Ecology

1.von Humboldt, A. Ansichten der Natur mit Wissenschaftlichen Erlauterungen (J.G. Cotta, 1808).2.Perrigo, A., Hoorn, C. & Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 47, 315–325 (2020).Article 

Google Scholar 
3.Badgley, C. et al. Biodiversity and topographic complexity: modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211–226 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
4.Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).Article 

Google Scholar 
6.Fjeldså, 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).Article 

Google Scholar 
7.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).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
8.Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).CAS 
Article 

Google Scholar 
9.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Gillooly, J. F., Allen, A. P., West, G. B. & Brown, J. H. The rate of DNA evolution: effects of body size and temperature on the molecular clock. Proc. Natl Acad. Sci. USA 102, 140–145 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Martin, A. P. & Palumbi, S. R. Body size, metabolic rate, generation time, and the molecular clock. Proc. Natl Acad. Sci. USA 90, 4087–4091 (1993).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).Article 

Google Scholar 
13.Allen, A. P., Gillooly, J. F., Savage, V. M. & Brown, J. H. Kinetic effects of temperature on rates of genetic divergence and speciation. Proc. Natl Acad. Sci. USA 103, 9130–9135 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Igea, J. & Tanentzap, A. J. Angiosperm speciation cools down in the tropics. Ecol. Lett. 23, 692–700 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Schluter, D. Speciation, ecological opportunity, and latitude (American Society of Naturalists address). Am. Nat. 187, 1–18 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
17.Anderson, K. J. & Jetz, W. The broad-scale ecology of energy expenditure of endotherms. Ecol. Lett. 8, 310–318 (2005).Article 

Google Scholar 
18.Clarke, A. & Gaston, K. J. Climate, energy and diversity. Proc. R. Soc. B 273, 2257–2266 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
19.Dowle, E. J., Morgan-Richards, M. & Trewick, S. A. Molecular evolution and the latitudinal biodiversity gradient. Heredity 110, 501–510 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).PubMed 
Article 

Google Scholar 
21.Stevens, G. C. The latitudinal gradient in geographical range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).Article 

Google Scholar 
22.Boucher-Lalonde, V. & Currie, D. J. Spatial autocorrelation can generate stronger correlations between range size and climatic niches than the biological signal — a demonstration using bird and mammal range maps. PLoS One 11, e0166243 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Cutter, A. D. & Gray, J. C. Ephemeral ecological speciation and the latitudinal biodiversity gradient. Evolution 70, 2171–2185 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Morales‐Barbero, J., Martinez, P. A., Ferrer‐Castán, D. & Olalla‐Tárraga, M. Á. Quaternary refugia are associated with higher speciation rates in mammalian faunas of the Western Palaearctic. Ecography 41, 607–621 (2018).Article 

Google Scholar 
25.Xing, Y. & Ree, R. H. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. Proc. Natl Acad. Sci. USA 114, E3444–E3451 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in Andean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
27.Testo, W. L., Sessa, E. & Barrington, D. S. The rise of the Andes promoted rapid diversification in Neotropical Phlegmariurus (Lycopodiaceae). New Phytol. 222, 604–613 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
28.Dowsett, H. et al. The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction. Climate 12, 1519–1538 (2016).
Google Scholar 
29.Hartley, A. J. Andean uplift and climate change. J. Geol. Soc. 160, 7–10 (2003).Article 

Google Scholar 
30.Aron, P. G. & Poulsen, C. J. in Mountains, Climate and Biodiversity (eds Hoorn, C., Perrugi, A. & Antonelli, A.) Ch. 8 (2018).31.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Wallis, G. P., Waters, J. M., Upton, P. & Craw, D. Transverse Alpine speciation driven by glaciation. Trends Ecol. Evol. 31, 916–926 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Luebert, F. & Muller, L. A. H. Effects of mountain formation and uplift on biological diversity. Front. Genet. 6, 54 (2015).34.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).Article 

Google Scholar 
35.Whittaker, R. J., Triantis, K. A. & Ladle, R. J. A general dynamic theory of oceanic island biogeography. J. Biogeogr. 35, 977–994 (2008).Article 

Google Scholar 
36.Li, Y. et al. Climate and topography explain range sizes of terrestrial vertebrates. Nat. Clim. Change 6, 498–502 (2016).Article 

Google Scholar 
37.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 
Article 

Google Scholar 
38.Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).Article 

Google Scholar 
39.Rowley, D. B. & Garzione, C. N. Stable isotope-based paleoaltimetry. Annu. Rev. Earth Planet. Sci. 35, 463–508 (2007).CAS 
Article 

Google Scholar 
40.Mulch, A. Stable isotope paleoaltimetry and the evolution of landscapes and life. Earth Planet. Sci. Lett. 433, 180–191 (2016).CAS 
Article 

Google Scholar 
41.Kuhn, T. S., Mooers, A. Ø. & Thomas, G. H. A simple polytomy resolver for dated phylogenies. Methods Ecol. Evol. 2, 427–436 (2011).Article 

Google Scholar 
42.Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).43.Meredith, R. W. et al. Impacts of the Cretaceous Terrestrial Revolution and KPg Extinction on mammal diversification. Science 334, 521–524 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
44.Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
46.Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Redding, D. W. & Mooers, A. Ø. Incorporating evolutionary measures into conservation prioritization. Conserv. Biol. 20, 1670–1678 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS One 9, e89543 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
50.Moore, B. R., Höhna, S., May, M. R., Rannala, B. & Huelsenbeck, J. P. Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures. Proc. Natl Acad. Sci. USA 113, 9569–9574 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Meyer, A. L. S., Román-Palacios, C. & Wiens, J. J. BAMM gives misleading rate estimates in simulated and empirical datasets. Evolution 72, 2257–2266 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
52.Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66, 477–498 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
53.Mitchell, J. S., Etienne, R. S. & Rabosky, D. L. Inferring diversification rate variation from phylogenies with fossils. Syst. Biol. 68, 1–18 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: what are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).Article 

Google Scholar 
55.Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Amante, C. & Eakins, B. W. ETOPO1 Arc-minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24 (NOAA, 2009).57.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

Google Scholar 
58.Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
59.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
60.Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63, v063i18 (2015).
Google Scholar  More

1.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 

Google Scholar 
2.Carpenter, S. R. et al. Science for managing ecosystem services: beyond the Millennium Ecosystem Assessment. Proc. Natl Acad. Sci. USA 106, 1305–1312 (2009).CAS 

Google Scholar 
3.Nelson, E. et al. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front. Ecol. Environ. 7, 4–11 (2009).
Google Scholar 
4.Maes, J. et al. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst. Serv. 17, 14–23 (2016).
Google Scholar 
5.Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).
Google Scholar 
6.Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Synthesis (Island Press, 2005).7.Summary for Policymakers. In Global Assessment Report on Biodiversity and Ecosystem Services (IPBES, 2019).8.Martínez-Harms, M. J. & Balvanera, P. Methods for mapping ecosystem service supply: a review. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 17–25 (2012).
Google Scholar 
9.Hauck, J. et al. ‘Maps have an air of authority’: potential benefits and challenges of ecosystem service maps at different levels of decision making. Ecosyst. Serv. 4, 25–32 (2013).
Google Scholar 
10.Balvanera, P. et al. Conserving biodiversity and ecosystem services. Science 291, 2047 (2001).CAS 

Google Scholar 
11.Dick, J., Maes, J., Smith, R. I., Paracchini, M. L. & Zulian, G. Cross-scale analysis of ecosystem services identified and assessed at local and European level. Ecol. Indic. 38, 20–30 (2014).
Google Scholar 
12.UK National Ecosystem Assessment. The UK National Ecosystem Assessment Technical Report. (UNEP-WCMC, 2011); http://uknea.unep-wcmc.org/13.Orsi, F., Ciolli, M., Primmer, E., Varumo, L. & Geneletti, D. Mapping hotspots and bundles of forest ecosystem services across the European Union. Land Use Policy 99, 104840 (2020).
Google Scholar 
14.Holland, R. A. et al. The influence of temporal variation on relationships between ecosystem services. Biodivers. Conserv. 20, 3285–3294 (2011).
Google Scholar 
15.Renard, D., Rhemtull, J. M. & Bennett, E. M. Historical dynamics in ecosystem service bundles. Proc. Natl Acad. Sci. USA 112, 13411–13416 (2015).CAS 

Google Scholar 
16.Rukundo, E. et al. Spatio-temporal dynamics of critical ecosystem services in response to agricultural expansion in Rwanda, East Africa. Ecol. Indic. 89, 696–705 (2018).
Google Scholar 
17.Stürck, J., Schulp, C. J. E. & Verburg, P. H. Spatio-temporal dynamics of regulating ecosystem services in Europe—the role of past and future land use change. Appl. Geogr. 63, 121–135 (2015).
Google Scholar 
18.Rau, A. L. et al. Temporal patterns in ecosystem services research: a review and three recommendations. Ambio 49, 1377–1393 (2020).
Google Scholar 
19.Sutherland, I. J., Bennett, E. M. & Gergel, S. E. Recovery trends for multiple ecosystem services reveal non-linear responses and long-term tradeoffs from temperate forest harvesting. For. Ecol. Manage. 374, 61–70 (2016).
Google Scholar 
20.Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650–8655 (2010).CAS 

Google Scholar 
21.Vanhanen, H. et al. Making Boreal Forests Work for People and Nature (IUFRO, 2012).22.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 

Google Scholar 
23.Moen, J. et al. Eye on the Taiga: removing global policy impediments to safeguard the boreal forest. Conserv. Lett. 7, 408–418 (2014).
Google Scholar 
24.Global Forest Industry (Swedish Forest Industries, 2019); https://www.forestindustries.se/forest-industry/statistics/global-forest-industry/25.Saastamoinen, O., Kangas, K. & Aho, H. The picking of wild berries in Finland in 1997 and 1998. Scand. J. For. Res. 15, 645–650 (2000).
Google Scholar 
26.Gamfeldt, L. et al. Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4, 1340 (2013).
Google Scholar 
27.Hou, Y., Li, B., Müller, F., Fu, Q. & Chen, W. A conservation decision-making framework based on ecosystem service hotspot and interaction analyses on multiple scales. Sci. Total Environ. 643, 277–291 (2018).CAS 

