Ecology

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

Insects and virusOryctes rhinoceros was collected from Amami, Kagoshima, Japan in 2017 and Ishigaki, Okinawa, Japan in 2018. The insects were brought back to the lab in Tokyo and maintained in a moisture mushroom mat substrate (Mushroom Mat, Tsukiyono Kinokoen, Japan) which was also served as food for larvae. The temperature was held at 25–30 °C with a 16-h light / 8-h dark photoperiod. To collect eggs, 2 or 3 female adults were put in a plastic case containing a moisture mushroom mat substrate with a male adult beetle. The insect jelly (Dorcus Jelly, Fujikon, Japan) was provided ad libitum as food for adults. After 2 weeks, we collected eggs, and about 10 eggs were placed in a plastic cup with a moisture mushroom mat substrate until hatched larvae developed to the second instar. This strain was used in all bioassays in this study. All Japanese O. rhinoceros were confirmed as CRB-G.The OrNV-X2B isolate used in this study was originally isolated from Philippine CRB and obtained from AgResearch in New Zealand.Cell culturesFRI-AnCu-35 (AnCu35) cells were obtained from Genebank of NARO (Tsukuba, Japan)27. This continuous cell line was developed from embryos of the cupreous chafer, Anomala cuprea (Coleptera: Scarabaeidae). The cells were maintained as adherent cultures in 25 cm2 tissue culture flasks (Falcon, Corning, USA) at 25 °C in 5 ml of 10% Fetal Bovine Serum (Gibco, Thermo Fisher Scientific, USA) supplemented Grace’s insect medium (Gibco). Cells were passaged in the above culture medium until the cell monolayer reached 70% confluence.DNA extraction and identification of haplotypes in Palauan populationCRB specimens were collected in Palau using pheromone traps containing ethyl 4-methyloctanoate (ChemTica Internacional, Costa Rica). Adults were dissected to collect midgut and gut tissues to avoid cross contamination between dissection of individuals, which were immediately soaked into 0.1 μg/ml gentamicin solution to prevent bacterial contamination during transportation at room temperature. Specimens were stored at − 30 °C after arrival to Tokyo. The tissues were homogenized in cell lysis solution (10 mM Tris–HCl, 100 mM EDTA, 1% SDS, pH 8.0) using pestles in 1.5 ml microcentrifuge tubes. Homogenates were centrifuged at 12,000× g for 5 min at 4 °C. Proteinase K (200 µg/ml final concentration) (Nippon Gene Co. Ltd., Japan) was added to the supernatant and incubated at 50 °C for 5 h. To remove contaminating RNA, RNase A solution (100 µg/ml final concentration) (Nippon Gene Co. Ltd.) was added. After a 30 min incubation at 37 °C, the mixture was placed on ice and supplemented with 200 μl of Protein Precipitation Solution (Qiagen, Germany), and then centrifuged at 17,000× g for 15 min at 4 °C. The supernatant was isopropanol-precipitated, pelleted by centrifugation, and washed with 70% ethanol. Finally, precipitated DNA was dissolved in distilled MilliQ water. The concentrations of each DNA solution were measured by using NanoVue Plus (GE Healthcare, Buckinghamshire, England, UK). The sample DNA was diluted to 10 ng/μl and used for PCR. The following primer pair was used to amplify a 523 bp fragment of the COI gene: C1-J-1718Oryctes (5′-GGAGGTTTCGGAAATTGACTTGTTCC-3′) and C1-N-2191Oryctes (5′-CCAGGTAGAATTAAAATRTATACCTC-3′)9. Each 10 μl PCR reaction contained: 5 μl Emerald Amp (Takara, Japan), 0.3 μl forward primer (10 μM), 0.3 μl reverse primer (10 μM), 3.4 μl Milli-Q water (Merck Millipore, USA), and 1 µl template DNA. PCR amplifications were performed in a Life ECO thermocycler (Bioer Technology, China) with a cycling profile of 35 cycles of 94 °C denaturation (30 s), 50 °C annealing (45 s), 72 °C extension (1 min) with an initial denaturation of 3 min at 94 °C and a final extension of 5 min at 72 °C. A 5 μl aliquot of each PCR amplicon was checked by agarose gel electrophoresis (1.5%, 1 × TBE), stained with Midori green (Nippon Genetics, Japan) and fluorescence visualized over UV light. Photographs were recorded using an E-BOX-VX2 /20 M (E & M, Japan).For direct sequencing, the PCR products were purified using a QIAquick PCR Purification Kit (Qiagen). The purified DNA was sequenced using BigDye Terminator Kit ver. 3.1 (Applied Biosystems, USA) and performed by the 3700 DNA analyzer (Applied Biosystems). The obtained sequences were analyzed using MEGA X software28 and the G haplotype was identified by the presence of the (A→G) point mutation in the COI region as previously described9.Virus detection in Palauan populationUsing the same samples as above, virus detection was carried out by PCR. The following primer pair was used to amplify a 944 bp fragment of the OrNV-gp054 gene (GrBNV-gp83-like protein): OrNV15a (5′-ATTACGTCGTAGAGGCAATC-3′) and OrNV15b (5′-ATGATCGATTCGTCTATGG-3′)29. PCR amplifications were performed as above.Transmission electron microscopy (TEM) was also used for detection of OrNV within a subset of PCR positive CRB tissue samples. After washing in phosphate-buffered saline (PBS), midgut and fat body samples of Palauan CRB adults from Melekeok and Aimeliik (respectively; two each), were subjected to following resin fixation as described previously30: tissues were fixed in 5% glutaraldehyde for 1 h, rinsed 4 times with Millonig’s phosphate buffer (0.18% NaH2PO4 × H2O, 2.33% Na2HPO4 × 7H2O, 0.5% NaCl, pH 7.4), post-fixed and stained in 1% OsO4 for 2 h and dehydrated in an ethanol series. Following the final dehydration