Ecology

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

In co-culture with the bacterivorous flagellate Poteriospumella lacustris, the prey bacterium Pseudomonas putida exhibited a characteristic succession of predation defenses. The initial and the final defense differed substantially from one another with regard to their mechanism and their population-level benefits to the bacteria.Our results strongly indicate that the initial bacterial defense falls into the category of chemical defense, and is regulated by phenotypic plasticity. This would require P. putida to be able to sense predator density and to regulate the excretion of inhibitory substances accordingly. Because a considerable proportion of the P. putida genome is known to be involved in regulation and signal transduction allowing for very flexible responses to environmental triggers [41] both conditions are likely to be met. The filtrate exposure tests (Fig. 3) provide specific evidence for the ability of P. putida KT2440 to up- and downregulate the excretion of compounds inhibiting flagellate growth in response to grazing pressure. Previous research [25] corroborated the ability of P. putida to escape grazing from bacterivorous flagellates through induced responses like aggregation or biofilm formation.To provide a possible characterization for the apparent bacterial toxin, the whole-genome sequences of P. putida KT2440 obtained here were aligned against the antiSMASH [42] database. The output suggests the existence of non-ribosomal peptide synthetase clusters mediating the production of pyoverdines, a particular class of siderophores. The latter are molecules released by bacteria into the environment, which enhance the uptake of essential metals like, e.g., iron under deficient conditions. Specific pyoverdines associated with P. putida KT2440 have previously been identified [43]. Recent findings have shown that the benefits from siderophore production are not limited to competitive advantages gained from enhanced resource exploitation [44]. Pyoverdines were also demonstrated to determine the virulence of Pseudomonads via the damage of mitochondria in colonized hosts [45]. Moreover, pyoverdines were shown to be involved in the inducible defense of P. putida against predatory myxobacteria [46]. Such multiple functions have been reported for a number of bacterial metabolites, especially in Pseudomonads [47], and the particular combination of pyoverdin effects would explain the observed simultaneous flagellate inhibition and promoted bacterial growth.In contrast to the initial chemical defense of P. putida, the subsequent filamentation clearly provides an example of rapid evolution. Although the responsible mutation(s) could only be pinpointed in a few isolates so far (Table S1), there is no doubt about the genetic manifestation and heritability of the filamentous phenotype due to its demonstrated non-reversible nature.Only recently, similar observations were made by long-term co-cultivation of Pseudomonas fluorescence with the amoeboid predator Neaglena grubei [48]. In that system, protective adaptations like enhanced biofilm formation and altered motility were traced down to mutations in two particular genes (wspF, amrZ).From the perspective of the bacterial population, filamentation appears to be a much less efficient defense mechanism than toxin production. This is clearly reflected by the ratio of prey to predator biomass, which differed by two orders of magnitude between the initial and final defense (Table 6). It raises the question of why bacteria would abandon a highly effective form of defense in favor of a much less effective one. As demonstrated experimentally, adaptation of predators to the toxin can be excluded as a cause (Fig. 4). Moreover, it was not instantly evident how the small-sized flagellate was ultimately able to persist in large numbers given a very high proportion of completely inedible prey individuals (Fig. 1D and Fig. S2).Table 6 Average abundance of predator and prey during the temporary steady state following the initial bacterial defense (day 13–16) and during the final steady state (beyond day 30).Full size tableTo develop a comprehensive understanding of the system addressing the questions raised above, we set up a semi-continuous differential equation model to simulate the dynamics of predator and prey phenotypes. The model considers seven state variables (carbon, densities of four bacterial phenotypes, flagellate density, and toxin concentration) whose dynamics are controlled by nine processes (Table 3, Fig. 2). In addition to microbial growth and grazing, the model implements a phenotypically plastic predation defense (toxin production) as well as a genetic defense (filamentation) which arises via mutation. The particular assumptions implemented in the model are as follows:Dual effect of bacterial metabolitesIn line with the above discussion on siderophore-like compounds, secondary metabolites excreted by P. putida were assumed to exhibit a dual function, both inhibiting the growth of flagellates and allowing for a more efficient exploitation of the resources by bacteria. The inhibition of predators was demonstrated directly (Figs. 3 and 4) while enhanced resource exploitation was inferred from bacterial abundances in co-cultures exceeding the carrying capacity observed in predator-free controls (Fig. 1A, day 11–18).Metabolite production is costlyThe production of bacterial metabolites was assumed to be associated with a slight fitness cost [49] since resources are diverted from reproduction, thus resulting in a lowered growth rate of toxin-producing bacteria. The assumed fitness cost of 11% (parameter cBx in Table 5) allowed for the best agreement between simulated and observed data and is in agreement with data on the cost of pyoverdine production by P. aeruginosa [50]. The cost only manifests when toxin production is upregulated.Predator recognition and quorum sensing interactIn the model, the production of bacterial metabolites is upregulated when the two conditions of high flagellate abundance and high bacterial abundance coincide. That is, the expression of the toxin-based bacterial defense is assumed to be jointly controlled by predator recognition and quorum sensing (QS). Examples for such joint control of bacterial defenses have been reported previously [8, 26, 51]. The involvement of QS in chemical defense strategies is particularly likely as effective toxin concentrations can only be reached when producers are highly abundant. While multiple QS systems have been described for other Pseudomonads, only a single system has been identified in P. putida KT2440 so far [52, 53].Mutation rates are conditional on stressThe emergence of mutations resulting in the filamentation of P. putida was assumed to be conditional on a high ambient concentration of bacterial metabolites. The latter was considered as a proxy for bacterial stress which can affect mutagenesis either directly or indirectly by a variety of mechanisms [54,55,56]. Without this assumption, the almost synchronous appearance of filaments in all replicates at a late point in time would be very difficult to explain. Specifically, if mutation frequencies were high, filaments would become the predominant phenotype early (Fig. S3) which contradicts observations. On the other hand, if frequencies were low but unconditional, the timing of filament appearance should vary between replicates, which is in contrast to observations either (Fig. 1B).Filamentation is associated with a fitness costMeasurements of growth rate constants revealed a significant fitness disadvantage of filamentous isolates in comparison to single-celled, undefended isolates (p  More