Google Scholar 
28.Blumstein, M. & Thompson, J. R. Land-use impacts on the quantity and configuration of ecosystem service provisioning in Massachusetts, USA. J. Appl. Ecol. 52, 1009–1019 (2015).
Google Scholar 
29.Fernandez-Campo, M., Rodríguez-Morales, B., Dramstad, W. E., Fjellstad, W. & Diaz-Varela, E. R. Ecosystem services mapping for detection of bundles, synergies and trade-offs: examples from two Norwegian municipalities. Ecosyst. Serv. 28, 283–297 (2017).
Google Scholar 
30.Gissi, E., Fraschetti, S. & Micheli, F. Incorporating change in marine spatial planning: a review. Environ. Sci. Policy 92, 191–200 (2019).
Google Scholar 
31.Maxwell, S. M., Gjerde, K. M., Conners, M. G. & Crowder, L. B. Mobile protected areas for biodiversity on the high seas. Science 367, 252–254 (2020).CAS 

Google Scholar 
32.Willcock, S. et al. Do ecosystem service maps and models meet stakeholders’ needs? A preliminary survey across sub-Saharan Africa. Ecosyst. Serv. 18, 110–117 (2016).
Google Scholar 
33.Jonsson, M., Bengtsson, J., Gamfeldt, L., Moen, J. & Snäll, T. Levels of forest ecosystem services depend on specific mixtures of commercial tree species. Nat. Plants 5, 141–147 (2019).
Google Scholar 
34.Pohjanmies, T. et al. Impacts of forestry on boreal forests: an ecosystem services perspective. Ambio 46, 743–755 (2017).
Google Scholar 
35.Miina, J., Hotanen, J.-P. & Salo, K. Modelling the abundance and temporal variation in the production of bilberry (Vaccinium myrtillus L.) in Finnish mineral soil forests. Silva Fenn. 43, 577–593 (2009).
Google Scholar 
36.Hertel, A. G. et al. Berry production drives bottom–up effects on body mass and reproductive success in an omnivore. Oikos 127, 197–207 (2018).
Google Scholar 
37.Thiffault, E. Boreal forests and soils. Dev. Soil Sci. 36, 59–82 (2019).
Google Scholar 
38.Jonsson, M., Bengtsson, J., Moen, J., Gamfeldt, L. & Snäll, T. Stand age and climate influence forest ecosystem service delivery and multifunctionality. Environ. Res. Lett. 15, 0940a8 (2020).
Google Scholar 
39.Stokland, J. N. Volume increment and carbon dynamics in boreal forest when extending the rotation length towards biologically old stands. For. Ecol. Manage. 488, 119017 (2021).
Google Scholar 
40.Harmon, M. E., Ferrell, W. K. & Franklin, J. F. Effects on carbon storage of conversion of old-growth forests to young forests. Science 247, 699–702 (1990).CAS 

Google Scholar 
41.Mazziotta, A. et al. Applying a framework for landscape planning under climate change for the conservation of biodiversity in the Finnish boreal forest. Glob. Change Biol. 21, 637–651 (2015).
Google Scholar 
42.Triviño, M. et al. Optimizing management to enhance multifunctionality in a boreal forest landscape. J. Appl. Ecol. 54, 61–70 (2017).
Google Scholar 
43.Qiu, J. & Turner, M. G. Spatial interactions among ecosystem services in an urbanizing agricultural watershed. Proc. Natl Acad. Sci. USA 110, 12149–12154 (2013).CAS 

Google Scholar 
44.Felipe-Lucia, M. R. et al. Multiple forest attributes underpin the supply of multiple ecosystem services. Nat. Commun. 9, 4839 (2018).
Google Scholar 
45.Eggers, J., Räty, M., Öhman, K. & Snäll, T. How well do stakeholder-defined forest management scenarios balance economic and ecological forest values? Forests 11, 86 (2020).
Google Scholar 
46.Eyvindson, K., Repo, A. & Mönkkönen, M. Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy. For. Policy Econ. 92, 119–127 (2018).
Google Scholar 
47.Rusch, A., Bommarco, R., Jonsson, M., Smith, H. G. & Ekbom, B. Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale. J. Appl. Ecol. 50, 345–354 (2013).
Google Scholar 
48.Schipanski, M. E. et al. A framework for evaluating ecosystem services provided by cover crops in agroecosystems. Agric. Syst. 125, 12–22 (2014).
Google Scholar 
49.Hufnagel, J., Reckling, M. & Ewert, F. Diverse approaches to crop diversification in agricultural research. A review. Agron. Sustain. Dev. 40, 14 (2020).
Google Scholar 
50.Guerry, A. D. et al. Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 107–121 (2012).
Google Scholar 
51.Wikström, P. et al. The Heureka Forestry Decision Support System: An Overview. Math. Comput. For Nat.-Resour. Sci. 3, 87–94 (2011).
Google Scholar 
52.Forest Statistics (Swedish University of Agricultural Sciences, 2020).53.Eriksson, A., Snäll, T. & Harrison, P. J. Analys av miljöförhållanden ‐ SKA 15. Report 11 (Swedish Forest Agency, 2015).54.Axelsson, A.-L. et al. in National Forest Inventories—Pathways for Common Reporting (eds Tomppo, E. et al.) 541–553 (Springer, 2010).55.Marklund, L. G. Biomass Functions for Pine, Spruce and Birch in Sweden (1988).56.Petersson, H. & Ståhl, G. Functions for below-ground biomass of Pinus sylvestris, Picea abies, Betula pendula and Betula pubescens in Sweden. Scand. J. For. Res. 21, 24–83 (2006).
Google Scholar 
57.Miina, J., Pukkala, T. & Kurttila, M. Optimal multi-product management of stands producing timber and wild berries. Eur. J. For. Res. 135, 781–794 (2016).
Google Scholar 
58.Schröter, M. & Remme, R. P. Spatial prioritisation for conserving ecosystem services: comparing hotspots with heuristic optimisation. Landsc. Ecol. 31, 431–450 (2016).
Google Scholar 
59.Wu, J., Feng, Z., Gao, Y. & Peng, J. Hotspot and relationship identification in multiple landscape services: a case study on an area with intensive human activities. Ecol. Indic. 29, 529–537 (2013).CAS 

Google Scholar 
60.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974).
Google Scholar 
61.R: A Language and Environment for Statistical Computing (R Development Core Team, 2014); https://www.R-project.org/ More

These authors contributed equally: Celso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa.Tropical Ecosystems and Environmental Sciences Laboratory (TREES), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, João B. C. Reis, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Yosio E. Shimabukuro, Liana O. Anderson & Luiz E. O. C. AragãoInstituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Luciana Gatti, Ana P. Aguiar, Yosio E. Shimabukuro & Luiz E. O. C. AragãoUniversidade Estadual do Maranhão (UEMA), São Luís, Maranhão, BrazilCelso H. L. Silva JuniorCentro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN), São José dos Campos, São Paulo, BrazilJoão B. C. Reis & Liana O. AndersonUniversity of Bristol, Bristol, UKViola Heinrich & Joanna HouseInstituto de Pesquisa Ambiental da Amazônia (IPAM), Brasília, Distrito Federal, BrazilAne Alencar, Camila Silva & Paulo BrandoUniversidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, BrazilDavid M. LapolaEcología del Paisaje y Modelación de Ecosistemas (ECOLMOD), Universidad Nacional de Colombia (UNAL), Bogota, ColombiaDolors ArmenterasUniversidade de Brasília, Brasília, Distrito Federal, BrazilEraldo A. T. MatricardiUniversity of Oxford, Oxford, UKErika BerenguerLancaster University, Lancaster, UKCamila Silva, Erika Berenguer & Jos BarlowSouth Dakota State University, Brookings, SD, USAIzaya NumataEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Amazônia Oriental, Belém, Pará, BrazilJoice FerreiraUniversity of California, Irvine, CA, USAPaulo BrandoWoodwell Climate Research Center, Falmouth, MA, USAPaulo BrandoInstituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Amazonas, BrazilPhilip M. FearnsideJet Propulsion Laboratory (JPL), Pasadena, CA, USASassan SaatchiUniversity of California, Los Angeles, CA, USASassan SaatchiUniversidade Federal do Acre (UFAC), Cruzeiro do Sul, Acre, BrazilSonaira SilvaUniversity of Exeter, Exeter, UKStephen Sitch & Luiz E. O. C. AragãoStockholm Resilience Centre, Stockholm University, Stockholm, SwedenAna P. AguiarSchool of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USACarlos A. SilvaEuropean Commission, Joint Research Centre (JRC), Ispra, VA, ItalyChristelle Vancutsem, Frédéric Achard & René BeuchleCenter for International Forestry Research (CIFOR), Bogor, IndonesiaChristelle Vancutsem More

1.Fisher MC, Garner TWJ. Chytrid fungi and global amphibian declines. Nat Rev Microbiol. 2020;18:332–43.CAS 
PubMed 
Article 

Google Scholar 
2.Skerratt LF, Berger L, Speare R, Cashins S, McDonald KR, Phillott AD, et al. Spread of chytridiomycosis has caused the rapid global decline and extinction of frogs. Ecohealth. 2007;4:125–34.Article 

Google Scholar 
3.Voyles J, Vredenburg VT, Tunstall TS, Parker JM, Briggs CJ, Rosenblum EB. Pathophysiology in Mountain Yellow-Legged Frogs (Rana muscosa) during a Chytridiomycosis Outbreak. PLoS ONE. 2012;7:e35374.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Voyles J, Young S, Berger L, Campbell C, Voyles WF, Dinudom A, et al. Pathogenesis of Chytridiomycosis, a cause of catastrophic amphibian declines. Science (80-). 2009;326:582–5.CAS 
Article 