step, the ethanol was replaced by QY-1 (Nisshin EM, Tokyo), and the tissues were embedded in epoxy resin comprising 47% TAAB EPON812, 19% DDSA, 32% MNA and 2% DMP30 (Nisshin EM, Tokyo). Then, they were cut into 70 nm thick sections with a diamond knife on an Ultracut N ultramicrotome (Leica, Vienna, Austria), attached to grids and observed using TEM (JEM-1400Plus, JEOL, Japan).Isolation of OrNV from Palauan samples and infectivity to Japanese CRB larvaeVirus isolation was carried out using a modification of a method previously described23. The frozen tissues of two virus positive CRB-G from Melekeok were washed with PBS twice, and after grounding with 1 ml PBS by pestles, centrifuged at 6,000 g × 5 min at 4 °C. The supernatant was filtered by 0.45 µm pore sized filter (Merck, USA) and transferred to a 1.5 ml ultracentrifuge tube in a clean bench. Virus was pelleted by centrifugation at 4 °C, 98,600 g for 30 min using a TLA55 rotor. After separation, the supernatant was discarded and the pellet was suspended in 500 μl of PBS and designated as “virus solution”. A portion of this solution (30 µl/larva) was intrahemocoelically injected into 82nd instar CRB to evaluate its infectivity. This experiment had no biological replicates due to the very small amount of inoculum available. Intrahemocoelically injected larvae were reared in the insect rearing mat at 25 °C for two weeks. Following death, larval cadavers were immediately dissected to collect midgut for following RNA extraction to detect expression of a viral gene, and electron microscopy observation. Total RNA was extracted from larval tissue samples using ISOGEN (Nippon Gene Co. Ltd., Tokyo, Japan), as described in the manufactural protocol. The total RNA samples were treated with RNAse-free recombinant DNAse I (TaKaRa, Japan) to remove the contaminating DNAs. The DNAse I treated total RNA samples (approximately 100 ng/µl) were used as templates for cDNA synthesis using a TaKaRa RNA PCR Kit (AMV) ver. 3.0 (TaKaRa, Japan). PCR reactions were conducted as above using OrNV15a and b primers (detects gene GrBNV-gp83-like gene). This experiment was conducted in triplicate.Inoculum preparation using FRI-AnCu-35 cellsOrNV isolates were propagated using the FRI-AnCu-35 (AnCu35) cell line for further analyses following methods previously described for the DSIR-Ha-1179 cell line system9,12. AnCu35 was a Coleopteran cell line readily available in Japan, and was inoculated with the Palau OrNV solution prepared above and the OrNV-X2B isolate which was provided by AgResearch, New Zealand. When the cell culture reached 25% confluency, a 100 µl aliquot of virus solution was inoculated and incubated at 25 °C. The virus-treated cells were observed by optical microscope.Quantification of viral copy number using qPCR was conducted as follows. To measure the amount of OrNV virus produced by the AnCu35 cell line, DNA was extracted as described above for tissue samples from 1.5 ml of the virus treated cell’s suspension at 10 dpi (3 suspensions per each virus isolate). The extracted DNA was subjected to quantitative PCR (qPCR) following previously described methods31. The primer pair for qPCR was designed from the genome sequence of the P74 homolog of OrNV, a viral structural protein that is conserved widely among nudiviruses, polydnaviruses and baculoviruses32, to amplify a region of 82 bp of OrNV-X2B-gp120 (OrNV-p74_f2026: 5′-ATCGCCGGTGTGTTTATGG-3′, OrNV-p74_r2107: 5′-AGAGGGCTAACGCTACGAC-3′). The qPCR reaction was performed by using Step One Plus Real-Time PCR System (Life Technologies, USA). The reaction mixture contained 10 ng of template DNA, 5 µl of FastStart Universal SYBR Green Master Mix (ROX) (Roche, Switzerland), 0.3 µl forward primer (10 µM), 0.3 µl reverse primer (10 µM), and 3.4 µl Milli-Q water. The qPCR cycle condition was as follows: 95 °C 10 min; 40 cycle of 95 °C 15 s, 60 °C 1 min. At the end of the cycles, a dissociation curve analysis of the amplified product was performed as follows: 95 °C 15 s, 60 °C 1 min, 95 °C 15 s. The Ct value of each sample DNA was measured twice using two wells as technical replicates. The quantity of the viral genome (ng) in each sample was calculated from a standard curve generated from 29.7 to 29.7 × 10–5 ng of purified PCR amplicon from the OrNV P74 gene. The viral copies in 1 ng of sample DNA was estimated from the molecular weight of qPCR target region (p74). The virus titer was determined from average copy numbers of three virus suspensions as follows. The p74 qPCR amplicon was 83 bp, and the molecular weight of the amplicon was calculated as the length of dsDNA (83 bp) × 330 daltons × 2 nt/bp = 54,780 daltons (g/mol). DNA weight of 1 copy of virus genome was calculated as 54,780 g/mol/Avogadro constant (6.023 × 1023 molecules/mol) = 9.095 × 10–20 g/ molecule. Amplicons of the above region was purified by QIA quick PCR purification kit (Qiagen) and 29.7 ng/ul of DNA was obtained for use as a quantification standard. This is equivalent to 3.266 × 1011 copies of p74 gene (because the amplicon is 9.095 × 10–20 g/copy). Based on qPCR using the serial dilutions (× 10 – 105) of the standard DNA prepared above, Ct values were examined by each concentration of viral DNA. Ct-value = − 3.3112x – 1.4219 (x: diluton factor of 10x). Accordingly, copy number of p74 = 3.266 × 1011+x. Viral copy number (copy number of p74 genes) was calculated from Ct-value from the above formula.Viral replication in CRB larvae by time course