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    Phototrophy by antenna-containing rhodopsin pumps in aquatic environments

    Balashov, S. P. et al. Xanthorhodopsin: a proton pump with a light-harvesting carotenoid antenna. Science 309, 2061–2064 (2005).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Imasheva, E. S., Balashov, S. P., Choi, A. R., Jung, K.-H. & Lanyi, J. K. Reconstitution of Gloeobacter violaceus rhodopsin with a light-harvesting carotenoid antenna. Biochemistry 48, 10948–10955 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Fuhrman, J. A., Schwalbach, M. S. & Stingl, U. Proteorhodopsins: an array of physiological roles? Nat. Rev. Microbiol. 6, 488–494 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vollmers, J. et al. Poles apart: Arctic and Antarctic Octadecabacter strains share high genome plasticity and a new type of xanthorhodopsin. PLoS ONE 8, e63422 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bertsova, Y. V., Arutyunyan, A. M. & Bogachev, A. V. Na+-translocating rhodopsin from Dokdonia sp. PRO95 does not contain carotenoid antenna. Biochem. Mosc. 81, 414–419 (2016).Article 
    CAS 

    Google Scholar 
    Misra, R., Eliash, T., Sudo, Y. & Sheves, M. Retinal–salinixanthin interactions in a thermophilic rhodopsin. J. Phys. Chem. B 123, 10–20 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Béjà, O. et al. Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science 289, 1902–1906 (2000).Article 
    ADS 
    PubMed 

    Google Scholar 
    Béjà, O., Spudich, E. N., Spudich, J. L., Leclerc, M. & DeLong, E. F. Proteorhodopsin phototrophy in the ocean. Nature 411, 786–789 (2001).Article 
    ADS 
    PubMed 

    Google Scholar 
    Atamna-Ismaeel, N. et al. Widespread distribution of proteorhodopsins in freshwater and brackish ecosystems. ISME J. 2, 656–662 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Frigaard, N.-U., Martinez, A., Mincer, T. J. & DeLong, E. F. Proteorhodopsin lateral gene transfer between marine planktonic Bacteria and Archaea. Nature 439, 847–850 (2006).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Finkel, O. M., Béjà, O. & Belkin, S. Global abundance of microbial rhodopsins. ISME J. 7, 448–451 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gómez-Consarnau, L. et al. Microbial rhodopsins are major contributors to the solar energy captured in the sea. Sci. Adv. 5, eaaw8855 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    DeLong, E. F. & Béjà, O. The light-driven proton pump proteorhodopsin enhances bacterial survival during tough times. PLoS Biol. 8, e1000359 (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Munson-McGee, J. H. et al. Decoupling of respiration rates and abundance in marine prokaryoplankton. Nature 612, 764–770 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, W.-W., Sineshchekov, O. A., Spudich, E. N. & Spudich, J. L. Spectroscopic and photochemical characterization of a deep ocean proteorhodopsin. J. Biol. Chem. 278, 33985–33991 (2003).Article 
    CAS 
    PubMed 

    Google Scholar 
    Man, D. Diversification and spectral tuning in marine proteorhodopsins. EMBO J. 22, 1725–1731 (2003).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lanyi, J. K. & Balashov, S. P. in Halophiles and Hypersaline Environments (eds. Ventosa, A., Oren, A. & Ma, Y.) 319–340 (Springer, 2011).Balashov, S. P. et al. Reconstitution of Gloeobacter rhodopsin with echinenone: role of the 4-keto group. Biochemistry 49, 9792–9799 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kopejtka, K. et al. A bacterium from a mountain lake harvests light using both proton-pumping xanthorhodopsins and bacteriochlorophyll-based photosystems. Proc. Natl Acad. Sci. USA 119, e2211018119 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Pushkarev, A. & Béjà, O. Functional metagenomic screen reveals new and diverse microbial rhodopsins. ISME J. 10, 2331–2335 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pushkarev, A. et al. A distinct abundant group of microbial rhodopsins discovered using functional metagenomics. Nature 558, 595–599 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Chazan, A. et al. Diverse heliorhodopsins detected via functional metagenomics in freshwater Actinobacteria, Chloroflexi and Archaea. Environ. Microbiol. 24, 110–121 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Inoue, K. et al. A light-driven sodium ion pump in marine bacteria. Nat. Commun. 4, 1678 (2013).Article 
    ADS 
    PubMed 

    Google Scholar 
    Bhosale, P. & Bernstein, P. S. Microbial xanthophylls. Appl. Microbiol. Biotechnol. 68, 445–455 (2005).Article 
    CAS 
    PubMed 

    Google Scholar 
    Demmig-Adams, B., Polutchko, S. K. & Adams, W. W. Structure–function–environment relationship of the isomers zeaxanthin and lutein. Photochem 2, 308–325 (2022).Article 

    Google Scholar 
    Barreiro C. & Barredo J. L. Microbial Carotenoids: Methods and Protocols (Humana Press, 2018).Ram, S., Mitra, M., Shah, F., Tirkey, S. R. & Mishra, S. Bacteria as an alternate biofactory for carotenoid production: a review of its applications, opportunities and challenges. J. Funct. Foods 67, 103867 (2020).Article 
    CAS 

