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    Dead trees play an under-appreciated role in climate change

    NATURE PODCAST
    01 September 2021

    Dead trees play an under-appreciated role in climate change

    How insects help release carbon stored in forests, and the upcoming biodiversity summit COP 15.

    Shamini Bundell

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    Nick Petrić Howe

    Shamini Bundell

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    In this episode:00:44 Fungi, insects, dead trees and the carbon cycleAcross the world forests play a huge role in the carbon cycle, removing enormous amounts of carbon dioxide from the atmosphere. But when those trees die, some of that carbon goes back into the air. A new project studies how fast dead wood breaks down in different conditions, and the important role played by insects.Research Article: Seibold et al.09:37 Research HighlightsMassive stars make bigger planets, and melting ice moves continents.Research Highlight: Why gassy planets are bigger around more-massive starsResearch Highlight: So much ice is melting that Earth’s crust is moving12:04 The UN’s Convention on Biological DiversityAfter several delays, the fifteenth Conference of the Parties (COP 15) to the United Nations Convention on Biological Diversity, is now slated to take place next year. Even communicating the issues surrounding biodiversity loss has been a challenge, and reaching the targets due to be set at the upcoming meeting will be an even bigger one.Editorial: The scientific panel on biodiversity needs a bigger role 19:32 Briefing ChatWe discuss some highlights from the Nature Briefing. This time, cannibal cane toads and a pterosaur fossil rescued from smugglers.News: Australia’s cane toads evolved as cannibals with frightening speedResearch Highlight: A plundered pterosaur reveals the extinct flyer’s extreme headgearNational Geographic: Stunning fossil seized in police raid reveals prehistoric flying reptile’s secretsSubscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.Never miss an episode: Subscribe to the Nature Podcast on Apple Podcasts, Google Podcasts, Spotify or your favourite podcast app. Head here for the Nature Podcast RSS feed.

    doi: https://doi.org/10.1038/d41586-021-02391-z

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    Impact of shredding degree on papermaking potential of recycled waste

    1.Campbell, W. B. The Cellulose-Water Relationship in Papermaking (Dept Of Interior, Forest Service Bulletin, 1933).
    Google Scholar 
    2.Przybysz, K. & Wandelt, P. Pulp quality control system Part 3 Fiber strength. Przeglad Pap. 61, 283–286 (2005).
    Google Scholar 
    3.Horn, R. A. Morphology of Wood Pulp Fiber from Softwoods and Influence on Paper Strength. Research Paper FPL-242 (U.S. Department of Agriculture, 1974).
    Google Scholar 
    4.Joutsimo, O., Wathén, R. & Tamminen, T. Effects of fiber deformations on pulp sheet properties and fiber strength. Pap. Puu-Pap. Tim. 87, 392–397 (2005).CAS 

    Google Scholar 
    5.Kerekes, R. & Senger, J. J. Characterizing refining action in low-consistency refiners by forces on fibres. J. Pulp Pap. Sci. 32, 1–8 (2006).CAS 

    Google Scholar 
    6.Karlström, A. & Eriksson, K. Fiber energy efficiency. Part 2: Forces acting on the refiner bars. Nord. Pulp Pap. Res. J. 06, 332–343 (2014).Article 

    Google Scholar 
    7.Zeng, X., Retulainen, E., Heinemann, S. & Fu, S. Fibre deformations induced by different mechanical treatment and their effect on zero-span strength. Nord. Pulp Paper Res. J. 27, 335–342 (2012).CAS 
    Article 

    Google Scholar 
    8.Joutsimo, O. & Asikainen, S. Effect of fiber wall pore structure on pulp sheet density of softwood kraft pulp fibers. BioRes. 8, 2719–2737 (2013).Article 