and killing speedField collected CRB-G larvae from Japan were inoculated with the OrNV-Palau1 and -X2B isolates to examine establishment of infection over time using qPCR. The inoculum was prepared from supernatant collected from OrNV infected AnCu35 cell cultures at 10 dpi, passed through a 0.45 µm filter, and preserved at 4 °C until use.Second instar CRB was inoculated intrahemocoelically with 30 μl of the virus solution prepared from cell-culture per larva using a microinjector (Kiya Kogyo Seisakusho, Japan) fitted with a micro-syringe (Ito Seisakusho, Japan). The virus doses of OrNV-Palau1 and -X2B strains used for inoculation were confirmed to be comparable by absolute quantification using the above qPCR method (Palau1: 3.1 × 105 copies/ng, X2B: 3.3 × 105copies/ng; the mean titer of 3 DNA templates, respectively). As a mock treatment, CRB was injected with 30 µl PBS. The inoculated larvae were kept individually in plastic containers with a rearing mat in a 25 °C incubator. The samples were collected at 3, 6, and 9 dpi (25–30 larvae per time point) into 15 ml tubes and stored at − 30 °C until the DNA was extracted as above. Total DNA was extracted from whole, individual larvae which were dissected to remove midgut contents to prevent interference to Taq polymerase, and subjected to qPCR as above. Changes in viral copy number within the same virus strain over time were analyzed by one-way, nonparametric Steel–Dwass tests using JMP@ 9.0.0 software (SAS Institute, Cary, NC). Differences in virus copy number between strains were analyzed in the same way, but to correct for errors in the test values due to multiple comparisons, Bonferroni’s correction was used to set the α-value for the test at 0.008333. Ten larvae were inoculated and examined per each treatment-time point with three replications.To estimate killing speed, CRB-G larvae from Japan were inoculated with the OrNV-Palau1 and -X2B isolates as described previously. Intrahemocoelically inoculated larvae were reared individually in plastic containers with a rearing mat in a 25 °C incubator. Mortality of inoculated larvae were observed every day. Forty larvae were examined in a replicate with three replications carried out for virus treatments (total 120 larvae). The mock PBS inoculation treatment was done only once (total 37 larvae).Genome sequencingGenome sequencing of the OrNV-Palau1 isolate and X2B isolate was conducted. For obtaining high quality DNA, virus particles were purified, from 3 mL of AnCu35 culture supernatant collected six days after inoculation with OrNV. Virus containing supernatant was transferred to Ultra-Clear polyallomer tubes (Beckman Coulter, USA) with a 20–50% (w/w) sucrose density gradient and subjected to ultracentrifugation at 72,100 g, 4 °C, for 1 h. After ultracentrifugation, the white virus band was collected in a 1.5 ml tube. The solution was then subjected to ultracentrifugation at 110,000 g, 4 °C for 1 h to precipitate the viral particles33. Then, DNA was extracted from purified OrNV virions as described above. For the sequencing analysis, DNA libraries were prepared using the Nextera XT DNA Library Prep Kit (Illumina, USA). Amplified libraries were sequenced on Illumina HiSeq 2500 instrument using paired-end 2 × 150 bp chemistry which was performed by Novogene (Beijing, China). Contigs of each strain from NGS reads were generated by assembly using Unicycler (version 0.4.8)34. The gaps between contigs were further closed with Sanger sequences obtained by PCR direct sequencing using appropriate specific primers, and the sequence was aligned by minimap2 (version 2.17)35. The assembly and sequences of contigs were also confirmed by mapping to the OrNV isolate Solomon Islands genome sequence (GenBank accession no. MN623374.1) with NGS reads and Sanger sequences using minimap2. The mapped reads (SAM files) were converted to BAM format using SAMtools (version 1.10)36. After the sorting and indexing of BAM files, the consensus sequences were generated using bcftools (version 1.10.2)37.ORFs of at least 50 codons in size that possessed significant amino acid sequence similarity with ORFs from OrNV-Ma07 were identified with Lasergene GeneQuest (DNAStar, v. 17) and BLASTp. ORFs with no significant matches to other sequences also were selected for annotation if (a) they did not overlap a larger ORF by  > 75 bp, and (b) they were predicted to be protein-encoding by both the fgenesV0 (http://www.softberry.com/berry.phtml?topic=index&group=programs&subgroup=gfindv) and Vgas38 programs.OrNV genome sequences were compared by pairwise alignment using the Martinez/Needleman-Wunsch method as implemented in Lasergene MegAlign 15. Pairwise sequence identities were determined from these alignments as previously described39. Differences in ORF content and distribution of selected OrNV genomic regions were visualized with Mauve version 2015022640.Phylogenetic inferenceTo infer the relationships among OrNV isolates on the basis of nucleotide sequence alignments, the DNA polymerase ORFs of completely sequenced isolates (Table 2), OrNV-PV50516, and a set of nine isolates from Indonesia17 were aligned by MUSCLE as implemented in Lasergene MegAlign Pro v. 17 (DNAStar). Phylogeny was inferred by maximum likelihood using MEGA X28 with the Tamura-Nei (TN93) model41, with ambiguous data eliminated prior to analysis. Tree reliability was evaluated by bootstrap with 500 replicates. 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