An, Z., Guo, F., Chen, Y., Bai, G. & Chen, Z. Rhizosphere bacterial and fungal communities during the growth of Angelica sinensis seedlings cultivated in an Alpine uncultivated meadow soil. PeerJ 8, e8541. https://doi.org/10.7717/peerj.8541 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Munkholm, L. J., Heck, R. J. & Deen, B. Long-term rotation and tillage effects on soil structure and crop yield. Soil Tillage Res. 127, 85–91. https://doi.org/10.1016/j.still.2012.02.007 (2013).Article 

Google Scholar 
Jiao, X. L. et al. Effects of maize rotation on the physicochemical properties and microbial communities of American ginseng cultivated soil. Sci. Rep. 9, 8615. https://doi.org/10.1038/s41598-019-44530-7 (2019).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, X., Chen, Y., Guo, F., Yuan, H. & Guo, Y. Effects of medicinal crop stubbles on physiological and biochemical characteristics of Angelica sinensis seedings. J. Chin. Med. Mater. 40, 2002–2006 (2017).
Google Scholar 
Jin, Y. et al. Effect of various crop residues on growth and disease resisitance of Angelica sinensis seedlings in Min County. Acta Pratacul. Sin. 27, 69–78 (2018).MathSciNet 

Google Scholar 
Bai, G., Guo, F., Chen, Y., Yuan, H. & Xiao, W. Differences in physiological resistance traits of Angelica sinensis seedlings from uncultivated and cultivated fields in Min County. Acta Pratacul. Sin. 28, 86–95 (2019).
Google Scholar 
Bai, G. et al. Regulated effects of preceding crop on soil property and cultivating seedlings for Angelica sinensis on cultivated farmland. Chin. J. Eco-Agric. 28, 701–712. https://doi.org/10.13930/j.cnki.cjea.190719 (2020).Article 
CAS 

Google Scholar 
Mendes, R., Garbeva, P. & Raaijmakers, J. M. The rhizosphere microbiome: Significance of plant beneficial, plant pathogenic, and human pathogenic microorganisms. FEMS Microbiol. Rev. 37, 634–663 (2013).Article 
CAS 
PubMed 

Google Scholar 
Tkacz, A., Cheema, J., Chandra, G., Grant, A. & Poole, P. S. Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition. Int. Soc. Microb. Ecol. 9, 2349–2359. https://doi.org/10.1038/ismej.2015.41 (2015).Article 
CAS 

Google Scholar 
Berg, G. et al. Microbiome definition re-visited: Old concepts and new challenges. Microbiome 8, 103. https://doi.org/10.1186/s40168-020-00875-0 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Chaparro, J. M., Badri, D. V. & Vivanco, J. M. Rhizosphere microbiome assemblage is affected by plant development. ISME J. 8, 790–803. https://doi.org/10.1038/ismej.2013.196 (2014).Article 
CAS 
PubMed 

Google Scholar 
Uroz, S. et al. Specific impacts of beech and Norway spruce on the structure and diversity of the rhizosphere and soil microbial communities. Sci. Rep. 6, 27756. https://doi.org/10.1038/srep27756 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chamberlain, L. A. et al. Crop rotation, but not cover crops, influenced soil bacterial community composition in a corn-soybean system in southern Wisconsin. Appl. Soil Ecol. 154, 103603. https://doi.org/10.1016/j.apsoil.2020.103603 (2020).Article 

Google Scholar 
Classen, A. T. et al. Direct and indirect effects of climate change on soil microbial and soil microbial-plant interactions: What lies ahead?. Ecosphere 6, 130. https://doi.org/10.1890/es15-00217.1 (2015).Article 

Google Scholar 
Tiemann, L. K. et al. Crop rotational diversity enhances belowground communities and functions in an agroecosystem. Ecol. Lett. 18, 761–771. https://doi.org/10.1111/ele.12453 (2015).Article 
CAS 
PubMed 

Google Scholar 
Maldonado, S. et al. Enhanced crop productivity and sustainability by using native phosphate solubilizing rhizobacteria in the agriculture of arid zones. Front. Sustain. Food Syst. 4, 607355. https://doi.org/10.3389/fsufs.2020.607355 (2020).Article 

Google Scholar 
Gómez Expósito, R., de Bruijn, I., Postma, J. & Raaijmakers, J. M. Current insights into the role of rhizosphere bacteria in disease suppressive soils. Front. Microbiol.y 8, 2529. https://doi.org/10.3389/fmicb.2017.02529 (2017).Article 

Google Scholar 
Li, X., Rui, J., Mao, Y., Yannarell, A. & Mackie, R. Dynamics of the bacterial community structure in the rhizosphere of a maize cultivar. Soil Biol. Biochem. 68, 392–401. https://doi.org/10.1016/j.soilbio.2013.10.017 (2014).Article 
CAS 

Google Scholar 
Fierer, N. et al. Comparative metagenomic, phylogenetic and physiological analyses of soil microbial communities across nitrogen gradients. Int. Soc. Microb. Ecol. 6, 1007–1017. https://doi.org/10.1038/ismej.2011.159 (2012).Article 
CAS 

Google Scholar 
Kuffner, M. et al. Culturable bacteria from Zn- and Cd-accumulating Salix caprea with differential effects on plant growth and heavy metal availability. J. Appl. Microbiol. 108, 1471–1484. https://doi.org/10.1111/j.1365-2672.2010.04670.x (2010).Article 
CAS 
PubMed 

Google Scholar 
De Corato, U. Disease-suppressive compost enhances natural soil suppressiveness against soil-borne plant pathogens: A critical review. Rhizosphere 13, 100192. https://doi.org/10.1016/j.rhisph.2020.100192 (2020).Article 

Google Scholar 
Brookes, P. C., Landman, A., Pruden, G. & Jenkinson, D. S. Chloroform fumigation chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837–842 (1985).Article 
CAS 