Google Scholar 
5.Antwis RE, Harrison XA. Probiotic consortia are not uniformly effective against different amphibian chytrid pathogen isolates. Mol Ecol. 2018;27:577–89.CAS 
PubMed 
Article 

Google Scholar 
6.Muletz-wolz CR, Almario JG, Barnett SE, DiRenzo GV, Martel A, Pasmans F, et al. Inhibition of fungal pathogens across genotypes and temperatures by amphibian skin bacteria. Front Microbiol. 2017;8:1551.7.Kueneman JG, Woodhams DC, Harris R, Archer HM, Knight R, McKenzie VJ, et al. Probiotic treatment restores protection against lethal fungal infection lost during amphibian captivity. Proc R Soc London B Biol Sci. 2016;283:20161553.8.Harris RN, Brucker RM, Walke JB, Becker MH, Schwantes CR, Flaherty DC, et al. Skin microbes on frogs prevent morbidity and mortality caused by a lethal skin fungus. ISME J. 2009;3:818–24.CAS 
PubMed 
Article 

Google Scholar 
9.Bletz MC, Loudon AH, Becker MH, Bell SC, Woodhams DC, Minbiole KP, et al. Mitigating amphibian chytridiomycosis with bioaugmentation: characteristics of effective probiotics and strategies for their selection and use. Ecol Lett. 2013;16:817–20.Article 

Google Scholar 
10.Becker MH, Harris RN, Minbiole KP, Schwantes CR, Rollins-Smith LA, Reinert LK, et al. Towards a better understanding of the use of probiotics for preventing chytridiomycosis in Panamanian golden frogs. Ecohealth. 2011;8:501–6.PubMed 
Article 

Google Scholar 
11.Woodhams DC, Geiger CC, Reinert LK, Rollins-Smith LA, Lam B, Harris RN, et al. Treatment of amphibians infected with chytrid fungus: learning from failed trials with itraconazole, antimicrobial peptides, bacteria, and heat therapy. Dis Aquat Organ. 2012;98:11–25.CAS 
PubMed 
Article 

Google Scholar 
12.Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: networks, competition, and stability. Science. 2015;350:663–6.CAS 
PubMed 
Article 

Google Scholar 
13.Oh J, Byrd AL, Park M, Kong HH, Segre JA. Temporal stability of the human skin microbiome. Cell. 2016;165:854–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Harrison XA, Price SJ, Hopkins K, Leung W, Sergeant C, Garner T. Diversity-stability dynamics of the amphibian skin microbiome and susceptibility to a lethal viral pathogen. Front Microbiol. 2019;10:1–13.CAS 
Article 

Google Scholar 
15.Walke JB, Becker MH, Krinos A, Chang E, Santiago C, Umile TP, et al. Seasonal changes and the unexpected impact of environmental disturbance on skin bacteria of individual amphibians in a natural habitat. FEMS Microbiol Ecol. 2021;97:1–14.Article 

Google Scholar 
16.Bletz MC, Perl R, Bobowski BT, Japke LM, Tebbe CC, Dohrmann AB, et al. Amphibian skin microbiota exhibits temporal variation in community structure but stability of predicted Bd-inhibitory function. ISME J. 2017;11:1521–34.PubMed 
PubMed Central 
Article 

Google Scholar 
17.Familiar López M, Rebollar EA, Harris RN, Vredenburg VT, Hero J. Temporal variation of the skin bacterial community and Batrachochytrium dendrobatidis infection in the terrestrial cryptic frog Philoria loveridgei. Front Microbiol. 2017;8:1–12.Article 

Google Scholar 
18.Longo AV, Savage AE, Hewson I, Zamudio KR. Seasonal and ontogenetic variation of skin microbial communities and relationships to natural disease dynamics in declining amphibians. R Soc Open Sci. 2015;2:140377.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Douglas AJ, Hug LA, Katzenback BA. Composition of the North American wood frog (Rana sylvatica) bacterial skin microbiome and seasonal variation in community structure. Microb Ecol. 2021;81:78–92.CAS 
PubMed 
Article 

Google Scholar 
20.Estrada A, Hughey MC, Medina D, Rebollar EA, Walke JB, Harris RN, et al. Skin bacterial communities of neotropical treefrogs vary with local environmental conditions at the time of sampling. PeerJ. 2019;2019:1–20.
Google Scholar 
21.Longo AV, Zamudio KR. Temperature variation, bacterial diversity and fungal infection dynamics in the amphibian skin. Mol Ecol. 2017;26:4787–97.PubMed 
Article 

Google Scholar 
22.Vredenburg VT, Bingham R, Knapp R, Morgan JAT, Moritz C, Wake D. Concordant molecular and phenotypic data delineate new taxonomy and conservation priorities for the endangered mountain yellow-legged frog. J Zool. 2007;271:361–74.Article 

Google Scholar 
23.Vredenburg VT, McNally S, Sulaeman H, Butler HM, Yap T, Koo MS, et al. Pathogen invasion history elucidates contemporary host pathogen dynamics. PLoS ONE. 2019;14:1–14.Article 
CAS 

Google Scholar 
24.Vredenburg VT, Knapp RA, Tunstall TS, Briggs CJ. Dynamics of an emerging disease drive large-scale amphibian population extinctions. Proc Natl Acad Sci USA. 2010;107:9689–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Briggs CJ, Knapp RA, Vredenburg VT. Enzootic and epizootic dynamics of the chytrid fungal pathogen of amphibians. Proc Natl Acad Sci USA. 2010;107:9695–700.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Knapp RA, Fellers GM, Kleeman PM, Miller DA, Vredenburg VT, Rosenblum EB, et al. Large-scale recovery of an endangered amphibian despite ongoing exposure to multiple stressors. Proc Natl Acad Sci USA. 2016;113:11889–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
27.Jani AJ, Briggs CJ. The pathogen Batrachochytrium dendrobatidis disturbs the frog skin microbiome during a natural epidemic and experimental infection. Proc Natl Acad Sci USA. 2014;111:E5049–E5058.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Ellison S, Knapp RA, Sparagon W, Swei A, Vredenburg VT. Reduced skin bacterial diversity correlates with increased pathogen infection intensity in an endangered amphibian host. Mol Ecol. 2019;28:127–40.PubMed 
Article 

Google Scholar 
29.Jani AJ, Briggs CJ. Host and aquatic environment shape the amphibian skin microbiome but effects on downstream resistance to the pathogen Batrachochytrium dendrobatidis are variable. Front Microbiol. 2018;9:1–17.Article 

Google Scholar 
30.Jani AJ, Knapp RA, Briggs CJ. Epidemic and endemic pathogen dynamics correspond to distinct host population microbiomes at a landscape scale. Proc R Soc B Biol Sci. 2017;284:20170944.31.Vredenburg VT. Reversing introduced species effects: experimental removal of introduced fish leads to rapid recovery of a declining frog. Proc Natl Acad Sci USA. 2004;101:7646–50.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
32.Joseph MB, Knapp RA. Disease and climate effects on individuals drive post-reintroduction population dynamics of an endangered amphibian. Ecosphere 2018;9:e02499.33.Boyle DG, Boyle DB, Olsen V, Morgan JAT, Hyatt AD. Rapid quantitative detection of chytridiomycosis (Batrachochytrium dendrobatidis) in amphibian samples using real-time Taqman PCR assay. Dis Aquat Organ. 2004;60:141–8.CAS 
PubMed 
Article 

Google Scholar 
34.Hyatt AD, Boyle DG, Olsen V, Boyle DB, Berger L, Obendorf D, et al. Diagnostic assays and sampling protocols for the detection of Batrachochytrium dendrobatidis. Dis Aquat Organ. 2007;73:175–92.CAS 
PubMed 
Article 

Google Scholar 
35.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS ONE. 2010;5:e9490.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.CAS 
Article 

Google Scholar 
40.Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 
Article 

Google Scholar 
41.Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.
Google Scholar 
42.Weiss S, Xu ZZ, Peddada S, Amir A, Bittinger K, Gonzalez A, et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome. 2017;5:27.PubMed 
PubMed Central 
Article 

Google Scholar 
43.Faith DP. Conservation evaluation and phylogenetic diversity. Biol Conserv. 1992;61:1–10.Article 

Google Scholar 
44.Pielou EC. The measurement of diversity in different types of biological collections. J Theor Biol. 1966;13:131–44.Article 

Google Scholar 
45.Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Lozupone CA, Hamady M, Kelley ST, Knight R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl Environ Microbiol. 2007;73:1576–85.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Chang Q, Luan Y, Sun F. Variance adjusted weighted UniFrac: a powerful beta diversity measure for comparing communities based on phylogeny. BMC Bioinformatics. 2011;12:118.PubMed 
PubMed Central 
Article 

Google Scholar 
48.Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, et al. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics. 2012;28:2106–13.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.McDonald D, Vázquez-Baeza Y, Koslicki D, McClelland J, Reeve N, Xu Z, et al. Striped UniFrac: enabling microbiome analysis at unprecedented scale. Nat Methods. 2018;15:847–48.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
50.Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Heal Dis. 2015;26:1–7.
Google Scholar 
51.Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013;10:57–59.CAS 
PubMed 
Article 

Google Scholar 
52.Woodhams DC, Alford RA, Antwis RE, Archer H, Becker MH, Belden LK, et al. Antifungal isolates database of amphibian skin-associated bacteria and function against emerging fungal pathogens. Ecology. 2015;96:595.Article 

Google Scholar 
53.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
54.R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2020.55.Lee G, You HJ, Bajaj JS, Joo SK, Yu J, Park S, et al. Distinct signatures of gut microbiome and metabolites associated with significant fibrosis in non-obese NAFLD. Nat Commun. 2020;11:1–13.
Google Scholar 
56.Lloyd-Price J, Mahurkar A, Rahnavard G, Crabtree J, Orvis J, Hall AB, et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature. 2017;550:61–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Knapp RA, Briggs CJ, Smith TC, Maurer JR. Nowhere to hide: impact of a temperature-sensitive amphibian pathogen along an elevation gradient in the temperate zone. Ecosphere. 2011;2:art93.Article 

