Ecology

1.Pucciarelli, S. et al. Molecular cold-adaptation of protein function and gene regulation: the case for comparative genomic analyses in marine ciliated protozoa. Mar Genomics 2, 57–66. https://doi.org/10.1016/j.margen.2009.03.008 (2009).Article 
PubMed 

Google Scholar 
2.Pucciarelli, S., Marziale, F., Di Giuseppe, G., Barchetta, S. & Miceli, C. Ribosomal cold-adaptation: characterization of the genes encoding the acidic ribosomal P0 and P2 proteins from the Antarctic ciliate Euplotes focardii. Gene 360, 103–110. https://doi.org/10.1016/j.gene.2005.06.007 (2005).CAS 
Article 
PubMed 

Google Scholar 
3.Pucciarelli, S. & Miceli, C. Characterization of the cold-adapted alpha-tubulin from the psychrophilic ciliate Euplotes focardii. Extremophiles 6, 385–389. https://doi.org/10.1007/s00792-002-0268-5 (2002).CAS 
Article 
PubMed 

Google Scholar 
4.Yang, G. et al. Characterization and comparative analysis of psychrophilic and mesophilic alpha-amylases from Euplotes species: a contribution to the understanding of enzyme thermal adaptation. Biochem Biophys Res Commun 438, 715–720. https://doi.org/10.1016/j.bbrc.2013.07.113 (2013).CAS 
Article 
PubMed 

Google Scholar 
5.Prescott, D. M. The DNA of ciliated protozoa. Microbiol Rev 58, 233–267 (1994).CAS 
Article 

Google Scholar 
6.Mollenbeck, M. & Klobutcher, L. A. De novo telomere addition to spacer sequences prior to their developmental degradation in Euplotes crassus. Nucleic Acids Res 30, 523–531 (2002).Article 

Google Scholar 
7.Swart, E. C. et al. The Oxytricha trifallax macronuclear genome: a complex eukaryotic genome with 16,000 tiny chromosomes. PLoS Biol 11, e1001473. https://doi.org/10.1371/journal.pbio.1001473 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Heyse, G., Jonsson, F., Chang, W. J. & Lipps, H. J. RNA-dependent control of gene amplification. Proc Natl Acad Sci U S A 107, 22134–22139. https://doi.org/10.1073/pnas.1009284107 (2010).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
9.Nowacki, M., Haye, J. E., Fang, W., Vijayan, V. & Landweber, L. F. RNA-mediated epigenetic regulation of DNA copy number. Proc Natl Acad Sci U S A 107, 22140–22144. https://doi.org/10.1073/pnas.1012236107 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Dayeh, V. R. et al. Comparing a ciliate and a fish cell line for their sensitivity to several classes of toxicants by the novel application of multiwell filter plates to Tetrahymena. Res Microbiol 156, 93–103. https://doi.org/10.1016/j.resmic.2004.08.005 (2005).ADS 
CAS 
Article 
PubMed 

Google Scholar 
11.Detrich, H. W., 3rd, Parker, S. K., Williams, R. C., Jr., Nogales, E. & Downing, K. H. Cold adaptation of microtubule assembly and dynamics. Structural interpretation of primary sequence changes present in the alpha- and beta-tubulins of Antarctic fishes. J Biol Chem 275, 37038–37047. https://doi.org/10.1074/jbc.M005699200 (2000).12.Manka, S. W. & Moores, C. A. Microtubule structure by cryo-EM: snapshots of dynamic instability. Essays Biochem 62, 737–751. https://doi.org/10.1042/EBC20180031 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
13.Inclan, Y. F. & Nogales, E. Structural models for the self-assembly and microtubule interactions of gamma-, delta- and epsilon-tubulin. J Cell Sci 114, 413–422 (2001).CAS 
Article 

Google Scholar 
14.Chiappori, F. et al. Structural thermal adaptation of beta-tubulins from the Antarctic psychrophilic protozoan Euplotes focardii. Proteins 80, 1154–1166. https://doi.org/10.1002/prot.24016 (2012).CAS 
Article 
PubMed 

Google Scholar 
15.Marziale, F. et al. Different roles of two gamma-tubulin isotypes in the cytoskeleton of the Antarctic ciliate Euplotes focardii: remodelling of interaction surfaces may enhance microtubule nucleation at low temperature. FEBS J 275, 5367–5382. https://doi.org/10.1111/j.1742-4658.2008.06666.x (2008).CAS 
Article 
PubMed 

Google Scholar 
16.Pucciarelli, S., Miceli, C. & Melki, R. Heterologous expression and folding analysis of a beta-tubulin isotype from the Antarctic ciliate Euplotes focardii. Eur J Biochem 269, 6271–6277 (2002).CAS 
Article 

Google Scholar 
17.Gromer, S., Urig, S. & Becker, K. The thioredoxin system–from science to clinic. Med Res Rev 24, 40–89. https://doi.org/10.1002/med.10051 (2004).CAS 
Article 
PubMed 

Google Scholar 
18.Birben, E., Sahiner, U. M., Sackesen, C., Erzurum, S. & Kalayci, O. Oxidative stress and antioxidant defense. World Allergy Organ J 5, 9–19. https://doi.org/10.1097/WOX.0b013e3182439613 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
19.Alin, P., Danielson, U. H. & Mannervik, B. 4-Hydroxyalk-2-enals are substrates for glutathione transferase. FEBS Lett 179, 267–270 (1985).CAS 
Article 

Google Scholar 
20.Juganson, K. et al. Mechanisms of toxic action of silver nanoparticles in the protozoan Tetrahymena thermophila: From gene expression to phenotypic events. Environ Pollut 225, 481–489. https://doi.org/10.1016/j.envpol.2017.03.013 (2017).CAS 
Article 
PubMed 

Google Scholar 
21.Clark, M. S., Fraser, K. P. & Peck, L. S. Antarctic marine molluscs do have an HSP70 heat shock response. Cell Stress Chaperones 13, 39–49. https://doi.org/10.1007/s12192-008-0014-8 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Tomanek, L. The heat-shock response: its variation, regulation and ecological importance in intertidal gastropods (genus Tegula). Integr Comp Biol 42, 797–807. https://doi.org/10.1093/icb/42.4.797 (2002).CAS 
Article 
PubMed 

Google Scholar 
23.Morimoto, R. I., Kline, M. P., Bimston, D. N. & Cotto, J. J. The heat-shock response: regulation and function of heat-shock proteins and molecular chaperones. Essays Biochem 32, 17–29 (1997).CAS 
PubMed 

Google Scholar 
24.Gonzalez-Aravena, M. et al. HSP70 from the Antarctic sea urchin Sterechinus neumayeri: molecular characterization and expression in response to heat stress. Biol Res 51, 8. https://doi.org/10.1186/s40659-018-0156-9 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
25.Hofmann, G. E., Buckley, B. A., Airaksinen, S., Keen, J. E. & Somero, G. N. Heat-shock protein expression is absent in the antarctic fish Trematomus bernacchii (family Nototheniidae). J Exp Biol 203, 2331–2339 (2000).CAS 
Article 

Google Scholar 
26.La Terza, A., Papa, G., Miceli, C. & Luporini, P. Divergence between two Antarctic species of the ciliate Euplotes, E. focardii and E. nobilii, in the expression of heat-shock protein 70 genes. Mol Ecol 10, 1061–1067. https://doi.org/10.1046/j.1365-294x.2001.01242.x (2001).27.Klobutcher, L. A. & Farabaugh, P. J. Shifty ciliates: frequent programmed translational frameshifting in euplotids. Cell 111, 763–766 (2002).CAS 
Article 

Google Scholar 
28.Lobanov, A. V. et al. Position-dependent termination and widespread obligatory frameshifting in Euplotes translation. Nat Struct Mol Biol 24, 61–68. https://doi.org/10.1038/nsmb.3330 (2017).CAS 
Article 
PubMed 

Google Scholar 
29.Coordinators, N. R. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 45, D12–D17. https://doi.org/10.1093/nar/gkw1071 (2017).CAS 
Article 

Google Scholar 
30.Pucciarelli, S. et al. Microbial consortium associated with the antarctic marine ciliate Euplotes focardii: an investigation from genomic sequences. Microb Ecol 70, 484–497. https://doi.org/10.1007/s00248-015-0568-9 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Klobutcher, L. A. et al. Conserved DNA sequences adjacent to chromosome fragmentation and telomere addition sites in Euplotes crassus. Nucleic Acids Res 26, 4230–4240. https://doi.org/10.1093/nar/26.18.4230 (1998).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Aeschlimann, S. H. et al. The draft assembly of the radically organized Stylonychia lemnae macronuclear genome. Genome Biol Evol 6, 1707–1723. https://doi.org/10.1093/gbe/evu139 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
33.Swart, E. C. (personal communication).34.Cavalcanti, A. R., Stover, N. A., Orecchia, L., Doak, T. G. & Landweber, L. F. Coding properties of Oxytricha trifallax (Sterkiella histriomuscorum) macronuclear chromosomes: analysis of a pilot genome project. Chromosoma 113, 69–76. https://doi.org/10.1007/s00412-004-0295-3 (2004).CAS 
Article 
PubMed 

Google Scholar 
35.Lozupone, C. A., Knight, R. D. & Landweber, L. F. The molecular basis of nuclear genetic code change in ciliates. Curr Biol 11, 65–74. https://doi.org/10.1016/s0960-9822(01)00028-8 (2001).CAS 
Article 
PubMed 

