Philippa Kaur

Schwartz, J. J. & Bee, M. A. in Animal communication and noise (ed Henrik Brumm) 91–132 (Springer, 2013).Wollerman, L. Acoustic interference limits call detection in a Neotropical frog Hyla ebraccata. Anim. Behav. 57, 529–536. https://doi.org/10.1006/anbe.1998.1013 (1999).CAS 
Article 
PubMed 

Google Scholar 
Gerhardt, H. C. & Schwartz, J. J. Interspecific interactions in anuran courtship. Amphib. Biol. 2, 603–632 (1995).
Google Scholar 
Gröning, J. & Hochkirch, A. Reproductive interference between animal species. Q. Rev. Biol. 83, 257–282 (2008).Article 

Google Scholar 
Popp, J. W., Ficken, R. W. & Reinartz, J. A. Short-term temporal avoidance of interspecific acoustic interference among forest birds. Auk 102, 744–748. https://doi.org/10.1093/auk/102.4.744 (1985).Article 

Google Scholar 
Luther, D. A. Signaller: Receiver coordination and the timing of communication in Amazonian birds. Biol. Let. 4, 651–654 (2008).Article 

Google Scholar 
Brumm, H. Signalling through acoustic windows: nightingales avoid interspecific competition by short-term adjustment of song timing. J. Comp. Physiol. A. 192, 1279–1285 (2006).Article 

Google Scholar 
Farina, A. Soundscape ecology: principles, patterns, methods and applications. (Springer, 2013).Krause, B. L. The niche hypothesis: a virtual symphony of animal sounds, the origins of musical expression and the health of habitats. Soundscape Newsl. 6, 6–10 (1993).
Google Scholar 
Littlejohn, M. & Martin, A. Acoustic interaction between two species of leptodactylid frogs. Anim. Behav. 17, 785–791. https://doi.org/10.1016/S0003-3472(69)80027-8 (1969).Article 

Google Scholar 
Ficken, R. W., Ficken, M. S. & Hailman, J. P. Temporal pattern shifts to avoid acoustic interference in singing birds. Science 183, 762–763. https://doi.org/10.1126/science.183.4126.762 (1974).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Sinsch, U., Lümkemann, K., Rosar, K., Schwarz, C. & Dehling, M. Acoustic niche partitioning in an anuran community inhabiting an Afromontane wetland (Butare, Rwanda). Afr. Zool. 47, 60–73 (2012).Article 

Google Scholar 
Lima, M., Pederassi, J., Pineschi, R. & Barbosa, D. Acoustic niche partitioning in an anuran community from the municipality of Floriano, Piauí Brazil. Brazil. J. Biol. 79, 566–576 (2019).CAS 
Article 

Google Scholar 
Gottsberger, B. & Gruber, E. Temporal partitioning of reproductive activity in a neotropical anuran community. J. Trop. Ecol. 1, 271–280 (2004).Article 

Google Scholar 
Villanueva-Rivera, L. J. Eleutherodactylus frogs show frequency but no temporal partitioning: Implications for the acoustic niche hypothesis. PeerJ 2, e496 (2014).Article 

Google Scholar 
Bignotte-Giró, I. & López-Iborra, G. M. Acoustic niche partitioning in five Cuban frogs of the genus Eleutherodactylus. Amphibia-Reptilia 40, 1–11 (2019).Article 

Google Scholar 
Hödl, W. Call differences and calling site segregation in anuran species from Central Amazonian floating meadows. Oecologia 28, 351–363 (1977).ADS 
Article 

Google Scholar 
Schmidt, A. K., Römer, H. & Riede, K. Spectral niche segregation and community organization in a tropical cricket assemblage. Behav. Ecol. 24, 470–480. https://doi.org/10.1093/beheco/ars187 (2013).Article 

Google Scholar 
Gotelli, N. J. & Graves, G. R. Null models in ecology. (1996).Chek, A. A., Bogart, J. P. & Lougheed, S. C. Mating signal partitioning in multi-species assemblages: A null model test using frogs. Ecol. Lett. 6, 235–247 (2003).Article 