Google Scholar 
58.Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, et al. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324:1190–2.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Bates KA, Clare FC, O’Hanlon S, Bosch J, Brookes L, Hopkins K, et al. Amphibian chytridiomycosis outbreak dynamics are linked with host skin bacterial community structure. Nat Commun. 2018;9:1–11.CAS 
Article 

Google Scholar 
60.Kinney VC, Heemeyer JL, Pessier AP, Lannoo MJ, Samples F. Seasonal pattern of Batrachochytrium dendrobatidis infection and mortality in Lithobates areolatus: affirmation of Vredenburg’ s “10, 000 Zoospore Rule”. PLoS ONE. 2011;6:e16708.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).ADS 
CAS 

Google Scholar 
2.Bradford, M. A. et al. Climate fails to predict wood decomposition at regional scales. Nat. Clim. Change 4, 625–630 (2014).ADS 
CAS 

Google Scholar 
3.Chambers, J. Q., Higuchi, N., Schimel, J. P. J., Ferreira, L. V. & Melack, J. M. Decomposition and carbon cycling of dead trees in tropical forests of the central Amazon. Oecologia 122, 380–388 (2000).ADS 
CAS 

Google Scholar 
4.González, G. et al. Decay of aspen (Populus tremuloides Michx.) wood in moist and dry boreal, temperate, and tropical forest fragments. Ambio 37, 588–597 (2008).
Google Scholar 
5.Stokland, J., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood (Cambridge Univ. Press, 2012).6.Lustenhouwer, N. et al. A trait-based understanding of wood decomposition by fungi. Proc. Natl Acad. Sci. USA 117, 11551–11558 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Ulyshen, M. D. Wood decomposition as influenced by invertebrates. Biol. Rev. Camb. Philos. Soc. 91, 70–85 (2016).
Google Scholar 
8.Pretzsch, H., Biber, P., Schütze, G., Uhl, E. & Rötzer, T. Forest stand growth dynamics in Central Europe have accelerated since 1870. Nat. Commun. 5, 4967 (2014).ADS 
CAS 

Google Scholar 
9.Büntgen, U. et al. Limited capacity of tree growth to mitigate the global greenhouse effect under predicted warming. Nat. Commun. 10, 2171 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
10.Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Change 7, 395–402 (2017).ADS 

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

Google Scholar 
12.Portillo-Estrada, M. et al. Climatic controls on leaf litter decomposition across European forests and grasslands revealed by reciprocal litter transplantation experiments. Biogeosciences 13, 1621–1633 (2016).ADS 
CAS 

Google Scholar 
13.Christenson, L. et al. Winter climate change influences on soil faunal distribution and abundance: implications for decomposition in the northern forest. Northeast. Nat. 24, B209–B234 (2017).
Google Scholar 
14.Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).ADS 
CAS 

Google Scholar 
15.Stephenson, N. L. et al. Rate of tree carbon accumulation increases continuously with tree size. Nature 507, 90–93 (2014).ADS 
CAS 

Google Scholar 
16.Martin, A., Dimke, G., Doraisami, M. & Thomas, S. Carbon fractions in the world’s dead wood. Nat. Commun. 12, 889 (2021).17.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).ADS 

Google Scholar 
18.Marshall, D. J., Pettersen, A. K., Bode, M. & White, C. R. Developmental cost theory predicts thermal environment and vulnerability to global warming. Nat. Ecol. Evol. 4, 406–411 (2020).
Google Scholar 
19.Buczkowski, G. & Bertelsmeier, C. Invasive termites in a changing climate: a global perspective. Ecol. Evol. 7, 974–985 (2017).PubMed 
PubMed Central 

Google Scholar 
20.Diaz, S., Settele, J. & Brondizio, E. Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovermental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).21.van Klink, R. et al. Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances. Science 368, 417–420 (2020).ADS 

Google Scholar 
22.Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 

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

Google Scholar 
24.Jacobsen, R. M., Sverdrup-Thygeson, A., Kauserud, H., Mundra, S. & Birkemoe, T. Exclusion of invertebrates influences saprotrophic fungal community and wood decay rate in an experimental field study. Funct. Ecol. 32, 2571–2582 (2018).
Google Scholar 
25.Skelton, J. et al. Fungal symbionts of bark and ambrosia beetles can suppress decomposition of pine sapwood by competing with wood-decay fungi. Fungal Ecol. 45, 100926 (2020).
Google Scholar 
26.Wu, D., Seibold, S., Ruan, Z., Weng, C. & Yu, M. Island size affects wood decomposition by changing decomposer distribution. Ecography 44, 456–468 (2021).
Google Scholar 
27.Harmon, M. E. et al. Release of coarse woody detritus-related carbon: a synthesis across forest biomes. Carbon Balance Manag. 15, 1 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Wall, D. H. et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob. Change Biol. 14, 2661–2677 (2008).ADS 

Google Scholar 
29.Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).ADS 
CAS 

Google Scholar 
30.Baldrian, P. et al. Responses of the extracellular enzyme activities in hardwood forest to soil temperature and seasonality and the potential effects of climate change. Soil Biol. Biochem. 56, 60–68 (2013).CAS 

Google Scholar 
31.A’Bear, A. D., Jones, T. H., Kandeler, E. & Boddy, L. Interactive effects of temperature and soil moisture on fungal-mediated wood decomposition and extracellular enzyme activity. Soil Biol. Biochem. 70, 151–158 (2014).
Google Scholar 
32.IPCC. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. (IPCC, 2014).33.Smyth, C. E., Kurz, W. A., Trofymow, J. A. & CIDET Working Group. Including the effects of water stress on decomposition in the Carbon Budget Model of the Canadian Forest Sector CBM-CFS3. Ecol. Modell. 222, 1080–1091 (2011).
Google Scholar 
34.Weedon, J. T. et al. Global meta-analysis of wood decomposition rates: a role for trait variation among tree species? Ecol. Lett. 12, 45–56 (2009).
Google Scholar 
35.Griffiths, H. M., Ashton, L. A., Evans, T. A., Parr, C. L. & Eggleton, P. Termites can decompose more than half of deadwood in tropical rainforest. Curr. Biol. 29, R118–R119 (2019).CAS 

Google Scholar 
36.Birkemoe, T., Jacobsen, R. M., Sverdrup-Thygeson, A. & Biedermann, P. H. W. in Saproxylic Insects (ed. Ulyshen, M. D.) 377–427 (Springer, 2018).37.Harvell, M. C. E. et al. Climate warming and disease risks for terrestrial and marine biota. Science 296, 2158–2162 (2002).ADS 
CAS 

Google Scholar 
38.Berkov, A. in Saproxylic Insects (ed. Ulyshen, M. D.) 547–580 (Springer, 2018).39.Wang, C., Bond-Lamberty, B. & Gower, S. T. Environmental controls on carbon dioxide flux from black spruce coarse woody debris. Oecologia 132, 374–381 (2002).ADS 

Google Scholar 
40.Peršoh, D. & Borken, W. Impact of woody debris of different tree species on the microbial activity and community of an underlying organic horizon. Soil Biol. Biochem. 115, 516–525 (2017).
Google Scholar 
41.Campbell, J., Donato, D., Azuma, D. & Law, B. Pyrogenic carbon emission from a large wildfire in Oregon, United States. J. Geophys. Res. 112, G04014 (2007).ADS 

Google Scholar 
42.van Leeuwen, T. T. et al. Biomass burning fuel consumption rates: a field measurement database. Biogeosciences 11, 7305–7329 (2014).ADS 

Google Scholar 
43.McDowell, N. G. et al. Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020).CAS 

Google Scholar 
44.Ulyshen, M. D. & Wagner, T. L. Quantifying arthropod contributions to wood decay. Methods Ecol. Evol. 4, 345–352 (2013).
Google Scholar 
45.Bässler, C., Heilmann-Clausen, J., Karasch, P., Brandl, R. & Halbwachs, H. Ectomycorrhizal fungi have larger fruit bodies than saprotrophic fungi. Fungal Ecol. 17, 205–212 (2015).
Google Scholar 
46.Ryvarden, L. & Gilbertson, R. L. The Polyporaceae of Europe (Fungiflora, 1994).47.Eriksson, J. & Ryvarden, L. The Corticiaceae of North Europe Parts 1–8 (Fungiflora, 1987).48.Boddy, L., Hynes, J., Bebber, D. P. & Fricker, M. D. Saprotrophic cord systems: dispersal mechanisms in space and time. Mycoscience 50, 9–19 (2009).
Google Scholar 
49.Moore, D. Fungal Morphogenesis (Cambridge Univ. Press, 1998).50.Clemencon, H. Anatomy of the Hymenomycetes (Univ. Lausanne, 1997).51.R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).52.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
53.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
54.Wood, S. N. Generalized Additive Models: an Introduction with R 2nd edn (Chapman and Hall/CRC, 2017).55.Robinson, D. Implications of a large global root biomass for carbon sink estimates and for soil carbon dynamics. Proc. R. Soc. B 274, 2753–2759 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Food and Agriculture Organization. Global Ecological Zones for FAO Forest Reporting: 2010 Update, Forest Resource Assessment Working Paper (Food and Agriculture Organization, 2012).57.Food and Agriculture Organization. Global Forest Resources Assessment 2015 (Food and Agriculture Organization, 2016).58.Christensen, M. et al. Dead wood in European beech (Fagus sylvatica) forest reserves. For. Eco. Man. 210, 267–282 (2005).
Google Scholar 
59.Kobayashi, T. et al. Production of global land cover data – GLCNMO2013. J. Geogr. Geol. 9, 1–15 (2017).
Google Scholar 
60.Harmon, M. E., Woodall, C. W., Fasth, B., Sexton, J. & Yatkov, M. Differences between Standing and Downed Dead Tree Wood Density Reduction Factors: A Comparison across Decay Classes and Tree Species Research Paper NRS-15 (US Department of Agriculture, Forest Service, Northern Research Station, 2011).61.Hararuk, O., Kurz, W. A. & Didion, M. Dynamics of dead wood decay in Swiss forests. For. Ecosyst. 7, 36 (2020).
Google Scholar 
62.Gora, E. M., Kneale, R. C., Larjavaara, M. & Muller-Landau, H. C. Dead wood necromass in a moist tropical forest: stocks, fluxes, and spatiotemporal variability. Ecosystems 22, 1189–1205 (2019).CAS 