Google Scholar 
Arnebrant, K. & Schnürer, J. Changes in atp content during and after chloroform fumigation. Soil Biol. Biochem. 22, 875–877 (1990).Article 
CAS 

Google Scholar 
Toju, H. et al. Community composition of root-associated fungi in a Quercus-dominated temperate forest: “codominance” of mycorrhizal and root-endophytic fungi. Ecol. Evol. 3, 1281–1293. https://doi.org/10.1002/ece3.546 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Magoč, T. & Salzberg, S. L. FLASH: Fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59. https://doi.org/10.1038/nmeth.2276 (2013).Article 
CAS 
PubMed 

Google Scholar 
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Haas, B. J. et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 21, 494–504. https://doi.org/10.1101/gr.112730.110 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604 (2013).Article 
CAS 
PubMed 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/AEM.00062-07 (2007).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).Article 
CAS 
PubMed 

Google Scholar 
Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248. https://doi.org/10.1016/j.funeco.2015.06.006 (2016).Article 

Google Scholar 
Sisk-Hackworth, L., Ortiz-Velez, A., Reed, M. B. & Kelley, S. T. Compositional data analysis of periodontal disease microbial communities. Front. Microbiol. 12, 617949. https://doi.org/10.3389/fmicb.2021.617949 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Khan, M. A. W. et al. Deforestation impacts network co-occurrence patterns of microbial communities in Amazon soils. FEMS Microbiol. Ecol. 95, fiy230. https://doi.org/10.1093/femsec/fiy230 (2019).Article 
CAS 
PubMed 

Google Scholar 
Zhang, B., Zhang, J., Liu, Y., Shi, P. & Wei, G. Co-occurrence patterns of soybean rhizosphere microbiome at a continental scale. Soil Biol. Biochem. 118, 178–186. https://doi.org/10.1016/j.soilbio.2017.12.011 (2018).Article 
CAS 

Google Scholar 
Huang, M., Jiang, L., Zou, Y., Xu, S. & Deng, G. Changes in soil microbial properties with no-tillage in Chinese cropping systems. Biol. Fertil. Soils 49, 373–377. https://doi.org/10.1007/s00374-013-0778-6 (2013).Article 

Google Scholar 
Unger, P. W. & Cassel, D. K. Tillage implement disturbance effects on soil properties related to soil and water conservation: A literature review. Soil Tillage Res. 19, 363–382 (1991).Article 

Google Scholar 
Alvarez, R. & Steinbach, H. S. A review of the effects of tillage systems on some soil physical properties, water content, nitrate availability and crops yield in the Argentine Pampas. Soil Tillage Res. 104, 1–15. https://doi.org/10.1016/j.still.2009.02.005 (2009).Article 

Google Scholar 
Essel, E. et al. Bacterial and fungal diversity in rhizosphere and bulk soil under different long-term tillage and cereal/legume rotation. Soil Tillage Res. 194, 104302. https://doi.org/10.1016/j.still.2019.104302 (2019).Article 

Google Scholar 
Zhu, Q., Wang, N., Duan, B., Wang, Q. & Wang, Y. Rhizosphere bacterial and fungal communities succession patterns related to growth of poplar fine roots. Sci. Total Environ. 756, 143839. https://doi.org/10.1016/j.scitotenv.2020.143839 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Guseva, K. et al. From diversity to complexity: Microbial networks in soils. Soil Biol. Biochem. 169, 108604. https://doi.org/10.1016/j.soilbio.2022.108604 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiang, B. et al. Analysis of microbial community structure and diversity in surrounding rock soil of different waste dump sites in fushun western opencast mine. Chemosphere 269, 128777. https://doi.org/10.1016/j.chemosphere.2020.128777 (2020).Article 
ADS 
MathSciNet 
CAS 
PubMed 

Google Scholar 
Liu, J. et al. Pecan plantation age influences the structures, ecological networks, and functions of soil microbial communities. Land Degrad. Dev. 33, 3294–3309. https://doi.org/10.1002/ldr.4389 (2022).Article 

Google Scholar 
Lv, X. et al. Strengthening insights in microbial ecological networks from theory to applications. mSystems 4, e00124-19. https://doi.org/10.1128/mSystems.00124-19 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Toju, H., Kishida, O., Katayama, N. & Takagi, K. Networks depicting the fine-scale co-occurrences of fungi in soil Horizons. PLoS ONE 11, e0165987. https://doi.org/10.1371/journal.pone.0165987 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chun, S. J., Cui, Y., Baek, S. H., Ahn, C. Y. & Oh, H. M. Seasonal succession of microbes in different size-fractions and their modular structures determined by both macro- and micro-environmental filtering in dynamic coastal waters. Sci. Total Environ. 784, 147046. https://doi.org/10.1016/j.scitotenv.2021.147046 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Cardinale, M., Grube, M., Erlacher, A., Quehenberger, J. & Berg, G. Bacterial networks and co-occurrence relationships in the lettuce root microbiota. Environ. Microbiol. 17, 239–252. https://doi.org/10.1111/1462-2920.12686 (2015).Article 
CAS 
PubMed 

Google Scholar 
Zhou, Z. et al. Increases in bacterial community network complexity induced by biochar-based fertilizer amendments to karst calcareous soil. Geoderma 337, 691–700. https://doi.org/10.1016/j.geoderma.2018.10.013 (2019).Article 
ADS 
CAS 

Google Scholar 
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl. Acad. Sci. U.S.A. 104, 19891–19896. https://doi.org/10.1073/pnas.0706375104 (2007).Article 
ADS 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Eisenhauer, N. et al. Root biomass and exudates link plant diversity with soil bacterial and fungal biomass. Sci. Rep. 7, 44641. https://doi.org/10.1038/srep44641 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hassan, M. K., McInroy, J. A. & Kloepper, J. W. The interactions of rhizodeposits with plant growth-promoting Rhizobacteria in the rhizosphere: A review. Agriculture 9, 142. https://doi.org/10.3390/agriculture9070142 (2019).Article 
CAS 