Google Scholar 
Tobias, J. A., Planqué, R., Cram, D. L. & Seddon, N. Species interactions and the structure of complex communication networks. Proc. Natl. Acad. Sci. 111, 1020–1025. https://doi.org/10.1073/pnas.1314337111 (2014).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sugai, L. S., Llusia, D., Siqueira, T. & Silva, T. S. Revisiting the drivers of acoustic similarities in tropical anuran assemblages. Ecology, e03380 (2021).Hart, P. J. et al. Acoustic niche partitioning in two tropical wet forest bird communities. bioRxiv (2020).Duellman, W. E. & Trueb, L. Biology of amphibians. (McGraw-Hill Book Company, 1986).Wells, K. D. The social behaviour of anuran amphibians. Anim. Behav. 25, 666–693. https://doi.org/10.1016/0003-3472(77)90118-X (1977).Article 

Google Scholar 
Woinarski, J., Fisher, A. & Milne, D. Distribution patterns of vertebrates in relation to an extensive rainfall gradient and variation in soil texture in the tropical savannas of the Northern Territory, Australia. J. Trop. Ecol. 1, 381–398 (1999).Article 

Google Scholar 
Allen-Ankins, S. & Schwarzkopf, L. Spectral overlap and temporal avoidance in a tropical savannah frog community. Anim. Behav. 180, 1–11. https://doi.org/10.1016/j.anbehav.2021.07.024 (2021).Article 

Google Scholar 
Gerhardt, H. C. The evolution of vocalization in frogs and toads. Ann. Rev. Ecol. Syst. 1, 293–324 (1994).Article 

Google Scholar 
Rowley, J. J. & Callaghan, C. T. The FrogID dataset: expert-validated occurrence records of Australia’s frogs collected by citizen scientists. ZooKeys 912, 139 (2020).Article 

Google Scholar 
Zelick, R. & Narins, P. M. Characterization of the advertisement call oscillator in the frogEleutherodactylus coqui. J. Comp. Physiol. A. 156, 223–229 (1985).Article 

Google Scholar 
Schwartz, J. J. & Wells, K. D. An experimental study of acoustic interference between two species of neotropical treefrogs. Anim. Behav. 31, 181–190. https://doi.org/10.1016/S0003-3472(83)80187-0 (1983).Article 

Google Scholar 
Smith, M. J. & Hunter, D. Temporal and geographic variation in the advertisement call of the booroolong frog (Litoria booroolongensis: Anura: Hylidae). Ethology 111, 1103–1115 (2005).Article 

Google Scholar 
Baraquet, M., Grenat, P. R., Salas, N. E. & Martino, A. L. Geographic variation in the advertisement call of Hypsiboas cordobae (Anura, Hylidae). Acta ethologica 18, 79–86 (2015).Ziegler, L., Arim, M. & Bozinovic, F. Intraspecific scaling in frog calls: The interplay of temperature, body size and metabolic condition. Oecologia 181, 673–681 (2016).ADS 
Article 

Google Scholar 
Navas, C. A. & Bevier, C. R. Thermal dependency of calling performance in the eurythermic frog Colostethus subpunctatus. Herpetologica, 384–395 (2001).Lougheed, S. C., Austin, J. D., Bogart, J. P., Boag, P. T. & Chek, A. A. Multi-character perspectives on the evolution of intraspecific differentiation in a neotropical hylid frog. BMC Evol. Biol. 6, 1–16 (2006).Article 

Google Scholar 
Littlejohn, M. Premating isolation in the Hyla ewingi complex (Anura: Hylidae). Evolution, 234–243 (1965).Lemmon, E. M. Diversification of conspecific signals in sympatry: geographic overlap drives multidimensional reproductive character displacement in frogs. Evolution: International Journal of Organic Evolution 63, 1155–1170 (2009).Jansen, M., Plath, M., Brusquetti, F. & Ryan, M. J. Asymmetric frequency shift in advertisement calls of sympatric frogs. Amphibia-Reptilia 37, 137–152 (2016).Article 