Google Scholar 
63.Hérault, B. et al. Modeling decay rates of dead wood in a neotropical forest. Oecologia 164, 243–251 (2010).ADS 

Google Scholar 
64.Thünen-Institut für Waldökosysteme. Der Wald in Deutschland – Ausgewählte Ergebnisse der dritten Bundeswaldinventur (Bundesministerium für Ernährung und Landwirtschaft, 2014).65.Puletti, N. et al. A dataset of forest volume deadwood estimates for Europe. Ann. For. Sci. 76, 68 (2019).
Google Scholar 
66.Richardson, S. J. et al. Deadwood in New Zealand’s indigenous forests. For. Ecol. Manage. 258, 2456–2466 (2009).
Google Scholar 
67.Shorohova, E. & Kapitsa, E. Stand and landscape scale variability in the amount and diversity of coarse woody debris in primeval European boreal forests. For. Ecol. Manage. 356, 273–284 (2015).
Google Scholar 
68.Szymañski, C., Fontana, G. & Sanguinetti, J. Natural and anthropogenic influences on coarse woody debris stocks in Nothofagus–Araucaria forests of northern Patagonia, Argentina. Austral Ecol. 42, 48–60 (2017).
Google Scholar 
69.Link, K. G. et al. A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow. PLoS One 13, e0200917 (2018).70.Saugier, B., Roy, J. & Mooney, H. A. in Terrestrial Global Productivity (eds J. Roy, B. Saugier & H. A. Mooney) 543–557 (Academic Press, 2001). More

1.Wilson, E. O. The little things that run the world (The importance and conservation of invertebrates). Conserv. Biol. 1, 344–346 (1987).Article 

Google Scholar 
2.Catalogue of Life. Catalogue of life: 2018 annual checklist. http://www.catalogueoflife.org/annual-checklist/2018/info/ac (2018).3.Stork, N. E. How many species of insects and other terrestrial arthropods are there on earth?. Annu. Rev. Entomol. 63, 31–45 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
5.Magurran, A. E. et al. Long-term datasets in biodiversity research and monitoring: Assessing change in ecological communities through time. Trends Ecol. Evol. 25, 574–582 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Nielsen, T. F., Sand-Jensen, K., Dornelas, M. & Bruun, H. H. More is less: Net gain in species richness, but biotic homogenization over 140 years. Ecol. Lett. 22, 1650–1657 (2019).Article 

Google Scholar 
7.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 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Wagner, D. L. Insect declines in the Anthropocene. Annu. Rev. Entomol. 65, 457–480 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Wagner, D. L., Grames, E. M., Forister, M. L., Berenbaum, M. R. & Stopak, D. Insect decline in the Anthropocene: Death by a thousand cuts. PNAS 118, 1–10 (2021).
Google Scholar 
11.Welti, E. A. R., Roeder, K. A., de Beurs, K. M., Joern, A. & Kaspari, M. Nutrient dilution and climate cycles underlie declines in a dominant insect herbivore. Proc. Natl. Acad. Sci. U. S. A. 117, 7271–7275 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl. Acad. Sci. U. S. A. 110, 19456 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Dornelas, M. et al. A balance of winners and losers in the Anthropocene. Ecol. Lett. 22, 847–854 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Rada, S. et al. Protected areas do not mitigate biodiversity declines: A case study on butterflies. Divers. Distrib. 25, 217–224 (2019).Article 

Google Scholar 
16.Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Magurran, A. E., Dornelas, M., Moyes, F. & Henderson, P. A. Temporal β diversity—A macroecological perspective. Glob. Ecol. Biogeogr. 28, 1949–1960 (2019).Article 

Google Scholar 
18.McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Múrria, C., Iturrarte, G. & Gutiérrez-Cánovas, C. A trait space at an overarching scale yields more conclusive macroecological patterns of functional diversity. Glob. Ecol. Biogeogr. 29, 1729–1742 (2020).Article 

Google Scholar 
20.Violle, C. et al. Let the concept of trait be functional!. Oikos 116, 882–892 (2007).Article 

Google Scholar 
21.Schmera, D., Heino, J., Podani, J., Erős, T. & Dolédec, S. Functional diversity: A review of methodology and current knowledge in freshwater macroinvertebrate research. Hydrobiologia 787, 27–44 (2017).Article 

Google Scholar 
22.Frainer, A., McKie, B. G. & Malmqvist, B. When does diversity matter? Species functional diversity and ecosystem functioning across habitats and seasons in a field experiment. J. Anim. Ecol. 83, 460–469 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Ceballos, G. et al. Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci. Adv. 1, e1400253 (2015).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: The bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).Article 

Google Scholar 
25.Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Habel, J. C., Samways, M. J. & Schmitt, T. Mitigating the precipitous decline of terrestrial European insects: Requirements for a new strategy. Biodivers. Conserv. 28, 1343–1360 (2019).Article 

Google Scholar 
28.Baranov, V., Jourdan, J., Pilotto, F., Wagner, R. & Haase, P. Complex and nonlinear climate-driven changes in freshwater insect communities over 42 years. Conserv. Biol. 34, 1241–1251 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Halsch, C. A. et al. Insects and recent climate change. PNAS 118, 1–9 (2021).Article 
CAS 

Google Scholar 
30.Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. PNAS 118, 1–6 (2021).Article 
CAS 

Google Scholar 
31.Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Jourdan, J., Baranov, V., Wagner, R., Plath, M. & Haase, P. Elevated temperatures translate into reduced dispersal abilities in a natural population of an aquatic insect. J. Anim. Ecol. 88, 1498–1509 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Bowler, D. E. et al. Cross-realm assessment of climate change impacts on species’ abundance trends. Nat. Ecol. Evol. 1, 1–7 (2017).Article 

Google Scholar 
34.Habel, J. C., Ulrich, W., Biburger, N., Seibold, S. & Schmitt, T. Agricultural intensification drives butterfly decline. Insect Conserv. Divers. 12, 289–295 (2019).
Google Scholar 
35.Januschke, K. & Verdonschot, R. C. M. Effects of river restoration on riparian ground beetles (Coleoptera: Carabidae) in Europe. Hydrobiologia 769, 93–104 (2016).Article 

Google Scholar 
36.Koivula, M. Useful model organisms, indicators, or both? Ground beetles (Coleoptera, Carabidae) reflecting environmental conditions. ZooKeys 100, 287–317 (2011).Article 

Google Scholar 
37.Homburg, K., Homburg, N., Schäfer, F., Schuldt, A. & Assmann, T. Carabids.org—a dynamic online database of ground beetle species traits (Coleoptera, Carabidae). Insect Conserv. Divers. 7, 195–205 (2014).38.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 100, 55–148 (2011).Article 

Google Scholar 
39.Rainio, J. & Niemelä, J. Ground beetles (Coleoptera: Carabidae) as bioindicators. Biodivers. Conserv. 12, 487–506 (2003).Article 

Google Scholar 
40.Pozsgai, G., Baird, J., Littlewood, N. A., Pakeman, R. J. & Young, M. R. Long-term changes in ground beetle (Coleoptera: Carabidae) assemblages in Scotland. Ecol. Entomol. 41, 157–167 (2016).Article 

Google Scholar 
41.Jambrošić, V. Ž & Šerić, J. L. Long term changes (1990–2016) in carabid beetle assemblages (Coleoptera: Carabidae) in protected forests on Dinaric Karst on Mountain Risnjak, Croatia. EJE 117, 56–67 (2020).
Google Scholar 
42.Marrec, R. et al. Multiscale drivers of carabid beetle (Coleoptera: Carabidae) assemblages in small European woodlands. Glob. Ecol. Biogeogr. 30, 165–182 (2021).Article 

Google Scholar 
43.Ribera, I., Dolédec, S., Downie, I. S. & Foster, G. N. Effect of land disturbance and stress on species traits of ground beetle assemblages. Ecology 82, 1112–1129 (2001).Article 

Google Scholar 
44.Gobbi, M. & Fontaneto, D. Biodiversity of ground beetles (Coleoptera: Carabidae) in different habitats of the Italian Po lowland. Agric. Ecosyst. Environ. 127, 273–276 (2008).Article 

Google Scholar 
45.Cajaiba, R. L. et al. How informative is the response of Ground Beetles’ (Coleoptera: Carabidae) assemblages to anthropogenic land use changes? Insights for ecological status assessments from a case study in the Neotropics. Sci. Total Environ. 636, 1219–1227 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Baulechner, D., Diekötter, T., Wolters, V. & Jauker, F. Converting arable land into flowering fields changes functional and phylogenetic community structure in ground beetles. Biol. Cons. 231, 51–58 (2019).Article 

Google Scholar 
47.Hallmann, C. A. et al. Declining abundance of beetles, moths and caddisflies in the Netherlands. Insect Conserv. Divers. 13, 127–139 (2020).Article 

Google Scholar 
48.Brooks, D. R. et al. Large carabid beetle declines in a United Kingdom monitoring network increases evidence for a widespread loss in insect biodiversity. J. Appl. Ecol. 49, 1009–1019 (2012).Article 