Google Scholar 
36.Salas-Marco, J. et al. Distinct paths to stop codon reassignment by the variant-code organisms Tetrahymena and Euplotes. Mol Cell Biol 26, 438–447. https://doi.org/10.1128/MCB.26.2.438-447.2006 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Klobutcher, L. A. Sequencing of random Euplotes crassus macronuclear genes supports a high frequency of +1 translational frameshifting. Eukaryot Cell 4, 2098–2105. https://doi.org/10.1128/EC.4.12.2098-2105.2005 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Wang, R., Xiong, J., Wang, W., Miao, W. & Liang, A. High frequency of +1 programmed ribosomal frameshifting in Euplotes octocarinatus. Sci Rep 6, 21139. https://doi.org/10.1038/srep21139 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Turanov, A. A. et al. Genetic code supports targeted insertion of two amino acids by one codon. Science 323, 259–261. https://doi.org/10.1126/science.1164748 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
40.Maehigashi, T., Dunkle, J. A., Miles, S. J. & Dunham, C. M. Structural insights into +1 frameshifting promoted by expanded or modification-deficient anticodon stem loops. Proc Natl Acad Sci U S A 111, 12740–12745. https://doi.org/10.1073/pnas.1409436111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Miceli, C., Ballarini, P., Di Giuseppe, G., Valbonesi, A. & Luporini, P. Identification of the tubulin gene family and sequence determination of one beta-tubulin gene in a cold-poikilotherm protozoan, the antarctic ciliate Euplotes focardii. J Eukaryot Microbiol 41, 420–427. https://doi.org/10.1111/j.1550-7408.1994.tb06100.x (1994).CAS 
Article 
PubMed 

Google Scholar 
42.Ricci, F. et al. The sub-chromosomic macronuclear pheromone genes of the ciliate Euplotes raikovi: comparative structural analysis and insights into the mechanism of expression. J Eukaryot Microbiol 66, 376–384. https://doi.org/10.1111/jeu.12677 (2019).CAS 
Article 
PubMed 

Google Scholar 
43.Wang, R., Liu, J., Di Giuseppe, G. & Liang, A. UAA and UAG may Encode Amino Acid in Cathepsin B Gene of Euplotes octocarinatus. J Eukaryot Microbiol 67, 144–149. https://doi.org/10.1111/jeu.12755 (2020).CAS 
Article 
PubMed 

Google Scholar 
44.Heaphy, S. M., Mariotti, M., Gladyshev, V. N., Atkins, J. F. & Baranov, P. V. Novel ciliate genetic code variants including the reassignment of all three stop codons to sense codons in condylostoma magnum. Mol Biol Evol 33, 2885–2889. https://doi.org/10.1093/molbev/msw166 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Swart, E. C., Serra, V., Petroni, G. & Nowacki, M. Genetic codes with no dedicated stop codon: context-dependent translation termination. Cell 166, 691–702. https://doi.org/10.1016/j.cell.2016.06.020 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
46.Roy, B., Leszyk, J. D., Mangus, D. A. & Jacobson, A. Nonsense suppression by near-cognate tRNAs employs alternative base pairing at codon positions 1 and 3. Proc Natl Acad Sci U S A 112, 3038–3043. https://doi.org/10.1073/pnas.1424127112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Dunn, J. G., Foo, C. K., Belletier, N. G., Gavis, E. R. & Weissman, J. S. Ribosome profiling reveals pervasive and regulated stop codon readthrough in Drosophila melanogaster. Elife 2, e01179. https://doi.org/10.7554/eLife.01179 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Frechin, M., Duchene, A. M. & Becker, H. D. Translating organellar glutamine codons: a case by case scenario?. RNA Biol 6, 31–34. https://doi.org/10.4161/rna.6.1.7564 (2009).CAS 
Article 
PubMed 

Google Scholar 
49.Wilcox, M. & Nirenberg, M. Transfer RNA as a cofactor coupling amino acid synthesis with that of protein. Proc Natl Acad Sci U S A 61, 229–236. https://doi.org/10.1073/pnas.61.1.229 (1968).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
50.Detrich, H. W. 3rd., Fitzgerald, T. J., Dinsmore, J. H. & Marchese-Ragona, S. P. Brain and egg tubulins from antarctic fishes are functionally and structurally distinct. J Biol Chem 267, 18766–18775 (1992).CAS 
Article 

Google Scholar 
51.Detrich, H. W. 3rd., Johnson, K. A. & Marchese-Ragona, S. P. Polymerization of Antarctic fish tubulins at low temperatures: energetic aspects. Biochemistry 28, 10085–10093 (1989).CAS 
Article 

Google Scholar 
52.Wloga, D. et al. Glutamylation on alpha-tubulin is not essential but affects the assembly and functions of a subset of microtubules in Tetrahymena thermophila. Eukaryot Cell 7, 1362–1372. https://doi.org/10.1128/EC.00084-08 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Eisen, J. A. et al. Macronuclear genome sequence of the ciliate Tetrahymena thermophila, a model eukaryote. PLoS Biol 4, e286. https://doi.org/10.1371/journal.pbio.0040286 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
54.Aury, J. M. et al. Global trends of whole-genome duplications revealed by the ciliate Paramecium tetraurelia. Nature 444, 171–178. https://doi.org/10.1038/nature05230 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
55.Pucciarelli, S. et al. Distinct functional roles of beta-tubulin isotypes in microtubule arrays of Tetrahymena thermophila, a model single-celled organism. PLoS ONE 7, e39694. https://doi.org/10.1371/journal.pone.0039694 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Pucciarelli, S. et al. Tubulin folding: the special case of a beta-tubulin isotype from the Antarctic psychrophilic ciliate Euplotes focardii. Polar Biol 36, 1833–1838. https://doi.org/10.1007/s00300-013-1390-9 (2013).Article 

Google Scholar 
57.Pucci, F. & Rooman, M. Physical and molecular bases of protein thermal stability and cold adaptation. Curr Opin Struct Biol 42, 117–128. https://doi.org/10.1016/j.sbi.2016.12.007 (2017).CAS 
Article 
PubMed 

Google Scholar 
58.Aqvist, J., Isaksen, G. V. & Brandsdal, B. O. Computation of enzyme cold adaptation. Nat Rev Chem 1, 0051. https://doi.org/10.1038/s41570-017-0051 (2017).CAS 
Article 

Google Scholar 
59.Lesser, M. P. Oxidative stress in marine environments: biochemistry and physiological ecology. Annu Rev Physiol 68, 253–278. https://doi.org/10.1146/annurev.physiol.68.040104.110001 (2006).CAS 
Article 
PubMed 

Google Scholar 
60.McCord, J. M. & Fridovich, I. Superoxide dismutase. An enzymic function for erythrocuprein (hemocuprein). J Biol Chem 244, 6049–6055 (1969).61.McCord, J. M. & Fridovich, I. Superoxide dismutase: the first twenty years (1968–1988). Free Radic Biol Med 5, 363–369 (1988).CAS 
Article 

Google Scholar 
62.Miller, A. F. Superoxide dismutases: ancient enzymes and new insights. FEBS Lett 586, 585–595. https://doi.org/10.1016/j.febslet.2011.10.048 (2012).CAS 
Article 
PubMed 

Google Scholar 
63.Benov, L. T. & Fridovich, I. Escherichia coli expresses a copper- and zinc-containing superoxide dismutase. J Biol Chem 269, 25310–25314 (1994).CAS 
Article 

Google Scholar 
64.Steinman, H. M. & Ely, B. Copper-zinc superoxide dismutase of Caulobacter crescentus: cloning, sequencing, and mapping of the gene and periplasmic location of the enzyme. J Bacteriol 172, 2901–2910. https://doi.org/10.1128/jb.172.6.2901-2910.1990 (1990).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
65.Antonyuk, S. V., Strange, R. W., Marklund, S. L. & Hasnain, S. S. The structure of human extracellular copper-zinc superoxide dismutase at 1.7 A resolution: insights into heparin and collagen binding. J Mol Biol 388, 310–326. https://doi.org/10.1016/j.jmb.2009.03.026 (2009).66.Marklund, S. L. Extracellular superoxide dismutase and other superoxide dismutase isoenzymes in tissues from nine mammalian species. Biochem J 222, 649–655. https://doi.org/10.1042/bj2220649 (1984).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
67.Bannister, J. V., Bannister, W. H. & Rotilio, G. Aspects of the structure, function, and applications of superoxide dismutase. CRC Crit Rev Biochem 22, 111–180 (1987).CAS 
Article 

Google Scholar 
68.James, E. R. Superoxide dismutase. Parasitol Today 10, 481–484. https://doi.org/10.1016/0169-4758(94)90161-9 (1994).CAS 
Article 
PubMed 

Google Scholar 
69.Ferro, D. et al. Cu, Zn superoxide dismutases from Tetrahymena thermophila: molecular evolution and gene expression of the first line of antioxidant defenses. Protist 166, 131–145. https://doi.org/10.1016/j.protis.2014.12.003 (2015).CAS 
Article 
PubMed 

Google Scholar 
70.Arnaiz, O. & Sperling, L. ParameciumDB in 2011: new tools and new data for functional and comparative genomics of the model ciliate Paramecium tetraurelia. Nucleic Acids Res 39, D632-636. https://doi.org/10.1093/nar/gkq918 (2011).CAS 
Article 
PubMed 

