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    Increased microbial expression of organic nitrogen cycling genes in long-term warmed grassland soils

    1.Schmidt MWI, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, et al. Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.CAS 
    PubMed 

    Google Scholar 
    2.Bond-Lamberty B, Bailey VL, Chen M, Gough CM, Vargas R. Globally rising soil heterotrophic respiration over recent decades. Nature. 2018;560:80–3.CAS 
    PubMed 

    Google Scholar 
    3.Bradford MA. Thermal adaptation of decomposer communities in warming soils. Front Microbiol. 2013;4:1–16.
    Google Scholar 
    4.Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Jansson JK, Hofmockel KS. Soil microbiomes and climate change. Nat Rev Microbiol. 2020;18:35–46.CAS 
    PubMed 

    Google Scholar 
    6.Liu L, Greaver TL. A global perspective on belowground carbon dynamics under nitrogen enrichment. Ecol Lett. 2010;13:819–28.PubMed 

    Google Scholar 
    7.Knicker H. Soil organic N – An under-rated player for C sequestration in soils? Soil Biol Biochem. 2011;43:1118–29.CAS 

    Google Scholar 
    8.Soong JL, Fuchslueger L, Marañon-Jimenez S, Torn MS, Janssens IA, Peñuelas J, et al. Microbial carbon limitation: The need for integrating microorganisms into our understanding of ecosystem carbon cycling. Glob Chang Biol. 2020;26:1953–61.
    Google Scholar 
    9.Mooshammer M, Wanek W, Hämmerle I, Fuchslueger L, Hofhansl F, Knoltsch A, et al. Adjustment of microbial nitrogen use efficiency to carbon:nitrogen imbalances regulates soil nitrogen cycling. Nat Commun. 2014;5:1–7.
    Google Scholar 
    10.Geisseler D, Horwath WR, Joergensen RG, Ludwig B. Pathways of nitrogen utilization by soil microorganisms – a review. Soil Biol Biochem. 2010;42:2058–67.CAS 

    Google Scholar 
    11.Wang X, Wang C, Cotrufo MF, Sun L, Jiang P, Liu Z, et al. Elevated temperature increases the accumulation of microbial necromass nitrogen in soil via increasing microbial turnover. Glob Chang Biol. 2020;26:5277–89.PubMed 

    Google Scholar 
    12.Simpson AJ, Simpson MJ, Smith E, Kelleher BP. Microbially derived inputs to soil organic matter: Are current estimates too low? Environ Sci Technol. 2007;41:8070–6.CAS 
    PubMed 

    Google Scholar 
    13.Kuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol. 2018;16:263–76.CAS 
    PubMed 

    Google Scholar 
    14.Walker TWN, Kaiser C, Strasser F, Herbold CW, Leblans NIW, Woebken D, et al. Microbial temperature sensitivity and biomass change explain soil carbon loss with warming. Nat Climate Change. 2018;8:885–9.CAS 

    Google Scholar 
    15.Marañón-Jiménez S, Peñuelas J, Richter A, Sigurdsson BD, Fuchslueger L, Leblans NIW, et al. Coupled carbon and nitrogen losses in response to seven years of chronic warming in subarctic soils. Soil Biol Biochem. 2019;134:152–61.
    Google Scholar 
    16.Nguyen TTH, Myrold DD, Mueller RS. Distributions of extracellular peptidases across prokaryotic genomes reflect phylogeny and habitat. Front Microbiol. 2019;10:1–14.
    Google Scholar 
    17.Zimmerman AE, Martiny AC, Allison SD. Microdiversity of extracellular enzyme genes among sequenced prokaryotic genomes. ISME J. 2013;7:1187–99.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    18.Beier S, Bertilsson S. Bacterial chitin degradation-mechanisms and ecophysiological strategies. Front Microbiol. 2013;4:1–12.
    Google Scholar 
    19.Kielak AM, Cretoiu MS, Semenov AV, Sørensen SJ, Van, Elsas JD. Bacterial chitinolytic communities respond to chitin and pH alteration in soil. Appl Environ Microbiol. 2013;79:263–72.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Weintraub MN, Schimel JP. Seasonal protein dynamics in Alaskan arctic tundra soils. Soil Biol Biochem. 2005;37:1469–75.CAS 

    Google Scholar 
    21.Boer VM, De Winde JH, Pronk JT, Piper MDW. The genome-wide transcriptional responses of Saccharomyces cerevisiae grown on glucose in aerobic chemostat cultures limited for carbon, nitrogen, phosphorus, or sulfur. J Biol Chem. 2003;278:3265–74.CAS 
    PubMed 

    Google Scholar 
    22.Kolkman A, Daran-Lapujade P, Fullaondo A, Olsthoorn MMA, Pronk JT, Slijper M, et al. Proteome analysis of yeast response to various nutrient limitations. Mol Syst Biol. 2006;2:1–16.
    Google Scholar 
    23.Silberbach M, Hüser A, Kalinowski J, Pühler A, Walter B, Krämer R, et al. DNA microarray analysis of the nitrogen starvation response of Corynebacterium glutamicum. J Biotechnol. 2005;119:357–67.CAS 
    PubMed 

    Google Scholar 
    24.Merrick MJ, Edwards RA. Nitrogen control in bacteria. Microbiol Rev. 1995;59:604–22.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Daebeler A, Abell GCJ, Bodelier PLE, Bodrossy L, Frampton DMF, Hefting MM, et al. Archaeal dominated ammonia-oxidizing communities in Icelandic grassland soils are moderately affected by long-term N fertilization and geothermal heating. Front Microbiol. 2012;3:1–14.
    Google Scholar 
    26.Yeager CM, Kornosky JL, Housman DC, Grote EE, Belnap J, Kuske CR. Diazotrophic community structure and function in two successional stages of biological soil crusts from the colorado plateau and Chihuahuan Desert. Appl Environ Microbiol. 2004;70:973–83.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Malik AA, Swenson T, Weihe C, Morrison EW, Martiny JBH, Brodie EL, et al. Drought and plant litter chemistry alter microbial gene expression and metabolite production. ISME J. 2020;14:2236–47.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Tveit A, Schwacke R, Svenning MM, Urich T. Organic carbon transformations in high-Arctic peat soils: Key functions and microorganisms. ISME J. 2013;7:299–311.CAS 
    PubMed 

    Google Scholar 
    29.Geisen S, Tveit AT, Clark IM, Richter A, Svenning MM, Bonkowski M, et al. Metatranscriptomic census of active protists in soils. ISME J. 2015;9:2178–90.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE. 2008;3:1–13.
    Google Scholar 
    31.Kallenbach CM, Frey SD, Grandy AS. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat Commun. 2016;7:1–10.
    Google Scholar 
    32.Walker TWN, Janssens IA, Weedon JT, Sigurdsson BD, Richter A, Peñuelas J, et al. A systemic overreaction to years versus decades of warming in a subarctic grassland ecosystem. Nat Ecol Evol. 2020;4:101–8.PubMed 

    Google Scholar 
    33.Sigurdsson BD, Wallander H, Gunnarsdóttir GE, Richter A, Sigurðsson P, Leblans NIW, et al. Geothermal ecosystems as natural climate change experiments: the ForHot research site in Iceland as a case study. Icelandic Agric Sci. 2016;29:53–71.
    Google Scholar 
    34.Söllinger A, Séneca J, Dahl MB, Prommer J, Verbruggen E, Sigurdsson BD, et al. Downregulation of the microbial protein biosynthesis machinery in response to weeks, years and decades of soil warming. 2021 Research Square preprint. https://doi.org/10.21203/rs.3.rs-132190/v235.Leblans N. Natural gradients in temperature and nitrogen: Iceland represents a unique environment to clarify long-term global change effects on carbon dynamics. Joint doctoral dissertation. Antwerp University and Agricultural University of Iceland, Reykjavik, Iceland; 2016:1–229.36.Angel R, Claus P, Conrad R. Methanogenic archaea are globally ubiquitous in aerated soils and become active under wet anoxic conditions. ISME J. 2012;6:847–62.CAS 
    PubMed 

    Google Scholar 
    37.Hyatt D, Chen G-L, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:5–11.
    Google Scholar 
    38.Gillespie CS. Fitting heavy tailed distributions: the poweRlaw Package. J Stat Softw. 2015;64:1–16.
    Google Scholar 
    39.El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2018;47:427–32.
    Google Scholar 
    40.Eddy SR. Accelerated profile HMM searches. PLOS Comput Biol. 2011;7:e1002195.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Petersen TN, Brunak S, von Heijne G, Nielsen H. SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8:785–6.CAS 
    PubMed 

    Google Scholar 
    42.Bendtsen JD, Kiemer L, Fausbøll A, Brunak S. Non-classical protein secretion in bacteria. BMC Microbiol. 2005;5:1–13.
    Google Scholar 
    43.Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, et al. PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010;26:1608–15.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Orsi WD. MetaProt: an integrated database of predicted proteins for improved annotation of metaomic datasets. Open Data LMU. 2020. https://doi.org/10.5282/ubm/data.18345.Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2013;42:490–5.
    Google Scholar 
    46.Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2014;12:59–60.PubMed 

    Google Scholar 
    47.Oksanen AJ, Blanchet FG, Kindt R, Legen- P, Minchin PR, Hara RBO, et al. vegan: Community Ecology Package. 2019. https://cran.r-project.org/package=vegan48.Lê S, Josse J, Husson F. FactoMineR: an R package for multivariate analysis. J Stat Softw. 2008;25:1–18.
    Google Scholar 
    49.Kolde R. pheatmap: pretty heatmaps. 2019. https://cran.r-project.org/package=pheatmap50.Noll L, Zhang S, Zheng Q, Hu Y, Wanek W. Wide-spread limitation of soil organic nitrogen transformations by substrate availability and not by extracellular enzyme content. Soil Biol Biochem. 2019;133:37–49.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Schimel JP, Bennett J. Nitrogen mineralization: challenges of a changing paradigm. Ecology. 2004;85:591–602.
    Google Scholar 
    52.Wild B, Ambus P, Reinsch S, Richter A. Resistance of soil protein depolymerization rates to eight years of elevated CO2, warming, and summer drought in a temperate heathland. Biogeochemistry. 2018;140:255–67.CAS 

    Google Scholar 
    53.Wanek W, Mooshammer M, Blöchl A, Hanreich A, Richter A. Determination of gross rates of amino acid production and immobilization in decomposing leaf litter by a novel 15N isotope pool dilution technique. Soil Biol Biochem. 2010;42:1293–302.CAS 

    Google Scholar 
    54.Liang C, Schimel JP, Jastrow JD. The importance of anabolism in microbial control over soil carbon storage. Nat Microbiol. 2017;2:1–6.
    Google Scholar 
    55.Vranova V, Rejsek K, Formanek P. Proteolytic activity in soil: a review. Appl Soil Ecol. 2013;70:23–32.
    Google Scholar 
    56.Schimel JP, Weintraub MN. The implications of exoenzyme activity on microbial carbon and nitrogen limitation in soil: a theoretical model. Soil Biol Biochem. 2003;35:549–63.CAS 

    Google Scholar 
    57.Rawlings ND, Waller M, Barrett AJ, Bateman A. MEROPS: The database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Res. 2014;42:503–9.
    Google Scholar 
    58.Vollmer W, Joris B, Charlier P, Foster S. Bacterial peptidoglycan (murein) hydrolases. FEMS Microbiol Rev. 2008;32:259–86.CAS 
    PubMed 

    Google Scholar 
    59.Vermassen A, Leroy S, Talon R, Provot C, Popowska M, Desvaux M. Cell wall hydrolases in bacteria: Insight on the diversity of cell wall amidases, glycosidases and peptidases toward peptidoglycan. Front Microbiol. 2019;10:1–27.
    Google Scholar 
    60.Donhauser J, Qi W, Bergk-Pinto B, Frey B. High temperatures enhance the microbial genetic potential to recycle C and N from necromass in high-mountain soils. Glob Chang Biol. 2020;27:1365–86.61.Vollmer W, Blanot D, De Pedro MA. Peptidoglycan structure and architecture. FEMS Microbiology Reviews. 2008;32:149–67.CAS 
    PubMed 

    Google Scholar 
    62.Semchenko M, Leff JW, Lozano YM, Saar S, Davison J, Wilkinson A, et al. Fungal diversity regulates plant-soil feedbacks in temperate grassland. Science Adv. 2018;4.63.Saary P, Mitchell AL, Finn RD. Estimating the quality of eukaryotic genomes recovered from metagenomic analysis. Genome Biol. 2020;21:244.PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Climate and land-use changes reduce the benefits of terrestrial protected areas

    1.Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).CAS 

    Google Scholar 
    2.Juffe-Bignoli, D. et al. Protected Planet Report 2014 (UNEP-WCMC, 2014).3.Gray, C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).4.Xu, W. et al. Strengthening protected areas for biodiversity and ecosystem services in China. Proc. Natl Acad. Sci. USA 114, 1601–1606 (2017).CAS 

    Google Scholar 
    5.Naidoo, R. et al. Evaluating the impacts of protected areas on human well-being across the developing world. Sci. Adv. 5, eaav3006 (2019).CAS 

