Ecology

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

Google Scholar 
Friedlingstein, P. et al. Global carbon budget 2022. Earth Syst. Sci. Data 14, 4811–4900 (2022).Article 

Google Scholar 
Peng, S.-S. et al. Afforestation in China cools local land surface temperature. Proc. Natl Acad. Sci. USA 111, 2915–2919 (2014).Article 

Google Scholar 
Li, Y. et al. Local cooling and warming effects of forests based on satellite observations. Nat. Commun. 6, 6603 (2015).Article 

Google Scholar 
Houghton, R. A. & Nassikas, A. A. Global and regional fluxes of carbon from land use and land cover change 1850-2015. Glob. Biogeochem. Cycles 31, 456–472 (2017).Article 

Google Scholar 
Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).Article 

Google Scholar 
Longo, M. et al. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob. Biogeochem. Cycles 30, 1639–1660 (2016).Article 

Google Scholar 
Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).Article 

Google Scholar 
Smith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K. & Thompson, J. R. Piecing together the fragments: elucidating edge effects on forest carbon dynamics. Front. Ecol. Environ. 16, 213–221 (2018).Article 

Google Scholar 
Franklin, C. M. A., Harper, K. A. & Clarke, M. J. Trends in studies of edge influence on vegetation at human-created and natural forest edges across time and space. Can. J. For. Res. 51, 274–282 (2020).Article 

Google Scholar 
Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).Article 

Google Scholar 
Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 

Google Scholar 
Baccini, A. et al. Tropical forests are a net carbon source based on aboveground measurements of gain and loss. Science 358, 230–234 (2017).Article 

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

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

Google Scholar 
Schoene, D., Killmann, W., Lüpke, H. V. & LoycheWilkie, M. Forests and Climate Change Working Paper 5: Definitional Issues Related to Reducing Emissions from Deforestation in Developing Countries (FAO, 2007).Goetz, S. J. et al. Measurement and monitoring needs, capabilities and potential for addressing reduced emissions from deforestation and forest degradation under REDD+. Environ. Res. Lett. 10, 123001 (2015).Article 

Google Scholar 
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).Article 

Google Scholar 
Cadenasso, M. L., Traynor, M. M. & Pickett, S. T. Functional location of forest edges: gradients of multiple physical factors. Can. J. For. Res. 27, 774–782 (1997).Article 

Google Scholar 
Schmidt, M., Jochheim, H., Kersebaum, K.-C., Lischeid, G. & Nendel, C. Gradients of microclimate, carbon and nitrogen in transition zones of fragmented landscapes – a review. Agric. For. Meteorol. 232, 659–671 (2017).Article 

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

Google Scholar 
Silva Junior, C. H. L. et al. Amazonian forest degradation must be incorporated into the COP26 agenda. Nat. Geosci. 14, 634–635 (2021).Article 

Google Scholar 
Bala, G. et al. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl Acad. Sci. USA 104, 6550–6555 (2007).Article 

Google Scholar 
Windisch, M. G., Davin, E. L. & Seneviratne, S. I. Prioritizing forestation based on biogeochemical and local biogeophysical impacts. Nat. Clim. Change 11, 867–871 (2021).Article 

Google Scholar 
Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data 13, 3927–3950 (2021).Article 

Google Scholar 
Chuvieco, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth Syst. Sci. Data 10, 2015–2031 (2018).Article 

Google Scholar 
Zhao, Z. et al. Fire enhances forest degradation within forest edge zones in Africa. Nat. Geosci. https://doi.org/10.1038/s41561-021-00763-8 (2021).Cook, M., Schott, J. R., Mandel, J. & Raqueno, N. Development of an operational calibration methodology for the Landsat thermal data archive and initial testing of the atmospheric compensation component of a land surface temperature (LST) product from the archive. Remote Sens. https://doi.org/10.3390/rs61111244 (2014).Wan, Z. New refinements and validation of the collection-6 MODIS land-surface temperature/emissivity product. Remote Sens. Environ. 140, 36–45 (2014).Article 

Google Scholar 
Broadbent, E. N. et al. Forest fragmentation and edge effects from deforestation and selective logging in the Brazilian Amazon. Biol. Conserv. 141, 1745–1757 (2008).Article 

Google Scholar 
Chaplin-Kramer, R. et al. Degradation in carbon stocks near tropical forest edges. Nat. Commun. 6, 10158 (2015).Article 

Google Scholar 
Silva Junior, C. et al. Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses. Sci. Adv. 6, eaaz8360 (2020).Article 

Google Scholar 
Laurance, W. F. et al. Biomass collapse in Amazonian forest fragments. Science 278, 1117–1118 (1997).Article 

Google Scholar 
Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).Article 

Google Scholar 
Zheng, C., Jia, L. & Hu, G. Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite Earth observations. J. Hydrol. 613, 128444 (2022).Article 

Google Scholar 
Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 8, 14855 (2017).Article 

Google Scholar 
Laurance, W. F. et al. The fate of Amazonian forest fragments: a 32-year investigation. Biol. Conserv. 144, 56–67 (2011).Article 