Google Scholar 
Sasse, J., Martinoia, E. & Northen, T. Feed your friends: Do plant exudates shape the root microbiome?. Trends Plant Sci. 23, 25–41. https://doi.org/10.1016/j.tplants.2017.09.003 (2018).Article 
CAS 
PubMed 

Google Scholar 
Zhang, F., Xu, X., Wang, G., Wu, B. & Xiao, Y. Medicago sativa and soil microbiome responses to Trichoderma as a biofertilizer in alkaline-saline soils. Appl. Soil Ecol. 153, 103573. https://doi.org/10.1016/j.apsoil.2020.103573 (2020).Article 

Google Scholar 
Woźniak, A. Chemical properties and enzyme activity of soil as affected by tillage system and previous crop. Agriculture 9, 262. https://doi.org/10.3390/agriculture9120262 (2019).Article 
CAS 

Google Scholar 
Choudhary, M. et al. Changes in soil biology under conservation agriculture based sustainable intensification of cereal systems in Indo-Gangetic Plains. Geoderma 313, 193–204. https://doi.org/10.1016/j.geoderma.2017.10.041 (2018).Article 
ADS 
CAS 

Google Scholar 
Ai, C. et al. Distinct responses of soil bacterial and fungal communities to changes in fertilization regime and crop rotation. Geoderma 319, 156–166. https://doi.org/10.1016/j.geoderma.2018.01.010 (2018).Article 
ADS 
CAS 

Google Scholar 
Gałązka, A., Gawyjołek, K., Perzyński, A., Gałązka, R. & Jerzy, K. Changes in enzymatic activities and microbial communities in soil under long-term maize monoculture and crop rotation. Pol. J. Environ. Stud. 26, 39–46. https://doi.org/10.15244/pjoes/64745 (2017).Article 
CAS 

Google Scholar 
Tremblay, C., Deslauriers, A., Lafond, J., Lajeunesse, J. & Paré, M. Effects of soil pH and fertilizers on haskap (Lonicera caerulea L) vegetative growth. Agriculture 9, 56. https://doi.org/10.3390/agriculture9030056 (2019).Article 
CAS 

Google Scholar 
Sirisuntornlak, N. et al. Interactive effects of silicon and soil pH on growth, yield and nutrient uptake of maize. SILICON 13, 289–299. https://doi.org/10.1007/s12633-020-00427-z (2021).Article 
CAS 

Google Scholar 
Xu, Y., Ge, Y., Song, J. & Rensing, C. Assembly of root-associated microbial community of typical rice cultivars in different soil types. Biol. Fertil. Soils 56, 249–260. https://doi.org/10.1007/s00374-019-01406-2 (2019).Article 
CAS 

Google Scholar 
Putranta, H., Permatasari, A. K., Sukma, T. A. & Dwandaru, W. S. B. The effect of pH, electrical conductivity, and nitrogen (N) in the soil at yogyakarta special region on tomato plant growth. TEM J.-Technol. Educ. Manag. Inform. 8, 860–865. https://doi.org/10.18421/TEM83-24 (2019).Article 

Google Scholar 
Wang, J. et al. Effects of alternate partial root-zone irrigation on soil microorganism and maize growth. Plant Soil 302, 45–52. https://doi.org/10.1007/s11104-007-9453-8 (2007).Article 
CAS 

Google Scholar 
Yang, X., Zhu, K., Loik, M. E. & Sun, W. Differential responses of soil bacteria and fungi to altered precipitation in a meadow steppe. Geoderma 384, 114812. https://doi.org/10.1016/j.geoderma.2020.114812 (2021).Article 
ADS 
CAS 

Google Scholar 
Balota, E. L., Colozzi Filho, A., Andrade, D. S. & Dick, R. P. Long-term tillage and crop rotation effects on microbial biomass and C and N mineralization in a Brazilian Oxisol. Soil Tillage Res. 77, 137–145. https://doi.org/10.1016/j.still.2003.12.003 (2004).Article 

Google Scholar 
Franchini, J., Crispino, C., Souza, R., Torres, E. & Hungria, M. Microbiological parameters as indicators of soil quality under various soil management and crop rotation systems in southern Brazil. Soil Tillage Res. 92, 18–29. https://doi.org/10.1016/j.still.2005.12.010 (2007).Article 

Google Scholar 
Li, X., Wang, T., Chang, S. X., Jiang, X. & Song, Y. Biochar increases soil microbial biomass but has variable effects on microbial diversity: A meta-analysis. Sci. Total Environ. 749, 141593. https://doi.org/10.1016/j.scitotenv.2020.141593 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Lynch, J. M. & Panting, L. M. Effects of season, cultivation and nitrogen fertiliser on the size of the soil microbial biomass. J. Sci. Food Agric. 33, 249–252 (1982).Article 
CAS 

Google Scholar 
Tan, G. et al. Effects of biochar application with fertilizer on soil microbial biomass and greenhouse gas emissions in a peanut cropping system. Environ. Technol. 42, 9–19. https://doi.org/10.1080/09593330.2019.1620344 (2021).Article 
CAS 
PubMed 

Google Scholar 
Liu, C. et al. Linkages between nutrient ratio and the microbial community in rhizosphere soil following fertilizer management. Environ. Res. 184, 109261. https://doi.org/10.1016/j.envres.2020.109261 (2020).Article 
CAS 
PubMed 

Google Scholar 
Li, H. et al. Film mulching, residue retention and N fertilization affect ammonia volatilization through soil labile N and C pools. Agric. Ecosyst. Environ. 308, 107272. https://doi.org/10.1016/j.agee.2020.107272 (2021).Article 
CAS 