    Google Scholar 
    6.Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
    Google Scholar 
    7.Cazalis, V. et al. Effectiveness of protected areas in conserving tropical forest birds. Nat. Commun. 11, 4461 (2020).8.Elsen, P. R., Monahan, W. B., Dougherty, E. R. & Merenlender, A. M. Keeping pace with climate change in global terrestrial protected areas. Sci. Adv. 6, eaay0814 (2020).
    Google Scholar 
    9.Hoffmann, S., Irl, S. D. H. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 4787 (2019).10.Batllori, E., Parisien, M. A., Parks, S. A., Moritz, M. A. & Miller, C. Potential relocation of climatic environments suggests high rates of climate displacement within the North American protection network. Glob. Change Biol. 23, 3219–3230 (2017).
    Google Scholar 
    11.Ward, M. et al. Just ten percent of the global terrestrial protected area network is structurally connected via intact land. Nat. Commun. 11, 4563 (2020).CAS 

    Google Scholar 
    12.Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).CAS 

    Google Scholar 
    13.Parks, S. A., Carroll, C., Dobrowski, S. Z. & Allred, B. W. Human land uses reduce climate connectivity across North America. Glob. Change Biol. 26, 2944–2955 (2020).
    Google Scholar 
    14.McGuire, J. L., Lawler, J. J., McRae, B. H., Nuñez, T. A. & Theobald, D. M. Achieving climate connectivity in a fragmented landscape. Proc. Natl Acad. Sci. USA 113, 7195–7200 (2016).CAS 

    Google Scholar 
    15.Watson, J. E. M., Iwamura, T. & Butt, N. Mapping vulnerability and conservation adaptation strategies under climate change. Nat. Clim. Change 3, 989–994 (2013).
    Google Scholar 
    16.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
    Google Scholar 
    17.Jones, C., Giorgi, F. & Asrar, G. The coordinated regional downscaling experiment: CORDEX–an international downscaling link to CMIP5. CLIVAR Exch. 16, 34–40 (2011).
    Google Scholar 
    18.Hurtt, G. C. et al. Harmonization of global land-use change and management for the period 850-2100 (LUH2) for CMIP6. Geosci. Model Dev. 13, 5425–5464 (2020).CAS 

    Google Scholar 
    19.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).CAS 

    Google Scholar 
    20.Ordonez, A., Martinuzzi, S., Radeloff, V. C. & Williams, J. W. Combined speeds of climate and land-use change of the conterminous US until 2050. Nat. Clim. Change 4, 811–816 (2014).
    Google Scholar 
    21.UN General Assembly Resolution A/RES/70/1 (UN, 2015).22.Harrop, S. R. ‘Living in harmony with nature’? Outcomes of the 2010 Nagoya conference of the convention on biological diversity. J. Environ. Law 23, 117–128 (2011).
    Google Scholar 
    23.Maxwell, S. L. et al. Area-based conservation in the twenty-first century. Nature 586, 217–227 (2020).CAS 

    Google Scholar 
    24.Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl Acad. Sci. USA 109, 8606–8611 (2012).CAS 

    Google Scholar 
    25.Chen, I. C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).CAS 

    Google Scholar 
    26.Schwalm, C. R., Glendon, S. & Duffy, P. B. RCP8.5 tracks cumulative CO2 emissions. Proc. Natl Acad. Sci. USA 117, 19656–19657 (2020).CAS 

    Google Scholar 
    27.Ando, A. W. & Mallory, M. L. Optimal portfolio design to reduce climate-related conservation uncertainty in the Prairie Pothole Region. Proc. Natl Acad. Sci. USA 109, 6484–6489 (2012).CAS 

    Google Scholar 
    28.Ackerly, D. D. et al. The geography of climate change: implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).
    Google Scholar 
    29.Dobrowski, S. Z. & Parks, S. A. Climate change velocity underestimates climate change exposure in mountainous regions. Nat. Commun. 7, 12349 (2016).30.Hoegh-Guldberg, O. et al. in Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) 175–311 (IPCC, WMO, 2018).31.Sandel, B. et al. The influence of late Quaternary climate-change velocity on species endemism. Science 334, 660–664 (2011).CAS 

    Google Scholar 
    32.Ordonez, A., Williams, J. W. & Svenning, J.-C. Mapping climatic mechanisms likely to favour the emergence of novel communities. Nat. Clim. Change 6, 1104–1109 (2016).
    Google Scholar 
    33.Carroll, C. et al. Scale-dependent complementarity of climatic velocity and environmental diversity for identifying priority areas for conservation under climate change. Glob. Change Biol. 23, 4508–4520 (2017).
    Google Scholar 
    34.Alexander, J. M. et al. Lags in the response of mountain plant communities to climate change. Glob. Change Biol. 24, 563–579 (2018).
    Google Scholar 
    35.Lawler, J. J. et al. Projected land-use change impacts on ecosystem services in the United States. Proc. Natl Acad. Sci. USA 111, 7492–7497 (2014).CAS 

    Google Scholar 
    36.Stein, B. A. et al. Preparing for and managing change: climate adaptation for biodiversity and ecosystems. Front. Ecol. Environ. 11, 502–510 (2013).
    Google Scholar 
    37.Elsen, P. R., Monahan, W. B. & Merenlender, A. M. Global patterns of protection of elevational gradients in mountain ranges. Proc. Natl Acad. Sci. USA 115, 6004–6009 (2018).CAS 

    Google Scholar 
    38.Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).CAS 

    Google Scholar 
    39.Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–495 (2014).CAS 

    Google Scholar 
    40.Fitzpatrick, M. C., Gove, A. D., Sanders, N. & Dunn, R. R. Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia. Glob. Change Biol. 14, 1337–1352 (2008).
    Google Scholar 
    41.Dynesius, M. & Jansson, R. Evolutionary consequences of changes in species’ geographical distributions driven by Milankovitch climate oscillations. Proc. Natl Acad. Sci. USA 97, 9115–9120 (2000).CAS 

    Google Scholar 
    42.Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).CAS 

    Google Scholar 
    43.Tittensor, D. P. et al. Integrating climate adaptation and biodiversity conservation in the global ocean. Sci. Adv. 5, eaay9969 (2019).
    Google Scholar 
    44.Osorio, F., Vallejos, R. & Cuevas, F. SpatialPack: Package for Analysis of Spatial Data. R package version 0.2-3 (2014).45.Williams, K. D. et al. The Met Office Global Coupled model 2.0 (GC2) configuration. Geosci. Model Dev. 8, 1509–1524 (2015).
    Google Scholar 
    46.Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project Phase 5. J. Adv. Model. Earth Syst. https://doi.org/10.1002/jame.20038 (2013).47.Knudsen, E. M. & Walsh, J. E. Northern Hemisphere storminess in the Norwegian Earth System Model (NorESM1-M). Geosci. Model Dev. 9, 2335–2355 (2016).
    Google Scholar 
    48.Brito-Morales, I. et al. Climate velocity can inform conservation in a warming world. Trends Ecol. Evol. 33, 441–457 (2018).
    Google Scholar 
    49.García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an R package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evol. 10, 2195–2202 (2019).
    Google Scholar 
    50.UNEP‐WCMC & IUCN Protected Planet: The World Database on Protected Areas (WDPA, 2018).51.Visconti, P. et al. Protected area targets post-2020. Science 364, eaav6886 (2019).
    Google Scholar 
    52.Farr, T. G. et al. The shuttle radar topography mission. Rev. Geophys. https://doi.org/10.1029/2005RG000183 (2007).53.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).54.Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, 30535 (2012).55.Asamoah, E. F. Climate Velocity and Land-use Instability 1971–2100 (Figshare, 2021); https://doi.org/10.6084/m9.figshare.14852955.v4 More

  • in

    Trees outside of forests as natural climate solutions

    1.Chao, S. Forest Peoples: Numbers Across the World (Forest Peoples Programme, 2021).2.Zomer, R. J. et al. Sci. Rep. 6, 29987 (2016).CAS 
    Article 

    Google Scholar 
    3.Schnell, S., Altrell, D., Ståhl, G. & Kleinn, C. Environ. Monit. Assess. 187, 4197 (2015).Article 

    Google Scholar 
    4.Brandt, M. et al. Nature 587, 78–82 (2020).Article 

    Google Scholar 
    5.Baccini, A. et al. Nat. Clim. Chang. 2, 182–185 (2012).CAS 
    Article 

    Google Scholar 
    6.Miller, D. C., Muñoz-Mora, J. C. & Christiaensen, L. Forest Policy Econ. 84, 47–61 (2017).Article 

    Google Scholar 
    7.Mbow, C., Smith, P., Skole, D., Duguma, L. & Bustamante, M. Curr. Opin. Environ. Sustain. 6, 8–14 (2014).Article 

    Google Scholar 
    8.Brandt, M. et al. Nat. Geosci. 11, 328–333 (2018).CAS 
    Article 

    Google Scholar 
    9.Mbow, C. et al. in Climate Change and Agriculture (ed. Deryng, D.) Ch. 10 (Burleigh Dodds Science Publishing, 2020).10.Akinyemi, F. O., Ghazaryan, G. & Dubovyk, O. Land Degrad. Dev. 32, 158–172 (2021).Article 

    Google Scholar 
    11.Sitch, S. et al. Biogeosciences 12, 653–679 (2015).Article 

    Google Scholar 
    12.Schnell, S., Kleinn, C. & Ståhl, G. Environ. Monit. Assess. 187, 600 (2015).Article 

    Google Scholar 
    13.Hansen, M. C. et al. Science 342, 850–853 (2013).CAS 
    Article 

    Google Scholar 
    14.Kuyah, S. et al. Agrofor. Syst. 86, 267–277 (2012).Article 

    Google Scholar 
    15.Nationally Determined Contributions Under the Paris Agreement Synthesis Report by the Secretariat FCCC/PA/CMA/2021/2/Add 2 (UNFCCC, 2021); https://unfccc.int/documents/26857316.Lohbeck, M. et al. Sci. Rep. 10, 15038 (2020).CAS 
    Article 

    Google Scholar 
    17.Chomba, S., Sinclair, F., Savadogo, P., Bourne, M. & Lohbeck, M. Front. For. Glob. Chang. 3, 571679 (2020).Article 

    Google Scholar 
    18.Griscom, B. W. et al. Proc. Natl Acad. Sci. USA 114, 11645–11650 (2017).CAS 
    Article 

    Google Scholar  More

  • in

    Reduced deforestation and degradation in Indigenous Lands pan-tropically

    1.Weisse, M. & Goldman, E. D. We Lost a Football Pitch of Primary Rainforest Every 6 Seconds in 2019 (World Resources Institute, 2020); https://www.wri.org/blog/2020/06/global-tree-cover-loss-data-20192.Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).CAS 

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

    Google Scholar 
    4.State of the World’s Indigenous Peoples: Rights to Lands, Territories and Resources (UN, 2021).5.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 

    Google Scholar 
    6.Larsen, P. B. et al. Understanding and responding to the environmental human rights defenders crisis: the case for conservation action. Conserv. Lett. 14, e12777 (2020).
    Google Scholar 
    7.Tauli-Corpuz, V., Alcorn, J., Molnar, A., Healy, C. & Barrow, E. Cornered by PAs: adopting rights-based approaches to enable cost-effective conservation and climate action. World Dev. 130, 104923 (2020).
    Google Scholar 
    8.Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).CAS 

    Google Scholar 
    9.Dudley, N. et al. The essential role of other effective area-based conservation measures in achieving big bold conservation targets. Glob. Ecol. Conserv. 15, e00424 (2018).
    Google Scholar 
    10.Zero Draft of the Post-2020 Global Biodiversity Framework CBD/WG2020/2/3 (Convention on Biological Diversity, 2020).11.NGO Concerns Over the Proposed 30% Target for Protected Areas and Absence of Safeguards for Indigenous Peoples and Local Communities (Rainforest Foundation UK, 2021).12.Reyes-García, V. et al. Recognizing Indigenous Peoples’ and local communities’ rights and agency in the post-2020 Biodiversity Agenda. Ambio https://doi.org/10.1007/s13280-021-01561-7 (2021).13.Territories of Life: 2021 Report 52 (ICCA Consortium, 2021); https://report.territoriesoflife.org14.Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).
    Google Scholar 
    15.Fa, J. E. et al. Importance of Indigenous Peoples’ lands for the conservation of intact forest landscapes. Front. Ecol. Environ. 18, 135–140 (2020).
    Google Scholar 
    16.Vergara-Asenjo, G. & Potvin, C. Forest protection and tenure status: the key role of indigenous peoples and protected areas in Panama. Glob. Environ. Change 28, 205–215 (2014).
    Google Scholar 
    17.Blackman, A. & Veit, P. Titled Amazon indigenous communities cut forest carbon emissions. Ecol. Econ. 153, 56–67 (2018).
    Google Scholar 
    18.Walker, W. S. et al. The role of forest conversion, degradation, and disturbance in the carbon dynamics of Amazon indigenous territories and protected areas. Proc. Natl Acad. Sci. USA 117, 3015–3025 (2020).CAS 