1.Cartwright, P. et al. Exceptionally preserved jellyfishes from the middle Cambrian. PLoS One 2, e1121 (2007).ADS 
Article 

Google Scholar 
2.Walcott, C. D. Cambrian Geology and Paleontology II: No. 3—Middle Cambrian Holothurians and Medusae Vol. 3 (Smithsonian Institution, 1911).
Google Scholar 
3.Willoughby, R. H. & Robison, R. A. Medusoids from the Middle Cambrian of Utah. J. Paleontol. 53, 494–500 (1979).
Google Scholar 
4.Rigby, S. & Milsom, C. V. Origins, evolution, and diversification of zooplankton. Annu. Rev. Ecol. Syst. 31, 293–313 (2000).Article 

Google Scholar 
5.Young, G. A. & Hagadorn, J. W. The fossil record of cnidarian medusae. Palaeoworld 19, 212–221 (2010).Article 

Google Scholar 
6.Technau, U. & Steele, R. E. Evolutionary crossroads in developmental biology: Cnidaria. Development 138, 1447 (2012).Article 

Google Scholar 
7.Hoegh-Guldberg, O., Poloczanska, E. S., Skirving, W. & Dove, S. Coral reef ecosystems under climate change and ocean acidification. Front. Mar. Sci. https://doi.org/10.3389/fmars.2017.00158 (2017).Article 

Google Scholar 
8.Hagadorn, J. W., Dott, R. H. & Damrow, D. Stranded on a Late Cambrian shoreline: Medusae from central Wisconsin. Geology 30, 147–150 (2002).ADS 
Article 

Google Scholar 
9.Boero, F. Review of jellyfish blooms in the Mediterranean and Black Sea. Studies and Reviews. General Fisheries Commission for the Mediterranean, Vol. 92 (FAO, Rome, 2013).10.Brotz, L., Cheung, W., Kleisner, K., Pakhomov, E. & Pauly, D. Increasing jellyfish populations: Trends in large marine ecosystems. Hydrobiologia 690, 3–20 (2012).Article 

Google Scholar 
11.Condon, R. H. et al. Recurrent jellyfish blooms are a consequence of global oscillations. Proc. Natl. Acad. Sci. 110, 1000–1005. https://doi.org/10.1073/pnas.1210920110 (2013).ADS 
Article 
PubMed 