Google Scholar 
de Paula, M. D., Costa, C. P. A. & Tabarelli, M. Carbon storage in a fragmented landscape of Atlantic forest: the role played by edge-affected habitats and emergent trees. Trop. Conserv. Sci. 4, 349–358 (2011).Article 

Google Scholar 
van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).Article 

Google Scholar 
Gillett, N. P., Arora, V. K., Matthews, D. & Allen, M. R. Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations. J. Clim. 26, 6844–6858 (2013).Article 

Google Scholar 
Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).Article 

Google Scholar 
Kozlowski, T. T. Responses of woody plants to flooding and salinity. Tree Physiol. 17, 490–490 (1997).Article 

Google Scholar 
Garnett, S. T. et al. A spatial overview of the global importance of Indigenous lands for conservation. Nat. Sustain. 1, 369–374 (2018).Article 

Google Scholar 
Sze, J. S., Carrasco, L. R., Childs, D. & Edwards, D. P. Reduced deforestation and degradation in Indigenous lands pan-tropically. Nat. Sustain. 5, 123–130 (2022).Article 

Google Scholar 
Masson-Delmotte, V. et al. IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds) (Cambridge Univ. Press, 2021).Santoro, M. & Cartus, O. ESA Biomass Climate Change Initiative (Biomass_cci): Global Datasets of Forest Above-Ground Biomass for the Years 2010, 2017 and 2018, v3 (NERC EDS Centre for Environmental Data Analysis, 2021); https://doi.org/10.5285/5f331c418e9f4935b8eb1b836f8a91b8Gerland, P. et al. World population stabilization unlikely this century. Science 346, 234–237 (2014).Article 

Google Scholar 
Alkama, R. et al. Vegetation-based climate mitigation in a warmer and greener world. Nat. Commun. 13, 606 (2022).Article 

Google Scholar 
Duveiller, G., Hooker, J. & Cescatti, A. The mark of vegetation change on Earth’s surface energy balance. Nat. Commun. 9, 679 (2018).Article 

Google Scholar 
Matthews, H. D., Gillett, N. P., Stott, P. A. & Zickfeld, K. The proportionality of global warming to cumulative carbon emissions. Nature 459, 829–832 (2009).Article 

Google Scholar 
Li, W. et al. Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences 14, 5053–5067 (2017).Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 

Google Scholar  More

Dillon, M. E., Wang, G. & Huey, R. B. Nature 467, 704–706 (2010).Article 
CAS 

Google Scholar 
Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnoy, E. L. Science 293, 2248–2251 (2001).Article 
CAS 

Google Scholar 
Alton, L. A. & Kellermann, V. Nat. Clim. Change https://doi.org/10.1038/s41558-023-01607-6 (2023).Article 

Google Scholar 
Lighton, J. R. B. Measuring Metabolic Rates: A Manual for Scientists (Oxford Univ. Press, 2019).Careau, V., Killen, S. S. & Metcalfe, N. B. in Integrative Organismal Biology (eds Martin, L. B. et al.) Ch. 14, 219–233 (John Wiley & Sons, 2014).Speakman, J. R., Selman, C., Mclaren, J. S. & Harper, E. J. J. Nutr. 132, 1583S–1597S (2002).Article 
CAS 

Google Scholar 
Janča, M. & Gvoždík, L. Sci. Rep. 7, 5177 (2017).Article 

Google Scholar 
Seebacher, F., White, C. R. & Franklin, C. E. Nat. Clim. Change 5, 61–66 (2016).Article 

Google Scholar  More

Tucker, C. et al. Nature 615, 80–86 (2023).Article 

Google Scholar 
Bayala, J. et al. Agric. Ecosyst. Environ. 205, 25–35 (2015).Article 

Google Scholar 
Keesstra, S. D. et al. Soil 2, 111–128 (2016).Article 

Google Scholar 
Dewi, S. et al. Int. J. Biodivers. Sci. Ecosyst. Serv. Mgmt 13, 312–329 (2017).Article 

Google Scholar 
Ahlström, A. et al. Science 348, 895–899 (2015).Article 
PubMed 

Google Scholar 
Poulter, B. et al. Nature 509, 600–603 (2014).Article 
PubMed 

Google Scholar 
Prăvălie, R. et al. Environ. Res. 201, 111580 (2021).Article 
PubMed 

Google Scholar 
Reij, C. P. & Smaling, E. M. A. Land Use Policy 25, 410–420 (2008).Article 

Google Scholar 
Zomer, R. J., Bossio, D. A., Trabucco, A., van Noordwijk, M. & Xu, J. Circ. Agric. Syst. 2, 3 (2022).Article 

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

Google Scholar 
Dakpogan, A., Bayala, J., Ouattara, I. & Ellington, E. in United for Lands: From National Coalitions to a Pipeline of Bankable Projects for the Great Green Wall 54–56 (United Nations, 2022).
Google Scholar 
Garrity, D. P. & Bayala, J. in Sustainable Development Through Trees on Farms: Agroforestry in its Fifth Decade (ed. van Noordwijk, M.) 153–175 (World Agroforestry, 2019).
Google Scholar 
Schnell, S., Kleinn, C. & Ståhl, G. Environ. Monit. Assess. 187, 600 (2015).Article 
PubMed 

Google Scholar  More

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

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

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

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

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