    Google Scholar 
    19.Nolte, C., Agrawal, A., Silvius, K. M. & Soares-Filho, B. S. Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 110, 4956–4961 (2013).CAS 

    Google Scholar 
    20.Schleicher, J., Peres, C. A., Amano, T., Llactayo, W. & Leader-Williams, N. Conservation performance of different conservation governance regimes in the Peruvian Amazon. Sci. Rep. 7, 11318 (2017).
    Google Scholar 
    21.Jusys, T. Changing patterns in deforestation avoidance by different protection types in the Brazilian Amazon. PLoS ONE 13, e0195900 (2018).
    Google Scholar 
    22.State of the World’s Indigenous Peoples (UN, 2009).23.Jackson, J. E. & Warren, K. B. Indigenous movements in Latin America, 1992–2004: controversies, ironies, new directions. Annu. Rev. Anthropol. 34, 549–573 (2005).
    Google Scholar 
    24.Vancutsem, C. et al. Long-term (1990–2019) monitoring of forest cover changes in the humid tropics. Sci. Adv. 7, eabe1603 (2021).
    Google Scholar 
    25.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 

    Google Scholar 
    26.Stuart, E. A. & Rubin, D. B. in Best Practices in Quantitative Methods (ed. Osborne, J.) 155–176 (SAGE Publications, 2008).27.Pfaff, A., Robalino, J., Lima, E., Sandoval, C. & Herrera, L. D. Governance, location and avoided deforestation from protected areas: greater restrictions can have lower impact, due to differences in location. World Dev. 55, 7–20 (2014).
    Google Scholar 
    28.Leberger, R., Rosa, I. M. D., Guerra, C. A., Wolf, F. & Pereira, H. M. Global patterns of forest loss across IUCN categories of protected areas. Biol. Conserv. 241, 108299 (2020).
    Google Scholar 
    29.Borrini-Feyerabend, G. et al. Governance of Protected Areas: From Understanding to Action (IUCN, 2013).30.Who Owns the World’s Land? A Global Baseline of Formally Recognized Indigenous and Community Land Rights (Rights and Resources Initiative, 2015); https://rightsandresources.org/wp-content/uploads/GlobalBaseline_web.pdf31.Dubertret, F. & Alden Wily, L. Percent of Indigenous and Community Lands (Landmark, 2015).32.Under the Cover of COVID: New Laws in Asia Favor Business at the Cost of Indigenous Peoples’ and Local Communities’ Land and Territorial Rights (Rights and Resources Initiative, 2020).33.Domínguez, L. & Luoma, C. Decolonising conservation policy: how colonial land and conservation ideologies persist and perpetuate indigenous injustices at the expense of the environment. Land 9, 65 (2020).
    Google Scholar 
    34.Pyhälä, A., Orozco, A. O. & Counsell, S. Protected Areas in the Congo Basin: Failing both people and biodiversity? (FAO, 2016).35.Pearson, T. R. H., Brown, S., Murray, L. & Sidman, G. Greenhouse gas emissions from tropical forest degradation: an underestimated source. Carbon Balance Manag. 12, 3 (2017).
    Google Scholar 
    36.Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).CAS 

    Google Scholar 
    37.Hansen, A. J. et al. A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nat. Ecol. Evol. 4, 1377–1384 (2020).
    Google Scholar 
    38.Milodowski, D. T. et al. The impact of logging on vertical canopy structure across a gradient of tropical forest degradation intensity in Borneo. J. Appl. Ecol. 58, 1764–1775 (2021).
    Google Scholar 
    39.Benítez-López, A., Santini, L., Schipper, A. M., Busana, M. & Huijbregts, M. A. J. Intact but empty forests? Patterns of hunting-induced mammal defaunation in the tropics. PLoS Biol. 17, e3000247 (2019).
    Google Scholar 
    40.Miettinen, J., Stibig, H.-J. & Achard, F. Remote sensing of forest degradation in Southeast Asia—aiming for a regional view through 5–30 m satellite data. Glob. Ecol. Conserv. 2, 24–36 (2014).
    Google Scholar 
    41.Yuliani, E. L. et al. Keeping the land: indigenous communities’ struggle over land use and sustainable forest management in Kalimantan, Indonesia. Ecol. Soc. 23, art49 (2018).
    Google Scholar 
    42.Berkes, F. Sacred Ecology (Routledge, 2017).43.Sheil, D., Boissière, M. & Beaudoin, G. Unseen sentinels: local monitoring and control in conservation’s blind spots. Ecol. Soc. 20, 39 (2015).
    Google Scholar 
    44.Sasaoka, M. & Laumonier, Y. Suitability of local resource management practices based on supernatural enforcement mechanisms in the local social-cultural context. Ecol. Soc. 17, 6 (2012).
    Google Scholar 
    45.Asante, E. A., Ababio, S. & Boadu, K. B. The use of indigenous cultural practices by the Ashantis for the conservation of forests in Ghana. SAGE Open 7, 215824401668761 (2017).
    Google Scholar 
    46.Schwartzman, S. et al. The natural and social history of the indigenous lands and protected areas corridor of the Xingu River basin. Philos. Trans. R. Soc. B 368, 20120164 (2013).
    Google Scholar 
    47.Hayes, T. M. & Murtinho, F. Are indigenous forest reserves sustainable? An analysis of present and future land-use trends in Bosawas, Nicaragua. Int. J. Sustain. Dev. World Ecol. 15, 497–511 (2008).
    Google Scholar 
    48.Tellman, B. et al. Illicit drivers of land use change: narcotrafficking and forest loss in central America. Glob. Environ. Change 63, 102092 (2020).
    Google Scholar 
    49.Bryan, J. For Nicaragua’s indigenous communities, land rights in name only: delineating boundaries among indigenous and black communities in eastern Nicaragua was supposed to guaranteed their land rights. Instead, it did the opposite. NACLA Rep. Am. 51, 55–64 (2019).
    Google Scholar 
    50.Seymour, F., La Vina, T. & Hite, K. Evidence Linking Community-level Tenure and Forest Condition: An Annotated Bibliography (Climate and Land Use Alliance, 2014).51.Tseng, T.-W. J. et al. Influence of land tenure interventions on human well-being and environmental outcomes. Nat. Sustain. 4, 242–251 (2021).
    Google Scholar 
    52.Robinson, B. E. et al. Incorporating land tenure security into conservation: conservation and land tenure security. Conserv. Lett. 11, e12383 (2018).
    Google Scholar 
    53.Smith, D. A., Holland, M. B., Michon, A., Ibáñez, A. & Herrera, F. The hidden layer of indigenous land tenure: informal forest ownership and its implications for forest use and conservation in Panama’s largest collective territory. Int. For. Rev. 19, 478–494 (2017).
    Google Scholar 
    54.Larson, A. M. & Springer, J. Recognition and Respect for Tenure Rights (IUCN, CEESP, CIFOR, 2016).55.Arizona, Y., Wicaksono, M. T. & Vel, J. The role of indigeneity NGOs in the legal recognition of adat communities and customary forests in Indonesia. Asia Pac. J. Anthropol. 20, 487–506 (2019).
    Google Scholar 
    56.Malavasi, M. The map of biodiversity mapping. Biol. Conserv. 252, 108843 (2020).
    Google Scholar 
    57.Witter, R. & Satterfield, T. The ebb and flow of indigenous rights recognitions in conservation policy: indigenous rights recognitions in conservation policy. Dev. Change 50, 1083–1108 (2019).
    Google Scholar 
    58.Dutta, A. et al. Response to a “global safety net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, eabb2824 (2021).
    Google Scholar 
    59.Herrera, D., Pfaff, A. & Robalino, J. Impacts of protected areas vary with the level of government: comparing avoided deforestation across agencies in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 116, 14916–14925 (2019).CAS 

    Google Scholar 
    60.Bebbington, A. J. et al. Resource extraction and infrastructure threaten forest cover and community rights. Proc. Natl Acad. Sci. USA 115, 13164–13173 (2018).CAS 

    Google Scholar 
    61.Johnson, C. J., Venter, O., Ray, J. C. & Watson, J. E. M. Growth‐inducing infrastructure represents transformative yet ignored keystone environmental decisions. Conserv. Lett. https://doi.org/10.1111/conl.12696 (2020).62.Davis, K. F., Yu, K., Rulli, M. C., Pichdara, L. & D’Odorico, P. Accelerated deforestation driven by large-scale land acquisitions in Cambodia. Nat. Geosci. 8, 772–775 (2015).CAS 

    Google Scholar 
    63.Conigliani, C., Cuffaro, N. & D’Agostino, G. Large-scale land investments and forests in Africa. Land Use Policy 75, 651–660 (2018).
    Google Scholar 
    64.Global Land Analysis & Discovery. Global 2010 Tree Cover (30m) (Department of Geographical Sciences, Univ. Maryland, 2013).65.Global Forest Watch. Tree Cover Loss version 1.6 (World Resources Institute, 2019).66.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 
    67.Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC & IUCN, accessed January 2020; www.protectedplanet.net68.Hanson, J. O. wdpar: Interface to the world database on protected areas (CRAN, 2020); https://CRAN.R-project.org/package=wdpar69.Global Forest Watch. Spatial Database of Planted Trees (World Resources Institute, data aaccessed May 2021).70.Transparent World & Global Forest Watch. Tree Plantations (World Resources Institute, date accessed May 2021).71.Nelson, A. & Chomitz, K. M. Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6, e22722 (2011).CAS 

    Google Scholar 
    72.Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
    Google Scholar 
    73.Global Forest Watch. Tree Cover 2000 version 1.2 (World Resources Institute, 2015).74.Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data 5, 180040 (2018).
    Google Scholar 
    75.Nelson, A. et al. A suite of global accessibility indicators. Sci. Data 6, 266 (2019).
    Google Scholar 
    76.Global Roads Open Access Data Set Version 1 (gROADSv1) (1980–2010) (NASA SEDAC, 2013).77.Lloyd, C. T., Sorichetta, A. & Tatem, A. J. High resolution global gridded data for use in population studies. Sci. Data 4, 170001 (2017).
    Google Scholar 
    78.GADM Database of Global Administrative Areas version 3.6 (FAO, 2018).79.Ho, D., Imai, K., King, G. & Stuart, E. matchIt: Nonparametric preprocessing for parametric causal inference (CRAN, 2018); https://CRAN.R-project.org/package=MatchIt80.Wood, S. mgcv: Mixed GAM computation vehicle with automatic smoothness estimation (CRAN, 2019); https://CRAN.R-project.org/package=mgcv More

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    Observed increases in extreme fire weather driven by atmospheric humidity and temperature

    1.Abatzoglou, J. T., Williams, A. P., Boschetti, L., Zubkova, M. & Kolden, C. A. Global patterns of interannual climate-fire relationships (2018). Glob. Change Biol. 24, 5164–5175 (2018).
    Google Scholar 
    2.Littell, J. S., McKenzie, D., Peterson, D. L. & Westerling, A. L. Climate and wildfire area burned in western US ecoprovinces, 1916-2003. Ecol. Appl. 19, 1003–1021 (2009).
    Google Scholar 
    3.Abatzoglou, J. T. & Kolden, C. A. Relationships between climate and macroscale area burned in the western United States. Int. J. Wildland Fire 22, 1003–1020 (2013).
    Google Scholar 
    4.Wang, X. et al. Projected changes in daily fire spread across Canada over the next century. Environ. Res. Lett. 12, 025005 (2017).
    Google Scholar 
    5.Hanes, C. C. et al. Fire-regime changes in Canada over the last half century. Can. J. Res. 49, 256–269 (2019).
    Google Scholar 
    6.Amiro, B. D. et al. Fire weather index system components of large fires in the Canadian boreal forest. Int. J. Wildland Fire 13, 391–400 (2004).
    Google Scholar 
    7.Flannigan, M. D., Krawchuck, M. A., de Groot, W. J., Wotton, B. M. & Gowman, L. M. Implications of changing climate for global wildland fire. Int. J. Wildland Fire 18, 483–507 (2009).
    Google Scholar 
    8.Bowman, D. M. J. S. et al. Human exposure and sensitivity to globally extreme wildfire events. Nat. Ecol. Evol. 1, 0058 (2017).
    Google Scholar 
    9.Coogan, S. C. P., Robinne, F.-N., Jain, P. & Flannigan, M. D. Scientists’ warning on wildfire—a Canadian perspective. Can. J. Res. 49, 1015–1023 (2019).
    Google Scholar 
    10.Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).
    Google Scholar 
    11.Van Wagner, C. E. et al. Development and Structure of the Canadian Forest Fire Weather Index System (Canadian Forestry Service Headquarters, 1987); https://www.eea.europa.eu/data-and-maps/indicators/forest-fire-danger-3/camia-et-al.-2008-past12.Flannigan, M. D. & Harrington, J. B. A study of the relation of meteorological variables to monthly provincial area burned by wildfire in Canada (1953-80). J. Appl. Meteorol. 27, 441–452 (1988).
    Google Scholar 
    13.Flannigan, M. D. et al. Fuel moisture sensitivity to temperature and precipitation: climate change implications. Clim. Change 134, 59–71 (2016).CAS 