Google Scholar 
Jiao, P. et al. Bacteria are more sensitive than fungi to moisture in eroded soil by natural grass vegetation restoration on the Loess Plateau. Sci. Total Environ. 756, 143899. https://doi.org/10.1016/j.scitotenv.2020.143899 (2021).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Sommer, J. et al. The tree species matters: Belowground carbon input and utilization in the myco-rhizosphere. Eur. J. Soil Biol. 81, 100–107. https://doi.org/10.1016/j.ejsobi.2017.07.001 (2017).Article 
CAS 

Google Scholar 
Yu, K., Pieterse, C. M. J., Bakker, P. A. H. M. & Berendsen, R. L. Beneficial microbes going underground of root immunity. Plant Cell Environ. 42, 2860–2870. https://doi.org/10.1111/pce.13632 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
de Varennes, A. & Goss, M. J. The tripartite symbiosis between legumes, rhizobia and indigenous mycorrhizal fungi is more efficient in undisturbed soil. Soil Biol. Biochem. 39, 2603–2607. https://doi.org/10.1016/j.soilbio.2007.05.007 (2007).Article 
CAS 

Google Scholar 
Wang, X. et al. Mycorrhizal symbiosis modulates the rhizosphere microbiota to promote rhizobia-legume symbiosis. Mol. Plant 14, 503–516. https://doi.org/10.1016/j.molp.2020.12.002 (2020).Article 
CAS 
PubMed 

Google Scholar 
Zhang, R., Vivanco, J. M. & Shen, Q. The unseen rhizosphere root-soil-microbe interactions for crop production. Curr. Opin. Microbiol. 37, 8–14. https://doi.org/10.1016/j.mib.2017.03.008 (2017).Article 
PubMed 

Google Scholar 
Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486. https://doi.org/10.1016/j.tplants.2012.04.001 (2012).Article 
CAS 
PubMed 

Google Scholar  More

Forster D, Bittner L, Karkar S, Dunthorn M, Romac S, Audic S, et al. Testing ecological theories with sequence similarity networks: marine ciliates exhibit similar geographic dispersal patterns as multicellular organisms. BMC Biol. 2015;13:16. http://bmcbiol.biomedcentral.com/articles/10.1186/s12915-015-0125-5.Article 
PubMed 
PubMed Central 

Google Scholar 
Galperin MY, Koonin EV. From complete genome sequence to ‘complete’ understanding? Trends Biotechnol. 2010;28:398–406. https://linkinghub.elsevier.com/retrieve/pii/S0167779910000892.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Modha S, Robertson DL, Hughes J, Orton RJ. Quantifying and cataloguing unknown sequences within human microbiomes. mSystems. 2022;7:e01468–21. https://journals.asm.org/doi/10.1128/msystems.01468-21.Article 
PubMed 
PubMed Central 

Google Scholar 
Wyman SK, Avila-Herrera A, Nayfach S, Pollard KS. A most wanted list of conserved microbial protein families with no known domains. PLoS One. 2018;13:e0205749. https://dx.plos.org/10.1371/journal.pone.0205749.Bernard G, Pathmanathan JS, Lannes R, Lopez P, Bapteste E. Microbial dark matter investigations: how microbial studies transform biological knowledge and empirically sketch a logic of scientific discovery. Genome Biol Evol. 2018;10:707–15. https://academic.oup.com/gbe/article/10/3/707/4840377.Article 
PubMed 
PubMed Central 

Google Scholar 
Vanni C, Schechter MS, Acinas SG, Barberán A, Buttigieg PL, Casamayor EO, et al. Unifying the known and unknown microbial coding sequence space. eLife. 2022;11:e67667. https://elifesciences.org/articles/67667.Article 
PubMed 
PubMed Central 

Google Scholar 
Jaroszewski L, Li Z, Krishna SS, Bakolitsa C, Wooley J, Deacon AM, et al. Exploration of uncharted regions of the protein universe. PLoS Biol. 2009;7:e1000205. https://dx.plos.org/10.1371/journal.pbio.1000205.Meng A, Corre E, Probert I, Gutierrez-Rodriguez A, Siano R, Annamale A, et al. Analysis of the genomic basis of functional diversity in dinoflagellates using a transcriptome-based sequence similarity network. Mol Ecol. 2018;27:2365–80. https://onlinelibrary.wiley.com/doi/10.1111/mec.14579.Article 
CAS 
PubMed 

Google Scholar 
Meng A, Marchet C, Corre E, Peterlongo P, Alberti A, Da Silva C, et al. A de novo approach to disentangle partner identity and function in holobiont systems. Microbiome. 2018;6:105. https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-018-0481-9.Article 
PubMed 
PubMed Central 

Google Scholar 
Carradec Q, Pelletier E, Da Silva C, Alberti A, Seeleuthner Y, Tara Oceans Coordinators et al. A global ocean atlas of eukaryotic genes. Nat Commun. 2018;9:373. http://www.nature.com/articles/s41467-017-02342-1.Ramond P, Sourisseau M, Simon N, Romac S, Schmitt S, Rigaut-Jalabert F, et al. Coupling between taxonomic and functional diversity in protistan coastal communities: functional diversity of marine protists. Environ Microbiol. 2019;21:730–49. http://doi.wiley.com/10.1111/1462-2920.14537.Article 
CAS 
PubMed 

Google Scholar 
Zamkovaya T, Foster JS, de Crécy-Lagard V, Conesa A. A network approach to elucidate and prioritize microbial dark matter in microbial communities. ISME J. 2021;15:228–44. https://www.nature.com/articles/s41396-020-00777-x.Article 
PubMed 

Google Scholar 
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359–1261359. https://www.sciencemag.org/lookup/doi/10.1126/science.1261359.Article 
PubMed 

Google Scholar 
de Vargas C, Audic S, Henry N, Decelle J, Mahe F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015;348:1261605–1261605. https://www.sciencemag.org/lookup/doi/10.1126/science.1261605.Article 
PubMed 

Google Scholar 
Strassert JFH, Karnkowska A, Hehenberger E, del Campo J, Kolisko M, Okamoto N, et al. Single cell genomics of uncultured marine alveolates shows paraphyly of basal dinoflagellates. ISME J. 2018;12:304–8. http://www.nature.com/articles/ismej2017167.Article 
CAS 
PubMed 