    Google Scholar 
    9.Tingjie, C. et al. Effect of refining on physical properties and paper strength of pinus massoniana and china fir cellulose fibers. BioRes. 11, 7839–7848 (2016).
    Google Scholar 
    10.Laine, C., Wang, X. S., Tenkanen, M. & Varhimo, A. Changes in the fiber wall during refining of bleached pine kraft pulp. Holzforschung 58, 233–240 (2004).CAS 
    Article 

    Google Scholar 
    11.Gharehkhani, S. et al. Basic effects of pulp refining on fiber properties: A review. Carbohydr. Polym. 115, 785–803 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.El-Sharkawy, K., Haavisto, S., Koskenhely, K. & Paulapuro, H. Effect of fiber flocculation and filling design on refiner loadability and refining characteristics. BioRes. 3, 403–424 (2008).Article 

    Google Scholar 
    13.Kerekes, R. Energy and forces in refining. J. Pulp Pap. Sci. 36, 10–15 (2010).CAS 

    Google Scholar 
    14.O’Rourke, D. Nongovernmental organization strategies to influence global production and consumption. J. Ind. Ecol. 9, 115–128 (2005).Article 

    Google Scholar 
    15.Holik, H. Handbook of Paper and Board 2nd edn. (Willey-VCH, 2013).Book 

    Google Scholar 
    16.Przybysz, K. Fibrillation of cellulose fibers. Przemysl Chem. 82, 1149–1151 (2003).CAS 

    Google Scholar 
    17.Ferritsius, R. et al. Development of fibre properties in full scale HC and LC refining. in 2016 International Mechanical Pulping Conference, Jacksonville, 26–28 (2016).18.Kane, M. W. Beating, fiber length distributions and tensile strength-part. Pulp Pap. Canada 60, 308–359 (1959).
    Google Scholar 
    19.Hartman, R. R. Mechanical Treatment of Pulps for Property Development. PhD Dissertation, Institute of Paper Science and Technology (1984).20.Constable, M. The paper shredder: Trails of law. Law Text Culture 23, 276–293 (2019).
    Google Scholar 
    21.Japanese Paper Recycle, Paper Recycling Promotion Center http://www.prpc.or.jp/document/publications/japan/.22.Paper Recycling Facts, University of Southern Indiana https://www.usi.edu/recycle/paper-recycling-facts/.23.Chauhan, V. S., Kumar, N., Kumar, M. & Thapar, S. K. Weighted average fiber length: An important parameter in papermaking. Taiwan Lin Ye Ke Xue 28, 51–65 (2013).
    Google Scholar 
    24.Wangaard, F. F. & Woodson, G. E. Fiber length–fiber strength, interrelationship for slash pine and its effect on pulp–sheet properties. Wood Sci. 5, 235–240 (1973).
    Google Scholar 
    25.Perng, Y. S., Wang, I. C., Cheng, Y. L. & Chen, Y. C. Effects of fiber morphological characteristics and refining on handsheet properties. Taiwan Lin Ye Ke Xue 24, 127–139 (2009).
    Google Scholar 
    26.Choi, E. Y. & Cho, B. U. Effect of beating and water impregnation on fiber swelling and paper properties. J. Korea TAPPI 45, 88–95 (2013).CAS 

    Google Scholar 
    27.Pruden, B. The effect of fines on paper properties. Pap. Technol. 46, 19–26 (2005).
    Google Scholar 
    28.Kibblewhite, R. P. Interrelations between pulp refining treatments, fibre and pulp fines quality, and pulp freeness. Pap. Puu-Pap. Tim. 57, 519–526 (1975).
    Google Scholar 
    29.Olejnik, K. Effect of the free swelling of refined cellulose fibres on the mechanical properties of paper. Fibres Text. East. Eur. 20, 113–116 (2012).CAS 