    Google Scholar 
    14.Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).CAS 

    Google Scholar 
    15.Touma, D., Stevenson, S., Lehner, F. & Coats, S. Human-driven greenhouse gas and aerosol emissions cause distinct regional impacts on extreme fire weather. Nat. Commun. 12, 212 (2021).16.Clarke, H. G., Smith, P. L. & Pitman, A. J. Regional signatures of future fire weather over eastern Australia from global climate models. Int. J. Wildland Fire 20, 550–562 (2011).
    Google Scholar 
    17.Bedia, J. et al. Sensitivity of fire weather index to different reanalysis products in the Iberian Peninsula. Nat. Hazards Earth Syst. Sci. 12, 699–708 (2012).
    Google Scholar 
    18.Jain, P., Wang, X. & Flannigan, M. D. Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015. Int. J. Wildland Fire 26, 1009–1020 (2017).
    Google Scholar 
    19.Dowdy, A. J. Climatological variability of fire weather in Australia. J. Appl. Meteorol. Climatol. 57, 221–234 (2018).
    Google Scholar 
    20.Zhao, F., Liu, Y. & Shu, L. Change in the fire season pattern from bimodal to unimodal under climate change: the case of Daxing’anling in Northeast China. Agric. Meteorol. 291, 108075 (2020).
    Google Scholar 
    21.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
    Google Scholar 
    22.Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).CAS 

    Google Scholar 
    23.Kirchmeier-Young, M. C., Gillet, N. P., Zwiers, F. W., Cannon, A. J. & Anslow, F. S. Attribution of the influence of human-induced climate change on an extreme fire season. Earths Future 7, 2–10 (2019).
    Google Scholar 
    24.Pausas, J. G. & Ribeiro, E. The global-fire productivity relationship. Glob. Ecol. Biogeogr. 22, 728–736 (2013).
    Google Scholar 
    25.Cochrane, M. A. Fire science for rainforests. Nature 421, 913–919 (2003).CAS 

    Google Scholar 
    26.Ziel, R. H. et al. A comparison of fire weather indices with MODIS fire days for the natural regions of Alaska. Forests 11, 516 (2020).
    Google Scholar 
    27.Giannaros, T. M., Kotroni, V. & Lagouvardos, K. Climatology and trend analysis (1987–2016) of fire weather in the Euro-Mediterranean. Int. J. Climatol. 41, E491–E508 (2021).
    Google Scholar 
    28.Harris, S. & Lucas, C. Understanding the variability of Australian fire weather between 1973 and 2017. PLoS ONE 14, e0222328 (2019).CAS 

    Google Scholar 
    29.Climate at a Glance (NOAA, 2021); https://www.ncdc.noaa.gov/cag/30.van Oldenborgh, G. J. et al. Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. 21, 941–960 (2021).
    Google Scholar 
    31.Barbero, R., Abatzoglou, J. T., Pimont, F., Ruffault, J. & Curt, T. Attributing increases in fire weather to anthropogenic climate change over France. Front. Earth Sci. https://doi.org/10.3389/feart.2020.00104 (2020).32.Byrne, M. P. & O’Gorman, P. A. Understanding decreases in land relative humidity with global warming: conceptual model and GCM simulations. J. Clim. 29, 9045–9061 (2016).
    Google Scholar 
    33.Willett, K. M., Jones, P. D., Gillett, N. P. & Thorne, P. W. Recent changes in surface humidity: development of the HadCRUH dataset. J. Clim. 21, 5364–5383 (2008).
    Google Scholar 
    34.Matsoukas, C. et al. Potential evaporation trends over land between 1983-2008: driven by radiative fluxes or vapour-pressure deficit? Atmos. Chem. Phys. 11, 7601–7616 (2011).CAS 

    Google Scholar 
    35.Grotjahn, R. & Huynh, J. Contiguous US summer maximum temperature and heat stress trends in CRU and NOAA climate division data plus comparisons to reanalyses. Sci. Rep. 8, 11146 (2018).CAS 

    Google Scholar 
    36.Denson, E., Wasko, C. & Peel, M. C. Decreases in relative humidity across Australia. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ac0aca (2021).37.Barkhordarian, A., Saatchi, S. S., Behrangi, A., Loikith, P. C. & Mechoso, C. R. A recent systematic increase in vapor pressure deficit over tropical South America. Sci. Rep. 9, 15331 (2019).
    Google Scholar 
    38.Findell, K. L. et al. The impact of anthropogenic land use and land cover change on regional climate extremes. Nat. Commun. 8, 989 (2017).39.McKinnon, K. A., Poppick, A. & Simpson, I. R. Hot extremes have become drier in the United States Southwest. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01076-9 (2021).40.Berg, A. et al. Land–atmosphere feedbacks amplify aridity increase over land under global warming. Nat. Clim. Change 6, 869–874 (2016).
    Google Scholar 
    41.Mishra, V. et al. Moist heat stress extremes in India enhanced by irrigation. Nat. Geosci. 13, 722–728 (2020).CAS 

    Google Scholar 
    42.Dong, B. & Dai, A. The influence of the interdecadal Pacific oscillation on temperature and precipitation over the globe. Clim. Dyn. 45, 2667–2681 (2015).
    Google Scholar 
    43.Fischer, E. M. & Knutti, R. Robust projections of combined humidity and temperature extremes. Nat. Clim. Change 3, 126–130 (2013).
    Google Scholar 
    44.Tymstra C., Flannigan M. D., Stocks B. J., Cai X. & Morrison K. Wildfire management in Canada: review, challenges and opportunities. Prog. Disaster Sci. https://doi.org/10.1016/j.pdisas.2019.100045 (2020).45.Flannigan, M. D., Stocks, B., Turetsky, M. & Wotton, M. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Change Biol. 15, 549–560 (2009).
    Google Scholar 
    46.Chen, Y. et al. Future increases in Arctic lightning and fire risk for permafrost carbon. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01011-y (2021).47.Hope, E. S., McKenney, D. W., Pedlar, J. H., Stocks, B. J. & Gauthier, S. Wildfire suppression costs for Canada under a changing climate. PLoS ONE 11, e0157425 (2016).
    Google Scholar 
    48.Podur, J. & Wotton, B. M. Will climate change overwhelm fire management capacity? Ecol. Modell. 221, 1301–1309 (2010).
    Google Scholar 
    49.Abatzoglou, J. T., Juang, C. S., Williams, A. P., Kolden, C. A. & Westerling, A. L. Increasing synchronous fire danger in forests of the western United States. Geophys. Res. Lett. 48, e2020GL091377 (2021).
    Google Scholar 
    50.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
    Google Scholar 
    51.Copernicus Climate Change Service Data Store (Copernicus Climate Change Service, accessed 4 March 2020); https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation52.Ramon, J., Lledo, L., Torralba, V., Soret, A. & Doblas-Reyes, F. J. What global reanalysis best represents near-surface winds? Q. J. R. Meteorol. Soc. 145, 3236–3251 (2019).
    Google Scholar 
    53.Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci. 23, 207–224 (2019).
    Google Scholar 
    54.Tarek, M., Brissette, F. P. & Arsenault, R. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrol. Earth Syst. Sci. 24, 2527–2544 (2020).
    Google Scholar 
    55.Torralba, V., Doblas-Reyes, F. J. & Gonzalez-Reviriego, N. Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison. Environ. Res. Lett. 12, 114019 (2017).
    Google Scholar 
    56.Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
    Google Scholar 
    57.Andela, N. et al. The global fire atlas of individual fire size, duration, speed and direction. Earth Syst. Sci. Data 11, 529–552 (2019).
    Google Scholar 
    58.Wotton, B. M. Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications. Environ. Ecol. Stat. 16, 107–131 (2009).CAS 

    Google Scholar 
    59.Field, R. D. et al. Development of a global fire weather database. Nat. Hazards Earth Syst. Sci. 15, 1407–1423 (2015).
    Google Scholar 
    60.Bedia, J. et al. Global patterns in the sensitivity of burned area to fire weather: implications for climate change. Agric. Meteorol. 214–215, 369–379 (2015).
    Google Scholar 
    61.McElhinny, M., Beckers, J. F., Hanes, C., Flannigan, M. & Jain, P. A high-resolution reanalysis of global fire weather from 1979 to 2018 – overwintering the Drought Code. Earth Syst. Sci. Data 12, 1823–1833 (2020).
    Google Scholar 
    62.Wotton, B. M. & Flannigan, M. D. Length of the fire season in a changing climate. Forestry Chron. 69, 187–192 (1993).
    Google Scholar 
    63.Sedano, F. & Randerson, J. T. Vapor pressure deficit controls on fire ignition and fire spread in boreal forest ecosystems. Biogeosciences 11, 1309–1353 (2014).
    Google Scholar 
    64.Williams, P. A. et al. Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States. Int. J. Wildland Fire 24, 14–26 (2014).
    Google Scholar 
    65.Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earths Future 7, 892–910 (2019).
    Google Scholar 
    66.Mueller, S. E. et al. Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015. For. Ecol. Manage. 460, 117861 (2020).
    Google Scholar 
    67.Alduchov, O. A. & Eskridge, R. E. Improved Magnus form approximation of saturation vapor pressure. J. Appl. Meteorol. 35, 601–609 (1996).
    Google Scholar 
    68.Knauer, J., El-Madany, T. S., Zaehle, S. & Migliavacca, M. Bigleaf—an R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLoS ONE 13, e0201114 (2018).
    Google Scholar 
    69.Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
    Google Scholar 
    70.Loveland, T. R. & Belward, A. S. The IGBP-DIS global 1 km land cover data set, DISCover: first results. Int. J. Remote Sens. 18, 3291–3295 (1997).
    Google Scholar 
    71.Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).
    Google Scholar 
    72.Kendall, M. G. Rank Correlation Methods (Griffin, 1975).
    Google Scholar 
    73.Theil, H. A rank-invariant method of linear and polynomial regression analysis. I, II, III. Nederl. Akad. Wetensch. Proc. 53, part I: 386–392; part II: 521–525; part III: 1397–1412 (1950).74.Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).
    Google Scholar 
    75.Yue, S., Pilon, P. & Phinney, B. Canadian streamflow trend detection: impacts of serial and cross-correlation. Hydrol. Sci. J. 48, 51–63 (2003).
    Google Scholar 
    76.Wilks, D. S. On ‘field significance’ and the false discovery rate. J. Appl. Meteorol. Climatol. 45, 1181–1189 (2006).
    Google Scholar 
    77.Wilks, D. ‘The stippling shows statistically significant grid points’: how research results are routinely overstated and overinterpreted, and what to do about it. Bull. Am. Meteorol. Soc. 97, 2263–2273 (2016).
    Google Scholar 
    78.Libiseller, C. & Grimvall, A. Performance of partial Mann–Kendall tests for trend detection in the presence of covariates. Environmetrics 13, 71–84 (2002).CAS 

    Google Scholar 
    79.Mediero, L., Santillán, D., Garrote, L. & Granados, A. Detection and attribution of trends in magnitude, frequency and timing of floods in Spain. J. Hydrol. 517, 1072–1088 (2014).
    Google Scholar 
    80.Dowdy, A. J., Mills, G. A., Finkele, K. & de Groot, W. Index sensitivity analysis applied to the Canadian Forest Fire Weather Index and the McArthur Forest Fire Danger Index. Meteorol. Appl. 17, 298–312 (2010).
    Google Scholar 
    81.Millard, S. P. EnvStats: An R Package for Environmental Statistics (Springer, 2013).82.Pohlert, T. trend: Non-Parametric Trend Tests and Change-Point Detection. R package v.1.1.4. https://CRAN.R-project.org/package=trend (2020). More

  • in

    Translation stalling proline motifs are enriched in slow-growing, thermophilic, and multicellular bacteria

    1.Russell JB, Cook GM. Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol Rev. 1995;59:48–62.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    2.Klumpp S, Scott M, Pedersen S, Hwa T. Molecular crowding limits translation and cell growth. PNAS. 2013;110:16754–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Pedersen S. Escherichia coli ribosomes translate in vivo with variable rate. EMBO J. 1984;3:2895–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Ran W, Higgs PG. Contributions of speed and accuracy to translational selection in bacteria. PLoS One. 2012;7:e51652.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Vieira-Silva S, Rocha E. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 2009;6:1–15.
    Google Scholar 
    6.Roller BRK, Stoddard SF, Schmidt TM. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat Microbiol. 2016;1:1–7.
    Google Scholar 
    7.Buskirk AR, Green R. Ribosome pausing, arrest and rescue in bacteria and eukaryotes. Philos Trans R Soc B. 2017;372:20160183–11.
    Google Scholar 
    8.Wohlgemuth I, Brenner S, Beringer M, Rodnina MV. Modulation of the rate of peptidyl transfer on the ribosome by the nature of substrates. J Biol Chem. 2008;283:32229–35.CAS 
    PubMed 

    Google Scholar 
    9.Pavlov MY, Watts RE, Tan Z, Cornish VW, Ehrenberg M, Forster AC. Slow peptide bond formation by proline and other N-alkylamino acids in translation. PNAS. 2009;106:50–54.CAS 
    PubMed 