Google Scholar 
Jang, Y. & Gerhardt, H. Divergence in the calling songs between sympatric and allopatric populations of the southern wood cricket Gryllus fultoni (Orthoptera: Gryllidae). J. Evol. Biol. 19, 459–472 (2006).CAS 
Article 

Google Scholar 
Both, C. & Grant, T. Biological invasions and the acoustic niche: The effect of bullfrog calls on the acoustic signals of white-banded tree frogs. Biol. Let. 8, 714–716 (2012).Article 

Google Scholar 
Hopkins, J. M., Edwards, W., Laguna, J. M. & Schwarzkopf, L. An endangered bird calls less when invasive birds are calling. J. Avian Biol. 52, 1 (2021).Article 

Google Scholar 
Medeiros, C. I., Both, C., Grant, T. & Hartz, S. M. Invasion of the acoustic niche: variable responses by native species to invasive American bullfrog calls. Biol. Invasions 19, 675–690 (2017).Article 

Google Scholar 
Wilczynski, W. & Ryan, M. J. in Geographic Variation in Behavior (eds S. A. Foster & J. A. Endler) 234–261 (Oxford University Press, 1999).Schwartz, J. J. & Gerhardt, H. C. Spatially mediated release from auditory masking in an anuran amphibian. J. Comp. Physiol. A. 166, 37–41 (1989).Article 

Google Scholar 
da Silveira Vasconcelos, T. & de Cerqueira Rossa-Feres, D. Habitat heterogeneity and use of physical and acoustic space in anuran communities in Southeastern Brazil. Phyllomedusa J. Herpetol. 7, 127–142 (2008).Herrick, S. Z., Wells, K. D., Farkas, T. E. & Schultz, E. T. Noisy neighbors: Acoustic interference and vocal interactions between two syntopic species of Ranid frogs, Rana clamitans and Rana catesbeiana. J. Herpetol. 52, 176–184. https://doi.org/10.1670/17-049 (2018).Article 

Google Scholar 
Rowley, J. J. et al. FrogID: citizen scientists provide validated biodiversity data on frogs of Australia. Herpetol. Conserv. Biol. 14, 155–170 (2019).
Google Scholar 
Koehler, J. et al. The use of bioacoustics in anuran taxonomy: theory, terminology, methods and recommendations for best practice. Zootaxa 4251, 1–124 (2017).Article 

Google Scholar 
Tonini, J. F. R. et al. Allometric escape from acoustic constraints is rare for frog calls. Ecol. Evol. 10, 3686–3695. https://doi.org/10.1002/ece3.6155 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Anstis, M. et al. Revision of the water-holding frogs, Cyclorana platycephala (Anura: Hylidae), from arid Australia, including a description of a new species. Zootaxa 4126, 451–479 (2016).Article 

Google Scholar 
Cardoso, G. C. Using frequency ratios to study vocal communication. Anim. Behav. 85, 1529–1532 (2013).Article 

Google Scholar 
Narins, P. & Zelick, R. in The evolution of the amphibian auditory system (eds B Fritzsch et al.) 511–536 (John Wiley and Sons, 1988).Amézquita, A., Flechas, S. V., Lima, A. P., Gasser, H. & Hödl, W. Acoustic interference and recognition space within a complex assemblage of dendrobatid frogs. Proc. Natl. Acad. Sci. 108, 17058–17063 (2011).ADS 
Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).Article 

Google Scholar 
Kassambara, A. & Mundt, F. factoextra: extract and visualize the results of multivariate data analyses. R package version 1.0.7 (2020). More

Luo H, Moran MA. Evolutionary ecology of the marine Roseobacter clade. Microbiol Mol Biol Rev. 2014;78:573–87.PubMed 
PubMed Central 

Google Scholar 
Wietz M, Gram L, Jørgensen B, Schramm A. Latitudinal patterns in the abundance of major marine bacterioplankton groups. Aquat Microbial Ecol. 2010;61:179–89.
Google Scholar 
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.PubMed 