Google Scholar 
Burki F, Sandin MM, Jamy M. Diversity and ecology of protists revealed by metabarcoding. Curr Biol. 2021;31:R1267–80. https://linkinghub.elsevier.com/retrieve/pii/S0960982221010563.Article 
CAS 
PubMed 

Google Scholar 
Cai R, Kayal E, Alves-de-Souza C, Bigeard E, Corre E, Jeanthon C, et al. Cryptic species in the parasitic Amoebophrya species complex revealed by a polyphasic approach. Sci Rep. 2020;10:2531. http://www.nature.com/articles/s41598-020-59524-z.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Singer D, Seppey CVW, Lentendu G, Dunthorn M, Bass D, Belbahri L, et al. Protist taxonomic and functional diversity in soil, freshwater and marine ecosystems. Environ Int. 2021;146:106262. https://linkinghub.elsevier.com/retrieve/pii/S0160412020322170.Article 
CAS 
PubMed 

Google Scholar 
Guillou L, Viprey M, Chambouvet A, Welsh RM, Kirkham AR, Massana R, et al. Widespread occurrence and genetic diversity of marine parasitoids belonging to Syndiniales (Alveolata). Environ Microbiol. 2008;10:3349–65. https://onlinelibrary.wiley.com/doi/10.1111/j.1462-2920.2008.01731.x.Article 
CAS 
PubMed 

Google Scholar 
Clarke LJ, Bestley S, Bissett A, Deagle BE. A globally distributed Syndiniales parasite dominates the Southern Ocean micro-eukaryote community near the sea-ice edge. ISME J. 2019;13:734–7. http://www.nature.com/articles/s41396-018-0306-7.Article 
CAS 
PubMed 

Google Scholar 
Cleary AC, Durbin EG. Unexpected prevalence of parasite 18S rDNA sequences in winter among Antarctic marine protists. J Plankton Res. 2016;38:401–17. https://academic.oup.com/plankt/article-lookup/doi/10.1093/plankt/fbw005.Article 
CAS 

Google Scholar 
Anderson SR, Harvey EL. Temporal variability and ecological interactions of parasitic marine syndiniales in coastal protist communities. mSphere. 2020;5. https://journals.asm.org/doi/10.1128/mSphere.00209-20.Käse L, Metfies K, Neuhaus S, Boersma M, Wiltshire KH, Kraberg AC. Host-parasitoid associations in marine planktonic time series: can metabarcoding help reveal them? Amato A, editor. PLoS One. 2021;16:e0244817. https://dx.plos.org/10.1371/journal.pone.0244817.Jephcott TG, Alves-de-Souza C, Gleason FH, van Ogtrop FF, Sime-Ngando T, Karpov SA, et al. Ecological impacts of parasitic chytrids, syndiniales and perkinsids on populations of marine photosynthetic dinoflagellates. Fungal Ecology. 2016; https://linkinghub.elsevier.com/retrieve/pii/S175450481500032X.Siano R, Alves-de-Souza C, Foulon E, Bendif EM, Simon N, Guillou L, et al. Distribution and host diversity of Amoebophryidae parasites across oligotrophic waters of the Mediterranean Sea. Biogeosciences. 2011;8:267–78. https://bg.copernicus.org/articles/8/267/2011/.Article 

Google Scholar 
Moran MA, Ferrer‐González FX, Fu H, Nowinski B, Olofsson M, Powers MA, et al. The Ocean’s labile DOCsupply chain. Limnol Oceanogr. 2022;lno.12053. https://onlinelibrary.wiley.com/doi/10.1002/lno.12053.Chambouvet A, Morin P, Marie D, Guillou L. Control of toxic marine dinoflagellate blooms by serial parasitic killers. Science. 2008;322:1254–7. https://www.science.org/doi/10.1126/science.1164387.Article 
CAS 
PubMed 

Google Scholar 
Shadrin AM, Kholodova MV, Pavlov DS. Geographic distribution and molecular genetic identification of the parasite of the genus Ichthyodinium causing mass mortality of fish eggs and larvae in coastal waters of Vietnam. Dokl Biol Sci. 2010;432:220–3. http://link.springer.com/10.1134/S0012496610030154.Article 
CAS 
PubMed 

Google Scholar 
Farhat S, Le P, Kayal E, Noel B, Bigeard E, Corre E, et al. Rapid protein evolution, organellar reductions, and invasive intronic elements in the marine aerobic parasite dinoflagellate Amoebophrya spp. BMC Biol. 2021;19:1. https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-020-00927-9.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chambouvet A, Alves-de-Souza C, Cueff V, Marie D, Karpov S, Guillou L. Interplay between the parasite Amoebophrya sp. (Alveolata) and the cyst formation of the red tide Dinoflagellate Scrippsiella trochoidea. Protist. 2011;162:637–49. https://linkinghub.elsevier.com/retrieve/pii/S1434461011000022.Article 
PubMed 

Google Scholar 
Okamura B, Hartigan A, Naldoni J. Extensive uncharted biodiversity: the parasite dimension. integrative and comparative biology. 2018. https://academic.oup.com/icb/advance-article/doi/10.1093/icb/icy039/5026008.Rohde K. Ecology and Biogeography, Future Perspectives: Example Marine Parasites. Geoinfor Geostat Overview. 2016;4; http://www.scitechnol.com/peer-review/ecology-and-biogeography-future-perspectives-example-marine-parasites-wRny.php?article_id=4869.Pawlowski J, Audic S, Adl S, Bass D, Belbahri L, Berney C, et al. CBOL Protist Working Group: barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms. PLoS Biol. 2012;10:e1001419. https://dx.plos.org/10.1371/journal.pbio.1001419.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
del Campo J, Sieracki ME, Molestina R, Keeling P, Massana R, Ruiz-Trillo I. The others: our biased perspective of eukaryotic genomes. Trends Ecol Evol. 2014;29:252–9. https://linkinghub.elsevier.com/retrieve/pii/S0169534714000640.Article 
PubMed 
PubMed Central 