    Google Scholar 
    Shibata, M. et al. Oligomeric states of microbial rhodopsins determined by high-speed atomic force microscopy and circular dichroic spectroscopy. Sci. Rep. 8, 8262 (2018).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Luecke, H. et al. Crystallographic structure of xanthorhodopsin, the light-driven proton pump with a dual chromophore. Proc. Natl Acad. Sci. USA 105, 16561–16565 (2008).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chuon, K. et al. Assembly of natively synthesized dual chromophores into functional actinorhodopsin. Front. Microbiol. 12, 652328 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yoshizawa, S., Kawanabe, A., Ito, H., Kandori, H. & Kogure, K. Diversity and functional analysis of proteorhodopsin in marine Flavobacteria. Environ. Microbiol. 14, 1240–1248 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ahmed, F. et al. Profiling of carotenoids and antioxidant capacity of microalgae from subtropical coastal and brackish waters. Food Chem. 165, 300–306 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Shihoya, W. et al. Crystal structure of heliorhodopsin. Nature 574, 132–136 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Kishi, K. E. et al. Structural basis for channel conduction in the pump-like channelrhodopsin ChRmine. Cell 185, 672–689.e23 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Balashov, S. P., Imasheva, E. S., Wang, J. M. & Lanyi, J. K. Excitation energy-transfer and the relative orientation of retinal and carotenoid in xanthorhodopsin. Biophys. J. 95, 2402–2414 (2008).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lakowicz, J. R. (ed.) in Principles of Fluorescence Spectroscopy 27–61 (Springer, 2006).Dana, J. et al. Testing the fate of nascent holes in CdSe nanocrystals with sub-10 fs pump–probe spectroscopy. Nanoscale 13, 1982–1987 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Polívka, T. et al. Femtosecond carotenoid to retinal energy transfer in xanthorhodopsin. Biophys. J. 96, 2268–2277 (2009).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Iyer, E. S. S., Gdor, I., Eliash, T., Sheves, M. & Ruhman, S. Efficient femtosecond energy transfer from carotenoid to retinal in Gloeobacter rhodopsin–salinixanthin complex. J. Phys. Chem. B 119, 2345–2349 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Doi, S., Tsukamoto, T., Yoshizawa, S. & Sudo, Y. An inhibitory role of Arg-84 in anion channelrhodopsin-2 expressed in Escherichia coli. Sci. Rep. 7, 41879 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nagiri, C. et al. Crystal structure of human endothelin ETB receptor in complex with peptide inverse agonist IRL2500. Commun. Biol. 2, 236 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yamashita, K., Hirata, K. & Yamamoto, M. KAMO: towards automated data processing for microcrystals. Acta Crystallogr. D Struct. Biol. 74, 441–449 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kabsch, W. XDS. Acta Crystallogr. D Biol. Crystallogr. 66, 125–132 (2010).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40, 658–674 (2007).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Afonine, P. V. et al. Towards automated crystallographic structure refinement with phenix.refine.Acta Crystallogr. D Biol. Crystallogr. 68, 352–367 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zivanov, J. et al. New tools for automated high-resolution cryo-EM structure determination in RELION-3.eLife 7, e42166 (2018).Punjani, A., Rubinstein, J. L., Fleet, D. J. & Brubaker, M. A. cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination. Nat. Methods 14, 290–296 (2017).Punjani, A., Zhang, H. & Fleet, D. J. Non-uniform refinement: adaptive regularization improves single-particle cryo-EM reconstruction. Nat. Methods 17, 1214–1221 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rosenthal, P. B. & Henderson, R. Optimal determination of particle orientation, absolute hand, and contrast loss in single-particle electron cryomicroscopy. J. Mol. Biol. 333, 721–745 (2003).Article 
    CAS 
    PubMed 

    Google Scholar 
    Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 60, 2126–2132 (2004).Article 
    PubMed 

    Google Scholar 
    Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yamashita, K., Palmer, C. M., Burnley, T. & Murshudov, G. N. Cryo-EM single-particle structure refinement and map calculation using Servalcat. Acta Crystallogr. D Struct. Biol. 77, 1282–1291 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Inoue, K. et al. Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design. Commun. Biol. 4, 362 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068–1083.e21 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chen, I.-M. A. et al. The IMG/M data management and analysis system v.6.0: new tools and advanced capabilities. Nucleic Acids Res. 49, D751–D763 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Nayfach, S. et al. A genomic catalog of Earth’s microbiomes. Nat. Biotechnol. 39, 499–509 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nat. Methods 10, 1196–1199 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wickham, H. in ggplot2 (eds Gentleman, R., Hornik, K. & Parmigiani, G.) 189–201 (Springer, 2016).Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hoang, D. T., Chernomor, O., von Haeseler, A., Minh, B. Q. & Vinh, L. S. UFBoot2: improving the ultrafast bootstrap approximation. Mol. Biol. Evol. 35, 518–522 (2018).Article 
    CAS 
    PubMed 

    Google Scholar  More

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    Open-source software for geospatial analysis

    Satellite imagery provides insight into where and how Earth’s surface changes, particularly in remote areas where in situ measurements are generally lacking. With the large volumes of data produced by satellites, we need streamlined computational pipelines for optimized processing capabilities. Although a multitude of platforms exists to process satellite data, these often have expensive license requirements that price out much of the geospatial community. Moreover, many of these platforms are propriety, but transparency is key when developing geospatial processing workflows. Open-source programming is critical to the creation of efficient imagery processing pipelines. More

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    Tropical deforestation causes large reductions in observed precipitation

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

    Google Scholar 
    Spracklen, D. V., Baker, J. C. A., Garcia-Carreras, L. & Marsham, J. H. The effects of tropical vegetation on rainfall. Annu. Rev. Environ. Resour. 43, 193–218 (2018).Article 

    Google Scholar 
    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Spracklen, D. V., Arnold, S. R. & Taylor, C. M. Observations of increased tropical rainfall preceded by air passage over forests. Nature 489, 282–285 (2012).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Change 8, 539–543 (2018).Article 
    ADS 

    Google Scholar 
    Baker, J. C. A. & Spracklen, D. V. Divergent representation of precipitation recycling in the Amazon and the Congo in CMIP6 models. Geophys. Res. Lett. 49, e2021GL095136 (2022).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Staal, A. et al. Hysteresis of tropical forests in the 21st century. Nat. Commun. 11, 4978 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–854 (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Chagnon, F. J. F. & Bras, R. L. Contemporary climate change in the Amazon. Geophys. Res. Lett. 32, L13703 (2005).Article 
    ADS 

    Google Scholar 
    Khanna, J., Medvigy, D., Fueglistaler, S. & Walko, R. Regional dry-season climate changes due to three decades of Amazonian deforestation. Nat. Clim. Change 7, 200–204 (2017).Article 
    ADS 

    Google Scholar 
    Garcia-Carreras, L. & Parker, D. J. How does local tropical deforestation affect rainfall? Geophys. Res. Lett. 38, L19802 (2011).Article 
    ADS 

    Google Scholar 
    Leite-Filho, A. T., Soares-Filho, B. S., Davis, J. L., Abrahão, G. M. & Börner, J. Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nat. Commun. 12, 2591 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McAlpine, C. A. et al. Forest loss and Borneo’s climate. Environ. Res. Lett. 13, 044009 (2018).Chapman, S. et al. Compounding impact of deforestation on Borneo’s climate during El Niño events. Environ. Res. Lett. 15, 084006 (2020).Spracklen, D. V. & Garcia-Carreras, L. The impact of Amazonian deforestation on Amazon basin rainfall. Geophys. Res. Lett. 42, 9546–9552 (2015).Article 
    ADS 

    Google Scholar 
    Jiang, Y. et al. Modeled response of South American climate to three decades of deforestation. J. Clim. 34, 2189–2203 (2021).Article 
    ADS 

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

    Google Scholar 
    Fassoni-Andrade, A. C. et al. Amazon hydrology from space: scientific advances and future challenges. Rev. Geophys. 59, e2020RG000728 (2021).Article 
    ADS 

    Google Scholar 
    Haiden, T., Janousek, M., Vitart, F., Ferranti, L. & Prates, F. Evaluation of ECMWF Forecasts, Including the 2019 Upgrade. ECMWF Technical Memorandum No. 853 (ECMWF, 2019).Esquivel-Muelbert, A. et al. Compositional response of Amazon forests to climate change. Glob. Change Biol. 25, 39–56 (2019).Article 
    ADS 

    Google Scholar 
    Brum, M. et al. ENSO effects on the transpiration of eastern Amazon trees. Philos. Trans. R. Soc. B 373, 20180085 (2018).Article 