    Google Scholar 
    10.Mandal A, Mandal S, Park MH. Genome-wide analyses and functional classification of proline repeat-rich proteins: potential role of eIF5A in eukaryotic evolution. PLoS One. 2014;9:e111800–13.PubMed 
    PubMed Central 

    Google Scholar 
    11.Adzhubei AA, Sternberg MJE, Makarov AA. Polyproline-II helix in proteins: structure and function. J Mol Biol. 2013;425:2100–32.CAS 
    PubMed 

    Google Scholar 
    12.Elam WA, Schrank TP, Campagnolo AJ, Hilser VJ. Evolutionary conservation of the polyproline II conformation surrounding intrinsically disordered phosphorylation sites. Protein Sci. 2013;22:405–17.PubMed 

    Google Scholar 
    13.Ball LJ, Kühne R, Schneider-Mergener J, Oschkinat H. Recognition of proline-rich motifs by protein-protein-interaction domains. Angew Chem Int Ed Engl. 2005;44:2852–69.CAS 
    PubMed 

    Google Scholar 
    14.Starosta AL, Lassak J, Peil L, Atkinson GC, Virumäe K, Tenson T, et al. Translational stalling at polyproline stretches is modulated by the sequence context upstream of the stall site. Nucleic Acids Res. 2014;42:10711–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Woolstenhulme CJ, Guydosh NR, Green R, Buskirk AR. High-precision analysis of translational pausing by ribosome profiling in bacteria lacking EFP. Cell Rep. 2015;11:13–21.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    16.Hersch SJ, Elgamal S, Katz A, Ibba M, Navarre WW. Translation initiation rate determines the impact of ribosome stalling on bacterial protein synthesis. J Biol Chem. 2014;289:28160–71.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Lassak J, Wilson DN, Jung K. Stall no more at polyproline stretches with the translation elongation factors EF‐P and IF‐5A. Mol Microbiol. 2016;99:219–35.CAS 
    PubMed 

    Google Scholar 
    18.Yanagisawa T, Sumida T, Ishii R, Takemoto C, Yokoyama S. A paralog of lysyl-tRNA synthetase aminoacylates a conserved lysine residue in translation elongation factor P. Nature. 2010;17:1136–43.CAS 

    Google Scholar 
    19.Park J-H, Johansson HE, Aoki H, Huang BX, Kim H-Y, Ganoza MC, et al. Post-translational modification by beta-lysylation is required for activity of Escherichia coli elongation factor P (EF-P). J Biol Chem. 2012;287:2579–90.CAS 
    PubMed 

    Google Scholar 
    20.Lassak J, Keilhauer E, Fürst M, Wuichet K, Gödeke J, Starosta AL, et al. Arginine-rhamnosylation as new strategy to activate translation elongation factor P. Nat Chem Biol. 2015;11:266–70.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Tollerson R, Witzky A, Ibba M. Elongation factor P is required to maintain proteome homeostasis at high growth rate. PNAS. 2018;115:1–6.
    Google Scholar 
    22.Peng WT, Banta LM, Charles TC, Nester EW. The chvH locus of Agrobacterium encodes a homologue of an elongation factor involved in protein synthesis. J Bacteriol. 2001;183:36–45.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    23.Rajkovic A, Hummels KR, Witzky A, Erickson S, Gafken PR, Whitelegge JP, et al. Translation control of swarming proficiency in Bacillus subtilis by 5-amino-pentanolylated elongation factor P. J Biol Chem. 2016;291:10976–85.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Navarre WW, Zou SB, Roy H, Xie JL, Savchenko A, Singer A, et al. PoxA, YjeK, and elongation factor P coordinately modulate virulence and drug resistance in Salmonella enterica. Mol Cell. 2010;39:209–21.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Hummels KR, Kearns DB. Suppressor mutations in ribosomal proteins and FliY restore Bacillus subtilis swarming motility in the absence of EF-P. PLoS Genet. 2019;15:e1008179–27.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Rajkovic A, Erickson S, Witzky A, Branson OE, Seo J, Gafken PR, et al. Cyclic rhamnosylated elongation factor P establishes antibiotic resistance in Pseudomonas aeruginosa. MBio. 2015;6:1–9.
    Google Scholar 
    27.Yanagisawa T, Takahashi H, Suzuki T, Masuda A, Dohmae N, Yokoyama S. Neisseria meningitidis translation elongation factor P and its active-site arginine residue are essential for cell viability. PLoS One. 2016;11:e0147907–27.PubMed 
    PubMed Central 

    Google Scholar 
    28.Krafczyk R, Qi F, Sieber A, Mehler J, Jung K, Frishman D, et al. Proline codon pair selection determines ribosome pausing strength and translation efficiency in bacteria. Commun Biol. 2021;4:1–11.
    Google Scholar 
    29.Qi F, Motz M, Jung K, Lassak J, Frishman D. Evolutionary analysis of polyproline motifs in Escherichia coli reveals their regulatory role in translation. PLoS Comput Biol. 2018;14:e1005987–19.PubMed 
    PubMed Central 

    Google Scholar 
    30.Karlin S, Mrázek J, Campbell A, Kaiser D. Characterizations of highly expressed genes of four fast-growing bacteria. J Bacteriol. 2001;183:5025–40.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Dethlefsen L, Schmidt TM. Performance of the translational apparatus varies with the ecological strategies of bacteria. J Bacteriol. 2007;189:3237–45.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Weissman JL, Hou S, Fuhrman JA. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. PNAS. 2021;118:1–10.
    Google Scholar 
    33.Hersch SJ, Wang M, Zou SB, Moon K-M, Foster LJ, Ibba M, et al. Divergent protein motifs direct elongation factor P-mediated translational regulation in Salmonella enterica and Escherichia coli. MBio. 2013;4:1–10.
    Google Scholar 
    34.Pinheiro B, Scheidler CM, Kielkowski P, Schmid M, Forné I, Ye S, et al. Structure and function of an elongation factor P subfamily in Actinobacteria. Cell Rep. 2020;30:4332–42. e5.CAS 
    PubMed 

    Google Scholar 
    35.Chen I-MA, Markowitz VM, Chu K, Palaniappan K, Szeto E, Pillay M, et al. IMG/M: integrated genome and metagenome comparative data analysis system. Nucleic Acids Res. 2017;45:D507–D516.CAS 
    PubMed 

    Google Scholar 
    36.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2020;36:1925–7.CAS 

    Google Scholar 
    38.Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195–16.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Madin JS, Nielsen DA, Brbic M, Corkrey R, Danko D, Edwards K, et al. A synthesis of bacterial and archaeal phenotypic trait data. Sci Data. 2020;7:1–8.
    Google Scholar 
    40.Novembre JA. Accounting for background nucleotide composition when measuring codon usage bias. Mol Biol Evol. 2002;19:1390–4.CAS 
    PubMed 

    Google Scholar 
    41.Erdos G, Dosztányi Z. Analyzing protein disorder with IUPred2A. Curr Protoc Bioinforma. 2020;70:1–15.
    Google Scholar 
    42.Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–50.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Pagel M. Inferring the historical patterns of biological evolution. Nature. 1999;401:877–84.CAS 
    PubMed 

    Google Scholar 
    45.Revell LJ. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol Evol. 2011;3:217–23.
    Google Scholar 
    46.Orme D, Freckleton R, Thomas G, Petzoldt T, Fritz S, Isaac N, et al. caper: comparative analyses of phylogenetics and evolution in R. 2018; https://CRAN.R-project.org/package=caper.47.Wickham H. ggplot2: elegant graphics for data analysis. 2016. Springer-Verlag New York.48.Symonds MRE, Blomberg SP. A primer on phylogenetic generalized least squares. In: Garamszegi L (eds). Modern phylogenetic comparative methods and their application in evolutionary biology. (Springer, Berlin, Heidelberg, 2014) pp. 105–30.49.Watanabe K, Suzuki Y. Protein thermostabilization by proline substitutions. J Mol Catal B Enzym. 1998;4:167–80.CAS 

    Google Scholar 
    50.Sabath N, Ferrada E, Barve A, Wagner A. Growth temperature and genome size in bacteria are negatively correlated, suggesting genomic streamlining during thermal adaptation. Genome Biol Evol. 2013;5:966–77.PubMed 
    PubMed Central 

    Google Scholar 
    51.Goldman BS, Nierman WC, Kaiser D, Slater SC, Durkin AS, Eisen JA, et al. Evolution of sensory complexity recorded in a myxobacterial genome. PNAS. 2006;103:15200–5.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Long AM, Hou S, Ignacio-Espinoza JC, Fuhrman JA. Benchmarking microbial growth rate predictions from metagenomes. ISME J. 2020;15:1–13.
    Google Scholar 
    53.Rocha EPC. Codon usage bias from tRNA’s point of view: redundancy, specialization, and efficient decoding for translation optimization. Genome Res. 2004;14:2279–86.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Klappenbach JA, Dunbar JM, Schmidt TM. rRNA operon copy number reflects ecological strategies of bacteria. Appl Environ Microbiol. 2000;66:1328–33.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012;40:D109–D114.CAS 
    PubMed 

    Google Scholar 
    56.Perez J, Castaneda-García A, Jenke-Kodama H, Muller R, Munoz-Dorado J. Eukaryotic-like protein kinases in the prokaryotes and the myxobacterial kinome. PNAS. 2008;105:15950–5.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Shi L, Pigeonneau N, Ravikumar V, Dobrinic P, Macek B, Franjevic D, et al. Cross-phosphorylation of bacterial serine/threonine and tyrosine protein kinases on key regulatory residues. Front Microbiol. 2014;5:1–13.CAS 

    Google Scholar 
    58.Jakob U, Kriwacki R, Uversky VN. Conditionally and transiently disordered proteins: awakening cryptic disorder to regulate protein function. Chem Rev. 2014;114:6779–805.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Starosta AL, Lassak J, Peil L, Atkinson GC, Woolstenhulme CJ, Virumäe K, et al. A conserved proline triplet in Val-tRNA synthetase and the origin of elongation factor P. Cell Rep. 2014;9:476–83.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Nariya H, Inouye S. A protein Ser/Thr kinase cascade negatively regulates the DNA-binding activity of MrpC, a smaller form of which may be necessary for the Myxococcus xanthus development. Mol Microbiol. 2006;60:1205–17.CAS 
    PubMed 

    Google Scholar 
    61.Stein EA, Cho K, Higgs PI, Zusman DR. Two Ser/Thr protein kinases essential for efficient aggregation and spore morphogenesis in Myxococcus xanthus. Mol Microbiol. 2006;60:1414–31.CAS 
    PubMed 

    Google Scholar 
    62.Iakoucheva LM, Radivojac P, Brown CJ, OConnor TR, Sikes JG, Obradovic Z, et al. The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 2004;32:1037–49.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Elsen S, Swem LR, Swem DL, Bauer CE. RegB/RegA, a highly conserved redox-responding global two-component regulatory system. Microbiol Mol Biol R. 2004;68:263–79.CAS 

    Google Scholar 
    64.Tawa P, Stewart RC. Kinetics of CheA autophosphorylation and dephosphorylation reactions. Biochemistry. 1994;33:7917–24.CAS 
    PubMed 

    Google Scholar 
    65.Yoshida T, jian CaiS, Inouye M. Interaction of EnvZ, a sensory histidine kinase, with phosphorylated OmpR, the cognate response regulator. Mol Microbiol. 2002;46:1283–94.CAS 
    PubMed 

    Google Scholar 
    66.Cho M-H, Wrabl JO, Taylor J, Hilser VJ. Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome. PNAS. 2020;117:1–11.
    Google Scholar  More