Google Scholar 
12.Arai, M. Pelagic coelenterates and eutrophication: A review. Hydrobiologia 451, 69–87. https://doi.org/10.1023/A:1011840123140 (2001).Article 

Google Scholar 
13.Purcell, J. E., Malej, A. & Benović, A. in Ecosystems at the Land-Sea Margin: Drainage Basin to Coastal Seas Vol. 55 Coastal and Estuarine Studies Ch. 8, 241–263 (American Geophysical Union, 1999).14.Lynam, C. P. et al. Have jellyfish in the Irish Sea benefited from climate change and overfishing?. Glob. Change Biol. 17, 767–782. https://doi.org/10.1111/j.1365-2486.2010.02352.x (2011).ADS 
Article 

Google Scholar 
15.Richardson, A. J., Bakun, A., Hays, G. C. & Gibbons, M. J. The jellyfish joyride: Causes, consequences and management responses to a more gelatinous future. Trends Ecol. Evol. 24, 312–322 (2009).Article 

Google Scholar 
16.Lucas, C. H., Graham, W. M. & Widmer, C. Jellyfish life histories: Role of polyps in forming and maintaining scyphomedusa populations. Adv. Mar. Biol. 63, 133–196 (2012).Article 

Google Scholar 
17.Helm, R. R. Evolution and development of scyphozoan jellyfish. Biol. Rev. 93, 1228–1250 (2018).Article 

Google Scholar 
18.Jarms, G. & Morandini, A. C. World Atlas of Jellyfish (Dölling und Galitz Verlag, Germany, 2019).
Google Scholar 
19.Piraino, S., Boero, F., Aeschbach, B. & Schmid, V. Reversing the life cycle: medusae transforming into polyps and cell transdifferentiation in Turritopsis nutricula (Cnidaria, Hydrozoa). Biol. Bull. 180, 302–312 (1996).Article 

Google Scholar 
20.De Vito, D., Piraino, S., Schmich, J., Bouillon, J. & Boero, F. Evidence of reverse development in Leptomedusae (Cnidaria, Hydrozoa): the case of Laodicea undulata (Forbes and Goodsir 1851). Mar. Biol. 149, 339–346 (2006).Article 

Google Scholar 
21.He, J., Zheng, L., Zhang, W. & Lin, Y. Life cycle reversal in Aurelia sp.1 (Cnidaria, Scyphozoa). PLoS One 10, e0145314 (2015).Article 

Google Scholar 
22.Sandrini, L. R. & Avian, M. Biological cycle of Pelagia noctiluca: Morphological aspects of the development from planula to ephyra. Mar. Biol. 74, 169–174. https://doi.org/10.1007/BF00413920 (1983).Article 

Google Scholar 
23.Jarms, G., Båmstedt, U., Tiemann, H., Martinussen, M. B. & Fosså, J. H. The holopelagic life cycle of the deep-sea medusa Periphylla periphylla (Scyphozoa, Coronatae). Sarsia 84, 55–65 (1999).Article 

Google Scholar 
24.Dawson, M. N. & Hamner, W. M. A character-based analysis of the evolution of jellyfish blooms: Adaptation and exaptation. Hydrobiologia 616, 193–215. https://doi.org/10.1007/s10750-008-9591-x (2009).Article 

Google Scholar 
25.Ceh, J., Gonzalez, J., Pacheco, A. S. & Riascos, J. M. The elusive life cycle of scyphozoan jellyfish—Metagenesis revisited. Sci. Rep. 5, 12037. https://doi.org/10.1038/srep12037. http://www.nature.com/srep/2015/150708/srep12037/abs/srep12037.html#supplementary-information (2015).26.Campos, L., Gonzállez, K. & Ceh, J. First report of a precocious form of strobilation in a jellyfish, the South American Pacific sea nettle Chrysaora plocamia. Mar. Biodivers. 50, 85 (2020).Article 

Google Scholar 
27.Henroth, L. & Grondähl, F. On the biology of Aurelia aurita (L.) 1. Release and growth of Aurelia aurita (L.) ephyrae in the Gullmar Fjiord, western Sweden, 1982–83. Ophelia 22, 189–199 (1983).Article 

Google Scholar 
28.Hirai, E. On the developmental cycles of Aurelia aurita and Dactylometra pacifica. Bull. Mar. Biol. Stn Asamushi IX, 81 (1958).
Google Scholar 
29.Kakinuma, Y. An experimental study of the life cycle and organ differentiation of Aurelia aurita Lamarck. Bull. Mar. Biol. Stn. Asamushi XV, 101–113 (1975).
Google Scholar 
30.Yasuda, T. Ecological studies on the jelly-fish, Aurelia aurita, in Urazoko Bay, Fukui Prefecture-XI. An observation on ephyra formation. Publ. Seto Mar. Biol. Lab. XXII, 75–80 (1975).Article 