Google Scholar 
71.Fink, R. C. & Scandalios, J. G. Molecular evolution and structure–function relationships of the superoxide dismutase gene families in angiosperms and their relationship to other eukaryotic and prokaryotic superoxide dismutases. Arch Biochem Biophys 399, 19–36. https://doi.org/10.1006/abbi.2001.2739 (2002).CAS 
Article 
PubMed 

Google Scholar 
72.Lee, Y. M., Friedman, D. J. & Ayala, F. J. Superoxide dismutase: an evolutionary puzzle. Proc Natl Acad Sci U S A 82, 824–828. https://doi.org/10.1073/pnas.82.3.824 (1985).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
73.Pischedda, A. et al. Antarctic marine ciliates under stress: superoxide dismutases from the psychrophilic Euplotes focardii are cold-active yet heat tolerant enzymes. Sci Rep 8, 14721. https://doi.org/10.1038/s41598-018-33127-1 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
74.Yang, G. et al. Characterization of the first eukaryotic cold-adapted patatin-like phospholipase from the psychrophilic Euplotes focardii: Identification of putative determinants of thermal-adaptation by comparison with the homologous protein from the mesophilic Euplotes crassus. Biochimie 95, 1795–1806. https://doi.org/10.1016/j.biochi.2013.06.008 (2013).CAS 
Article 
PubMed 

Google Scholar 
75.Li, J., Zhou, L., Lin, X., Yi, Z. & Al-Rasheid, K. A. Characterizing dose-responses of catalase to nitrofurazone exposure in model ciliated protozoan Euplotes vannus for ecotoxicity assessment: enzyme activity and mRNA expression. Ecotoxicol Environ Saf 100, 294–302. https://doi.org/10.1016/j.ecoenv.2013.08.021 (2014).CAS 
Article 
PubMed 

Google Scholar 
76.Prast-Nielsen, S., Huang, H. H. & Williams, D. L. Thioredoxin glutathione reductase: its role in redox biology and potential as a target for drugs against neglected diseases. Biochim Biophys Acta 1262–1271, 2011. https://doi.org/10.1016/j.bbagen.2011.06.024 (1810).CAS 
Article 

Google Scholar 
77.Kabani, M. & Martineau, C. N. Multiple hsp70 isoforms in the eukaryotic cytosol: mere redundancy or functional specificity?. Curr Genomics 9, 338–248. https://doi.org/10.2174/138920208785133280 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
78.La Terza, A., Miceli, C. & Luporini, P. The gene for the heat-shock protein 70 of Euplotes focardii, an Antarctic psychrophilic ciliate. Antarct. Sci. 16, 23–28. https://doi.org/10.1017/S0954102004001774 (2004).ADS 
Article 

Google Scholar 
79.Chen, X. et al. Genome analyses of the new model protist Euplotes vannus focusing on genome rearrangement and resistance to environmental stressors. Mol Ecol Resour 19, 1292–1308. https://doi.org/10.1111/1755-0998.13023 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
80.Chen, Z. et al. Transcriptomic and genomic evolution under constant cold in Antarctic notothenioid fish. Proc Natl Acad Sci U S A 105, 12944–12949. https://doi.org/10.1073/pnas.0802432105 (2008).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
81.Li, Y. et al. Comparative transcriptomic analysis reveals gene expression associated with cold adaptation in the tea plant Camellia sinensis. BMC Genomics 20, 624. https://doi.org/10.1186/s12864-019-5988-3 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
82.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
83.Andrews, S. (2010).84.Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 19, 455–477. https://doi.org/10.1089/cmb.2012.0021 (2012).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
85.Nikolenko, S. I., Korobeynikov, A. I. & Alekseyev, M. A. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14 Suppl 1, S7. https://doi.org/10.1186/1471-2164-14-S1-S7 (2013).86.Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075. https://doi.org/10.1093/bioinformatics/btt086 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
87.Boscaro, V., Husnik, F., Vannini, C. & Keeling, P. J. Symbionts of the ciliate Euplotes: diversity, patterns and potential as models for bacteria-eukaryote endosymbioses. Proc Biol Sci 286, 20190693. https://doi.org/10.1098/rspb.2019.0693 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
88.Serra, V. et al. Morphology, ultrastructure, genomics, and phylogeny of Euplotes vanleeuwenhoeki sp. nov. and its ultra-reduced endosymbiont “Candidatus Pinguicoccus supinus” sp. nov. Sci Rep 10, 20311. https://doi.org/10.1038/s41598-020-76348-z (2020).89.Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res 34, W435-439. https://doi.org/10.1093/nar/gkl200 (2006).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
90.Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323. https://doi.org/10.1186/1471-2105-12-323 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
91.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
Article 
PubMed 

Google Scholar 
92.Gotz, S. et al. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36, 3420–3435. https://doi.org/10.1093/nar/gkn176 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
93.Parra, G., Bradnam, K. & Korf, I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics 23, 1061–1067. https://doi.org/10.1093/bioinformatics/btm071 (2007).CAS 
Article 
PubMed 

Google Scholar 
94.Laslett, D. & Canback, B. ARAGORN, a program to detect tRNA genes and tmRNA genes in nucleotide sequences. Nucleic Acids Res 32, 11–16. https://doi.org/10.1093/nar/gkh152 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
95.Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubock, R. & Hofacker, I. L. The Vienna RNA websuite. Nucleic Acids Res 36, W70-74. https://doi.org/10.1093/nar/gkn188 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
96.Popenda, M. et al. Automated 3D structure composition for large RNAs. Nucleic Acids Res 40, e112. https://doi.org/10.1093/nar/gks339 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
97.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
98.Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659. https://doi.org/10.1093/bioinformatics/btl158 (2006).CAS 
Article 
PubMed 

Google Scholar 
99.Shigematsu, M. et al. YAMAT-seq: an efficient method for high-throughput sequencing of mature transfer RNAs. Nucleic Acids Res 45, e70. https://doi.org/10.1093/nar/gkx005 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
100.Bushnell, B., Rood, J. & Singer, E. BBMerge: accurate paired shotgun read merging via overlap. PLoS ONE 12, e0185056. https://doi.org/10.1371/journal.pone.0185056 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
101.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. 2011 17, 3. https://doi.org/10.14806/ej.17.1.200 (2011).102.Holmes, A. D., Howard, J. M., Chan, P. P. & Lowe, T. M. tRNA Analysis of eXpression (tRAX): A tool for integrating analysis of tRNAs, tRNA-derived small RNAs, and tRNA modifications. (Submitted) (2020).103.Sievers, F. & Higgins, D. G. Clustal omega. Curr Protoc Bioinformatics 48, 3 13 11–16. https://doi.org/10.1002/0471250953.bi0313s48 (2014).104.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
105.Webb, B. & Sali, A. Comparative protein structure modeling using MODELLER. Curr Protoc Bioinform. 54, 5 6 1–5 6 37. https://doi.org/10.1002/cpbi.3 (2016).106.Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46, W296–W303. https://doi.org/10.1093/nar/gky427 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
107.Ichikawa, M. et al. Tubulin lattice in cilia is in a stressed form regulated by microtubule inner proteins. Proc Natl Acad Sci U S A 116, 19930–19938. https://doi.org/10.1073/pnas.1911119116 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
108.Chaaban, S. et al. The Structure and Dynamics of C. elegans Tubulin Reveals the Mechanistic Basis of Microtubule Growth. Dev Cell 47, 191–204 e198. https://doi.org/10.1016/j.devcel.2018.08.023 (2018).109.Kikkawa, M. et al. Switch-based mechanism of kinesin motors. Nature 411, 439–445. https://doi.org/10.1038/35078000 (2001).ADS 
CAS 
Article 
PubMed 

Google Scholar 
110.Howes, S. C. et al. Structural differences between yeast and mammalian microtubules revealed by cryo-EM. J Cell Biol 216, 2669–2677. https://doi.org/10.1083/jcb.201612195 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
111.Ma, M. et al. Structure of the Decorated Ciliary Doublet Microtubule. Cell 179, 909–922 e912. https://doi.org/10.1016/j.cell.2019.09.030 (2019).112.Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25. https://doi.org/10.1016/j.softx.2015.06.001 (2015).ADS 
Article 

Google Scholar 
113.Morrison, T. B., Weis, J. J. & Wittwer, C. T. Quantification of low-copy transcripts by continuous SYBR Green I monitoring during amplification. Biotechniques 24, 954–958, 960, 962 (1998).114.Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29, e45. https://doi.org/10.1093/nar/29.9.e45 (2001).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
115.Pfaffl, M. W., Horgan, G. W. & Dempfle, L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res 30, e36. https://doi.org/10.1093/nar/30.9.e36 (2002).Article 
PubMed 
PubMed Central 

Google Scholar  More

1.Kaul, I., Grunberg, I. & Stern, M. A. Global Public Goods: International Cooperation in the 21st Century (United Nations Development Programme, 1999).2.Kindleberger, C. P. in Comparative Political Economy: A Retrospective (eds Kindleberger, C. P.) Ch. 20 (MIT Press, 2003); https://doi.org/10.7551/mitpress/1977.003.00253.Horvat, M. & Gong, P. Science support for Belt and Road. Science 364, 513 (2019).CAS 
Article 

Google Scholar 
4.Belt and Road Economics: Opportunities and Risks of Transport Corridors (The World Bank, 2019).5.Hughes, A. C. et al. Horizon scan of the Belt and Road Initiative. Trends Ecol. Evol. 35, 583–593 (2020).Article 