    Google Scholar 
    Bagley, J. E., Desai, A. R., Harding, K. J., Snyder, P. K. & Foley, J. A. Drought and deforestation: has land cover change influenced recent precipitation extremes in the Amazon? J. Clim. 27, 345–361 (2014).Article 
    ADS 

    Google Scholar 
    Wunderling, N. et al. Recurrent droughts increase risk of cascading tipping events by outpacing adaptive capacities in the Amazon rainforest. Proc. Natl Acad. Sci. USA 119, e2120777119 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fu, R. & Li, W. The influence of the land surface on the transition from dry to wet season in Amazonia. Theor. Appl. Climatol. 78, 97–110 (2004).Article 
    ADS 

    Google Scholar 
    Leite-Filho, A. T., de Sousa Pontes, V. Y. & Costa, M. H. Effects of deforestation on the onset of the rainy season and the duration of dry spells in southern Amazonia. J. Geophys. Res. Atmos. 124, 5268–5281 (2019).Article 
    ADS 

    Google Scholar 
    Negri, A. J., Adler, R. F., Xu, L. & Surratt, J. The Impact of Amazonian deforestation on dry season rainfall. J. Clim. 17, 1306–1319 (2004).Article 
    ADS 

    Google Scholar 
    Chagnon, F. J. F., Bras, R. L. & Wang, J. Climatic shift in patterns of shallow clouds over the Amazon. Geophys. Res. Lett. 31, L24212 (2004).Article 
    ADS 

    Google Scholar 
    Chambers, J. Q. & Artaxo, P. Biosphere–atmosphere interactions: deforestation size influences rainfall. Nat. Clim. Change 7, 175–176 (2017).Article 
    ADS 

    Google Scholar 
    Baudena, M., Tuinenburg, O. A., Ferdinand, P. A. & Staal, A. Effects of land-use change in the Amazon on precipitation are likely underestimated. Glob. Change Biol. 27, 5580–5587 (2021).Article 
    CAS 

    Google Scholar 
    Duku, C. & Hein, L. The impact of deforestation on rainfall in Africa: a data-driven assessment. Environ. Res. Lett. 16, 064044 (2021).Akkermans, T., Thiery, W. & Van Lipzig, N. P. M. The regional climate impact of a realistic future deforestation scenario in the Congo basin. J. Clim. 27, 2714–2734 (2014).Article 
    ADS 

    Google Scholar 
    Staal, A. et al. Feedback between drought and deforestation in the Amazon. Environ. Res. Lett. 15, 044024 (2020).Xu, X. et al. Deforestation triggering irreversible transition in Amazon hydrological cycle. Environ. Res. Lett. 17, 034037 (2022).Kooperman, G. J. et al. Forest response to rising CO2 drives zonally asymmetric rainfall change over tropical land. Nat. Clim. Change 8, 434–440 (2018).Article 
    ADS 

    Google Scholar 
    Chen, Z. et al. Global land monsoon precipitation changes in CMIP6 projections. Geophys. Res. Lett. 47, e2019GL086902 (2020).Stickler, C. M. et al. Dependence of hydropower energy generation on forests in the Amazon Basin at local and regional scales. Proc. Natl Acad. Sci. USA 110, 9601–9606 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).Article 
    ADS 

    Google Scholar 
    Strand, J. et al. Spatially explicit valuation of the Brazilian Amazon forest’s ecosystem services. Nat. Sustain. 1, 657–664 (2018).Article 

    Google Scholar 
    Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).Article 

    Google Scholar 
    Li, Y. et al. Deforestation-induced climate change reduces carbon storage in remaining tropical forests. Nat. Commun. 13, 1964 (2022).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aragão, L. E. O. C. et al. Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia. Philos. Trans. R. Soc. B 363, 1779–1785 (2008).Article 

    Google Scholar 
    Marengo, J. A. et al. Changes in climate and land use over the Amazon region: current and future variability and trends. Front. Earth Sci. https://doi.org/10.3389/feart.2018.00228 (2018).Jiang, Y. et al. Widespread increase of boreal summer dry season length over the Congo rainforest. Nat. Clim. Change https://doi.org/10.1038/s41558-019-0512-y (2019).Van Der Ent, R. J. & Savenije, H. H. G. Length and time scales of atmospheric moisture recycling. Atmos. Chem. Phys. 11, 1853–1863 (2011).Article 
    ADS 

    Google Scholar 
    Sorí, R., Nieto, R., Vicente-Serrano, S. M., Drumond, A. & Gimeno, L. A Lagrangian perspective of the hydrological cycle in the Congo River basin. Earth Syst. Dyn. 8, 653–675 (2017).Article 
    ADS 

    Google Scholar 
    van der Ent, R. J., Savenije, H. H. G., Schaefli, B. & Steele-Dunne, S. C. Origin and fate of atmospheric moisture over continents. Water Resour. Res. 46, W09525 (2010).ADS 

    Google Scholar 
    Feng, Y. et al. Doubling of annual forest carbon loss over the tropics during the early twenty-first century. Nat. Sustain. 4, 441–451 (2022).
    Google Scholar 
    Tuinenburg, O. A., Bosmans, J. H. C. & Staal, A. The global potential of forest restoration for drought mitigation. Environ. Res. Lett. 17, 034045 (2022).Met Office. Cartopy: a cartographic python library with a Matplotlib interface 2010–2015. Met Office https://scitools.org.uk/cartopy (2022).Hoyer, S. & Hamman, J. xarray: N-D labeled arrays and datasets in Python. J. Open Res. Softw. https://doi.org/10.5334/jors.148 (2017).Zhuang, J. xESMF. Zenodo https://doi.org/10.5281/zenodo.1134365 (2022).Baker, J. C. A. & Spracklen, D. V. Climate benefits of intact Amazon forests and the biophysical consequences of disturbance. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2019.00047 (2019).Schaaf, C. & Wang, Z. MCD43A3 MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global – 500m V006. NASA EOSDIS Land Processes DAAC https://doi.org/10.5067/modis/mcd43a3.006 (2015).Waskom, M. Seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).Article 
    ADS 

    Google Scholar 
    Chen, M. et al. Global land use for 2015–2100 at 0.05° resolution under diverse socioeconomic and climate scenarios. Sci. Data 7, 320 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xie, P. et al. NOAA Climate Data Record (CDR) of CPC Morphing technique (CMORPH) high resolution global precipitation estimates, version 1. NOAA National Centers for Environmental Information https://doi.org/10.25921/w9va-q159 (2019).Xie, P. et al. A gauge-based analysis of daily precipitation over East Asia. J. Hydrometeorol. 8, 607–626 (2007).Article 
    ADS 

    Google Scholar 
    Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).Article 
    ADS 