Google Scholar 
Sibbald SJ, Archibald JM. More protist genomes needed. Nat Ecol Evol. 2017;1:0145. http://www.nature.com/articles/s41559-017-0145.Article 

Google Scholar 
Egge E, Elferink S, Vaulot D, John U, Bratbak G, Larsen A, et al. An 18S V4 rRNA metabarcoding dataset of protist diversity in the Atlantic inflow to the Arctic Ocean, through the year and down to 1000 m depth. Earth Syst Sci Data. 2021;13:4913–28. https://essd.copernicus.org/articles/13/4913/2021/.Article 

Google Scholar 
Mugnai F, Meglécz E, Abbiati M, Bavestrello G, Bertasi F, Bo M, et al. Are well-studied marine biodiversity hotspots still blackspots for animal barcoding? Global Ecol Conserv. 2021;32:e01909. https://linkinghub.elsevier.com/retrieve/pii/S2351989421004595.Article 

Google Scholar 
Bittner L, Gobet A, Audic S, Romac S, Egge ES, Santini S, et al. Diversity patterns of uncultured Haptophytes unravelled by pyrosequencing in Naples Bay. Mol Ecol. 2013;22:87–101. https://onlinelibrary.wiley.com/doi/10.1111/mec.12108.Article 
CAS 
PubMed 

Google Scholar 
Malviya S, Scalco E, Audic S, Vincent F, Veluchamy A, Poulain J, et al. Insights into global diatom distribution and diversity in the world’s ocean. Proc Natl Acad Sci USA. 2016;113. https://pnas.org/doi/full/10.1073/pnas.1509523113.Kochin BF, Bull JJ, Antia R. Parasite evolution and life history theory. PLoS Biol. 2010;8:e1000524. https://dx.plos.org/10.1371/journal.pbio.1000524.Article 
PubMed 
PubMed Central 

Google Scholar 
Sheath DJ, Dick JTA, Dickey JWE, Guo Z, Andreou D, Britton JR. Winning the arms race: host–parasite shared evolutionary history reduces infection risks in fish final hosts. Biol Lett. 2018;14:20180363. https://royalsocietypublishing.org/doi/10.1098/rsbl.2018.0363.Article 
PubMed 
PubMed Central 

Google Scholar 
Mahé F, de Vargas C, Bass D, Czech L, Stamatakis A, Lara E, et al. Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests. Nat Ecol Evol. 2017;1:0091. http://www.nature.com/articles/s41559-017-0091.Article 

Google Scholar 
Blanco-Bercial L, Parsons R, Bolaños L, Johnson R, Giovannoni S, Curry R. The protist community mirrors seasonality and mesoscale hydrographic features in the oligotrophic Sargasso Sea. 2022. https://www.authorea.com/users/453879/articles/551657-the-protist-community-mirrors-seasonality-and-mesoscale-hydrographic-features-in-the-oligotrophic-sargasso-sea?commit=ba32b47ec0dffb4865eb448dd0b5dd27d5f8cd15.Lepère C, Domaizon I, Debroas D. Unexpected importance of potential parasites in the composition of the freshwater small-eukaryote community. Appl Environ Microbiol. 2008;74:2940–9. https://journals.asm.org/doi/10.1128/AEM.01156-07.Article 
PubMed 
PubMed Central 

Google Scholar 
Decelle J, Martin P, Paborstava K, Pond DW, Tarling G, Mahé F, et al. Diversity, ecology and biogeochemistry of cyst-forming acantharia (radiolaria) in the Oceans. PLoS One. 2013;8:e53598. https://dx.plos.org/10.1371/journal.pone.0053598.Stern RF, Horak A, Andrew RL, Coffroth MA, Andersen RA, Küpper FC, et al. Environmental barcoding reveals massive dinoflagellate diversity in marine environments. PLoS One. 2010;5:e13991. https://dx.plos.org/10.1371/journal.pone.0013991.Stoeck T, Bass D, Nebel M, Christen R, Jones MDM, Breiner HW, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Eco. 2010;19:21–31. http://doi.wiley.com/10.1111/j.1365-294X.2009.04480.x.Article 
CAS 

Google Scholar 
Chambouvet A, Gower DJ, Jirků M, Yabsley MJ, Davis AK, Leonard G, et al. Cryptic infection of a broad taxonomic and geographic diversity of tadpoles by Perkinsea protists. Proc Natl Acad Sci USA. 2015;112. https://pnas.org/doi/full/10.1073/pnas.1500163112.Chauvet M, Debroas D, Moné A, Dubuffet A, Lepère C. Temporal variations of Microsporidia diversity and discovery of new host–parasite interactions in a lake ecosystem. Environ Microbiol. 2022;1462-2920.15950. https://onlinelibrary.wiley.com/doi/10.1111/1462-2920.15950.Bjorbækmo MFM, Evenstad A, Røsæg LL, Krabberød AK, Logares R. The planktonic protist interactome: where do we stand after a century of research? ISME J. 2020;14:544–59. http://www.nature.com/articles/s41396-019-0542-5.Article 
PubMed 

Google Scholar 
Dallas TA, Han BA, Nunn CL, Park AW, Stephens PR, Drake JM. Host traits associated with species roles in parasite sharing networks. Oikos. 2019;128:23–32. https://onlinelibrary.wiley.com/doi/10.1111/oik.05602.Article 

Google Scholar 
Lima-Mendez G, Faust K, Henry N, Decelle J, Colin S, Carcillo F, et al. Determinants of community structure in the global plankton interactome. Science. 2015;348:1262073. https://www.science.org/doi/10.1126/science.1262073.Article 
PubMed 

Google Scholar 
Hayashi A, Crombie A, Lacey E, Richardson A, Vuong D, Piggott A, et al. Aspergillus Sydowii marine fungal bloom in Australian coastal waters, its metabolites and potential impact on symbiodinium dinoflagellates. Marine Drugs. 2016;14:59. http://www.mdpi.com/1660-3397/14/3/59.Article 
PubMed 
PubMed Central 