Google Scholar 
6.Laurance, W. F. & Arrea, I. B. Roads to riches or ruin? Science 358, 442–444 (2017).CAS 
Article 

Google Scholar 
7.Carter, N., Killion, A., Easter, T., Brandt, J. & Ford, A. Road development in Asia: assessing the range-wide risks to tigers. Sci. Adv. 6, eaaz9619 (2020).Article 

Google Scholar 
8.Liu, X. et al. Risks of biological invasion on the Belt and Road. Curr. Biol. 29, 499–505 (2019).CAS 
Article 

Google Scholar 
9.Farhadinia, M. S. et al. Belt and Road Initiative may create new supplies for illegal wildlife trade in large carnivores. Nat. Ecol. Evol. 3, 1267–1268 (2019).Article 

Google Scholar 
10.Hanna, P. & Vanclay, F. Human rights, Indigenous peoples and the concept of free, prior and informed consent. Impact Assess. Proj. Appraisal 31, 146–157 (2013).Article 

Google Scholar 
11.Hall, T. D. & Fenelon, J. V. Indigenous Peoples and Globalization: Resistance and Revitalization (Routledge, 2015); https://doi.org/10.4324/978131563396112.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 
13.Ascensão, F. et al. Environmental challenges for the Belt and Road Initiative. Nat. Sustain. 1, 206–209 (2018).Article 

Google Scholar 
14.Teo, H. C. et al. Environmental impacts of infrastructure development under the Belt and Road Initiative. Environments 6, 72 (2019).Article 

Google Scholar 
15.Thomas, V. & Chindarkar, N. in Economic Evaluation of Sustainable Development (eds Thomas, V. & Chindarkar, N.) Ch. 1 (Springer Nature, 2019); https://doi.org/10.1007/978-981-13-6389-4_116.Chinese Academy of International Trade and Economic Cooperation, Research Centre of the State-owned Assets Supervision and Administration & United Nations Development Programme China 2017 Report on the Sustainable Development of Chinese Enterprises Overseas (UNDP China, 2017).17.Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–232 (2014).CAS 
Article 

Google Scholar 
18.Guo, H. Steps to the digital Silk Road. Nature 554, 25–27 (2018).CAS 
Article 

Google Scholar 
19.BenYishay, A., Parks, B., Runfola, D. & Trichler, R. Forest cover impacts of Chinese development projects in ecologically sensitive areas Working Paper 32 (AidData, 2016).20.Hughes, A. C. Understanding and minimizing environmental impacts of the Belt and Road Initiative. Conserv. Biol. 33, 883–894 (2019).Article 

Google Scholar 
21.Narain, D., Maron, M., Teo, H. C., Hussey, K. & Lechner, A. M. Best-practice biodiversity safeguards for Belt and Road Initiative’s financiers. Nat. Sustain. 3, 650–657 (2020).Article 

Google Scholar 
22.Ray, R., Gallagher, K. P., Kring, W., Pitts, J. & Simmons, B. A. Geolocated dataset of Chinese overseas development finance. Sci. Data (in the press).23.Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).Article 

Google Scholar 
24.Ray, R., Gallagher, K. P. & Sanborn, C. A. Development Banks and Sustainability in the Andean Amazon (Routledge, 2019); https://doi.org/10.4324/978042933019325.Humphrey, C. & Michaelowa, K. Shopping for development: multilateral lending, shareholder composition and borrower preferences. World Dev. 44, 142–155 (2013).Article 

Google Scholar 
26.Green BRI and 2013 Agenda for Sustainable Development (China Council for International Cooperation on Environment and Development, 2020).27.Miller, D. C. Explaining global patterns of international aid for linked biodiversity conservation and development. World Dev. 59, 341–359 (2014).Article 

Google Scholar 
28.McKinnon, M. C. et al. What are the effects of nature conservation on human well-being? A systematic map of empirical evidence from developing countries. Environ. Evid. 5, 8 (2016).Article 

Google Scholar 
29.Hale, T., Liu, C. & Urepelainen, J. Belt and Road Decision-Making in China and Recipient Countries: How and to What Extent Does Sustainability Matter? (ISEP, BSG, and ClimateWorks, 2020).30.Laurance, W. F. et al. Reducing the global environmental impacts of rapid infrastructure expansion. Curr. Biol. 25, 259–262 (2015).Article 

Google Scholar 
31.Ministry of Ecology and Environment Belt and Road Ecological and Environmental Cooperation Plan (Ministry of Ecology and Environment, 2017); https://eng.yidaiyilu.gov.cn/zchj/qwfb/13392.htm32.Ministry of Ecology and Environment The Guidance on Promoting Green Belt and Road (Ministry of Ecology and Environment, 2017); https://eng.yidaiyilu.gov.cn/zchj/qwfb/12479.htm33.Watson, J. E. M., Dudley, N., Segan, D. B. & Hockings, M. The performance and potential of protected areas. Nature 515, 67–73 (2014).CAS 
Article 

Google Scholar 
34.Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).Article 

Google Scholar 
35.Golden Kroner, R. E. et al. The uncertain future of protected lands and waters. Science 364, 881–886 (2019).CAS 
Article 

Google Scholar 
36.Indigenous and Tribal Peoples Convention, 1989 (No. 169) (International Labour Organisation, 1989); https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C16937.United Nations General Assembly United Nations Declaration on the Rights of Indigenous Peoples (UN, 2007); https://www.un.org/development/desa/indigenouspeoples/declaration-on-the-rights-of-indigenous-peoples.html38.United Nations Economic Commission for Latin America and the Caribbean Escazú Agreement: Regional Agreement on Access to Information, Public Participation and Justice in Environmental Matters in Latin America and the Caribbean (UN, 2021).39.Dhir, R. K., Cattaneo, U., Ormaza, M. V. C., Coronado, H. & Oelz, M. Implementing the ILO Indigenous and Tribal Peoples Convention No. 169: Towards an Inclusive, Sustainable and Just Future (International Labour Organization, 2020).40.Ward, T. The right to free, prior, and informed consent: Indigenous peoples’ participation rights within international law. J. Hum. Rights 10, 54–84 (2011).
Google Scholar 
41.Global Critical Habitat Screening Layer Version 1.0 (UN Environment Programme World Conservation Monitoring Centre, 2017); https://data.unep-wcmc.org/datasets/4442.Brauneder, K. M. et al. Global screening for critical habitat in the terrestrial realm. PLoS ONE 13, e0193102 (2018).Article 

Google Scholar 
43.Martin, C. S. et al. A global map to aid the identification and screening of critical habitat for marine industries. Mar. Policy 53, 45–53 (2015).Article 

Google Scholar 
44.World Database on Protected Areas (WDPA) (IUCN & UNEP-WCMC, 2018); https://www.protectedplanet.net/en/search-areas?geo_type=site45.IUCN Red List of Threatened Species Version 2020.6 (IUCN, 2020).46.Data Zone (BirdLife International, accessed 19 June 2020); http://datazone.birdlife.org/species/requestdis47.Allan, J. R. et al. Hotspots of human impact on threatened terrestrial vertebrates. PLoS Biol. 17, e3000158 (2019).Article 

Google Scholar 
48.Lewis, J., Hoover, J. & MacKenzie, D. Mining and environmental health disparities in Native American communities. Curr. Environ. Health Rep. 4, 130–141 (2017).Article 

Google Scholar 
49.Pfaff, A. et al. Road investments, spatial spillovers, and deforestation in the Brazilian Amazon. J. Reg. Sci. 47, 109–123 (2007).Article 

Google Scholar 
50.Turschwell, M. P., Brown, C. J., Pearson, R. M. & Connolly, R. M. China’s Belt and Road Initiative: conservation opportunities for threatened marine species and habitats. Mar. Policy 112, 103791 (2020).Article 

Google Scholar 
51.Bolam, F. C. et al. Using the value of information to improve conservation decision making. Biol. Rev. 94, 629–647 (2019).Article 

Google Scholar 
52.Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
53.Yeboah, J., Buor D., Mensah, C. Environmental and Health Impact of Mining on Surrounding Communities: A Case Study of Anglogold Ashanti In Obuasi (Kwame Nkrumah University of Science and Technology, 2008).54.Barber, C. P., Cochrane, M. A., Souza, C. M. & Laurance, W. F. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol. Conserv. 177, 203–209 (2014).Article 

Google Scholar 
55.Richter, B. D. et al. Lost in development’s shadow: the downstream human consequences of dams. Water Altern. 3, 14–42 (2010).
Google Scholar 
56.Newman, P. The environmental impact of cities. Environ. Urban. 18, 275–295 (2006).Article 

Google Scholar 
57.Hansen, A. J. et al. Effects of exurban development on biodiversity: patterns, mechanisms, and research needs. Ecol. Appl. 15, 1893–1905 (2011).Article 

Google Scholar 
58.Nolte, C. High-resolution land value maps reveal underestimation of conservation costs in the United States. Proc. Natl Acad. Sci. USA 117, 29577–29583 (2020).CAS 
Article 

Google Scholar 
59.Dorries, H. Planning for coexistence? Recognizing Indigenous rights through land-use planning in Canada and Australia. Plan. Theory Pract. 19, 313–315 (2018).Article 