    Google Scholar 
    Elke, R., Hänsel, S., Finger, P., Schneider, U. & Ziese, M. GPCC Climatology Version 2022 at 0.25°: monthly land-surface precipitation climatology for every month and the total year from rain-gauges built on GTS-based and historical data. GPCC https://doi.org/10.5676/DWD_GPCC/CLIM_M_V2022_025 (2022).Huffman, G. J. A., Behrangi, R. F., Adler, D. T., Bolvin, E. J. & Nelkin, G. G. Introduction to the new version 3 GPCP monthly global precipitation analysis. GPCP https://docserver.gesdisc.eosdis.nasa.gov/public/project/MEaSUREs/GPCP/Release_Notes.GPCPV3.2.pdf (2022).Hou, A. Y. et al. The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 95, 701–722 (2014).Article 
    ADS 

    Google Scholar 
    Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Japan 93, 5–48 (2015).Article 
    ADS 

    Google Scholar 
    Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).Article 
    ADS 

    Google Scholar 
    Chen, M., Xie, P. & Janowiak, J. E. Global land precipitation: a 50-yr monthly analysis based on gauge observations. J. Hydrometeorol. 3, 249–266 (2002).Article 
    ADS 

    Google Scholar 
    Nguyen, P. et al. The CHRS data portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Sci. Data 6, 1180296 (2019).Article 

    Google Scholar 
    Ashouri, H. et al. PERSIANN-CDR: daily precipitation climate data record from multisatellite observations for hydrological and climate studies. Bull. Am. Meteorol. Soc. 96, 69–83 (2015).Article 
    ADS 

    Google Scholar 
    Nguyen, P. et al. Persiann dynamic infrared–rain rate (PDIR-now): a near-real-time, quasi-global satellite precipitation dataset. J. Hydrometeorol. 21, 2893–2906 (2020).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sadeghi, M. et al. PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies. Sci. Data 8, 157 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huffman, G. J. et al. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).Article 
    ADS 

    Google Scholar 
    Matsuura, K. & Willmott, C. J. Terrestrial precipitation: 1900-2017 gridded monthly time series. Global Precipitation Archive http://climate.geog.udel.edu/~climate/html_pages/Global2017/README.GlobalTsP2017.html (2018). More

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    Observed reductions in rainfall due to tropical deforestation

    RESEARCH BRIEFINGS
    01 March 2023

    Tropical deforestation affects local and regional precipitation, but the effects are uncertain and have not been determined using observations. Satellite data sets were used to show reductions in precipitation over areas of tropical forest loss, with stronger reductions seen as the deforested area expands. More

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    Coastal algal blooms have intensified over the past 20 years

    RESEARCH BRIEFINGS
    01 March 2023

    Global spatial and temporal patterns of coastal phytoplankton blooms were characterized using daily satellite imaging between 2003 and 2020. These blooms were identified on the coast of 126 of the 153 ocean-bordering countries examined. The extent and frequency of blooms have increased globally over the past two decades. More

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    Untitled public forestlands threat Amazon conservation

    There is a recent change in the modus operandi of Brazilian Amazon deforestation. The proportion of illegal deforestation in public land increased from ~43–44% (2015–2018) to ~49–52% (2019–2021)10. Land grabbers occupy public lands (deforesting or raising cattle) in a high-risk expectation of receiving title to the land and/or trading the land with significant returns (land speculation)6,7. Therefore, we argue that it is crucial to rapidly assign most of the Amazon’s UPFs to land tenure regimes associated with conservation. Land-tenure security will bring greater governance and protection to these areas. Achieving this goal requires a combination of three measures: (1) careful attention to the choice of land tenure categories for UPFs, (2) technological improvements, and (3) law enforcement.Choice of land tenure category for UPFsPublic lands in Brazil include several categories, such as conservation areas (with several subcategories under law number 9985/2000), Indigenous lands, and rural settlements, among others. Therefore, the category choice for each undesignated public land area requires studies to determine those lands’ social, environmental, or productive suitability, taking note of their histories of occupation, cultural importance, and potential uses. The unpopulated forest is a myth. Most of the areas in the Amazon have been occupied by human populations—traditional communities, indigenous villages, uncontacted tribes, “riverside” (ribeirinho) peoples, or small farmers—for generations. Ancestral occupation of land without proof or associated studies, however, does not guarantee land rights. Therefore, to avoid unfair competition for land and unilateral political decisions, the best choice of land category for a given UPF to meet social, ecological and economic demands would benefit from active social participation, multidisciplinary scientific studies, in situ observations, and innovative technologies (e.g., remote sensing, data processing capabilities, machine learning, cloud computing) to provide fast, scalable, and quality information.Final allocation decisions, however, must be preceded by participatory and transparent consultation processes to avoid conflicts and safeguard land rights. The measure of assigning tenure categories to the UPFs has a high level of complexity in itself and may benefit from the support of multi-actors (e.g., governments, academia, civil society, private sector) at multi-levels (e.g., studies, participation processes, decision-making processes) and multi-scales (local, regional and national). Despite the complexity, there are examples in the early 2000s of joint efforts to allocate land (“Terra Legal” Program) and create protected areas on a large scale and in a short period of time in the Brazilian Amazon. We emphasize, however, that the tenure categories selected for the UPFs need to maintain forest cover, remain in the public domain in compliance with national laws, and enhance long-term Amazon conservation, respecting the rights of resident populations.Technological improvements to control land grabbing in UPFLasting conservation of the Amazon rainforest depends on ending land-grabbing and illegal deforestation in public forests (designated or undesignated). However, land grabbers are using a self-declaratory tool to declare illegally invaded public lands as private properties, which demands immediate technological improvements to the system.The Rural Environmental Registry (CAR is the Portuguese acronym) is a mechanism of environmental oversight of private lands under the Brazilian Forest Code (Law 12,651/2012). CARs are registered on a web-based platform (Rural Environmental Registry System – SICAR). By law, landowners must self-declare their property boundaries and land use types (e.g., residential, agricultural, protection) in SICAR, respecting legally required protection of certain forest areas and watercourses. Then, a state environmental agency must validate the information. Unfortunately, the validation process has been extremely slow (e.g., More

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    Individual personality predicts social network assemblages in a colonial bird

    Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. Lond. B 365, 4051–4063 (2010).Article 

    Google Scholar 
    Gosling, S. D. From mice to men: What can we learn about personality from animal research?. Psychol. Bull. 127, 45 (2001).Article 
    CAS 

    Google Scholar 
    Dingemanse, N. J., Class, B. & Holtmann, B. Nonrandom mating for behavior in the wild?. Trends Ecol. Evol. 36, 177–179 (2021).Article 

    Google Scholar 
    Croft, D. P. et al. Behavioural trait assortment in a social network: Patterns and implications. Behav. Ecol. Sociobiol. 63, 1495–1503 (2009).Article 

    Google Scholar 
    Morton, F. B., Weiss, A., Buchanan-Smith, H. M. & Lee, P. C. Capuchin monkeys with similar personalities have higher-quality relationships independent of age, sex, kinship and rank. Anim. Behav. 105, 163–171 (2015).Article 

    Google Scholar 
    Su, X. et al. Agonistic behaviour and energy metabolism of bold and shy swimming crabs Portunus trituberculatus. J. Exp. Biol. https://doi.org/10.1242/jeb.188706 (2019).Article 