  • in

    Rank-invariant estimation of inbreeding coefficients

    Statistical samplingWe can describe the dependence between pairs of uniting alleles in a single population without invoking an evolutionary model for the history of the population. In this “statistical sampling” framework (Weir, 1996) we do not consider the variation associated with evolutionary processes but we do consider the variation among samples from the same population. Although extensive sets of genetic data allow individual-level inbreeding coefficients to be estimated with high precision, we start with population-level estimation.Allelic dependencies can be quantified with the within-population inbreeding coefficient, written here as fW to emphasize it is a within-population quantity, defined by$${H}_{l}=2{p}_{l}(1-{p}_{l})(1-{f}_{W})$$
    (1)
    where Hl is the population proportion of heterozygotes for the reference allele at SNP l and pl is the population proportion of that allele. The same value of fW is assumed to apply for all SNPs. An immediate consequence of this definition is that the population proportions of homozygotes for the reference and alternative alleles are ({p}_{l}^{2}+{p}_{l}(1-{p}_{l}){f}_{W}) and ({(1-{p}_{l})}^{2}+{p}_{l}(1-{p}_{l}){f}_{W}) respectively. This formulation allows fW to be negative, with the maximum of −pl/(1 − pl) and −(1 − pl)/pl as lower bound. It is bounded above by 1. Hardy–Weinberg equilibrium, HWE, corresponds to fW = 0 and textbooks (e.g., (Hedrick, 2000)) point out that negative values of fW indicate more heterozygotes than expected under HWE.Observed heterozygote proportions ({tilde{H}}_{l}) have Hl as within-population expectation ({{{{{{mathcal{E}}}}}}}_{W}) over samples from the study population, ({{{{{{mathcal{E}}}}}}}_{W}({tilde{H}}_{l})={H}_{l}), and this would provide a simple estimator of fW if the population allele proportions were known. In practice, however, these proportions are unknown. Steele et al. (2014) suggested use of data external to the study sample to provide reference allele proportions in forensic applications where a reference database is used for making inferences about the population relevant for a particular crime. The more usual approach is to use study sample proportions ({tilde{p}}_{l}) in place of the true proportions pl, as in equation 1 of Li & Horvitz (1953):$${hat{f}}_{{W}_{l}}=1-frac{{tilde{H}}_{l}}{2{tilde{p}}_{l}(1-{tilde{p}}_{l})}$$
    (2)
    The moment estimator in Eq. (2) is also an MLE of fW when only one locus is considered, but it is biased (Robertson & Hill, 1984) since not only is it a ratio of statistics but also the expected value ({{{{{{mathcal{E}}}}}}}_{W}[2{tilde{p}}_{l}(1-{tilde{p}}_{l})]) over repeated samples of n from the population is 2pl(1 − pl)[1 − (1 + fW)/(2n)] (e.g., (Weir, 1996), p39).This approach can be used to estimate the within-population inbreeding coefficient fj for each individual j in a sample from one population. These are the “simple” estimators of Hall et al. (2012) and the ({hat{f}}_{{{{{{{rm{HOM}}}}}}}_{j}}) of Yengo et al. (2017):$${hat{f}}_{{{{{{{rm{HOM}}}}}}}_{jl}}=1-frac{{tilde{H}}_{jl}}{2{tilde{p}}_{l}(1-{tilde{p}}_{l})}$$
    (3)
    The sample heterozygosity indicator ({tilde{H}}_{jl}) is one if individual j is heterozygous at SNP l and is zero otherwise. Averaging Eq. (3) over individuals gives the estimator based on SNP l in Eq. (2).A single SNP provides estimates that are either 1 or a negative value depending on ({tilde{p}}_{l}), so many SNPs are used in practice. In both Hall et al. (2012) and Yengo et al. (2017) data were combined over loci as weighted or “ratio of averages” estimators:$${hat{f}}_{{{{{{{rm{Hom}}}}}}}_{j}}=1-frac{{sum }_{l}({tilde{H}}_{jl})}{{sum }_{l}[2{tilde{p}}_{l}(1-{tilde{p}}_{l})]}$$
    (4)
    Gazal et al. (2014) referred to this estimator as fPLINK as it is an option in PLINK. We show below the good performance of this weighted estimator for large sample sizes and large numbers of loci. We will consider throughout that a large number L of SNPs are used so that ratios of sums of statistics over loci, such as in Eq. (4), have expected values equal to the ratio of expected values of their numerators and denominators. Ochoa & Storey (2021) showed statistics of the form ({tilde{A}}_{L}/{tilde{B}}_{L}), where ({tilde{A}}_{L}=mathop{sum }nolimits_{l = 1}^{L}{a}_{l}/L) and ({tilde{B}}_{L}=mathop{sum }nolimits_{l = 1}^{L}{b}_{l}/L), have expected values that converge almost surely to the ratio A/B when ({{{{{{mathcal{E}}}}}}}_{W}({tilde{A}}_{L})=A{c}_{L}) and ({{{{{{mathcal{E}}}}}}}_{W}({tilde{B}}_{L})=B{c}_{L}). This result rests on the expectations ({{{{{{mathcal{E}}}}}}}_{W}({a}_{l})=A{c}_{l}) and ({{{{{{mathcal{E}}}}}}}_{W}({b}_{l})=B{c}_{l}) with ({c}_{L}=mathop{sum }nolimits_{l = 1}^{L}{c}_{l}/L). It requires ∣al∣, ∣bl∣ to both be no greater than some finite quantity C, cL to converge to a finite value c as L increases, and for Bc not to be zero. For the ratio in Eq. (4), ({a}_{l}={tilde{H}}_{jl}), ({b}_{l}=2{tilde{p}}_{l}(1-{tilde{p}}_{l})) so A = (1 − fj), B = 1 for large sample sizes n, and cL = ∑l2pl(1 − pl)/L ≤ 1/2. The conditions are satisfied providing at least one SNP is polymorphic. For an “average of ratios” estimator of the form (mathop{sum }nolimits_{l = 1}^{L}({a}_{l}/{b}_{l})/L), the denominators bl can be very small and convergence of its expected value is not assured.As an alternative to using sample allele frequencies, Hall et al. (2012) used maximum likelihood to estimate population allele proportions for multiple loci whereas Ayres & Balding (1998) used Markov chain Monte Carlo methods in a Bayesian approach that integrated out the allele proportion parameters. Neither of those papers considered data of the size we now face in sequence-based studies of many organisms, and we doubt the computational effort to estimate, or integrate over, hundreds of millions of allele proportions in Eqs. (2) or (4) adds much value to inferences about f. The allele-sharing estimators we describe below regard allele probabilities as unknown nuisance parameters and we show how to avoid estimating them or assigning them values.Hall et al. (2012) used an EM algorithm to find MLEs for fj when population allele proportions were regarded as being known and equal to sample proportions. Alternatively, a grid search can be conducted over the range of validity for the single parameter fj that maximizes the log-likelihood$${{{{mathrm{ln}}}}},[{{{{{rm{Lik}}}}}}({f}_{j})]={{{{{rm{Constant}}}}}}+mathop{sum }limits_{l=1}^{L}{{tilde{H}}_{jl}{{{{mathrm{ln}}}}},[(1-{f}_{j})]+(1-{tilde{H}}_{jl}){{{{mathrm{ln}}}}},[1-2{tilde{p}}_{l}(1-{tilde{p}}_{l})(1-{f}_{j})]}$$Estimation of the within-population inbreeding coefficients fW (FIS of (Wright, 1922)) and fj does not require any information beyond genotype proportions in samples from a study population, nor does it make any assumptions about that population or the evolutionary forces that shaped the population. The coefficients are simply measures of dependence of pairs of alleles within individuals.Genetic samplingInbreeding parameters of most interest in genetic studies are those that recognize the contribution of previous generations to inbreeding in the present study population. This requires accounting for “genetic sampling” (Weir, 1996) between generations, thereby leading to an ibd interpretation of inbreeding: ibd alleles descend from a single allele in a reference population. It also allows the prediction of inbreeding coefficients by path counting when pedigrees are known (Wright, 1922). If individual J is ancestral to both individuals (j^{prime}) and j″, and if there are n individuals in the pedigree path joining (j^{prime}) to j″ through J, then Fj = ∑(0.5)n(1 + FJ) where FJ is the inbreeding coefficient of ancestor J and Fj is the inbreeding coefficient of offspring j of parents (j^{prime}) and j″. The sum is over all ancestors J and all paths joining (j^{prime}) to j″ through J. The expression is also the coancestry ({theta }_{j^{prime} j^{primeprime} }) of (j^{prime}) and j″: the probability an allele drawn randomly from (j^{prime}) is ibd to an allele drawn randomly from j″.The allele proportion pl in a study population has expectation πl over evolutionary replicates of the population from an ancestral reference population to the present time. Sample allele proportions ({tilde{p}}_{l}) provide information about the population proportions pl, and their statistical sampling properties follow from the binomial distribution. We do not invoke a specific genetic sampling distribution for the pl about their expectations πl although we do assume the second moments of that distribution depend on probabilities of ibd for pairs of alleles. One consequence of the assumed moments is that the probability of individual j in the study sample being heterozygous, i.e., the total expected value ({{{{{{mathcal{E}}}}}}}_{T}) of the heterozygosity indicator over replicates of the history of that individual, is$${{{{{{mathcal{E}}}}}}}_{T}({tilde{H}}_{{j}_{l}})=2{pi }_{l}(1-{pi }_{l})(1-{F}_{j})$$
    (5)
    The quantity Fj is the individual-specific version of FIT of Wright (1922) and we can regard it as the probability the two alleles at any locus for individual j are ibd. There is an implicit assumption in Eq. (5) that the reference population needed to define ibd is infinite and in HWE: there is probability Fj that j has homologous alleles with a single ancestral allele in that population and probability (1 − Fj) of j having homologous alleles with distinct ancestral alleles there. In the first place, the single ancestral allele has probability π of being the reference allele for that locus and the implicit assumption is that two ancestral alleles are both the reference type with probability π2. This does not mean there is an actual ancestral population with those properties, any more than use of ({{{{{{mathcal{E}}}}}}}_{T}) means there are actual replicates of the history of any population or individual, and we note that Eq. (5) does not allow higher heterozygosity than predicted by HWE. Nonetheless, the concept of ibd allows theoretical constructions of great utility and we now present a framework for approaching empirical situations.Inbreeding, or ibd, implies a common ancestral origin for uniting alleles and statements about sample allele proportions ({tilde{p}}_{l}) require consideration of possible ibd for other pairs of alleles in the sample. The total expectation of (2{tilde{p}}_{l}(1-{tilde{p}}_{l})) over samples from the population and over evolutionary replicates of the study population is ((Weir, 1996), p176)$${{{{{{mathcal{E}}}}}}}_{T}[2{tilde{p}}_{l}(1-{tilde{p}}_{l})]=2{pi }_{l}(1-{pi }_{l})left[(1-{theta }_{S})-frac{1}{2n}left(1+{F}_{W}-2{theta }_{S}right)right]$$
    (6)
    where FW is the parametric inbreeding coefficient averaged over sample members, ({F}_{W}=mathop{sum }nolimits_{j = 1}^{n}{F}_{j}/n), and θS is the average parametric coancestry in the sample, ({theta }_{S}=mathop{sum }nolimits_{j = 1}^{n}{sum }_{j^{prime} ne j}{theta }_{jj^{prime} }/[n(n-1)]). Equivalent expressions were given by McPeek et al. (2004) and DeGiorgio and Rosenberg (2009). We note the relationship fW = (FW − θS)/(1 − θS) given by Wright (1922) and we showed in WG17 the equivalent expression fj = (Fj − θS)/(1 − θS) for individual-specific values (θS is Wright’s FST).For a large number of SNPs, the expectation of a ratio estimator of the type considered here is the ratio of expectations (Ochoa & Storey, 2021). Therefore, the total expectations of the ({hat{f}}_{{{{{{{rm{Hom}}}}}}}_{j}}), taking into account both statistical and genetic sampling, are$${{{{{{mathcal{E}}}}}}}_{T}({hat{f}}_{{{{{{{rm{HOM}}}}}}}_{j}})=1-frac{1-{F}_{j}}{(1-{theta }_{S})-frac{1}{2n}left(1+{F}_{W}-2{theta }_{S}right)}=frac{{f}_{j}-frac{1}{2n}(1+{f}_{W})}{1-frac{1}{2n}(1+{f}_{W})}$$
    (7)