Google Scholar 
31.Suzuki, K. S. et al. Seasonal alternation of the ontogenetic development of the moon jellyfish Aurelia coerulea in Maizuru Bay, Japan. PLoS One 14, e0225513. https://doi.org/10.1371/journal.pone.0225513 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Avian, M. In Workshop on Jellyfish in the Mediterranean Sea Vol. 2 (eds Rottini Sandrini, L. & Avian, M.) 47–59 (Nova Thalassia, 1986).
Google Scholar 
33.Costello, J. et al. Project Meduza in the context of its historical time. Ann. Ser. Hist. Nat. 19, 1–18 (2009).
Google Scholar 
34.Margiotta, F. et al. Do plankton reflect the environmental quality status? The case of a post-industrial Mediterranean Bay. Mar. Environ. Res. 160, 104980 (2020).CAS 
Article 

Google Scholar 
35.Schiariti, A. et al. Asexual reproduction strategies and blooming potential in Scyphozoa. Mar. Ecol. Prog. Ser. 510, 241–253 (2014).ADS 
Article 

Google Scholar 
36.Yasuda, T. Ecological studies on the jelly-fish, Aurelia aurita, in Urazoko Bay, Fukui Prefecture-IV. Monthly change in the bell-length composition and breeding season. Bull. Jpn. Soc. Sci. Fish. 37, 364–370 (1971).Article 

Google Scholar 
37.Suryan, R. M. et al. Environmental forcing on life history strategies: Evidence for multi-trophic level responses at ocean basin scales. Prog. Oceanogr. 81, 214–222 (2009).ADS 
Article 

Google Scholar 
38.Dawson, M. N. Macro-morphological variation among cryptic species of the moon jellyfish, Aurelia (Cnidaria: Scyphozoa). Mar. Biol. 143, 369–379 (2003).Article 

Google Scholar 
39.Benović, A. et al. Ecological characteristics of the Mljet Island seawater lakes (South Adriatic Sea) with special reference to their resident population of medusae. Sci. Mar. 64, 197–206 (2000).Article 

Google Scholar 
40.Prieto, L., Astorga, D., Navarro, G. & Ruiz, J. Environmental control of phase transition and polyp survival of a massive-outbreaker jellyfish. PLoS One 5, e13793. https://doi.org/10.1371/journal.pone.0013793 (2010).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
41.Purcell, J. et al. Temperature effects on asexual reproduction rates of scyphozoan polyps from the NW Mediterranean Sea. Hydrobiologia 690, 169–180 (2012).CAS 
Article 

Google Scholar 
42.Kikinger, R. Cotylorhiza tuberculata (Cnidaria: Scyphozoa)—Life history of a stationary population. PSZN Mar. Ecol. 13, 333–362 (1992).Article 

Google Scholar 
43.Djeghri, N., Pondaven, P., Stibor, H. & Dawson, M. N. Review of the diversity, traits, and ecology of zooxanthellate jellyfishes. Mar. Biol. 166, 147 (2019).Article 

Google Scholar 
44.Glynn, P. W. & Colgan, M. W. Sporadic disturbances in fluctuating coral reef environments: El Niño and coral reef development in the Eastern Pacific. Am. Zool. 32, 707–718. https://doi.org/10.1093/icb/32.6.707 (1999).Article 

Google Scholar  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

1.Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Esparza-Olguin, L. & Mendoza-Vega, J. Carbon stocks and changes in tropical secondary forests of southern Mexico. Agr. Ecosyst. Environ. 195, 220–230 (2014).Article 

Google Scholar 
2.Aryal, D. R., De Jong, B. H., Ochoa-Gaona, S., Mendoza-Vega, J. & Esparza-Olguin, L. Successional and seasonal variation in litterfall and associated nutrient transfer in semi-evergreen tropical forests of SE Mexico. Nutr. Cycl. Agroecosys. 103(1), 45–60 (2015).CAS 
Article 

Google Scholar 
3.Aryal, D. R. et al. Soil organic carbon depletion from forests to grasslands conversion in Mexico: A review. Trop. Agric. 8, 181 (2018).CAS 

Google Scholar 
4.Gao, W., Yang, J., Ren, S. R. & Liu, H. L. The trend of soil organic carbon, total nitrogen, and wheat and maize productivity under different long-term fertilizations in the upland fluvo-aquic soil of North China. Nutr. Cycl. Agroecosys. 103, 61–73 (2015).CAS 
Article 

Google Scholar 
5.Qi, H., Paz-Kagan, T., Karnieli, A., Jin, X. & Li, S. Evaluating calibration methods for predicting soil available nutrients using hyperspectral VNIR data. Soil Till Res. 175, 267–275 (2018).Article 