    Google Scholar 
    Jolles, J. W., King, A. J. & Killen, S. S. The role of individual heterogeneity in collective animal behaviour. Trends Ecol. Evol. 35, 278–291 (2020).Article 

    Google Scholar 
    Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).Article 

    Google Scholar 
    Frost, A. J., Winrow-Giffen, A., Ashley, P. J. & Sneddon, L. U. Plasticity in animal personality traits: Does prior experience alter the degree of boldness?. P. Roy. Soc. B-Biol. Sci. 274, 333–339 (2007).
    Google Scholar 
    Krause, J., James, R. & Croft, D. P. Personality in the context of social networks. Philos. Trans. R. Soc. Lond. B 365, 4099 (2010).Article 
    CAS 

    Google Scholar 
    David, M., Auclair, Y. & Cézilly, F. Personality predicts social dominance in female zebra finches, Taeniopygia guttata, in a feeding context. Anim. Behav. 81, 219–224 (2011).Article 

    Google Scholar 
    Favati, A., Leimar, O. & Løvlie, H. Personality predicts social dominance in male domestic fowl. PLoS ONE 9, e103535 (2014).Article 
    ADS 

    Google Scholar 
    McGhee, K. E. & Travis, J. Repeatable behavioural type and stable dominance rank in the Bluefin killifish. Anim. Behav. 79, 497–507 (2010).Article 

    Google Scholar 
    Krause, J., Croft, D. P. & James, R. Social network theory in the behavioural sciences: Potential applications. Behav. Ecol. Sociobiol. 62, 15–27 (2007).Article 
    CAS 

    Google Scholar 
    Flack, J. C., Girvan, M., de Waal, F. & Krakauer, D. C. Policing stabilizes construction of social niches in primates. Nature 439, 426–429 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Croft, D. P., James, R. & Krause, J. Exploring Animal Social Networks (Princeton University Press, 2008).Book 

    Google Scholar 
    Patriquin, K. J., Leonard, M. L., Broders, H. G. & Garroway, C. J. Do social networks of female northern long-eared bats vary with reproductive period and age?. Behav. Ecol. Sociobiol. 64, 899–913 (2010).Article 

    Google Scholar 
    Gomes, A. C. R., Beltrão, P., Boogert, N. J. & Cardoso, G. C. Familiarity, dominance, sex and season shape common waxbill social networks. Behav. Ecol. 33, 526–540 (2022).Article 

    Google Scholar 
    Croft, D. P., Krause, J. & James, R. Social networks in the guppy (Poecilia reticulata). P. Roy. Soc. B-Biol. Sci. 271, S516–S519 (2004).Article 

    Google Scholar 
    Pike, T. W., Samanta, M., Lindström, J. & Royle, N. J. Behavioural phenotype affects social interactions in an animal network. P. Roy. Soc. B-Biol. Sci. 275, 2515–2520 (2008).
    Google Scholar 
    Aplin, L. M. et al. Individual personalities predict social behaviour in wild networks of great tits (Parus major). Ecol. Lett. 16, 1365–1372 (2013).Article 
    CAS 

    Google Scholar 
    Massen, J. J. & Koski, S. E. Chimps of a feather sit together: Chimpanzee friendships are based on homophily in personality. Evol. Hum. Behav. 35, 1–8 (2014).Article 

    Google Scholar 
    Rault, J.-L. Friends with benefits: Social support and its relevance for farm animal welfare. Appl. Anim. Behav. Sci. 136, 1–14 (2012).Article 

    Google Scholar 
    Schneider, G. & Krueger, K. Third-party interventions keep social partners from exchanging affiliative interactions with others. Anim. Behav. 83, 377–387 (2012).Article 

    Google Scholar 
    Fraser, O. N. & Bugnyar, T. Do ravens show consolation? Responses to distressed others. PLoS ONE 5, e10605 (2010).Article 
    ADS 

    Google Scholar 
    Rose, P. & Croft, D. The potential of social network analysis as a tool for the management of zoo animals. Anim. Welf. 24, 123–138 (2015).Article 

    Google Scholar 
    Clark, F. E. Space to choose: network analysis of social preferences in a captive chimpanzee community, and implications for management. Am. J. Primatol. 73, 748–757 (2011).Article 

    Google Scholar 
    Corner, L., Pfeiffer, D. & Morris, R. Social-network analysis of Mycobacterium bovis transmission among captive brushtail possums (Trichosurus vulpecula). Prev. Vet. Med. 59, 147–167 (2003).Article 
    CAS 

    Google Scholar 
    Hansen, H., McDonald, D. B., Groves, P., Maier, J. A. & Ben-David, M. Social networks and the formation and maintenance of river otter groups. Ethology 115, 384–396 (2009).Article 

    Google Scholar 
    Radosevich, L. M., Jaffe, K. E. & Minier, D. E. The utility of social network analysis for informing zoo management: Changing network dynamics of a group of captive hamadryas baboons (Papio hamadryas) following an introduction of two young males. Zoo Biol. 40, 503–516 (2021).Article 

    Google Scholar 
    Pacheco, X. P. & Madden, J. R. Does the social network structure of wild animal populations differ from that of animals in captivity?. Behav. Processes 190, 104446 (2021).Article 

    Google Scholar 
    Watters, J. V. & Powell, D. M. Measuring animal personality for use in population management in zoos: Suggested methods and rationale. Zoo Biol. 31, 1–12 (2012).Article 

    Google Scholar 
    Koski, S. E. Social personality traits in chimpanzees: temporal stability and structure of behaviourally assessed personality traits in three captive populations. Behav. Ecol. Sociobiol. 65, 2161–2174 (2011).Article 

    Google Scholar 
    Račevska, E. & Hill, C. M. Personality and social dynamics of zoo-housed western lowland gorillas (Gorilla gorilla gorilla). J. Zoo Aqua. Res. 5, 116–122 (2017).
    Google Scholar 
    Stoinski, T. S., Jaicks, H. F. & Drayton, L. A. Visitor effects on the behavior of captive western lowland gorillas: The importance of individual differences in examining welfare. Zoo Biol. 31, 586–599 (2012).Article 

    Google Scholar 
    Wielebnowski, N. C. Behavioral differences as predictors of breeding status in captive cheetahs. Zoo Biol. 18, 335–349 (1999).Article 

    Google Scholar 
    Barrett, L. P. et al. Personality assessment of headstart Texas horned lizards (Phrynosoma cornutum) in human care prior to release. Appl. Anim. Behav. Sci. 254, 105690 (2022).Article 

    Google Scholar 
    Rose, P. E., Brereton, J. E. & Croft, D. P. Measuring welfare in captive flamingos: Activity patterns and exhibit usage in zoo-housed birds. Appl. Anim. Behav. Sci. 205, 115–125 (2018).Article 