    Google Scholar 
    30.Sundblad, S. Predictions of Pulp and Paper Properties Based on Fiber Morphology. Master Thesis in Macromolecular Materials, KTH Vetenskap Och Konst, Stockholm, Sweden (2015).31.Retulainen, E. The Role of Fibre Bonding in Paper Properties (National Technical Information Service, Espoo, 1997).
    Google Scholar 
    32.Hietanen, S. E. K. Fundamental aspects of the refining process. Pap. Puu-Pap. Tim. 72, 158–170 (1990).CAS 

    Google Scholar 
    33.Wang, X., Maloney, T. & Paulapuro, H. Fibre fibrillation and its impact on sheet properties. Pap. Puu-Pap. Tim. 89, 148–151 (2007).CAS 

    Google Scholar 
    34.Lindqvist, H. et al. The effect of fibre properties, fines content and surfactant addition on dewatering, wet and dry web properties. Nord. Pulp Pap. Res. J. 27, 104–111 (2012).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    35.Kekäläinen, K., Illikainen, M. & Niinimäki, J. Morphological changes in never-dried kraft fibers under mechanical shearing. Cellulose 19, 879–889 (2012).Article 
    CAS 

    Google Scholar 
    36.Heymer, J. O., Olson, J. A. & Kerekes, R. The role of multiple loading cycles on pulp in refiners. Nord. Pulp Pap. Res. 26, 283–287 (2018).Article 

    Google Scholar 
    37.Vishtal, A. & Retulainen, E. Boosting the extensibility potential of fibre networks: A review. BioRes. 9, 7933–7983 (2014).Article 

    Google Scholar 
    38.Cheng, Q., Wang, J., McNeel, J. & Jacobson, P. Water retention value measurements of cellulosic materials using a centrifuge technique. BioRes. 5, 1945–1954 (2010).CAS 

    Google Scholar 
    39.Scallan, A. M. & Carles, J. The correlation of the water retention value with the fibre saturation point. Sven Papperstidning 75, 699–703 (1972).CAS 

    Google Scholar 
    40.Bäckström, M., Kolar, M. & Htun, M. Characterisation of fines from unbleached kraft pulps and their impact on sheet properties. Holzforschung 62, 546–552 (2008).Article 
    CAS 

    Google Scholar 
    41.Ferreira, P. J., Matos, S. & Figueiredo, M. M. Size characterization of fibres and fines in hardwood kraft pulps. Part. Part. Syst. Charact. 16, 20–24 (1999).CAS 
    Article 

    Google Scholar 
    42.Ciesielski, K. & Olejnik, K. Application of neural networks for estimation of paper properties based on refined pulp properties. Fibres Text. East. Eur. 5, 126–132 (2014).
    Google Scholar 
    43.Paavilainen, L. Importance of particle size: fibre length and fines: for the characterization of softwood kraft pulp. Pap. Puu-Pap. Tim. 72, 516–526 (1990).CAS 

    Google Scholar 
    44.Hai, L. V., Park, H. J. & Seo, Y. B. Effect of PFI mill and Valley beater refining on cellulose degree of polymerization, alpha cellulose contents, and crystallinity of wood and cotton fibers. J. Korea TAPPI 45, 27–33 (2013).CAS 

    Google Scholar 
    45.Wathén, R. Studies on Fiber Strength and its Effect on Paper Properties. Dissertation for the degree of Doctor of Science in Technology, KCL Communications 11, Helsinki University of Technology (2006).46.Motamedian, H. R., Halilovic, A. E. & Kulachenko, A. Mechanisms of strength and stiffness improvement of paper after PFI refining with a focus on the effect of fines. Cellulose 26, 4099–4124 (2019).Article 

    Google Scholar 
    47.Nordström, B. & Hermansson, L. Effect of fiber length on formation and strength efficiency in twin-wire roll forming. Nord. Pulp Pap. Res. 32, 119–125 (2017).Article 

    Google Scholar 
    48.Biermann, C. J. Refining and Pulp Characterization. Handbook of Pulping and Papermaking 138–139 (Academic Press, 1996).
    Google Scholar 
    49.Jang, H. F. & Seth, R. S. Determining the mean values for fibre physical properties. Nord. Pulp Pap. Res. J. 19, 372–378 (2004).CAS 
    Article 