Google Scholar 
60.Dreher, A. et al. African leaders and the geography of China’s foreign assistance. J. Dev. Econ. 140, 44–71 (2019).Article 

Google Scholar 
61.World Bank Projects & Operations (World Bank, 2020); https://datacatalog.worldbank.org/dataset/world-bank-projects-operations62.World Bank Geocoded Research Release Version 1.4.2 (AidData, 2017); https://www.aiddata.org/data/world-bank-geocoded-research-release-level-1-v1-4-263.Buchanan, G. M. et al. The local impacts of World Bank development projects near sites of conservation significance. J. Environ. Dev. 27, 299–322 (2018).Article 

Google Scholar 
64.Ray, R. & Pitts, J. Geolocated dataset of Chinese overseas development finance. OSF https://doi.org/10.17605/OSF.IO/7WUXV (2020). More

German Centre for Integrative Biodiversity Research (iDiv) – Halle-Jena-Leipzig, Puschstrasse 4, 04103, Leipzig, GermanyFrancesco Maria SabatiniMartin-Luther-Universität Halle-Wittenberg, Institut für Biologie, Am Kirchtor 1, 06108, Halle, GermanyFrancesco Maria SabatiniHumboldt-Universität zu Berlin, Geography Department, Unter den Linden 6, 10099, Berlin, GermanyHendrik BluhmFrankfurt Zoological Society, Bernhard-Grzimek-Allee 1, 60316, Frankfurt, GermanyZoltan KunNGO “Transparent World”, Rossolimo str. 5/22, building 1, 119021, Moscow, RussiaDmitry AksenovEUROPARC-Spain/Fundación Fernando González Bernáldez. ICEI Edificio A. Campus de Somosaguas, E28224, Pozuelo de Alarcón, SpainJosé A. AtauriThe Danish Nature Agency, Gjøddinggård, Førstballevej 2, DK-7183, Randbøl, DenmarkErik BuchwaldSapienza University of Rome, Department of Environmental Biology, P.le Aldo Moro 5, 00185, Rome, ItalySabina BurrascanoRéserves Naturelles de France, La Bourdonnerie, Dijon cedex, 21000, FranceEugénie CateauPSEDA-ILIRIA. Forestry department, Tirana, 1000, AlbaniaAbdulla DikuCentre for Applied Ecology “Professor Baeta Neves” (CEABN), InBIO, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349‐017, Lisbon, PortugalInês Marques DuarteParque Nacional de Garajonay. Avda. V Centenario, edif. Las Creces, local 1, portal 3, 38800 San Sebastian de La Gomera, Tenerife, SpainÁngel B. Fernández LópezUniversity of Torino, Department DISAFA L.go Paolo Braccini 2, Grugliasco, 10095, ItalyMatteo GarbarinoForest Research Institute, Vassilika, 57006, Thessaloniki, GreeceNikolaos GrigoriadisCentre for Ecological Research, Institute of Ecology and Botany, Alkotmány u. 2-4, 2163, Vácrátót, HungaryFerenc HorváthFaculty of Forestry, University of Agriculture in Krakow, aleja 29-Listopada 46, 31-415, Krakow, PolandSrđan KerenLatvian State Forest Research Institute “Silava”, Rigas street 111, Salaspils, LV-2169, LatviaMara KitenbergaInstitute for Nature Conservation of Vojvodina Province, Radnička 20a, Novi Sad, 21000, SerbiaAlen KišUniversity of Tartu, Institute of Ecology and Earth Sciences, Vanemuise 46, EE-51014, Tartu, EstoniaAnn KrautCentre for Econics and Ecosystem Management, Faculty of Forest and Environment, Eberswalde University for Sustainable Development, Alfred-Möller-Str. 1, 16225, Eberswalde, GermanyPierre L. IbischUniversité de Toulouse, INRAE, UMR DYNAFOR, 24 Chemin de Borde-Rouge Auzeville CS 52627, Castanet-Tolosan, 31326, FranceLaurent LarrieuCRPF-Occitanie, antenne de Tarbes, place du foirail, 65000, Tarbes, FranceLaurent LarrieuMediterranean University of Reggio Calabria, Agraria Department, Loc. Feo di Vito, 89122, Reggio Calabria, ItalyFabio LombardiUniversity of Novi Sad, Institute of Lowland Forestry and Environment, Antona Cehova 13d, Novi Sad, 21102, SerbiaBratislav MatovicWorld Wide Fund for nature (CEE), Lunga street 190, Brasov, 500051, RomaniaRadu Nicolae MeluNorthwest German Forest Research Institute, Department Forest Nature Conservation, Professor-Oelkers-Straße 6, 34346, Hann. Münden, GermanyPeter MeyerAsplan Viak A.S.Kjörboveien 20, postboks 24, N-1300, Sandvika, NorwayRein MidtengUniversity of Zagreb, Faculty of Forestry, Svetosimunska cesta 25, 10000, Zagreb, CroatiaStjepan MikacCzech University of Life Sciences, Faculty of Forestry and Wood Sciences, Kamýcka cesta 1176, CZ-, 16521, Praha6-Suchdol, Czech RepublicMartin MikolášPRALES, Odtrnovie 563, SK-01322, Rosina, SlovakiaMartin MikolášVytautas Magnus University, K. Donelaičio g. 58, LT-44248, Kaunas, LithuaniaGintautas MozgerisUniversity of Forestry, Dendrology Department, bulevard “Sveti Kliment Ohridski” 10, 1756, Sofia, BulgariaMomchil PanayotovSlovenia Forest Service, Department for forest management planning, Vecna pot 2, 1000, Ljubljana, SloveniaRok PisekCentre for Applied Ecology “Professor Baeta Neves” (CEABN), InBIO, School of Agriculture, University of Lisbon, Tapada da Ajuda 1349‐017, Lisbon, PortugalLeónia NunesGreensway AB, Ulls väg 24A, 756 51, Uppsala, SwedenAlejandro RueteFreelance forest expert and book author, Vienna, AustriaMatthias SchickhoferSs. Cyril and Methodius University in Skopje, Hans Em Faculty of Forest Sciences, Landscape Architecture and Environmental Engineering, Department of Botany and Dendrology, P.O. Box 235, MK-1000, Skopje, North MacedoniaBojan SimovskiSwiss Federal Research Institute for Forest, Snow and Landscape Research WSL, Forest Resources and Management, Zürcherstrasse 111, 8903, Birmensdorf, SwitzerlandJonas StillhardUniversity of Novi Sad, Institute of Lowland Forestry and Environment, Antona Cehova 13d, Novi Sad, 21000, SerbiaDejan StojanovicDepartment of Forest Biodiversity, University of Agriculture, Kraków, PolandJerzy SzwagrzykUniversity of Eastern Finland, School of forest Sciences, Yliopistokatu 7, 80100, Joensuu, FinlandOlli-Pekka TikkanenAgricultural University of Tirana, Forestry Department, Kodër Kamëz, SH1, 1029, Tirana, AlbaniaElvin ToromaniWorld Wide Fund for nature (DCP) Ukraine, Mushaka 48, Lviv, 79011, UkraineRoman VolosyanchukEcosphera NGO, Kapushans’ka 82a, Uzhhorod, 88000, UkraineRoman VolosyanchukSilva Tarouca Research Institute, Department of Forest Ecology, Lidická 25/27, 602 00, Brno, Czech RepublicTomáš VrškaCentre for Econics and Ecosystem Management, Faculty of Forest and Environment, Eberswalde University for Sustainable Development, Alfred-Möller-Str. 1, 16225, Eberswalde, GermanyMarcus WaldherrInstitute of Experimental Botany of the National Academy of Sciences of Belarus, Laboratory of Productivity & Stability of Plant Communities, 220072, Academicheskaya St. 27, Minsk, BelarusMaxim YermokhinInstitute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, 2 Gagarin Street, 1113, Sofia, BulgariaTzvetan ZlatanovSaint-Petersburg State University, Department of Vegetation Science, University Embankment, 7/9, St Petersburg, 199034, RussiaAsiya ZagidullinaHumboldt-Universität zu Berlin, Geography Department & Integrative Research Institute on Transformation in Human-Environment Systems, Unter den Linden 6, 10099, Berlin, GermanyTobias KuemmerleCorrespondence to
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1.Redecker D, Kodner R, Graham LE. Glomalean fungi from the Ordovician. Science. 2000;289:1920–1.CAS 
PubMed 

Google Scholar 
2.Parniske M. Arbuscular mycorrhiza: the mother of plant root endosymbioses. Nat Rev Microbiol. 2008;6:763–75.CAS 
PubMed 

Google Scholar 
3.Raven JA, Lambers H, Smith SE, Westoby M. Costs of acquiring phosphorus by vascular land plants: patterns and implications for plant coexistence. N Phytol. 2018;217:1420–7.CAS 

Google Scholar 
4.Field KJ, Pressel S. Unity in diversity: structural and functional insights into the ancient partnerships between plants and fungi. N Phytol. 2018;220:996–1011.CAS 

Google Scholar 
5.Harrison MJ, Vanbuuren ML. A phosphate transporter from the mycorrhizal fungus Glomus versiforme. Nature. 1995;378:626–9.CAS 
PubMed 