    Google Scholar 
    Rose, P. E. & Croft, D. P. Social bonds in a flock bird: Species differences and seasonality in social structure in captive flamingo flocks over a 12-month period. Appl. Anim. Behav. Sci. 193, 87–97 (2017).Article 

    Google Scholar 
    Rose, P. E. & Croft, D. P. Quantifying the social structure of a large captive flock of greater flamingos (Phoenicopterus roseus): Potential implications for management in captivity. Behav. Processes 150, 66–74 (2018).Article 

    Google Scholar 
    Rose, P. E., Croft, D. P. & Lee, R. A review of captive flamingo (Phoenicopteridae) welfare: A synthesis of current knowledge and future directions. Intern. Zoo Yearb. 48, 139–155 (2014).Article 

    Google Scholar 
    Rose, P. E. & Croft, D. P. Evaluating the social networks of four flocks of captive flamingos over a five-year period: Temporal, environmental, group and health influences on assortment. Behav. Processes 175, 104118 (2020).Article 

    Google Scholar 
    Munson, A. A., Jones, C., Schraft, H. & Sih, A. You’re just my type: Mate choice and behavioral types. Trends Ecol. Evol. 35, 823–833 (2020).Article 

    Google Scholar 
    Schuett, W., Tregenza, T. & Dall, S. R. Sexual selection and animal personality. Biol. Rev. 85, 217–246 (2010).Article 

    Google Scholar 
    Jackson, W. M. Why do winners keep winning?. Behav. Ecol. Sociobiol. 28, 271–276 (1991).Article 

    Google Scholar 
    Dammhahn, M. & Almeling, L. Is risk taking during foraging a personality trait? A field test for cross-context consistency in boldness. Anim. Behav. 84, 1131–1139 (2012).Article 

    Google Scholar 
    Van Oers, K., Drent, P. J., De Goede, P. & Van Noordwijk, A. J. Realized heritability and repeatability of risk-taking behaviour in relation to avian personalities. P. Roy. Soc. B-Biol. Sci. 271, 65–73 (2004).Article 

    Google Scholar 
    Hinton, M. G. et al. Patterns of aggression among captive American flamingos (Phoenicopterus ruber). Zoo Biol. 32, 445–453 (2013).Article 

    Google Scholar 
    Royer, E. A. & Anderson, M. J. Evidence of a dominance hierarchy in captive Caribbean flamingos and its relation to pair bonding and physiological measures of health. Behav. Processes 105, 60–70 (2014).Article 

    Google Scholar 
    Carere, C., Drent, P. J., Privitera, L., Koolhaas, J. M. & Groothuis, T. G. Personalities in great tits, Parus major: Stability and consistency. Anim. Behav. 70, 795–805 (2005).Article 

    Google Scholar 
    Jouventin, P., Lequette, B. & Dobson, F. S. Age-related mate choice in the wandering albatross. Anim. Behav. 57, 1099–1106 (1999).Article 
    CAS 

    Google Scholar 
    Black, J. M. Partnerships in Birds: The Study of Monogamy (Oxford University Press, USA, 1996).
    Google Scholar 
    Estevez, I., Andersen, I.-L. & Nævdal, E. Group size, density and social dynamics in farm animals. Appl. Anim. Behav. Sci. 103, 185–204 (2007).Article 

    Google Scholar 
    Pickering, S. The comparative breeding biology of flamingos Phoenicopteridae at the Wildfowl and Wetlands Trust Centre, Slimbridge. Intern. Zoo Yearbook 31, 139–146 (1992).Article 

    Google Scholar 
    Whitehead, H. Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis (University of Chicago Press, 2008).Book 

    Google Scholar 
    Wilson, A. D., Krause, S., Dingemanse, N. J. & Krause, J. Network position: A key component in the characterization of social personality types. Behav. Ecol. Sociobiol. 67, 163–173 (2013).Article 

    Google Scholar 
    Renner, M. J. & Kelly, A. L. Behavioral decisions for managing social distance and aggression in captive polar bears (Ursus maritimus). J. Appl. Anim. Welf. Sci. 9, 233–239 (2006).Article 
    CAS 

    Google Scholar 
    Stevens, E. F. & Pickett, C. Managing the social environments of flamingos for reproductive success. Zoo Biol. 13, 501–507 (1994).Article 

    Google Scholar 
    Franks, D. W., Ruxton, G. D. & James, R. Sampling animal association networks with the gambit of the group. Behav. Ecol. Sociobiol. 64, 493–503 (2010).Article 

    Google Scholar 
    Haddadi, H. et al. Determining association networks in social animals: Choosing spatial–temporal criteria and sampling rates. Behav. Ecol. Sociobiol. 65, 1659–1668 (2011).Article 

    Google Scholar 
    Whitehead, H. & Dufault, S. Techniques for analyzing vertebrate social structure using identified individuals. Adv. Stud. Behav. 28, 33–74 (1999).Article 

    Google Scholar 
    Borgatti, S.P., M., E., G., & C., F.L. UCINET for windows: software for social network analysis. Analytic Technologies: Harvard, MA (2002).Borgatti, S. P. NetDraw: graph visualization software (Analytic Technologies, 2002).
    Google Scholar 
    Bejder, L., Fletcher, D. & Bräger, S. A method for testing association patterns of social animals. Anim. Behav. 56, 719–725 (1998).Article 
    CAS 

    Google Scholar 
    Farine, D. R. & Whitehead, H. Constructing, conducting and interpreting animal social network analysis. J. Anim. Ecol. 84, 1144–1163 (2015).Article 

    Google Scholar 
    Perdue, B. M., Gaalema, D. E., Martin, A. L., Dampier, S. M. & Maple, T. L. Factors affecting aggression in a captive flock of Chilean flamingos (Phoenicopterus chilensis). Zoo Biol. 30, 59–64 (2011).
    Google Scholar 
    IBMCorp. IBM SPSS Statistics for Windows. IBM Corp: Armonk, NY (2012).Clarke, K.R. & Gorley, R.N. PRIMER v6: User Manual/Tutorial. PRIMER-E, Plymouth. (2006).Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. (2020).RCoreTeam. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. (2021).Budaev, S. V. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology 116, 472–480 (2010).Article 

    Google Scholar 
    Whitehead, H. SOCPROG programs: Analysing animal social structures. Behav. Ecol. Sociobiol. 63, 765–778 (2009).Article 

    Google Scholar 
    Whitehead, H. SOCPROG: Programs for analyzing social structure: Whitehead Lab (2019).Hanneman, R.A. & Riddle, M., Chapter 18: Some Statistical Tools. In: Introduction to Social Network Methods. (University of California, Riverside 2005). http://faculty.ucr.edu/~hanneman/.(2005) More