    Google Scholar 
    50.Bajpai, P. The Pulp and Paper Industry. Pulp and Paper Industry: Emerging Waste Water Treatment Technologies 23–25 (Elesiver, 2017).
    Google Scholar 
    51.Fišerová, M., Gigac, J. & Balberčák, J. Relationship between fibre characteristics and tensile strength of hardwood and softwood kraft pulps. Cell. Chem. Technol. 44, 249–253 (2010).
    Google Scholar 
    52.Johansson, A. Correlations Between Fibre Properties and Paper Properties. Master Thesis in Pulp Technology, KTH Vetenskap Och Konst (2011).53.Sjöberg, J. & Höglund, H. Refining system for sack paper pulp: Part 1 HC refining under pressurised conditions and subsequent LC refining. Nord. Pulp Pap. Res. 20, 320–328 (2005).Article 

    Google Scholar 
    54.Larsson, P. T., Lindström, T., Carlsson, L. A. & Fellers, C. Fiber length and bonding effects on tensile strength and toughness of kraft paper. J. Mater. Sci. 53, 3006–3015 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    55.Watson, A. J. & Dadswell, H. E. Influence of fibre morphology on paper properties. Part 1: fibre length. Appita J. 14, 168–178 (1961).CAS 

    Google Scholar 
    56.Horn, R. A. Morphology of Pulp Fiber from Hardwoods and Influence on Paper Strength. USDA Forest Service, Research Paper FPL 312, Forest Products Laboratory, 1–10 (1978).57.Seth, R. S. The measurement and significance of fines. Pulp Pap. Canada 104, 41–44 (2003).CAS 

    Google Scholar 
    58.Odabas, N., Henniges, U., Potthast, A. & Rosenau, T. Cellulosic fines: properties and effects. Prog. Mater. Sci. 83, 574–594 (2016).CAS 
    Article 

    Google Scholar 
    59.Sirviö, J. & Nurminen, I. Systematic changes in paper properties caused by fines. Pulp Pap. Canada 105, 39–42 (2004).
    Google Scholar 
    60.Bossu, J. et al. Fine cellulosic materials produced from chemical pulp: The combined effect of morphology and rate of addition on paper properties. Nanomaterials 9, 321 (2019).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    61.Niskanen, K. (ed.) Paper Physics, Papermaking Science and Technology, Book 16 (Finnish Paper Engineers Association and TAPPI, 1998).
    Google Scholar 
    62.Maloney, T. C., Todorovic, A. & Paulapuro, H. The effect of fiber swelling on press dewatering. Nord. Pulp Pap. Res. 13, 285–291 (1998).CAS 
    Article 

    Google Scholar 
    63.Fischer, W. J. et al. Pulp fines-characterization, sheet formation, and comparison to microfibrillated cellulose. Polymers 9, 366–378 (2017).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    64.Park, J. Y., Melani, L., Lee, H. & Kim, H. J. Effect of pulp fibers on the surface softness component of hygiene paper. Holzforschung 74, 497–504 (2020).CAS 
    Article 

    Google Scholar 
    65.Jonsson, D. K. et al. Energy at your service: Highlighting energy usage systems in the context of energy efficiency analysis. Energy Effic. 4, 355–369 (2011).Article 

    Google Scholar  More

  • in

    Selecting when to bloom

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    Longitudinal patterns in the skin microbiome of wild, individually marked frogs from the Sierra Nevada, California

    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

  • in

    The contribution of insects to global forest deadwood decomposition

    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

  • in

    Long-term data reveal unimodal responses of ground beetle abundance to precipitation and land use but no changes in taxonomic and functional diversity

    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

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    How deregulation, drought and increasing fire impact Amazonian biodiversity

    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