Google Scholar 
49.Kotze, D. J. & O’Hara, R. B. Species decline—but why? Explanations of carabid beetle (Coleoptera, Carabidae) declines in Europe. Oecologia 135, 138–148 (2003).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Homburg, K. et al. Where have all the beetles gone? Long-term study reveals carabid species decline in a nature reserve in Northern Germany. Insect Conserv. Divers. 12, 268–277 (2019).
Google Scholar 
51.Thiele, H. U. Carabid Beetles in their Environments: A Study on Habitat Selection by Adaptations in Physiology and Behaviour. (Springer, 1977). https://doi.org/10.1007/978-3-642-81154-8.52.Hengeveld, R. Dynamics of Dutch Beetle Species During the Twentieth Century (Coleoptera, Carabidae). J. Biogeogr. 12, 389–411 (1985).Article 

Google Scholar 
53.Engel, J. et al. Pitfall trap sampling bias depends on body mass, temperature, and trap number: Insights from an individual-based model. Ecosphere 8, e01790 (2017).Article 

Google Scholar 
54.Eyre, M. D., Rushton, S. P., Luff, M. L. & Telfer, M. G. Investigating the relationships between the distribution of British ground beetle species (Coleoptera, Carabidae) and temperature, precipitation and altitude. J. Biogeogr. 32, 973–983 (2005).Article 

Google Scholar 
55.Paetzold, A., Schubert, C. J. & Tockner, K. Aquatic terrestrial linkages along a braided-river: Riparian arthropods feeding on aquatic insects. Ecosystems 8, 748–759 (2005).Article 

Google Scholar 
56.Van Looy, K., Vanacker, S., Jochems, H., de Blust, G. & Dufrêne, M. Ground beetle habitat templets and riverbank integrity. River Res. Appl. 21, 1133–1146 (2005).Article 

Google Scholar 
57.Lambeets, K., Vandegehuchte, M. L., Maelfait, J.-P. & Bonte, D. Understanding the impact of flooding on trait-displacements and shifts in assemblage structure of predatory arthropods on river banks. J. Anim. Ecol. 77, 1162–1174 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Kotze, D. J., Niemelä, J., O’Hara, R. B. & Turin, H. Testing abundance-range size relationships in European carabid beetles (Coleoptera, Carabidae). Ecography 26, 553–566 (2003).Article 

Google Scholar 
59.Barber, H. S. Traps for cave-inhabiting insects. J. Elisha Mitchell Sci. Soc. 46, 259–266 (1931).
Google Scholar 
60.Dunger, W. Praktische Erfahrungen mit Bodenfallen. Entomologische Nachrichten 7, 41–46 (1963).
Google Scholar 
61.Trautner, J. Handfänge als effektive und vergleichbare Methode zur Laufkäfer-Erfassung an Fließgewässern-Ergebnisse eines Tests an der Aich. Angewandte Carabidologie Supplement 1, 139–144 (1999).
Google Scholar 
62.Trautner, J. Laufkäfer – Methoden der Bestandsaufnahme und Hinweise für die Auswertung bei Naturschutz- und Eingriffsplanungen. in Arten- und Biotopschutz in der Planung: Methodische Standards zur Erfassung von Tierartengruppen (ed. Trautner, J.) 145–162 (1992).63.Linke, S., Bailey, R. C. & Schwindt, J. Temporal variability of stream bioassessments using benthic macroinvertebrates. Freshw. Biol. 42, 575–584 (1999).Article 

Google Scholar 
64.Albrecht, L. Grundlagen, Ziele und Methodik der waldökologischen Forschung in Naturreservaten. vol. 1 (1990).65.Renner, K. Faunistisch-ökologische Untersuchungen der Käferfauna pflanzensoziologisch unterschiedlicher Biotope im Evessell-Buch bei Bielefeld-Sennestadt. Ber. Naturw. V. Bielefeld 145–176 (1980).66.Müller-Motzfeld, G. Die Käfer Mitteleuropas. vol. 2 (Springer Spektrum, 2004).67.Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).MathSciNet 
MATH 
Article 

Google Scholar 
68.Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication. (University of Illinois Press, 1949).69.Simpson, E. H. Measurement of diversity. Nature 163, 688 (1949).ADS 
MATH 
Article 

Google Scholar 
70.Pielou, E. C. Mathematical Ecology. (Wiley, 1977).71.Smith, B. & Wilson, J. B. A consumer’s guide to Evenness indices. Oikos 76, 70–82 (1996).Article 

Google Scholar 
72.Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: Consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Article 

Google Scholar 
73.Schmera, D., Podani, J., Heino, J., Erős, T. & Poff, N. L. A proposed unified terminology of species traits in stream ecology. Freshw. Sci. 34, 823–830 (2015).Article 

Google Scholar 
74.Villéger, S., Grenouillet, G. & Brosse, S. Decomposing functional β-diversity reveals that low functional β-diversity is driven by low functional turnover in European fish assemblages. Glob. Ecol. Biogeogr. 22, 671–681 (2013).Article 

Google Scholar 
75.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
76.Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: The primary components of functional diversity. Oikos 111, 112–118 (2005).Article 

Google Scholar 
77.Pakeman, R. J. Functional trait metrics are sensitive to the completeness of the species’ trait data?. Methods Ecol. Evol. 5, 9–15 (2014).Article 

Google Scholar 
78.Mouillot, D. et al. Functional over-redundancy and high functional vulnerability in global fish faunas on tropical reefs. PNAS 111, 13757–13762 (2014).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Chevene, F., Doléadec, S. & Chessel, D. A fuzzy coding approach for the analysis of long-term ecological data. Freshw. Biol. 31, 295–309 (1994).Article 

Google Scholar 
80.Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M. & Jones, P. D. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).Article 

Google Scholar 
81.Haylock, M. R. et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res. Atmos. 113, 1–12 (2008).Article 

Google Scholar 
82.Jourdan, J. et al. Effects of changing climate on European stream invertebrate communities: A long-term data analysis. Sci. Total Environ. 621, 588–599 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Büttner, G. Corine land cover and land cover change products. in Land Use and Land Cover Mapping in Europe: Practices & Trends (eds. Manakos, I. & Braun, M.) 55–74 (Springer Netherlands, 2014). https://doi.org/10.1007/978-94-007-7969-3_5.84.Erős, T., Czeglédi, I., Tóth, R. & Schmera, D. Multiple stressor effects on alpha, beta and zeta diversity of riverine fish. Sci. Total Environ. 748, 141407 (2020).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
85.Oksanen, J. et al. Vegan: Community ecology package. https://CRAN.R-project.org/package=vegan (2019).86.Sanders, H. L. Marine benthic diversity: A comparative study. Am. Nat. 102, 243–282 (1968).Article 

Google Scholar 
87.Maire, A., Thierry, E., Viechtbauer, W. & Daufresne, M. Poleward shift in large-river fish communities detected with a novel meta-analysis framework. Freshw. Biol. 64, 1143–1156 (2019).Article 

Google Scholar 
88.R Development Core Team. R: A language and environment for statistical computing. R Foundation For Statistical Computing, Vienna, Austria https://www.r-project.org/ (2019).89.Lahti, L. & Shetty, S. Microbiome R package. http://microbiome.github.io (2012).90.Laliberté, E., Legendre, P. & Shipley, B. FD: Measuring functional diversity from multiple traits, and other tools for functional ecology. https://cran.r-project.org/web/packages/FD/citation.html (2014).91.Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
92.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Development Core Team. Nlme: linear and nonlinear mixed effects models. https://CRAN.R-project.org/package=nlme (2020).93.Boscaini, A., Franceschini, A. & Maiolini, B. River ecotones: Carabid beetles as a tool for quality assessment. Hydrobiologia 422, 173–181 (2000).Article 

Google Scholar 
94.Magura, T., Lövei, G. L. & Tóthmérész, B. Does urbanization decrease diversity in ground beetle (Carabidae) assemblages?. Glob. Ecol. Biogeogr. 19, 16–26 (2010).Article 

Google Scholar 
95.Kędzior, R., Szwalec, A., Mundała, P. & Skalski, T. Ground beetle (Coleoptera, Carabidae) life history traits as indicators of habitat recovering processes in postindustrial areas. Ecol. Eng. 142, 105615 (2020).Article 

Google Scholar 
96.Post, D. M. The long and short of food-chain length. Trends Ecol. Evol. 17, 269–277 (2002).Article 

Google Scholar 
97.Pilotto, F. et al. Meta-analysis of multidecadal biodiversity trends in Europe. Nat. Commun. 11, 3486 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
98.Skarbek, C. J., Kobel-Lamparski, A. & Dormann, C. F. Trends in monthly abundance and species richness of carabids over 33 years at the Kaiserstuhl, southwest Germany. Basic Appl. Ecol. 50, 107–118 (2021).Article 

Google Scholar 
99.Chase, J. M. et al. Species richness change across spatial scales. Oikos 128, 1079–1091 (2019).Article 

Google Scholar 
100.Prather, R. M. & Kaspari, M. Plants regulate grassland arthropod communities through biomass, quality, and habitat heterogeneity. Ecosphere 10, e02909 (2019).Article 

Google Scholar 
101.Desender, K., Dekoninck, W., Dufrêne, M. & Maes, D. Changes in the distribution of carabid beetles in Belgium revisited: Have we halted the diversity loss?. Biol. Cons. 143, 1549–1557 (2010).Article 

Google Scholar 
102.Haase, P. et al. The next generation of site-based long-term ecological monitoring: Linking essential biodiversity variables and ecosystem integrity. Sci. Total Environ. 613–614, 1376–1384 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar  More