  • in

    Alcobiosis, an algal-fungal association on the threshold of lichenisation

    Wilkinson, D. At cross purposes. Nature 412, 485 (2001).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    de Bary, H. A. Über Symbiose [On Symbiosis]. Tageblatt für die Versammlung Dtsch. Naturforscher und Aerzte (in Cassel) [Daily J. Conf. Ger. Sci. Phys.] (in Ger. 51, 121–126 (1878).Lücking, R., Leavitt, S. D. & Hawksworth, D. L. Species in lichen-forming fungi: balancing between conceptual and practical considerations, and between phenotype and phylogenomics. Fungal Div.109, 99–154 (Springer, Netherlands, 2021).de Vries, J. & Archibald, J. M. Plant evolution: Landmarks on the path to terrestrial life. New Phytol. 217, 1428–1434 (2018).Article 
    PubMed 

    Google Scholar 
    Ahmadjian, V. The Lichen Symbiosis (John Wiley & Sons, 1993).
    Google Scholar 
    Lücking, R., Hodkinson, B. P. & Leavitt, S. D. The 2016 classification of lichenized fungi in the Ascomycota and Basidiomycota-approaching one thousand genera. Bryologist 119, 361–416 (2016).Article 

    Google Scholar 
    Schneider, K., Resl, P. & Spribille, T. Escape from the cryptic species trap: lichen evolution on both sides of a cyanobacterial acquisition event. Mol. Ecol. 25, 3453–3468 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wedin, M., Döring, H. & Gilenstam, G. Saprotrophy and lichenization as options for the same fungal species on different substrata: Environmental plasticity and fungal lifestyles in the Stictis-Conotrema complex. New Phytol. 164, 459–465 (2004).Article 

    Google Scholar 
    Muggia, L., Baloch, E., Stabentheiner, E., Grube, M. & Wedin, M. Photobiont association and genetic diversity of the optionally lichenized fungus Schizoxylon albescens. FEMS Microbiol. Ecol. 75, 255–272 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sanders, W. B., Moe, R. L. & Ascaso, C. Ultrastructural study of the brown alga Petroderma maculiforme (Phaeophyceae) in the free-living state and in lichen symbiosis with the intertidal marine fungus Verrucaria tavaresiae (Ascomycotina). Eur. J. Phycol. 40, 353–361 (2005).Article 
    CAS 

    Google Scholar 
    Vondrák, J. et al. From Cinderella to Princess. Preslia 94, 143–181 (2022).Article 

    Google Scholar 
    Hawksworth, D. L. The variety of fungal-algal symbioses, their evolutionary significance, and the nature of lichens. Bot. J. Linn. Soc. 96, 3–20 (1988).Article 

    Google Scholar 
    Larsson, K. H. & Ryvarden, L. Corticioid fungi of Europe 1. Acanthobasidium–Gyrodontium. Synop. Fungorum 43, 1–266 (2021).
    Google Scholar 
    Albertini, J. B., von Schweinitz, L. D. Conspectus fungorum in Lusatiae Superioris agro Niskiensi crescentium, e methodo Persooniana. (DE: Sumtibus Kummerianis, Lipsiae 1805) https://doi.org/10.5962/bhl.title.3601.Poelt, J. & Jülich, W. Über die Beziehungen zweier corticioider Basidiomyceten zu Algen. Österr. Bot. Zeitschrift 116, 400–410 (1969).Article 

    Google Scholar 
    Voytsekhovich, A., Ordynets, O. & Akimov, Y. Optionally lichenized fungi of Hyphodontia (Agaricomycetes, Schizoporaceae) and their photobiont composition. Aктyaльнi Пpoблeми Бoтaнiки Ta Eкoлoгiї. Maтepiaли Miжнapoднoї Кoнфepeнцiї Moлoдиx Учeниx 65 (2013).Voytsekhovich, A., Mikhailyuk, T., Akimov, Y., Ordynets, A., Gustavs, L. Optionally lichenized fungi of Hyphodontia (Agaricomycetes, Schizoporaceae). 8th Congress of the International Symbiosis Society, Lisbon, 12–18 July 2015. Lisbon, PT:, 217 (Conf. abstract) (2015).Gustavs L, Schiefelbein U, Darienko T, P. T. Symbioses of the green algal genera Coccomyxa and Elliptochloris (Trebouxiophyceae, Chlorophyta). in Algal and Cyanobacteria Symbioses (ed. Grube M, Seckbach J) 169–208 (2017).Darienko, T., Gustavs, L., Eggert, A., Wolf, W. & Pröschold, T. Evaluating the species boundaries of green microalgae (Coccomyxa, Trebouxiophyceae, Chlorophyta) using integrative taxonomy and DNA barcoding with further implications for the species identification in environmental samples. PLoS ONE 10, 1–31 (2015).Article 

    Google Scholar 
    Malavasi, V. et al. DNA-based taxonomy in ecologically versatile microalgae: A re-evaluation of the species concept within the coccoid green algal genus Coccomyxa (Trebouxiophyceae, Chlorophyta). PLoS ONE 11, e0151137 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Green, T. G. A., Nash, T. H. Lichen Biology. In Lichen Biology, Second Edition 152–181 (Cambridge University Press, Cambridge, 2008) https://doi.org/10.1017/CBO9780511790478.Lindgren, H. et al. Cophylogenetic patterns in algal symbionts correlate with repeated symbiont switches during diversification and geographic expansion of lichen-forming fungi in the genus Sticta (Ascomycota, Peltigeraceae). Mol. Phylogenet. Evol. 150, 106860 (2020).Article 
    PubMed 

    Google Scholar 
    Kulichová, J., Škaloud, P. & Neustupa, J. Molecular diversity of green corticolous microalgae from two sub-mediterranean European localities. Eur. J. Phycol. 49, 345–355 (2014).Article 

    Google Scholar 
    Pröschold, T. & Darienko, T. The green puzzle Stichococcus (Trebouxiophyceae, Chlorophyta): New generic and species concept among this widely distributed genus. Phytotaxa 441, 113–142 (2020).Article 

    Google Scholar 
    Meier, F. A., Scherrer, S. & Honegger, R. Faecal pellets of lichenivorous mites contain viable cells of the lichen-forming ascomycete Xanthoria parietina and its green algal photobiont. Trebouxia arboricola. Biol. J. Linn. Soc. 76, 259–268 (2002).Article 

    Google Scholar 
    Bernicchia, A. & Gorjón, S. P. Corticiaceae s.l. 1008 (2010), ISBN: 9788890105791.Parmasto, E. Descriptiones taxorum novorum. Combinationes novae. Proc. Acad. Sci. Est. SSR. Biol. 16, 377–394 (1967).
    Google Scholar 
    Hjortstam, K., Larsson, K., Ryvarden, L. & Eriksson, J. The Corticiaceae of North Europe. (Oslo: Fungiflora, 1988).Jaag, O. Coccomyxa schmidle Monographie einer algengattung. Beitr. Kryptogamenflora Schweiz 8, 1–132 (1933).
    Google Scholar 
    Oberwinkler, F. Die gattungen der Basidiolichenen. Vorträge aus dem Gesamtgebiet der Botanik. Herausgegeb. v. d. Deutsch. bot. Ges. Neue Folge 4, 139–169 (1970).
    Google Scholar 
    Poelt, J. Basidienflechten, eine in den Alpen lange übersehene Pflanzengruppe. Jahrb. Vereins Schutze Alpenpfl. Tiere 40, 81–92 (1975).
    Google Scholar 
    Eriksson, J., Hjortstam, K. The Corticiaceae of North Europe. Vol. 6. (Grønlands Eskefabrikk, 1981).Oberwinkler, F. Basidiolichens. In Fungal Association 211–225 (Springer, Berlin Heidelberg, Berlin, 2001). https://doi.org/10.1007/978-3-662-07334-6_12.Chapter 