Google Scholar 
Drew GC, Stevens EJ, King KC. Microbial evolution and transitions along the parasite–mutualist continuum. Nat Rev Microbiol. 2021;19:623–38. https://www.nature.com/articles/s41579-021-00550-7.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hehenberger E, Tikhonenkov D, Cooney E, Jacko-Reynolds V, Irwin N, Keeling P. Free-living relatives of highly abundant unicellular marine parasites elucidate plastid loss. 2022. https://www.researchsquare.com/article/rs-1472581/v1.Sures B, Nachev M, Selbach C, Marcogliese DJ. Parasite responses to pollution: what we know and where we go in ‘Environmental Parasitology’. Parasites Vectors. 2017;10:65. http://parasitesandvectors.biomedcentral.com/articles/10.1186/s13071-017-2001-3.Article 
PubMed 
PubMed Central 

Google Scholar 
Payne RJ. Seven reasons why protists make useful bioindicators. Acta Protozoologica. 2013;52:105–13. https://doi.org/10.4467/16890027AP.13.0011.1108Article 

Google Scholar 
Vaulot D, Sim CWH, Ong D, Teo B, Biwer C, Jamy M, et al. metaPR 2: a database of eukaryotic 18S rRNAmetabarcodes with an emphasis on protists. Mol Ecol Resour. 2022;1755-0998.13674. https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13674.Vernette C, Henry N, Lecubin J, Vargas C, Hingamp P, Lescot M. The Ocean barcode atlas: a web service to explore the biodiversity and biogeography of marine organisms. Mol Ecol Resour. 2021;21:1347–58. https://onlinelibrary.wiley.com/doi/10.1111/1755-0998.13322.Article 
CAS 
PubMed 

Google Scholar 
Chust G, Vogt M, Benedetti F, Nakov T, Villéger S, Aubert A, et al. Mare incognitum: a glimpse into future plankton diversity and ecology research. Front Mar Sci. 2017;4. http://journal.frontiersin.org/article/10.3389/fmars.2017.00068/full.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucl Acids Res. 2012;41:D597–604. http://academic.oup.com/nar/article/41/D1/D597/1064851/The-Protist-Ribosomal-Reference-database-PR2-a.Article 
PubMed 
PubMed Central 

Google Scholar 
Coppola L, Raimbault P, Mortier L, Testor P. Monitoring the Environment in the Northwestern Mediterranean Sea. Eos 2019;100. https://doi.org/10.1029/2019EO125951.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43;. http://www.nature.com/articles/ismej2017119.Article 
PubMed 
PubMed Central 

Google Scholar 
RStudio Team. RStudio: integrated development for R. Boston, MA: RStudio, PBC; 2020. http://www.rstudio.com/.Caracciolo M, Rigaut‐Jalabert F, Romac S, Mahé F, Forsans S, Gac J, et al. Seasonal dynamics of marine protist communities in tidally mixed coastal waters. Mol Ecol. 2022;31:3761–83. https://onlinelibrary.wiley.com/doi/10.1111/mec.16539.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Giner CR, Balagué V, Krabberød AK, Ferrera I, Reñé A, Garcés E, et al. Quantifying long‐term recurrence in planktonic microbial eukaryotes. Mol Ecol. 2019;28:923–35. https://onlinelibrary.wiley.com/doi/10.1111/mec.14929.Article 
PubMed 

Google Scholar 
Giner CR, Pernice MC, Balagué V, Duarte CM, Gasol JM, Logares R, et al. Marked changes in diversity and relative activity of picoeukaryotes with depth in the world ocean. ISME J. 2020;14:437–49. http://www.nature.com/articles/s41396-019-0506-9.Article 
PubMed 

Google Scholar 
Lambert S, Tragin M, Lozano JC, Ghiglione JF, Vaulot D, Bouget FY, et al. Rhythmicity of coastal marine picoeukaryotes, bacteria and archaea despite irregular environmental perturbations. ISME J. 2019;13:388–401. http://www.nature.com/articles/s41396-018-0281-z.Article 
PubMed 

Google Scholar 
Logares R, Deutschmann IM, Junger PC, Giner CR, Krabberød AK, Schmidt TSB, et al. Disentangling the mechanisms shaping the surface ocean microbiota. Microbiome. 2020;8:55. https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-020-00827-8.Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pernice MC, Giner CR, Logares R, Perera-Bel J, Acinas SG, Duarte CM, et al. Large variability of bathypelagic microbial eukaryotic communities across the world’s oceans. ISME J. 2016;10:945–58. http://www.nature.com/articles/ismej2015170.Article 
PubMed 

Google Scholar 
Massana R, Gobet A, Audic S, Bass D, Bittner L, Boutte C, et al. Marine protist diversity in European coastal waters and sediments as revealed by high-throughput sequencing: protist diversity in European coastal areas. Environ Microbiol. 2015;17:4035–49. http://doi.wiley.com/10.1111/1462-2920.12955.Article 
CAS 
PubMed 

Google Scholar 
Csardi G, Nepusz T. The igraph software package for complex network research. InterJ Complex Syst. 2006;1695:1–9.
Google Scholar 
Bray JR, Curtis JT. An ordination of the upland forest communities of Southern Wisconsin. Ecol Monographs. 1957;27:325–49. https://onlinelibrary.wiley.com/doi/10.2307/1942268.Article 

Google Scholar 
Robert P, Escoufier Y. A unifying tool for linear multivariate statistical methods: the RV- coefficient. J R Stat Soc Ser C Appl Stat. 1976;25:257–65. https://doi.org/10.2307/2347233Article 

Google Scholar 
Ruf T. The lomb-scargle periodogram in biological rhythm research: analysis of incomplete and unequally spaced time-series. Biol Rhythm Res. 1999;30:178–201. https://www.tandfonline.com/doi/full/10.1076/brhm.30.2.178.1422.Article 

Google Scholar  More