We acknowledge the herbaria that contributed data to this work: HA, FCO, MFU, UNEX, VDB, ASDM, BPI, BRI, CLF, L, LPB, AD, TAES, FEN, FHO, A, ANSM, BCMEX, RB, TRH, AAH, ACOR, AJOU, UI, AK, ALCB, AKPM, EA, AAU, ALU, AMES, AMNH, AMO, ANA, GH, ARAN, ARM, AS, CICY, ASU, BAI, AUT, B, BA, BAA, BAB, BACP, BAF, BAL, COCA, BARC, BBS, BC, BCN, BCRU, BEREA, BG, BH, BIO, BISH, SEV, BLA, BM, MJG, BOL, CVRD, BOLV, BONN, BOUM, BR, BREM, BRLU, BSB, BUT, C, CAMU, CAN, CANB, CAS, CAY, CBG, CBM, CEN, CEPEC, CESJ, CHR, ENCB, CHRB, CIIDIR, CIMI, CLEMS, COA, COAH, COFC, CP, COL, COLO, CONC, CORD, CPAP, CPUN, CR, CRAI, FURB, CU, CRP, CS, CSU, CTES, CTESN, CUZ, DAO, HB, DAV, DLF, DNA, DS, DUKE, DUSS, E, HUA, EAC, ECU, EIF, EIU, GI, GLM, GMNHJ, K, GOET, GUA, EKY, EMMA, HUAZ, ERA, ESA, F, FAA, FAU, UVIC, FI, GZU, H, FLAS, FLOR, HCIB, FR, FTG, FUEL, G, GB, GDA, HPL, GENT, GEO, HUAA, HUJ, CGE, HAL, HAM, IAC, HAMAB, HAS, HAST, IB, HASU, HBG, IBUG, HBR, IEB, HGI, HIP, IBGE, ICEL, ICN, ILL, SF, NWOSU, HO, HRCB, HRP, HSS, HU, HUAL, HUEFS, HUEM, HUSA, HUT, IAA, HYO, IAN, ILLS, IPRN, FCQ, ABH, BAFC, BBB, INPA, IPA, BO, NAS, INB, INEGI, INM, MW, EAN, IZTA, ISKW, ISC, GAT, IBSC, UCSB, ISU, IZAC, JBAG, JE, SD, JUA, JYV, KIEL, ECON, TOYA, MPN, USF, TALL, RELC, CATA, AQP, KMN, KMNH, KOR, KPM, KSTC, LAGU, UESC, GRA, IBK, KTU, KU, PSU, KYO, LA, LOMA, SUU, UNITEC, NAC, IEA, LAE, LAF, GMDRC, LCR, LD, LE, LEB, LI, LIL, LINN, AV, HUCP, MBML, FAUC, CNH, MACF, CATIE, LTB, LISI, LISU, MEXU, LL, LOJA, LP, LPAG, MGC, LPD, LPS, IRVC, MICH, JOTR, LSU, LBG, WOLL, LTR, MNHN, CDBI, LYJB, LISC, MOL, DBG, AWH, NH, HSC, LMS, MELU, NZFRI, M, MA, UU, UBT, CSUSB, MAF, MAK, MB, KUN, MARY, MASS, MBK, MBM, UCSC, UCS, JBGP, OBI, BESA, LSUM, FULD, MCNS, ICESI, MEL, MEN, TUB, MERL, CGMS, FSU, MG, HIB, TRT, BABY, ETH, YAMA, SCFS, SACT, ER, JCT, JROH, SBBG, SAV, PDD, MIN, SJSU, MISS, PAMP, MNHM, SDSU, BOTU, MPU, MSB, MSC, CANU, SFV, RSA, CNS, JEPS, BKF, MSUN, CIB, VIT, MU, MUB, MVFA, SLPM, MVFQ, PGM, MVJB, MVM, MY, PASA, N, HGM, TAM, BOON, MHA, MARS, COI, CMM, NA, NCSC, ND, NU, NE, NHM, NHMC, NHT, UFMA, NLH, UFRJ, UFRN, UFS, ULS, UNL, US, NMNL, USP, NMR, NMSU, XAL, NSW, ZMT, BRIT, MO, NCU, NY, TEX, U, UNCC, NUM, O, OCLA, CHSC, LINC, CHAS, ODU, OKL, OKLA, CDA, OS, OSA, OSC, OSH, OULU, OXF, P, PACA, PAR, UPS, PE, PEL, SGO, PEUFR, PH, PKDC, SI, PMA, POM, PORT, PR, PRC, TRA, PRE, PY, QMEX, QCA, TROM, QCNE, QRS, UH, R, REG, RFA, RIOC, RM, RNG, RYU, S, SALA, SANT, SAPS, SASK, SBT, SEL, SING, SIU, SJRP, SMDB, SNM, SOM, SP, SRFA, SPF, STL, STU, SUVA, SVG, SZU, TAI, TAIF, TAMU, TAN, TEF, TENN, TEPB, TI, TKPM, TNS, TO, TU, TULS, UADY, UAM, UAS, UB, UC, UCR, UEC, UFG, UFMT, UFP, UGDA, UJAT, ULM, UME, UMO, UNA, UNM, UNR, UNSL, UPCB, UPNA, USAS, USJ, USM, USNC, USZ, UT, UTC, UTEP, UV, VAL, VEN, VMSL, VT, W, WAG, WII, WELT, WIS, WMNH, WS, WTU, WU, Z, ZSS, ZT, CUVC, AAS, AFS, BHCB, CHAM, FM, PERTH and SAN. X.F., D.S.P., E.A.N., A.L. and J.R.B. were supported by the University of Arizona Bridging Biodiversity and Conservation Science program. Z.L. was supported by NSFC (41922006) and K. C. Wong Education Foundation. The BIEN working group was supported by the National Center for Ecological Analysis and Synthesis, a centre funded by NSF EF-0553768 at the University of California, Santa Barbara, and the State of California. Additional support for the BIEN working group was provided by iPlant/Cyverse via NSF DBI-0735191. B.J.E., B.M. and C.M. were supported by NSF ABI-1565118. B.J.E. and C.M. were supported by NSF ABI-1565118 and NSF HDR-1934790. B.J.E., L.H. and P.R.R. were supported by the Global Environment Facility SPARC project grant (GEF-5810). D.D.B. was supported in part by NSF DEB-1824796 and NSF DEB-1550686. S.R.S. was supported by NSF DEB-1754803. X.F. and A.L. were partly supported by NSF DEB-1824796. B.J.E. and D.M.N. were supported by NSF DEB-1556651. M.M.P. is supported by the São Paulo Research Foundation (FAPESP), grant 2019/25478-7. D.M.N. was supported by Instituto Serrapilheira/Brazil (Serra-1912-32082). E.I.N. was supported by NSF HDR-1934712. We thank L. López-Hoffman and L. Baldwin for constructive comments. More

1.Schmidt, M. W. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Drobnik, T., Greiner, L., Keller, A. & Grêt-Regamey, A. Soil quality indicators-from soil functions to ecosystem services. Ecol. Ind. 94, 151–169 (2018).Article 

Google Scholar 
3.Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).ADS 
Article 
CAS 

Google Scholar 
4.Viscarra Rossel, R. et al. A global spectral library to characterize the world’s soil. Earth Sci. Rev. 155, 198–230 (2016).5.Amundson, R. & Biardeau, L. Opinion: Soil carbon sequestration is an elusive climate mitigation tool. Proc. Natl. Acad. Sci. 115, 11652–11656 (2018).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
6.Smith, P. et al. How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal. Glob. Change Biol. 26, 219–241 (2020).ADS 
Article 

Google Scholar 
7.Zhang, S., Yu, Z., Lin, J. & Zhu, B. Responses of soil carbon decomposition to drying-rewetting cycles: A meta-analysis. Geoderma 361, 114069 (2020).ADS 
CAS 
Article 

Google Scholar 
8.Bot, A. & Benites, J. The Importance of Soil Organic Matter: Key to Drought-Resistant Soil and Sustained Food Production. 80 (Food & Agriculture Org., 2005).9.Rawles, W. J. & Brakensiek, D. Estimating soil water retention from soil properties. J. Irrig. Drain. Div. 108, 166–171 (1982).Article 

Google Scholar 
10.Zhao, D., Zhao, X., Khongnawang, T., Arshad, M. & Triantafilis, J. A Vis–NIR spectral library to predict clay in Australian cotton growing soil. Soil Sci. Soc. Am. J. 82, 1347–1357 (2018).ADS 
CAS 
Article 

Google Scholar 
11.Demattê, J. A., Campos, R. C., Alves, M. C., Fiorio, P. R. & Nanni, M. R. Visible–NIR reflectance: A new approach on soil evaluation. Geoderma 121, 95–112 (2004).ADS 
Article 
CAS 

Google Scholar 
12.Viscarra Rossel, R. Fine-resolution multiscale mapping of clay minerals in Australian soils measured with near infrared spectra. J. Geophys. Res. Earth Surf. 116 (2011).13.Viscarra Rossel, R., Walvoort, D., McBratney, A., Janik, L. J. & Skjemstad, J. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131, 59–75 (2006).14.Viscarra Rossel, R. & Lark, R. Improved analysis and modelling of soil diffuse reflectance spectra using wavelets. Eur. J. Soil Sci. 60, 453–464 (2009).15.Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010).Article 

Google Scholar 
16.Næs, T. & Martens, H. Principal component regression in NIR analysis: Viewpoints, background details and selection of components. J. Chemom. 2, 155–167 (1988).Article 

Google Scholar 
17.Geladi, P. & Kowalski, B. R. Partial least-squares regression: A tutorial. Anal. Chim. Acta 185, 1–17 (1986).CAS 
Article 

Google Scholar 
18.Rossel, R. V. Robust modelling of soil diffuse reflectance spectra by “bagging-partial least squares regression”. J. Near Infrared Spectrosc. 15, 39–47 (2007).19.Viscarra Rossel, R. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54 (2010).20.Tsakiridis, N. L., Keramaris, K. D., Theocharis, J. B. & Zalidis, G. C. Simultaneous prediction of soil properties from VNIR–SWIR spectra using a localized multi-channel 1-d convolutional neural network. Geoderma 367, 114208 (2020).ADS 
CAS 
Article 

Google Scholar 
21.Yang, J., Wang, X., Wang, R. & Wang, H. Combination of convolutional neural networks and recurrent neural networks for predicting soil properties using Vis–NIR spectroscopy. Geoderma 380, 114616 (2020).ADS 
CAS 
Article 