Google Scholar 
6.Smith SE, Jakobsen I, Gronlund M, Smith FA. Roles of arbuscular mycorrhizas in plant phosphorus nutrition: interactions between pathways of phosphorus uptake in arbuscular mycorrhizal roots have important implications for understanding and manipulating plant phosphorus acquisition. Plant Physiol. 2011;156:1050–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Zhang L, Feng G, Declerck S. Signal beyond nutrient, fructose, exuded by an arbuscular mycorrhizal fungus triggers phytate mineralization by a phosphate solubilizing bacterium. ISME J. 2018;12:23–51.
Google Scholar 
8.Zhang L, Xu MG, Liu Y, Zhang FS, Hodge A, Feng G. Carbon and phosphorus exchange may enable cooperation between an arbuscular mycorrhizal fungus and a phosphate-solubilizing bacterium. N Phytol. 2016;210:1022–32.CAS 

Google Scholar 
9.Koide RT, Kabir Z. Extraradical hyphae of the mycorrhizal fungus Glomus intraradices can hydrolyse organic phosphate. N Phytol. 2000;148:511–7.CAS 

Google Scholar 
10.Jiang FY, Zhang L, Zhou JC, George TS, Feng G. Arbuscular mycorrhizal fungi enhance mineralisation of organic phosphorus by carrying bacteria along their extraradical hyphae. N Phytol. 2021;230:304–15.CAS 

Google Scholar 
11.Tisserant E, Malbreil M, Kuo A, Kohler A, Symeonidi A, Balestrini R, et al. Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis. Proc Natl Acad Sci USA. 2013;110:20117–22.CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Miyauchi S, Kiss E, Kuo A, Drula E, Kohler A, Sanchez-Garcia M, et al. Large-scale genome sequencing of mycorrhizal fungi provides insights into the early evolution of symbiotic traits. Nat Commun. 2020;11:5125.CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Johansen A, Jakobsen I, Jensen ES. Hyphal transport by a vesicular-arbuscular mycorrhizal fungus of N applied to the soil as ammonium or nitrate. Biol Fert Soils. 1993;16:66–70.CAS 

Google Scholar 
14.Wipf D, Krajinski F, van Tuinen D, Recorbet G, Courty PE. Trading on the arbuscular mycorrhiza market: from arbuscules to common mycorrhizal networks. N Phytol. 2019;223:1127–42.CAS 

Google Scholar 
15.Johansen A, Jensen ES. Transfer of N and P from intact or decomposing roots of pea to barley interconnected by an arbuscular mycorrhizal fungus. Soil Biol Biochem. 1996;28:73–81.CAS 

Google Scholar 
16.Hodge A, Campbell CD, Fitter AH. An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature. 2001;413:297–9.CAS 
PubMed 

Google Scholar 
17.Hodge A, Stewart J, Robinson D, Griffiths BS, Fitter AH. Competition between roots and soil micro-organisms for nutrients from nitrogen-rich patches of varying complexity. J Ecol. 2000;88:150–64.
Google Scholar 
18.Bukovská P, Bonkowski M, Konvalinková T, Beskid O, Hujslová M, Püschel D, et al. Utilization of organic nitrogen by arbuscular mycorrhizal fungi–is there a specific role for protists and ammonia oxidizers? Mycorrhiza. 2018;28:465.PubMed 

Google Scholar 
19.Püschel D, Janoušková M, Hujslová M, Slavíková R, Gryndlerová H, Jansa J. Plant-fungus competition for nitrogen erases mycorrhizal growth benefits of Andropogon gerardii under limited nitrogen supply. Ecol Evol. 2016;6:4332–46.PubMed 
PubMed Central 

Google Scholar 
20.Bukovská P, Rozmoš M, Kotianová M, Gančarčíková K, Dudáš M, Hršelová H, et al. Arbuscular mycorrhiza mediates efficient recycling from soil to plants of nitrogen bound in chitin. Front Microbiol. 2021;12:574060.PubMed 
PubMed Central 

Google Scholar 
21.Bunn RA, Simpson DT, Bullington LS, Lekberg Y, Janos DP. Revisiting the ‘direct mineral cycling’ hypothesis: arbuscular mycorrhizal fungi colonize leaf litter, but why? ISME J. 2019;13:1891–8.PubMed 
PubMed Central 

Google Scholar 
22.Nuccio EE, Hodge A, Pett-Ridge J, Herman DJ, Weber PK, Firestone MK. An arbuscular mycorrhizal fungus significantly modifies the soil bacterial community and nitrogen cycling during litter decomposition. Environ Microbiol. 2013;15:1870–81.CAS 
PubMed 

Google Scholar 
23.Herman DJ, Firestone MK, Nuccio E, Hodge A. Interactions between an arbuscular mycorrhizal fungus and a soil microbial community mediating litter decomposition. FEMS Microbiol Ecol. 2012;80:236–47.CAS 
PubMed 

Google Scholar 
24.Emmett BD, Lévesque-Tremblay V, Harrison MJ. Conserved and reproducible bacterial communities associate with extraradical hyphae of arbuscular mycorrhizal fungi. ISME J. 2021;e-pub ahead of print 1 March 2021; https://doi.org/10.1038/s41396-021-00920-2.25.Trap J, Bonkowski M, Plassard C, Villenave C, Blanchart E. Ecological importance of soil bacterivores for ecosystem functions. Plant Soil. 2016;398:1–24.CAS 

Google Scholar 
26.Jansa J, Hodge A. Swimming, gliding, or hyphal riding? On microbial migration along the arbuscular mycorrhizal hyphal highway and functional consequences thereof. N Phytol. 2021;230:14–6.
Google Scholar 
27.Morin E, Miyauchi S, San Clemente H, Chen ECH, Pelin A, de la Providencia I, et al. Comparative genomics of Rhizophagus irregularis, R. cerebriforme, R. diaphanus and Gigaspora rosea highlights specific genetic features in Glomeromycotina. N Phytol. 2019;222:1584–98.CAS 

Google Scholar 
28.Gil-Cardeza ML, Calonne-Salmon M, Gomez E, Declerck S. Short-term chromium (VI) exposure increases phosphorus uptake by the extraradical mycelium of the arbuscular mycorrhizal fungus Rhizophagus irregularis MUCL 41833. Chemosphere. 2017;187:27–34.CAS 
PubMed 

Google Scholar 
29.Voets L, Dupre de Boulois H, Renard L, Strullu DG, Declerck S. Development of an autotrophic culture system for the in vitro mycorrhization of potato plantlets. FEMS Microbiol Lett. 2005;248:111–8.CAS 
PubMed 

Google Scholar 
30.Kiers ET, Duhamel M, Beesetty Y, Mensah JA, Franken O, Verbruggen E, et al. Reciprocal rewards stabilize cooperation in the mycorrhizal symbiosis. Science. 2011;333:880–2.CAS 
PubMed 

Google Scholar 
31.van’t Padje A, Galvez LO, Klein M, Hink MA, Postma M, Shimizu T, et al. Temporal tracking of quantum-dot apatite across in vitro mycorrhizal networks shows how host demand can influence fungal nutrient transfer strategies. ISME J. 2021;15:435–49.PubMed 

Google Scholar 
32.Gryndler M, Šmilauer P, Püschel D, Bukovská P, Hršelová H, Hujslová M, et al. Appropriate nonmycorrhizal controls in arbuscular mycorrhiza research: a microbiome perspective. Mycorrhiza. 2018;28:435–50.PubMed 

Google Scholar 
33.Jansa J, Šmilauer P, Borovička J, Hršelová H, Forczek ST, Slámová K, et al. Dead Rhizophagus irregularis biomass mysteriously stimulates plant growth. Mycorrhiza. 2020;30:63–77.PubMed 

Google Scholar 
34.Bukovská P, Püschel D, Hršelová H, Jansa J, Gryndler M. Can inoculation with living soil standardize microbial communities in soilless potting substrates? Appl Soil Ecol. 2016;108:278–87.
Google Scholar 
35.Cranenbrouck S, Voets L, Bivort C, Renard L, Strullu DG, Declerck S. Methodologies for in vitro cultivation of arbuscular mycorrhizal fungi with root organs. In: Declerck S, Strullu DG, Fortin JA, (eds.). In vitro culture of mycorrhizas. Berlin: Springer; 2005. p. 341–75. pp
Google Scholar 
36.Ohno T, Zibilske LM. Determination of low concentrations of phosphorus is soil extracts using malachite green. Soil Sci Soc Am J. 1991;55:892–5.CAS 

Google Scholar 
37.Püschel D, Janoušková M, Voříšková A, Gryndlerová H, Vosátka M, Jansa J. Arbuscular mycorrhiza stimulates biological nitrogen fixation in two Medicago spp. through improved phosphorus acquisition. Front Plant Sci. 2017;8:390.PubMed 
PubMed Central 

Google Scholar 
38.Phillips DL, Gregg JW. Uncertainty in source partitioning using stable isotopes. Oecologia 2001;127:171–9.PubMed 

Google Scholar 
39.Perez-Tienda J, Valderas A, Camanes G, Garcia-Agustin P, Ferrol N. Kinetics of NH4+ uptake by the arbuscular mycorrhizal fungus Rhizophagus irregularis. Mycorrhiza. 2012;22:485–91.CAS 
PubMed 

Google Scholar 
40.He XX, Chen YQ, Liu SJ, Gunina A, Wang XL, Chen W, et al. Cooperation of earthworm and arbuscular mycorrhizae enhanced plant N uptake by balancing absorption and supply of ammonia. Soil Biol Biochem. 2018;116:351–9.CAS 