Google Scholar 
6.Dong, X., Tian, J., Zhang, R., He, D. & Chen, Q. Study on the relationship between soil emissivity spectra and content of soil element. Spectrosc. Spect. Anal. 37(02), 557–564 (2017).CAS 

Google Scholar 
7.Kemper, T. & Sommer, S. Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy. Environ. Sci. Technol. 36(12), 2742–2747 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Panigrahi, N. & Das, B. S. Canopy spectral reflectance as a predictor of soil water potential in rice. Water Resour. Res. 54(4), 2544–2560 (2018).ADS 
Article 

Google Scholar 
9.Peddle, D. R., White, H. P., Soffer, R. J., Miller, J. R. & LeDrew, E. F. Reflectance processing of remote sensing spectroradiometer data. Comput. Geoences. 27(2), 203–213 (2001).ADS 

Google Scholar 
10.Ben-Dor, E. et al. Using imaging spectroscopy to study soil properties. Remote Sens. Environ. 113, S38–S55 (2009).Article 

Google Scholar 
11.Rossel, R. A., Walvoort, D. J., Mcbratney, A. B., Janik, L. J. & Skjemstad, J. O. Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131(1), 59–75 (2006).ADS 
Article 
CAS 

Google Scholar 
12.Cheng, H. et al. Estimating heavy metal concentrations in suburban soils with reflectance spectroscopy. Geoderma 336, 59–67 (2019).ADS 
CAS 
Article 

Google Scholar 
13.Ding, J., Yang, A., Wang, J., Sagan, V. & Yu, D. Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy. PeerJ 6(3), e5714 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
14.Gobrecht, A., Bendoula, R., Roger, J.-M. & Bellon-Maurel, V. A new optical method coupling light polarization and vis–NIR spectroscopy to improve the measurement of soil carbon content. Soil Till Res. 155, 461–470 (2016).Article 

Google Scholar 
15.Gu, X., Wang, Y., Song, X. & Xu, X. The Inversion Model of Soil Organic Matter of Cultivated Land Based on Hyperspectral Technology. Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII (International Society for Optics and Photonics, 2015).
Google Scholar 
16.Nawar, S., Buddenbaum, H., Hill, J., Kozak, J. & Mouazen, A. M. Estimating the soil clay content and organic matter by means of different calibration methods of vis-NIR diffuse reflectance spectroscopy. Soil Till Res. 155, 510–522 (2016).Article 

Google Scholar 
17.Yu, X., Liu, Q., Wang, Y., Liu, X. & Liu, X. Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. CATENA 137, 340–349 (2016).CAS 
Article 

Google Scholar 
18.Ji, W. J., Li, X., Li, C. X., Zhou, Y. & Shi, Z. Using different data mining algorithes to predict soil organic matter based on visible-near infrared spectroscopy. Spectrosc. Spect. Anal. 32(09), 2393–2397 (2012).CAS 

Google Scholar 
19.Douglas, R. K., Nawar, S., Alamar, M. C., Mouazen, A. M. & Coulon, F. Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques. Sci. Total Environ. 616, 147–155 (2018).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
20.Mouazen, A. M. & Al-Asadi, R. A. Influence of soil moisture content on assessment of bulk density with combined frequency domain reflectometry and visible and near infrared spectroscopy under semi field conditions. Soil Till Res. 176, 95–103 (2018).Article 

Google Scholar 
21.Rossel, R. A. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158(1), 46–54 (2010).ADS 
CAS 
Article 

Google Scholar 
22.Nawar, S. & Mouazen, A. M. Comparison between random forests, artificial neural networks and gradient boosted machines methods of on-line vis-NIR spectroscopy measurements of soil total nitrogen and total carbon. Sensors. 17(10), 2428 (2017).ADS 
PubMed Central 
Article 
CAS 

Google Scholar 
23.Wang, J., Chen, Y., Chen, F., Shi, T. & Wu, G. Wavelet-based coupling of leaf and canopy reflectance spectra to improve the estimation accuracy of foliar nitrogen concentration. Agr. Forest Meteorol. 248, 306–315 (2018).ADS 
Article 

Google Scholar 
24.Hong, Y. et al. Combining fractional order derivative and spectral variable selection for organic matter estimation of homogeneous soil samples by vis–NIR spectroscopy. Remote Sens. 10(3), 479 (2018).ADS 
Article 

Google Scholar 
25.Sorenson, P. T. et al. Monitoring organic carbon, total nitrogen, and pH for reclaimed soils using field reflectance spectroscopy. Can J Soil Sci. 97(2), 241–248 (2017).CAS 
Article 

Google Scholar 
26.Gomez, C., Rossel, R. A. V. & Mcbratney, A. B. Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study. Geoderma 146(3–4), 403–411 (2008).ADS 
CAS 
Article 