Google Scholar 
22.Shen, Z. & Viscarra Rossel, R. A. Automated spectroscopic modelling with optimised convolutional neural networks. Sci. Rep. 11, 208. https://doi.org/10.1038/s41598-020-80486-9 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
23.Li, F., Wang, L., Liu, J., Wang, Y. & Chang, Q. Evaluation of leaf n concentration in winter wheat based on discrete wavelet transform analysis. Remote Sens. 11, 1331 (2019).ADS 
Article 

Google Scholar 
24.Meng, X. et al. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. Int. J. Appl. Earth Obs. Geoinf. 89, 102111 (2020).Article 

Google Scholar 
25.Jiang, B. Geospatial analysis requires a different way of thinking: The problem of spatial heterogeneity. GeoJournal 80, 1–13 (2015).Article 

Google Scholar 
26.Song, Y., Wang, J., Ge, Y. & Xu, C. An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data. GISci. Remote Sens. 57, 593–610 (2020).Article 

Google Scholar 
27.Yang, Z. et al. The effect of environmental heterogeneity on species richness depends on community position along the environmental gradient. Sci. Rep. 5, 1–7 (2015).
Google Scholar 
28.Jenny, H. Factors of Soil Formation (McGraw-Hill, 1941).29.Ye, H. et al. Effects of different sampling densities on geographically weighted regression kriging for predicting soil organic carbon. Spat. Stat. 20, 76–91 (2017).MathSciNet 
Article 

Google Scholar 
30.Viscarra Rossel, R. & Webster, R. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database. Eur. J. Soil Sci. 63. https://doi.org/10.1111/j.1365-2389.2012.01495.x (2012).31.Sila, A., Pokhariyal, G. & Shepherd, K. Evaluating regression-kriging for mid-infrared spectroscopy prediction of soil properties in western Kenya. Geoderma Reg. 10, 39–47 (2017).Article 

Google Scholar 
32.Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Wiley, 2003).33.Brunsdon, C., Fotheringham, A. S. & Charlton, M. E. Geographically weighted regression: A method for exploring spatial nonstationarity. Geogr. Anal. 28, 281–298 (1996).Article 

Google Scholar 
34.Bidanset, P. E. & Lombard, J. R. The effect of kernel and bandwidth specification in geographically weighted regression models on the accuracy and uniformity of mass real estate appraisal. J. Prop. Tax Assess. Admin. 11, 5–14 (2014).
Google Scholar 
35.Brunsdon, C., Fotheringham, A. & Charlton, M. Geographically weighted summary statistics? A framework for localised exploratory data analysis. Comput. Environ. Urban Syst. 26, 501–524 (2002).MATH 
Article 

Google Scholar 
36.Comber, A. et al. The GWR route map: A guide to the informed application of geographically weighted regression. arXiv preprint arXiv:2004.06070 (2020).37.Fotheringham, A. S., Yang, W. & Kang, W. Multiscale geographically weighted regression (MGWR). Ann. Am. Assoc. Geogr. 107, 1247–1265 (2017).
Google Scholar 
38.Yu, H. et al. Inference in multiscale geographically weighted regression. Geogr. Anal. 52, 87–106 (2020).Article 

Google Scholar 
39.Wheeler, D. & Tiefelsdorf, M. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J. Geogr. Syst. 7, 161–187 (2005).Article 

Google Scholar 
40.Harris, P., Fotheringham, A. S. & Juggins, S. Robust geographically weighted regression: A technique for quantifying spatial relationships between freshwater acidification critical loads and catchment attributes. Ann. Assoc. Am. Geogr. 100, 286–306 (2010).Article 

Google Scholar 
41.Harris, P., Brunsdon, C., Lu, B., Nakaya, T. & Charlton, M. Introducing bootstrap methods to investigate coefficient non-stationarity in spatial regression models. Spat. Stat. 21, 241–261 (2017).MathSciNet 
Article 

Google Scholar 
42.Cho, S.-H., Lambert, D. M. & Chen, Z. Geographically weighted regression bandwidth selection and spatial autocorrelation: An empirical example using Chinese agriculture data. Appl. Econ. Lett. 17, 767–772 (2010).Article 

Google Scholar 
43.Lu, B., Yang, W., Ge, Y. & Harris, P. Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths. Comput. Environ. Urban Syst. 71, 41–57 (2018).Article 

Google Scholar 
44.Arabameri, A., Pradhan, B. & Rezaei, K. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in gis. J. Environ. Manag. 232, 928–942 (2019).Article 

Google Scholar 
45.Li, X. et al. Mapping soil organic carbon and total nitrogen in croplands of the corn belt of northeast china based on geographically weighted regression kriging model. Comput. Geosci. 135, 104392 (2020).CAS 
Article 

Google Scholar 
46.Cao, K., Diao, M. & Wu, B. A big data-based geographically weighted regression model for public housing prices: A case study in Singapore. Ann. Am. Assoc. Geogr. 109, 173–186 (2019).
Google Scholar 
47.Ge, Y. et al. Geographically weighted regression-based determinants of malaria incidences in northern China. Trans. GIS 21, 934–953 (2017).Article 

Google Scholar 
48.Viscarra Rossel, R. A. & Hicks, W. S. Soil organic carbon and its fractions estimated by visible–near infrared transfer functions. Eur. J. Soil Sci. 66(3), 438–450 (2015).CAS 
Article 

Google Scholar 
49.Wight, J. P., Ashworth, A. J. & Allen, F. L. Organic substrate, clay type, texture, and water influence on NIR carbon measurements. Geoderma 261, 36–43 (2016).ADS 
CAS 
Article 

Google Scholar 
50.Costa, L. R., Tonoli, G. H. D., Milagres, F. R. & Hein, P. R. G. Artificial neural network and partial least square regressions for rapid estimation of cellulose pulp dryness based on near infrared spectroscopic data. Carbohyd. Polym. 224, 115186 (2019).Article 
CAS 

Google Scholar 
51.Murphy, R. J., Schneider, S., Taylor, Z. & Nieto, J. Mapping clay minerals in an open-pit mine using hyperspectral imagery and automated feature extraction. In Vertical Geology, From Remote Sensing to 3D Geological Modelling. Proceedings of the first Vertical Geology Conference, Lausanne, Switzerland, 5–7 (2014).52.Todorova, M. H. & Atanassova, S. L. Near infrared spectra and soft independent modelling of class analogy for discrimination of chernozems, luvisols and vertisols. J. Near Infrared Spectrosc. 24, 271–280 (2016).ADS 
CAS 
Article 

Google Scholar 
53.Stenberg, B., Viscarra Rossel, R., Mouazen, A. & Wetterlind, J. Visible and Near Infrared Spectroscopy in Soil Science, vol. 107 (Academic Press, 2010).54.Harris, P., Fotheringham, A., Crespo, R. & Charlton, M. The use of geographically weighted regression for spatial prediction: An evaluation of models using simulated data sets. Math. Geosci. 42, 657–680 (2010).MathSciNet 
CAS 
MATH 
Article 

Google Scholar 
55.Department of Primary Industries and Regional Development, Western Australia. South West Agricultural Region (dpird-008) (2020).56.Australian Bureau of Statistics. Value of Agricultural Commodities Produced, Australia (2020).57.Department of Primary Industries and Regional Development, Western Australia. Western Australian Wheat Industry (2019).58.Rayment, G. E. & Lyons, D. J. Soil Chemical Methods—Australasia (CSIRO Publishing, 2010).59.Dolui, S. et al. Structural correlation-based outlier rejection (score) algorithm for arterial spin labeling time series. J. Magn. Reson. Imaging 45, 1786–1797 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
60.Pollet, T. V. & van der Meij, L. To remove or not to remove: The impact of outlier handling on significance testing in testosterone data. Adapt. Hum. Behav. Physiol. 3, 43–60 (2017).Article 

Google Scholar 
61.Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).Article 

Google Scholar 
62.Mallat, S. G. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11, 674–693 (1989).ADS 
MATH 
Article 

Google Scholar 
63.Whitcher, B. waveslim: Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing (2020).64.O’brien, R. M. A caution regarding rules of thumb for variance inflation factors. Qual. Quant. 41, 673–690 (2007).65.Akinwande, M. O. et al. Variance inflation factor: As a condition for the inclusion of suppressor variable (s) in regression analysis. Open J. Stat. 5, 754 (2015).Article 

Google Scholar 
66.Webster, R. & Oliver, M. A. Sample adequately to estimate variograms of soil properties. J. Soil Sci. 43, 177–192 (1992).Article 

Google Scholar 
67.Atteia, O., Dubois, J.-P. & Webster, R. Geostatistical analysis of soil contamination in the Swiss Jura. Environ. Pollut. 86, 315–327 (1994).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Brunsdon, C., Fotheringham, S. & Charlton, M. Geographically weighted regression-modelling spatial non-stationarity. J. R. Stat. Soc. Ser. D (The Statistician) 47, 431–443 (1998).Article 

Google Scholar 
69.Gollini, I., Lu, B., Charlton, M., Brunsdon, C. & Harris, P. Gwmodel: An r package for exploring spatial heterogeneity using geographically weighted models. arXiv preprint arXiv:1306.0413 (2013).70.Lu, B., Harris, P., Charlton, M. & Brunsdon, C. The GWmodel R package: Further topics for exploring spatial heterogeneity using geographically weighted models. Geo Spat. Inf. Sci. 17, 85–101 (2014).Article 

Google Scholar 
71.Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Science & Business Media, 2009).72.Burnham, K. P. & Anderson, D. R. A practical information-theoretic approach. Model selection and multimodel inference 2 (2002).73.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, Austria 2020).74.Mevik, B.-H., Wehrens, R., Liland, K. H. & Hiemstra, P. pls: Partial Least Squares and Principal Component Regression (2020).75.Bivand, R., Yu, D., Nakaya, T. & Garcia-Lopez, M. spgwr: Geographically weighted regression. R Package Version 0.6-34. http://cran.r-project.org/web/packages/spgwr/. Accessed August 30th 2020 (2020). More