Google Scholar 
41.Hestrin R, Weber PK, Pett-Ridge J, Lehmann J. Plants and mycorrhizal symbionts acquire substantial soil nitrogen from gaseous ammonia transport. New Phytol. 2021;e-pub ahead of print 2 June 2021; https://doi.org/10.1111/nph.1752742.Everett DH, Wynne-Jones WFK. The dissociation of the ammonium ion and the basic strength of ammonia in water. P R Soc Lond A Mat. 1938;169:190–204.CAS 

Google Scholar 
43.Bidondo LF, Colombo R, Bompadre J, Benavides M, Scorza V, Silvani V, et al. Cultivable bacteria associated with infective propagules of arbuscular mycorrhizal fungi. Implications for mycorrhizal activity. Appl Soil Ecol. 2016;105:86–90.
Google Scholar 
44.Cruz AF, Ishii T. Arbuscular mycorrhizal fungal spores host bacteria that affect nutrient biodynamics and biocontrol of soil-borne plant pathogens. Biol Open. 2012;1:52–7.PubMed 

Google Scholar 
45.Scheublin TR, Sanders IR, Keel C, van der Meer JR. Characterisation of microbial communities colonising the hyphal surfaces of arbuscular mycorrhizal fungi. ISME J. 2010;4:752–63.PubMed 

Google Scholar 
46.Toljander JF, Artursson V, Paul LR, Jansson JK, Finlay RD. Attachment of different soil bacteria to arbuscular mycorrhizal fungal extraradical hyphae is determined by hyphal vitality and fungal species. FEMS Microbiol Lett. 2006;254:34–40.CAS 
PubMed 

Google Scholar 
47.Jaderlund L, Arthurson V, Granhall U, Jansson JK. Specific interactions between arbuscular mycorrhizal fungi and plant growth-promoting bacteria: as revealed by different combinations. FEMS Microbiol Lett. 2008;287:174–80.PubMed 

Google Scholar 
48.Larsen J, Jaramillo-Lopez P, Najera-Rincon M, Gonzalez-Esquivel CE. Biotic interactions in the rhizosphere in relation to plant and soil nutrient dynamics. J Soil Sci Plant Nut. 2015;15:449–63.
Google Scholar 
49.Mansfeld-Giese K, Larsen J, Bodker L. Bacterial populations associated with mycelium of the arbuscular mycorrhizal fungus Glomus intraradices. FEMS Microbiol Ecol. 2002;41:133–40.CAS 
PubMed 

Google Scholar 
50.Hildebrandt U, Janetta K, Bothe H. Towards growth of arbuscular mycorrhizal fungi independent of a plant host. Appl Environ Microbiol. 2002;68:1919–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.Cruz AF, Horii S, Ochiai S, Yasuda A, Ishii T. Isolation and analysis of bacteria associated with spores of Gigaspora margarita. J Appl Microbiol. 2008;104:1711–7.CAS 
PubMed 

Google Scholar 
52.Luthfiana N, Inamura N, Tantriani, Sato T, Saito K, Oikawa A, et al. Metabolite profiling of the hyphal exudates of Rhizophagus clarus and Rhizophagus irregularis under phosphorus deficiency. Mycorrhiza. 2021;31:403–12.CAS 
PubMed 

Google Scholar 
53.Oliverio AM, Geisen S, Delgado-Baquerizo M, Maestre FT, Turner BL, Fierer N. The global-scale distributions of soil protists and their contributions to belowground systems. Sci Adv. 2020;6:eaax8787.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Averill C, Turner BL, Finzi AC. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature. 2014;505:543–5.CAS 
PubMed 

Google Scholar 
55.Mäder P, Fliessbach A, Dubois D, Gunst L, Fried P, Niggli U. Soil fertility and biodiversity in organic farming. Science 2002;296:1694–7.PubMed 

Google Scholar 
56.Cavagnaro TR. Biologically regulated nutrient supply systems: compost and arbuscular mycorrhizas—a review. Adv Agron. 2015;129:293–321.
Google Scholar  More

This dataset relies on two types of technical validation: ensuring the accuracy of (1) project attributes and, where applicable, (2) their geographic locations.Project attribute validation: the double-verification methodExisting sources for Chinese overseas development finance rely on a variety of verification standards. The present dataset extends the most stringent approach of the existing “double verification” methods pioneered by the China Africa Research Initiative at the Johns Hopkins University School of Advanced International Studies (SAIS-CARI) to create a harmonized, global standard.The double verification method is based on academic literature showing a tendency to overstate, rather than understate, finance commitments. For example, Ebeke and Ölçer49 show that major infrastructure projects are often timed for announcements to coincide with political campaigns. Regional case studies9,50 show patterns of planners avoiding the publication of projects’ environmental and social risks, but simultaneously maximizing the visibility of the projects and their financial commitments, often before they are finalized. For this reason, earlier datasets have struggled to correctly identify and exclude projects that have been publicized but never materialized, resulting in sometimes significant over-estimations51.The possibility remains of under-counting. As Horn, Reinhart, and Trebesch (2019)15 point out, in reference to “hidden” Chinese finance, many overseas Chinese loans are never fully disclosed. For this reason, we cast the widest possible net for financing commitments and then narrowing those findings by applying the standard of double-verification. It is for this reason also that we perform annual updates, and in each update include previous years’ data, in order to include any additional projects that may not have been disclosed until a much later date.Our aim is to provide the most evidence-based supported data in order to have a more empirical based understanding of Chinese overseas development finance. Erring on the side of caution then, double verification is admittedly a more conservative set of estimates but grants all scholars and stakeholders the confidence that every record in the dataset does indeed exist.Without public reporting by CDB and ExImBank of their lending operations, we are limited to reporting by government (and government-affiliated) sources, academic, civil society, and press reports. The system of double verification ensures accuracy in this context, requiring agreement on the core characteristics of each loan agreement between at least one Chinese source and at least one international source.For China-side verification, we rely on official and quasi-official sources associated with the Chinese government or Chinese Communist Party. We include the following sources:

1.

Chinese government and DFI websites (including CDB.com.cn, ExImBank.gov.cn, and any other source with a domain ending in .gov.cn)

2.

Websites of Chinese embassies abroad

3.

Chinese government or CCP-affiliated press sites:

a.

China Daily, http://www.chinadaily.com.cn

b.

China Global Television Network, https://www.cgtn.com

c.

China News, http://www.chinanews.com

d.

China Plus, http://chinaplus.cri.cn

e.

Guangming Daily, http://www.gmw.cn

f.

People, http://www.people.cn

g.

Xinhua, http://www.xinhuanet.com

For international verification, we rely similarly on government reports, supplemented with academic, civil society, and private press reports. As mentioned above, when differences emerge among sources, we resolve these conflicts by giving government sources top priority, followed by academic sources, civil society sources, and private press sources. Government press sources, such as the Chinese sources listed above, are given the weight of government sources. This method coincides with that of other datasets with double verification7,8,21.Because of the stringency of the double-verification standard used here, we exclude the smallest finance agreements (those below $25 million USD). Excluding these low-level loans necessarily involves a small degree of under-counting. For example, Brautigam et al. (2020)8 show that loans of less than $25 million each comprise just $389 million in total commitments, out of a total of $148 billion in financing commitments by CDB and ExImBank between 2008 and 2018 in Africa: approximately 0.2% of the total. However, including these loans would introduce significant geographic bias toward countries with particularly transparent governments and open media environments. As the purpose of the present effort is to enable more reliable geospatial analysis, the inclusion of this additional activity was not deemed worthy of the cost to the reliability of analysis using it.It is worth comparing these results to those of other datasets for context. Among other independent datasets of Chinese lending, only AidData11,12 and Horn, Reinhart, and Trebesch15 have global coverage, and of those two, only AidData differentiates by lender, allowing a strict comparison. As Fig. 1 shows, AidData includes $463 billion in policy bank loans between 2008 and 2014 that would meet the standard for inclusion in the present dataset if they could be validated. However, in that same time period, our methodology found that only $271 billion of loans could pass the validation standards introduced here.This process of double-verification results in a dataset that excludes some countries that appear in other datasets. For example, in the case of four countries, this process resulted in the present dataset having no loans listed, even though CDB and/or ExImBank loans appear in AidData, the largest global dataset, with loans that would qualify for inclusion here if they could be validated. Those four are: Central African Republic (for which we were unable to find doubly verified validation for the Boali No. 3 hydropower plant project), Dominica (for which we were unable to double verify the source of the loan for rehabilitation of State College), Turkey (whose Turk Telecom was privatized before the loan listed in AidData), and Yemen (for which we were unable to find Chinese validation for the Bajal cement factory project). In addition to these four countries, three others are included in AidData but with no loans of $25 million or more: Burundi, Colombia, and Sierra Leone.As with other researchers in this space7,8,21 we understand that individual projects within such funds can be hidden from public view until the line of credit or framework agreement is renewed or laid down unused. Thus, we include such financing agreements when they are initially drawn up, but then withdraw them from subsequent updates if it comes to light that they were unused. If they are renewed, as lines of credit frequently are, such renewals do not represent new financing but simply a relaxation of the time period for use of the original commitment. For this reason, renewals are not considered separately.Finally, not all projects in this dataset have been completed as of this writing. We have removed all projects that have been publicly cancelled, but ongoing projects with active financing commitments remain, even if construction has not yet begun or has been suspended. For this reason, we refer to each observation as a commitment or agreement, rather than a loan. Funds may or may not have been disbursed as of this writing, but commitments have been made and remain valid. In all, this double-verification process resulted in a final dataset of 857 finance commitments in 93 countries from 2008 through 2019.Location validationOf the 857 finance commitments in the final dataset, 664 have a geographic footprint of some type. These projects – encompassing agriculture, extraction, manufacturing, utilities, infrastructure, and other installations – were located according to the following procedure.Several of the existing datasets listed above include the location of financed projects: AidData, CSIS, Dayant and Pryke, and the World Bank11,13,14,26. Among these datasets, CSIS’ Reconnecting Asia merits special mention, as it displays project locations through embedded Google Maps. For projects originating in this dataset, we queried CSIS for the coordinates in these maps (using code available in R as CSIS_to_coord_str.R on the project repository). For these observations, we used these reported locations as initial estimates, to be visually validated thereafter. For energy projects not listed in these project datasets, we used the following sources for initial estimates of project locations:

Power plants: Global Power Plant Database52.