    For all sample sizes, ({hat{f}}_{{{{{{{rm{HOM}}}}}}}_{j}}) has an expected value less than the true value fj, with the bias being of the order of 1/n. The ranking of ({{{{{{mathcal{E}}}}}}}_{T}({hat{f}}_{{{{{{{rm{HOM}}}}}}}_{j}})) values, however, is the same as the ranking of the fj and, therefore, of the Fj. For large sample sizes, Eq. (7) reduces to ({{{{{{mathcal{E}}}}}}}_{T}({hat{f}}_{{{{{{{rm{HOM}}}}}}}_{j}})={f}_{j}). Averaging over individuals shows that ({{{{{{mathcal{E}}}}}}}_{T}({hat{f}}_{{{{{{rm{HOM}}}}}}})={f}_{W}): the population-level estimator in Eq. (2) has total expectation of fW, not FW.A different outcome is found for the ({hat{f}}_{{{{{{{rm{UNI}}}}}}}_{j}}) estimator of Yengo et al. (2017) (i.e., ({hat{f}}^{III}) of Yang et al. (2011); ({hat{f}}_{{{{{{rm{GCTA}}}}}}3}) of (Gazal et al., 2014)). This estimator, with the weighted (w) ratio of averages over loci we recommend, as opposed to the unweighted (u) average of ratios over loci used in their papers, is$${hat{f}}_{{{{{{{rm{UNI}}}}}}}_{j}}^{w}=frac{mathop{sum }nolimits_{l = 1}^{L}[{X}_{jl}^{2}-(1+2{tilde{p}}_{l}){X}_{jl}+2{tilde{p}}_{l}^{2}]}{mathop{sum }nolimits_{l = 1}^{L}2{tilde{p}}_{l}(1-{tilde{p}}_{l})}$$
    (8)
    In this equation Xjl is the reference allele dosage, the number of copies of the reference allele, at SNP l for individual j. It is equivalent to the estimator given by (Ritland (1996), eq. 5) and attributed by him to Li & Horvitz (1953).Ochoa & Storey (2021) showed that ({hat{f}}_{{{{{{{rm{UNI}}}}}}}_{j}}^{w}) has expectation, for a large number of SNPs and a large sample size, of$${{{{{{mathcal{E}}}}}}}_{T}({hat{f}}_{{{{{{{rm{UNI}}}}}}}_{j}}^{w})=frac{{F}_{j}-2{{{Psi }}}_{j}+{theta }_{S}}{1-{theta }_{S}}={f}_{j}-2{psi }_{j}$$
    (9)
    where Ψj is the average coancestry of individual j with other members of the study sample: ({{{Psi }}}_{j}=mathop{sum }nolimits_{j^{prime} = 1,j^{prime} ne j}^{n}{theta }_{jj^{prime} }/(n-1)). We term ψj = (Ψj − θS)/(1 − θS) the within-population individual-specific average kinship coefficient. The Ψj have an average of θS over members of the sample, so the average of the ψj’s is zero and expected value of the average of the ({hat{f}}_{{{{{{{rm{UNI}}}}}}}_{j}}^{w}) is fW, as is the case for ({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}) below.Equation (9) shows that the ({hat{f}}_{{{{{{{rm{UNI}}}}}}}_{j}}^{w}) have expected values with the same ranking as the Fj values only if every individual j in the sample has the same average kinship ψj with other sample members.Finally, we mention another common estimator described by VanRaden (2008), termed fGCTA1 by Gazal et al. (2014) and available from the GCTA software (Yang et al., 2011) with option –ibc. We referred to this as the “standard” estimator in WG17. The weighted version for multiple loci is$${hat{f}}_{{{{{{{rm{STD}}}}}}}_{j}}^{w}=frac{{sum }_{l}{({X}_{jl}-2{tilde{p}}_{l})}^{2}}{{sum }_{l}2{tilde{p}}_{l}(1-{tilde{p}}_{l})}-1$$
    (10)
    and it has the large-sample expectation of (fj − 4ψj) as is implied by WG17 (Eq. 13) and as was given by Ochoa & Storey (2021). We summarize the various measures of inbreeding and coancestry in Table 1, and we include sample sizes in the expectations shown in Table 2.Table 1 Measures of inbreeding and coancestry.Full size tableTable 2 Estimators of inbreeding.Full size tableThe ({hat{f}}_{{{{{{rm{HOM}}}}}}}), ({hat{f}}_{{{{{{rm{UNI}}}}}}},{hat{f}}_{{{{{{rm{STD}}}}}}}) and ({hat{f}}_{{{{{{rm{MLE}}}}}}}) estimators of individual or population inbreeding coefficients make explicit use of sample allele proportions. This means that all four have small-sample biases, and none of the four provide estimates of the ibd quantities F or Fj. We showed that ({hat{f}}_{{{{{{rm{HOM}}}}}}}) is actually estimating the within-population inbreeding coefficients: the total inbreeding coefficients relative to the average coancestry of pairs of individuals in the sample, but ({hat{f}}_{{{{{{rm{UNI}}}}}}}) and ({hat{f}}_{{{{{{rm{STD}}}}}}}) are estimating expressions that also involve average kinships ψ.Allele sharingIn a genetic sampling framework, and with the ibd viewpoint, we consider within-individual allele sharing proportions Ajl for SNP l in individual j (we wrote M rather than A in WG17 and in (Goudet et al., 2018)). These equal one for homozygotes and zero for heterozygotes and sample values can be expressed in terms of allele dosages, ({tilde{A}}_{jl}={({X}_{jl}-1)}^{2}). We also consider between-individual sharing proportions ({A}_{jj^{prime} l}) for SNP l and individuals j and (j^{prime}). These are equal to one for both individuals being the same homozygote, zero for different homozygotes, and 0.5 otherwise. Observed values can be written as ({tilde{A}}_{jj^{prime} l}=[1+({X}_{jl}-1)({X}_{j^{prime} l}-1)]/2), with an average over all pairs of distinct individuals in a sample of ({tilde{A}}_{Sl}). Astle & Balding (2009) introduced ({tilde{A}}_{jj^{prime} l}) as a measure of identity in state of alleles chosen randomly from individuals j and (j^{prime}), and Ochoa & Storey (2021) used a simple transformation of this quantity. The allele sharing for an individual with itself is Ajjl = (1 + Ajl)/2.The same logic that led to Eq. (5) provides total expectations for allele-sharing proportions for all (j,j^{prime}):$$begin{array}{lll}{{{{{{mathcal{E}}}}}}}_{T}({tilde{A}}_{jj^{prime} l})&=&1-2{pi }_{l}(1-{pi }_{l})(1-{theta }_{jj^{prime} })\ {{{{{{mathcal{E}}}}}}}_{T}({tilde{A}}_{Sl})&=&1-2{pi }_{l}(1-{pi }_{l})(1-{theta }_{S})end{array}$$Note that θjj = (1 + Fj)/2. The nuisance parameter 2πl(1 − πl) cancels out of the ratio ({{{{{{mathcal{E}}}}}}}_{T}({tilde{A}}_{jj^{prime} l}-{tilde{A}}_{Sl})/{{{{{{mathcal{E}}}}}}}_{T}(1-{tilde{A}}_{Sl})) and this motivates definitions of allele-sharing estimators of the inbreeding coefficient for individual j and the kinship coefficient for individuals (j,j^{prime}) as$${hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}=frac{{sum }_{l}({tilde{A}}_{{j}_{l}}-{tilde{A}}_{{S}_{l}})}{{sum }_{l}(1-{tilde{A}}_{Sl})},{hat{psi }}_{{{{{{{rm{AS}}}}}}}_{jj^{prime} }}=frac{{sum }_{l}({tilde{A}}_{jj^{prime} l}-{tilde{A}}_{{S}_{l}})}{{sum }_{l}(1-{tilde{A}}_{Sl})}$$
    (11)
    For a large number of SNPs, these are unbiased for fj and ({psi }_{jj^{prime} }) for all sample sizes. We showed in WG17 there is no need to filter on minor allele frequency to preserve the lack of bias. Note that ({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}) is a linear function of the form ({a}_{S}+{b}_{S}{tilde{A}}_{j}) with ({tilde{A}}_{j}) being the total homozygosity for j and constants aS, bS being the same for all individuals j. Changing the scope of the study, from population to world for example, preserves linearity (with different values of aS, bS). The changed estimates are linear functions of the old estimates: old and new estimates are completely correlated and are rank invariant over all samples that include particular individuals, i.e., over all reference populations. Unlike the case for ({hat{f}}_{{{{{{rm{UNI}}}}}}}) or ({hat{f}}_{{{{{{rm{STD}}}}}}}), rank invariance is guaranteed for ({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}) for any two individuals even if only one more individual is added to the study.For large sample sizes, ((1-{tilde{A}}_{Sl})approx 2{tilde{p}}_{l}(1-{tilde{p}}_{l})). Under that approximation, ({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}) is the same as ({hat{f}}_{{{{{{{rm{Hom}}}}}}}_{j}}) but the approximation is not necessary in computer-based analyses. Summing the large-sample estimates over individuals not equal to j gives an estimator for the average individual kinship ψj:$${hat{psi }}_{{{{{{{rm{AS}}}}}}}_{j}}=-frac{{sum }_{l}({X}_{jl}-2{tilde{p}}_{l})(1-2{tilde{p}}_{l})}{{sum }_{l}4{tilde{p}}_{l}(1-{tilde{p}}_{l})}$$
    (12)
    Adding (2{hat{psi }}_{{{{{{{rm{AS}}}}}}}_{j}}) to ({hat{f}}_{{{{{{{rm{UNI}}}}}}}_{j}}^{w}) gives ({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}), as expected, as does adding (4{hat{psi }}_{{{{{{{rm{AS}}}}}}}_{j}}) to ({hat{f}}_{{{{{{{rm{STD}}}}}}}_{j}}^{w}). Similarly, ({hat{psi }}_{{{{{{{rm{AS}}}}}}}_{jj^{prime} }}) is obtained by adding ({hat{psi }}_{{{{{{{rm{AS}}}}}}}_{j}}) and ({hat{psi }}_{{{{{{{rm{AS}}}}}}}_{j^{prime} }}) to ({hat{psi }}_{{{{{{{rm{STD}}}}}}}_{jj^{prime} }}), where (Yang et al., 2011)$${hat{psi }}_{{{{{{{rm{STD}}}}}}}_{jj^{prime} }}=frac{mathop{sum}nolimits_{l}({X}_{jl}-2{tilde{p}}_{l})({X}_{j^{prime} l}-2{tilde{p}}_{l})}{mathop{sum}nolimits_{l}4{tilde{p}}_{l}(1-{tilde{p}}_{l})}$$These are the elements of the first method for constructing the GRM given by VanRaden (2008).When inbreeding and coancestry coefficients are defined as ibd probabilities they are non-negative, but the within-population values f and ψ will be negative for individuals, or pairs of individuals, having smaller ibd allele probabilities than do pairs of individuals in the sample, on average. Individual-specific values of f always have the same ranking as the individual-specific F values, and they are estimable. Negative estimates can be avoided by the transformation to (({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}-{hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}^{min })/(1-{hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}^{min })) where ({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}}^{min }) is the smallest value over individuals of the ({hat{f}}_{{{{{{{rm{AS}}}}}}}_{j}})’s. We don’t see the need for this transformation, and we noted above the recognition of the utility of negative values. Ochoa & Storey (2021) wished to estimate Fj rather than fj and, to overcome the lack of information about the ancestral population serving as a reference point for ibd, they assumed the least related pair of individuals in a sample have a coancestry of zero. We showed in WG17 that this brings estimates in line with path-counting predicted values when founders are assumed to be not inbred and unrelated, but we prefer to avoid the assumption. We stress that, absent external information or assumptions, F is not estimable. Instead, linear functions of F that describe ibd of target pairs of alleles relative to ibd in a specified set of alleles are estimable and have utility in empirical studies.Runs of homozygosityEach of the inbreeding estimators considered so far has been constructed for individual SNPs and then combined over SNPs. Observed values of allelic state are used to make inferences about the unobserved state of identity by descent. Estimators based on ROH, however, suppose that ibd for a region of the genome can be observed. Although F is the probability an individual has ibd alleles at any single SNP, in fact ibd occurs in blocks within which there has been no recombination in the paths of descent from common ancestor to the individual’s parents. Whereas a single SNP can be homozygous without the two alleles being ibd, if many adjacent SNPs are homozygous the most likely explanation is that they are in a block of ibd (Gibson et al., 2006). There can be exceptions, from mutation for example, and several publications give strategies for identifying runs of homozygotes for which ibd may be assumed (e.g., Gazal et al. (2014); (Joshi et al., 2015)). These strategies include adjusting the size of the blocks, the numbers of heterozygotes or missing values allowed per block, the minor allele frequency, and so on. These software parameters affect the size of the estimates (Meyermans et al., 2020). Some methods (e.g., Gazal et al. (2014); (Narasimhan et al., 2016)) use hidden Markov models where ibd is the hidden status of an observed homozygote. Model-based approaches necessarily have assumptions, such as HWE in the sampled population.We provide more details elsewhere, but we note here that ROH methods offer a useful alternative to SNP-by-SNP methods even though they cannot completely compensate for lack of information on the ibd reference population. We note also that shorter runs of ibd result from more distant relatedness of an individual’s parents, and ROH procedures can be set to distinguish recent (familial) ibd from distant (evolutionary) ibd. SNP-by-SNP estimators do not make a distinction between these two time scales. More