Google Scholar 
27.Shi, T. Z. et al. Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy. Plant Soil. 366(1–2), 363–375 (2013).CAS 
Article 

Google Scholar 
28.Stenberg, B., Rossel, R. A. V., Mouazen, A. M. & Wetterlind, J. Chapter five-visible and near infrared spectroscopy in soil science. Adv. Agron. 107, 163–215 (2010).CAS 
Article 

Google Scholar 
29.Uddin, M. P., Mamun, M. A. & Hossain, M. A. PCA-based feature reduction for hyperspectral remote sensing image classification. IETE Tech. Rev. 5, 1–21 (2020).
Google Scholar 
30.Cambule, A. H., Rossiter, D. G., Stoorvogel, J. J. & Smaling, E. M. A. Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park Mozambique. Geoderma 183, 41–48 (2012).ADS 
Article 
CAS 

Google Scholar 
31.Kawamura, K. et al. Vis-NIR spectroscopy and PLS regression with waveband selection for estimating the total C and N of paddy soils in Madagascar. Remote Sens. 9(10), 1081 (2017).ADS 
Article 

Google Scholar 
32.Leone, A. P., Viscarra-Rossel, R. A., Amenta, P. & Buondonno, A. Prediction of soil properties with PLSR and vis-NIR spectroscopy: Application to mediterranean soils from southern Italy. Curr. Anal. Chem. 8(2), 283–299 (2012).CAS 
Article 

Google Scholar 
33.Wang, S., Chen, Y., Wang, M., Zhao, Y. & Li, J. SPA-based methods for the quantitative estimation of the soil salt content in saline-alkali land from field spectroscopy data: A case study from the Yellow River irrigation regions. Remote Sens. 11(8), 967 (2019).ADS 
CAS 
Article 

Google Scholar 
34.Barnes, E. M. et al. Remote- and ground-based sensor techniques to map soil properties. Photogramm. Eng Rem S. 69(6), 619–630 (2003).Article 

Google Scholar 
35.Priori, S. et al. Field-scale mapping of soil carbon stock with limited sampling by coupling gamma-ray and vis-NIR spectroscopy. Soil Sci Soc Am J. 80(4), 954–964 (2016).ADS 
CAS 
Article 

Google Scholar 
36.Amin, I., Fikrat, F., Mammadov, E. & Babayev, M. Soil organic carbon prediction by vis-NIR spectroscopy: Case study the Kur-Aras plain Azerbaijan. Commun. Soil Sci. Plan. 51(6), 726–734 (2020).CAS 
Article 

Google Scholar 
37.Yu, L. et al. Hyperspectral estimation of soil organic matter content based on partial least squares regression. Trans. CSAE. 31(14), 103–109 (2015).
Google Scholar 
38.Liu, Y. F., Lu, Y. N., Guo, L., Xiao, F. T. & Chen, Y. Y. Construction of calibration set based on the land use types in visible and near-infrared (VIS-NIR)model for soil organic matter estimation. Acta Pedol. Sin. 53, 332–341 (2016).
Google Scholar 
39.Zhou, X. M. & Zhang, T. Analysis of the April 2019 atmospheric circulation and weather. Meteor. Mon. 45(7), 1028–1036 (2019).
Google Scholar 
40.Guan, L. & Zhang, T. Analysis of the May 2019 atmospheric circulation and weather. Meteor. Mon. 45(8), 1181–1188 (2019).
Google Scholar 
41.Li, X., He, Y. & Wu, C. Non-destructive discrimination of paddy seeds of different storage age based on Vis/NIR spectroscopy. J. Stored Prod. Res. 44(3), 264–268 (2008).Article 

Google Scholar 
42.Boško, M. & Bensa, A. Prediction of soil organic carbon using VIS-NIR spectroscopy: Application to Red Mediterranean soils from Croatia. Eurasian J. Soil Sci. 6(4), 365–373 (2017).
Google Scholar 
43.McCarty, G. W., Reeves, J. B. III., Reeves, V. B., Follett, R. F. & Kimble, J. M. Mid-infrared and near-infrared diffuse reflectance spectroscopy for soil carbon measurement. Soil Sci. Soc. Am. J. 66(2), 640–646 (2002).ADS 
CAS 
Article 

Google Scholar 
44.Gholizadeh, A. et al. Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil Water Res. 10(4), 218–227 (2015).CAS 
Article 

Google Scholar 
45.Wang, X., Xue, L., He, X. W. & Liu, M. H. Vitamin C content estimation of chilies using Vis/NIR spectroscopy. Int. Conf. Electr. Inf. Control Eng. 2011, 1894–1897 (2011).
Google Scholar 
46.Lee, K. S. et al. Wavelength identification and diffuse reflectance estimation for surface and profile soil properties. Am. Soc. Agric. Biol. Eng. 52(3), 683–695 (2009).CAS 

Google Scholar  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