Coal-fired power plants: Global Energy Monitor53

Fossil fuel pipelines and related infrastructure: Global Fossil Infrastructure Tracker54

For other observations, we developed an API to query Google Maps for the locations of each (available in R as GoogleMaps_OSM_API_query.R on the OSF project repository).For all observations – those included in previous geolocated datasets, those located through querying Google Maps and Open Street Maps, and those with no query response – we validated the locations visually through the use of Google Maps, Open Street Maps, and Open Route Services, as shown in Fig. 3 below.Fig. 3Examples of point, line, and polygon footprints. Left to right: Rehabilitation of Sam Lord’s Castle, Barbados; Soyo-Kapary Electrical Transmission and Transformation Project, Angola; Kirirom III hydropower plant (reservoir), Cambodia.Full size imageThis process represents a significant elevation of requirement needing to be met for projects to be reported as having a precise location, in comparison to previous geocoded datasets. For example, AidData allows projects to be reported at the most precise location category based on the precise boundaries of an area of uncertainty around a project—including populated places or the political seats of geographic areas—rather than the precise point or boundaries of the true project site(s). The resulting high-precision category includes 579 sovereign finance commitments by CDB and ExImBank identified by AidData during our period of study, of which only 105 geotags are associated with specific sites of projects. The remaining projects’ location are defined by the administrative division or the political seats thereof. This is in contrast to the more stringent precision classification scheme in our dataset. Projects marked with a precision code of “1” in the present dataset have all been visually located as site-specific project footprints. The introduction of this new level of precision allows for linear and polygonal projects to be represented with their complete footprints, rather than representative points, which enables a more thorough analysis of environmental risks and impacts, including for example, the impacts of the entire length of a highway or the entire area of a mine. Analysts using this dataset will be able to avoid the under-estimation of environmental impacts necessarily introduced by relying on representative points. Our first such analysis uses these precise footprints to compare location-based social and ecological risks of Chinese overseas development finance to World Bank projects, based on their proximity to the boundaries of national protected areas, possible critical habitats, and indigenous territories48. The dataset also supports holistic environmental analysis of interconnected networks of projects, based on their collective footprints. Yang et al (2021) use these collective footprints to examine the environmental and social sensitivity of Chinese overseas development finance locations, and find that the total footprint is significantly concentrated in more sensitive territory than World Bank projects during the same time period55.To accurately reflect the variety of types of footprints across various types of finance projects, we classified each geolocated observation as a point (or collection of points), line (or collection of discontinuous lines), or polygon (or collection of discontinuous polygons). Points are used for individual buildings or installations. Lines are used for linear infrastructure including roads, rails, power distribution, wired communications networks, and pipelines. Polygons show projects with footprints that are larger than single buildings or installations, with well-defined boundaries, including dam reservoirs, oil and gas fields, and clusters of buildings such as housing or stadium complexes. The distribution of projects among footprint types is listed in Table 4.Table 4 Footprint types.Full size tableA few examples merit further explanation regarding their classification of footprint type. First, wind farms are comprised of turbines along access roads; to accurately show the total geographic footprints, we show them as linear infrastructure comprised of their access roads. In addition, projects with lower levels of geographic precision (at the national level or first/second-level administrative division level) are shown as polygons that encompass these areas, showing the municipal, provincial, or national boundaries48. More

We make the first report of Microsporidia MB in An. gambiae s.s and An. coluzzii following identification of the symbiont in An. arabiensis. This does not only demonstrate the existence of the microsporidian in another predominant malaria vector species in Africa but also extends its incidence from East to West Africa. The prevalence of MB-positive mosquitoes was estimated to be 1.8%, which is within the rate of  More

Field measurementsWe used two sets of field measurements of soil moisture, VPD, and stomatal conductance of maize at the daily scale to illustrate a proof-of-concept for the co-regulation of soil moisture and VPD on stomatal conductance.The first set was measurements from greenhouse experiments of maize (seed: Dekalb hybrid DKC52-04) at Colorado State University during the 2013 growing season (planted on June 10, 2013)49. There were two treatments (well-watered, WW, and water-stressed, WS) with five plants per treatment. The soil of the greenhouse experiments was the air-dried soilless substrate (8.8 kg) consisting of a 1:1.3 by volume ratio of Greens GradeTM, Turface® Quick Dry® and Fafard 2SV in 26 L pots49. The soil moisture measurements came from soil moisture sensors (Decagon5TM sensors) installed in the middle of the pots (~6 inches from top). The greenhouse measurements of leaf-level stomatal conductance and soil moisture were performed in approximately 2-week intervals beginning in the vegetative stage and continuing until plant senescence (DOY 198–199, 210–211, 217–218, 233–234, 247), with 11 replicates for each plant under two treatments (WW and WS). The environmental variables, such as relative humidity and air temperature, were continuously measured in minutes. Other detailed experimental setups can be found in Miner and Bauerle (2017)49.The second set was eddy-covariance measurements of maize cropping systems (seed: Pioneer 33P67/33B51) from 2001 to 2012 at three AmeriFlux sites (US-Ne1, Ne2, and Ne3). US-Ne1 and Ne2 were irrigated sites, with a continuous maize cropping system during 2001–2012 for US-Ne1 and with a maize-soybean rotation cropping system during 2001-2009 and then a continuous maize cropping system during 2010-2012 for US-Ne2. US-Ne3 was rainfed with a maize-soybean rotation cropping system during 2001–2012. The soil at the three AmeriFlux sites was a deep silty clay loam consisting of four soil series: Yutan, Tomek, Filbert, and Filmore. There are three replicates with the soil moisture sensors (theta probes: ML2, Dynamax Inc.) installed horizontally with the profile of soil depth (10, 25, 50, and 100 cm) in the US-Ne1 and US-Ne2, and four replicates with soil moisture sensors (theta probes: ML2, Dynamax Inc.) installed horizontally with the profile of soil depth (10, 25, 50, and 100 cm) in the US-Ne3 (http://csp.unl.edu/public/G_moist.htm). The soil moisture data used here was from the top soil layer (10–25 cm). The canopy-level stomatal conductance (Gs) was derived by inverting the Penman-Monteith equation50 (Equations 1 and 2) from the eddy-covariance measurements at the hourly scale18,24,51, and the averaged value near midday (from 12:00 to 14:00) was applied as the daily canopy-level stomatal conductance to remove the diurnal cycle. This inversion was only conducted during peak growing season (July and August) to avoid the impact of LAI24. The impact of evaporation from canopy interception and of low incoming shortwave radiation was removed by data filtering24, i.e., excluding the data within 2 days following every precipitation and irrigation event, and periods of low incoming shortwave radiation conditions ( More

Mineralogy and thermal propertiesThe bulk mineral composition of sapropels is detailed in Table 1. The XRD analysis indicates that Amara and Tekirghiol sapropels are enriched in silicates, i.e., quartz (30.8% and 29.1% respectively), plagioclase-albite (10.1% and 8.9%), carbonates, mainly calcite (6.8%) and aragonite (13.1%) in Amara, and calcite (8.7%) in Tekirghiol (Fig. 2). By contrast, Ursu sapropel contains lower concentrations of silicates, mainly quartz (15.4%), plagioclase (5.5% albite and 8% andesine), sulfides, i.e., pyrite (1.5%) and is enriched in halite (34.5%). The major clay components in the sapropels were 2:1 dioactahedral and 2:1 trioctahedral clays, representing 28.9%, 23.6% and 20.8% of clay minerals in Tekirghiol, Amara and Ursu samples, respectively. Muscovite was detected in similar concentrations in Tekirghiol (4.5%) and Amara (4.2%). Quantitative mineralogical clay composition of the fraction  90% in each sample), and kaolinite and chlorite as minor fractions (Table 2; Fig. 3).Table 1 Quantitative bulk mineralogical compositions of saline sapropels collected from Tekirghiol, Amara and Ursu lakes.Full size tableFigure 2X-ray diffraction patterns on the raw mud samples (upper image) collected from the three lakes. The main minerals that contribute to the most important reflections are indicated. Chl: Chlorite, M: Muscovite, K: Kaolinite Group minerals, Q: Quartz, A: Anatase, 2:1: 2:1 phyllosilicate (e.g., illite and smectite), Ca: Calcite, Pl: Plagioclase/Albite/Andesine, R: Rutile, P: Pyrite, Ar: Aragonite, H: Halite.Full size imageTable 2 Quantitative mineralogical clay composition of the fraction  More