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    Species richness and identity both determine the biomass of global reef fish communities

    Reef life surveyReef fish communities were censused by a combination of experienced marine scientists and trained recreational SCUBA divers using globally standardized Reef Life Survey methods. All surveys were undertaken on 50 m long transects laid along a contour (at consistent depth) on predominantly hard substrate (usually rocky or coral reef) in shallow waters (depth range of transects 1 to 20 m, average ~7.2 m). Full details of fish census methods, data quality, and training of divers are provided in refs. 22,34,35 and in an online methods manual (www.reeflifesurvey.com). Fish abundance counts and size estimates per 500 m2 transect area (2 ×250 m2 blocks) were converted to biomass using length–weight relationships for each species obtained from Fishbase (www.fishbase.org). In cases where length–weight relationships were provided in Fishbase using standard length or fork length, rather than total length as estimated by divers, length–length relationships provided in Fishbase allowed conversion to the total length. For improved accuracy in biomass assessments, observed sizes were also adjusted to account for the bias in divers’ perception of fish size underwater using an empirical calibration36. Length–weight coefficients from similar-shaped close relatives were used for those species where length–weight relationships were not available in Fishbase. All transects were collapsed into a single average value of biomass for each species at a location to account for any differences in the total number of transect surveys performed.Decomposition of difference in ecosystem functioningOur equation was inspired by previous decompositions, principally the Price equation originally derived in the field of evolutionary biology as a means of separating genetic and environmental influences on phenotypic change over time37. Fox38 and later Fox and Kerr12 modified the Price equation to describe how the difference in the ecological function between two communities can be decomposed into components with different ecological interpretations. We follow a similar approach but use a different decomposition where the resulting components are similar to, but not the same as, the components proposed by Fox and Kerr12.We begin by assuming that the ecological function of the community, such as biomass, is a simple additive function of the contributions of its constituent species. We go on to compare two communities, one of which we consider the “reference” community and the other we refer to as the “comparison” community. The species present in the reference community can be classified into two types: species that are unique to the reference community (i.e., not present in the comparison community) and those that are in common with the comparison community. Let suB be the number of unique species in the reference community, and sc be the number in common between the two communities. Let ({bar{z}}_{{uB}}) be the average ecological function contributed per unique species to the reference community, and ({bar{z}}_{{cB}}) be the average ecological function contributed per shared species in the reference community. The total ecological function TB of the reference community can thus be decomposed as:$${T}_{B}={s}_{{uB}}{bar{z}}_{{uB}}+{s}_{c}{bar{z}}_{{cB}}$$
    (1)
    where the first term represents the ecological function contributed by species that are unique to the reference community (i.e., not present in the comparison community) and the latter term represents the contribution from species that are also found in the comparison community.Analogously, in the comparison community, the total ecological function can be decomposed as:$${T}_{F}={s}_{{uF}}{bar{z}}_{{uF}}+{s}_{c}{bar{z}}_{{cF}}$$
    (2)
    with a similar interpretation to Eq. (1). Though there are sc species in common between the two communities, the average per species contribution need not be the same in the two communities (i.e., ({bar{z}}_{{cB}}) may differ from ({bar{z}}_{{cF}})).The species in common between the two communities can serve as a reference point for comparison between communities. It is useful to define ({delta }_{B}={bar{z}}_{{uB}}-{bar{z}}_{{cB}}) and ({delta }_{F}={bar{z}}_{{uF}}-{bar{z}}_{{cF}}) as the difference in average ecological function per species of unique species versus shared species in reference and comparison communities, respectively. From this perspective, we consider the average ecological function of a species unique to the reference community as being equal to the average ecological function of shared species (as measured in the same community) plus the deviation from this value ({bar{z}}_{{uB}}={bar{z}}_{{cB}}+{delta }_{B}). Using this equality and the analogous one for ({bar{z}}_{{uF}}), along with Eqs. (1) and (2), the difference in the ecological function between communities can be decomposed as$$Delta T={T}_{F}-{T}_{B}={-s}_{{uB}}{bar{z}}_{{cB}}-{s}_{{uB}}{delta }_{B}+{s}_{{uF}}{bar{z}}_{{cF}}+{s}_{{uF}}{delta }_{F}+{s}_{c}left({bar{z}}_{{cF}}-{bar{z}}_{{cB}}right)$$
    (3)
    The first two terms represent the loss in ecological function in the comparison community due to the loss of species that are unique to the reference community. Specifically, the first term represents the loss in ecological function due to the absence of unique species if these species had the same average value of functioning as each of the shared species. In other words, it is the amount by which biomass is expected to decline if species were interchangeable. Therefore, we interpret this term as the “richness loss” or the loss in functioning due strictly to the loss of species: RICH-L ((={-s}_{{uB}}{bar{z}}_{{cB}})). It will always be negative, assuming there is at least one species unique to the reference population. In cases where ({bar{z}}_{{cB}} > {bar{z}}_{{uB}}), it is possible for RICH-L to exceed the total functioning observed at the reference site, which complicates interpretation of the raw values. In this case, it is useful to consider only the relative quantities (each component is scaled by the sum of the absolute values of all components). We note that this situation arises only 41 times out of 2867 comparisons in our analysis, and removing these cases has no effect on our findings. We advise future applications be aware of this potential issue and test for its influence.The second term accounts for the fact that the true loss in ecological function due to these lost species will often differ from the “richness expectation” because the lost species differ in value from the average value of shared species. In other words, this term reflects the deviation in the actual contributions of lost species from the average of shared species, which implies that not all species contribute equally (and that the identities of the species are important in determining differences in biomass between the two communities). We, therefore, interpret this term as indicating “compositional loss,” or the degree to which loss in biomass is due to loss of particular species: COMP-L ((= – {s}_{{uB}}{delta}_{B})). If the average lost species provide a higher contribution to the reference community than the average shared species (({bar{z}}_{{uB}} > {bar{z}}_{{cB}})), the COMP-L term will be negative. On the other hand, if the average lost species represent lower contributions, the COMP-L term will be positive (({bar{z}}_{{uB}} < {bar{z}}_{{cB}})).The next two terms are analogous to the first two terms but instead represent the increase in ecological function in the comparison community due to the “gain” of unique species that are lacking from the reference community. The third term represents the expected increase in ecological function due to an increase in species richness assuming these gained species had the same per species contribution as the shared species: RICH-G ((={+s}_{{uF}}{bar{z}}_{{cF}})). It is always positive, assuming the comparison community has at least one unique species. The fourth term, COMP-G ((=+{s}_{{uF}}{delta }_{F})), reflects the difference in composition (with respect to average value) of gained versus shared species. This term can be positive or negative, being positive if the gained species have a higher per species value than the shared species.The final term focuses on the changes in biomass considering only the species that are present in both communities. This can be thought of as holding richness and composition constant and considering changes in the community biomass that are controlled extrinsically, i.e., by underlying gradients in resource availability and other environmental factors. Historically, this term has been referred to as the “context-dependent effect,” or CDE, and is the number of shared species (({s}_{c})), multiplied by the difference in biomasses among shared species at both sites ((={s}_{c}({bar{z}}_{{cF}}-{bar{z}}_{{cB}}))). It can be of either sign: positive if shared species have a higher value in the comparison community than in the reference, negative if they have a higher value in the reference community. The number of shared species has the potential to bias away from the CDE term if it is very low. However, we note that, on average, 49.1 ± 0.003% of species are shared for each comparison at the 100-km scale, and this value is remarkably consistent regardless of spatial scale (51.3–50.0% for 15–50 km).Our decomposition is similar to, but not the same as, that of Fox and Kerr12, though both are mathematically sound. Only the CDE term is mathematically identical across the two decompositions and, thus, shares the same interpretation. By extension, the sum across the loss and gain terms (the total diversity effect, or DIV) must also be identical, because both equations partition the same total quantity. Thus, it is important to note that using either decomposition yields the same inference with respect to comparisons of DIV and CDE.Our decomposition differs from Fox and Kerr’s because the two approaches use different reference points. We take the perspective that the shared species form the basis for comparison between two communities, so we then evaluate the average value of a unique species with respect to its deviation from an average value of a shared species. In contrast, Fox and Kerr effectively evaluate the average value of a unique species with respect to its deviation from the average value of any species in that community (averaging over both unique and shared species). In both decompositions, the “composition” components only exist if there is some difference in the average value of shared and unique species. We prefer our decomposition for this case because it works with that difference directly rather than indirectly via the difference between unique and all species (which is the average of unique and shared species). Moreover, our composition makes intuitive sense that the function of the “average” species is determined by the ones that are known to exist at both sites. A full comparison of the Fox and Kerr formulation and ours is provided in the Supplementary Materials.Statistical analysisA general function to conduct our new decomposition from a site-by-species biomass matrix, and a second function to perform the simulations, can be found here: https://gist.github.com/jslefche/76c076c1c7c5d200e5cb87113cdb9fb4.We first ordered all sites by decreasing total biomass. Beginning with the highest biomass site of all sites as the first reference site, we identified all other sites within a certain spatial radius (15-, 25-, 50-, or 100-km) to serve as the comparison sites. Setting the reference to be the site with the highest community biomass constrains the sum of the terms to be negative. This choice simplifies the language used to discuss the output13 and allows us to speak directly to the consequences of real-world activities like overharvesting (and their implications).We then computed the components for each set of comparisons. We standardized the output to the same scale (−1, 1) by first taking the sum of the absolute value of all components, and then dividing each component by this value. This relativization was done to account for the fact that raw biomass may differ substantially among sites and regions and to make our results comparable across the entire dataset. Once the scaled components were computed, the reference and comparison sites were removed from the ordered list from any further comparisons to prevent any bias that might arise from including the same site multiple times. We then moved onto the next most productive site in the list, identified the comparison sites within 100 km, computed the components, and so on, until all sites were analyzed. From these individual comparisons, we computed the means of all components while omitting any reference sites for which there were fewer than five comparison sites. We alternately averaged the components for all comparisons for each reference site and then took the grand mean of these averaged values, although this additional level of aggregation did not qualitatively change our results (Supplementary Fig. 6). We have chosen to present the raw values in the main text to demonstrate the full range of variability inherent in the individual comparisons, which might otherwise be condensed by showing only the means for each reference site. We repeated the analysis over multiple spatial radii to assess whether the spatial extent and therefore the size and composition of the species pool, might influence our results.We calculated the relative strength of the total diversity effect vs. the context-dependent effect for each comparison as the ratio of DIV/CDE, and of compositional vs. richness losses as:$${{{{{rm{Q}}}}}}=frac{(-{s}_{{uB}}{delta }_{B}{-s}_{{uB}}{bar{z}}_{{cB}})}{{-s}_{{uB}}{bar{z}}_{{cB}}}=frac{{bar{z}}_{{uB}}}{{bar{z}}_{{cB}}}$$ (4) In this case, Q = (COMP-L + RICH-L)/RICH-L, which reduces to the average value of unique species relative to the average value of shared species at the reference site. This quantity reflects the magnitude to which species unique to the reference site contribute to biomass relative to the “expected” contribution per species. To avoid biases associated with averaging ratios, we report the geometric mean of both quantities. Bootstrapped 95% confidence intervals were derived by randomly resampling DIV/CDE and Q for a total of 5000 times. For DIV/CDE, some values were negative, so we excluded them in both the original data and bootstrap samples. As an alternative approach that focused on the magnitude of effect, we examined the absolute value of |DIV | / | CDE | . In this case, the ratio was 6.9x with bootstrap 95% CIs of [6.2, 7.7].To explore the drivers of the components of our decomposition, we applied random forest analysis to account for potential collinearity and interactions among the suite of predictors previously selected in ref. 39. Depth was recorded on the surveys while the following predictors were obtained from the combination of remote sensed and in situ measurements compiled in the Bio-ORACLE database: mean, minimum, maximum, and range of sea surface temperature; mean, minimum and maximum for surface chlorophyll-a; mean salinity; mean PAR; mean dissolved oxygen; mean nitrate concentration; mean phosphate concentration40. Finally, an index of human population density was calculated by fitting a smoothly tapered surface to each settlement point on the year 2010 world-population density grid using a quadratic kernel function described previously41. Random forests were fit using the default settings in the randomForest package42 in R version 4.1.143. Variable importance was determined using the percent increase in the mean-square error after randomly permuting the predictor of interest for each tree in the random forest, averaging the error of the models, and then computing the difference relative to the accuracy of the original model.Null simulationsA key finding of our analysis is that compositional losses are considerably greater than losses due to other aspects of the reef fish community. We wanted to evaluate the possibility of whether such a result could be an artifact of applying our decomposition to a dataset in which we assign the site with the higher total biomass as the “reference” community and the site with lower total biomass as the “focal” community. To do so, we conducted simulations in which we created communities with species richness values matching the observed data, but for community compositions that were random. Following the same procedure we used with the real communities, we applied our decomposition to these simulated communities to generate null distributions for the average values of each of the five terms when community composition is random. Comparing our observed values to these null distributions tells us if the values of the compositional components (or indeed any component) we observed arose as an artifact of our procedure or, alternatively, because high-biomass sites actually contain more high-biomass species than expected under random community assembly.Our simulation procedure focused on the site-by-species biomass matrix from each set of comparisons used in the main 100-km analysis. We divided this matrix by the corresponding site-by-species abundance matrix to yield the observed per capita contribution of each species in each community. We then averaged the per capita contributions of each species across all communities where the species was present to yield a single vector representing mean per capita contributions for all S species within that set of comparisons.We initially constructed each simulated community by populating it with every species in the region (“maximum richness”). To determine the biomass of each species in each community we applied the following procedure. First, we identified the minimum and maximum observed abundance of each species across all communities where it is present. For a single community, we sampled an integer value between the minimum and maximum abundance for each species to yield a single vector of random abundance values of length S, and then multiplied this vector by the vector of average per capita contributions. This procedure yielded a new vector representing a new total contribution to biomass by every species. We repeated this for all n communities in the original site-by-species matrix and bound these vectors together in a new “maximum richness” version of the site-by-species matrix. For the ith row (community) in the original dataset, we calculated the richness, si. We then randomly subsampled si species at random from the simulated “maximum richness” site-by-species matrix and set the biomass of any remaining species to zero. We repeated this for each community to yield a simulated “observed richness” site-by-species matrix with the same dimensions as the original matrix. This procedure ensures that richness is held at the observed levels and that the biomass contribution of each species are within the observed range.These communities were intentionally constructed randomly with respect to composition as our goal was to test whether the observed compositional effects in the real data are significantly different than under this null hypothesis with respect to composition. Thus, using the simulated “observed richness” site-by-species matrix, we computed the (scaled) components as we had with the real data and took their means across all communities. We repeated the randomization procedure 1000 times to yield 1000 total average values of each component. We compared the observed mean to the distribution of expected means using a one-tailed t-test to determine whether the observed components were more or less extreme than would be expected by chance.Reporting SummaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More