    Google Scholar 
    Jülich, W. A new lichenized Athelia from Florida. Persoonia 10, 149–151 (1978).
    Google Scholar 
    Zavada, M. S. & Simoes, P. The possible demi-lichenization of the basidiocarps of Trametes Versicolor (L.:Fries) pilat (polyporaceae). Northeast. Nat. 8, 101–112 (2001).
    Google Scholar 
    Neustroeva, N., Mukhin, V., Novakovskaya, I. & Patova, E. Biodiversity of symbiotic algae of wood decay Basidimycetes in the Central Urals. III Russ. Natl. Conf. “Information Technol. Biodivers. Res. 1, 83–92 (2020).
    Google Scholar 
    Zavada, M. S., DiMichele, L. & Toth, C. R. The possible demi-lichenization of Trametes versicolor (L.: Fries) Pilát (Polyporaceae): The transfer of fixed 14CO2 from epiphytic algae to T. versicolor. Northeast. Nat. 11, 33–40 (2004).Article 

    Google Scholar 
    Mukhin, V. A., Patova, E. N., Kiseleva, I. S., Neustroeva, N. V. & Novakovskaya, I. V. Mycetobiont symbiotic algae of wood-decomposing fungi. Russ. J. Ecol. 47, 133–137 (2016).Article 
    CAS 

    Google Scholar 
    Sanders, W. B. & Masumoto, H. Lichen algae: The photosynthetic partners in lichen symbioses. Lichenologist 53, 347–393 (2021).Article 

    Google Scholar 
    Krause, G. & Weis, E. Chlorophyll fluorescence and photosynthesis: the basics. Annu. Rev. Plant Biol. 42(1), 313–349 (1991).Article 
    CAS 

    Google Scholar 
    Lüttge, U. & Büdel, B. Resurrection kinetics of photosynthesis in desiccation-tolerant terrestrial green algae (Chlorophyta) on tree bark. Plant Biol. 12, 437–444 (2010).Article 
    PubMed 

    Google Scholar 
    Lange, O. L. Moisture content and CO2 exchange of lichens: I. Influence of temperature on moisture-dependent net photosynthesis and dark respiration in Ramalina maciformis. Oecologia 45, 82–87 (1980).Article 
    ADS 
    PubMed 

    Google Scholar 
    Palmqvist, K. & Sundberg, B. Light use efficiency of dry matter gain in five macrolichens: Relative impact of microclimate conditions and species-specific traits. Plant Cell Environ. 23, 1–14 (2000).Article 

    Google Scholar 
    Vondrak, J. & Kubásek, J. Algal stacks and fungal stacks as adaptations to high light in lichens. Lichenol. 45(1), 115 (2013).Article 

    Google Scholar 
    Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: Incorporating acclimation to temperature and CO2. Glob. Chang. Biol. 19, 45–63 (2013).Article 
    ADS 
    PubMed 

    Google Scholar 
    Medeiros, P. M. & Simoneit, B. R. T. Analysis of sugars in environmental samples by gas chromatography-mass spectrometry. J. Chromatogr. A 1141, 271–278 (2007).Article 
    CAS 
    PubMed 

    Google Scholar 
    Honegger, R. Functional aspects of the lichen symbiosis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 42, 553–578 (1991).Article 
    CAS 

    Google Scholar 
    Honegger, R. The lichen symbiosis—What is so spectacular about it?. Lichenologist 30, 193–212 (1998).Article 

    Google Scholar 
    Kirk, P. M. et al. (eds) Dictionary of the Fungi 10th edn. (CABI, Netherlands, 2008).
    Google Scholar 
    Ahmadjian, V. The lichen alga Trebouxia: Does it occur free-living?. Plant Syst. Evol. 158, 243–247 (1988).Article 

    Google Scholar 
    Sanders, W. B. Complete life cycle of the lichen fungus Calopadia puiggarii (Pilocarpaceae, Ascomycetes) documented in situ: Propagule dispersal, establishment of symbiosis, thallus development, and formation of sexual and asexual reproductive structures. Am. J. Bot. 101, 1836–1848 (2014).Article 
    PubMed 

    Google Scholar 
    Rindi, F. & Guiry, M. Composition and spatial variability of terrestrial algal assemblages occurring at the bases of urban walls in Europe. Phycologia 43, 225–235 (2004).Article 

    Google Scholar 
    Stonyeva, M. P., Uzunov, B. A. & Gärtner, G. Aerophytic green algae, epimycotic on Fomes fomentarius (L. ex Fr.) Kickx. Annu. Sofia Univ “St. Kliment Ohridski”. Fac. Biol. 99, 19–25 (2015).
    Google Scholar 
    Aras, S. & Cansaran, D. Isolation of DNA for sequence analysis from herbarium material of some lichen specimens. Turk. J. Bot. 30, 449–453 (2006).
    Google Scholar 
    Hall, T. BioEdit: A userfriendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 41, 95–98 (1999).CAS 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Posada, D. jModelTest: Phylogenetic model averaging. Mol. Biol. Evol. 25, 1253–1256 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vondrák, J. & Kubásek, J. Algal stacks and fungal stacks as adaptations to high light in lichens. Lichenol. 45, 115–124 (2013).Article 

    Google Scholar 
    Kubásek, J., Hájek, T. & Glime, J. M. Bryophyte photosynthesis in sunflecks: Greater relative induction rate than in tracheophytes. J. Bryol. 36, 110–117 (2014).Article 

    Google Scholar 
    Kubásek, J. et al. Moss stomata do not respond to light and CO2 concentration but facilitate carbon uptake by sporophytes: A gas exchange, stomatal aperture, and C-13-labelling study. New Phytol. 230, 1815–1828 (2021).Article 
    PubMed 

    Google Scholar 
    Feige, G. & Kremer, B. Unusual carbohydrate pattern in Trentepohlia species. Phytochemistry 19, 1844–1845 (1980).Article 
    CAS 

    Google Scholar 
    Tonon, T., Li, Y. & McQueen-Mason, S. Mannitol biosynthesis in algae: More widespread and diverse than previously thought. New Phytol. 213, 1573–1579 (2017).Article 
    PubMed 

    Google Scholar 
    Gustavs, L., Görs, M. & Karsten, U. Polyol patterns in biofilm-forming aeroterrestrial green algae (Trebouxiophyceae, Chlorophyta). J. Phycol. 47, 533–537 (2011).Article 
    PubMed 

    Google Scholar  More