Google Scholar 
González JM, Simó R, Massana R, Covert JS, Casamayor EO, Pedrós-Alió C, et al. Bacterial community structure associated with a dimethylsulfoniopropionate-producing North Atlantic algal bloom. Appli Environ Microbiol. 2000;66:4237–46.
Google Scholar 
González JM, Moran MA. Numerical dominance of a group of marine bacteria in the alpha-subclass of the class Proteobacteria in coastal seawater. Appl Environ Microbiol. 1997;63:4237–42.PubMed 
PubMed Central 

Google Scholar 
Grossart HP, Levold F, Allgaier M, Simon M, Brinkhoff T. Marine diatom species harbour distinct bacterial communities. Environ Microbiol. 2005;7:860–73.CAS 
PubMed 

Google Scholar 
Amin SA, Parker MS, Armbrust EV. Interactions between diatoms and bacteria. Microbiol Mol Biol Rev. 2012;76:667–84.CAS 
PubMed 
PubMed Central 

Google Scholar 
Alavi M, Miller T, Erlandson K, Schneider R, Belas R. Bacterial community associated with Pfiesteria‐like dinoflagellate cultures. Environ Microbiol. 2001;3:380–96.CAS 
PubMed 

Google Scholar 
Jasti S, Sieracki ME, Poulton NJ, Giewat MW, Rooney-Varga JN. Phylogenetic diversity and specificity of bacteria closely associated with Alexandrium spp. and other phytoplankton. Appl Environ Microbiol. 2005;71:3483–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zubkov MV, Fuchs BM, Archer SD, Kiene RP, Amann R, Burkill PH. Rapid turnover of dissolved DMS and DMSP by defined bacterioplankton communities in the stratified euphotic zone of the North Sea. Deep Sea Res Top Stud Oceanogr. 2002;49:3017–38.CAS 

Google Scholar 
Zubkov MV, Fuchs BM, Archer SD, Kiene RP, Amann R, Burkill PH. Linking the composition of bacterioplankton to rapid turnover of dissolved dimethylsulphoniopropionate in an algal bloom in the North Sea. Environ Microbiol. 2001;3:304–11.CAS 
PubMed 

Google Scholar 
Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
PubMed 

Google Scholar 
Voget S, Wemheuer B, Brinkhoff T, Vollmers J, Dietrich S, Giebel H-A, et al. Adaptation of an abundant Roseobacter RCA organism to pelagic systems revealed by genomic and transcriptomic analyses. ISME J. 2015;9:371–84.CAS 
PubMed 

Google Scholar 
Billerbeck S, Wemheuer B, Voget S, Poehlein A, Giebel H-A, Brinkhoff T, et al. Biogeography and environmental genomics of the Roseobacter-affiliated pelagic CHAB-I-5 lineage. Nat Microbiol. 2016;1:16063.CAS 
PubMed 

Google Scholar 
Buchan A, LeCleir GR, Gulvik CA, González JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
PubMed 

Google Scholar 
Amin S, Hmelo L, van Tol H, Durham B, Carlson L, Heal K, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98–101.CAS 
PubMed 

Google Scholar 
Landa M, Burns AS, Durham BP, Esson K, Nowinski B, Sharma S, et al. Sulfur metabolites that facilitate oceanic phytoplankton–bacteria carbon flux. ISME J. 2019;13:2536–50.CAS 
PubMed 
PubMed Central 

Google Scholar 
Durham BP, Boysen AK, Carlson LT, Groussman RD, Heal KR, Cain KR, et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat Microbiol. 2019;4:1706–15.CAS 
PubMed 

Google Scholar 
Howard EC, Henriksen JR, Buchan A, Reisch CR, Bürgmann H, Welsh R, et al. Bacterial taxa that limit sulfur flux from the ocean. Science. 2006;314:649–52.CAS 
PubMed 

Google Scholar 
Levine NM, Toole DA, Neeley A, Bates NR, Doney SC, Dacey JW. Revising upper-ocean sulfur dynamics near Bermuda: new lessons from 3 years of concentration and rate measurements. Environ Chem. 2016;13:302–13.CAS 

Google Scholar 
Kiene RP. Turnover of dissolved DMSP in estuarine and shelf waters of the northern Gulf of Mexico. In Biological and environmental chemistry of DMSP and related sulfonium compounds. Boston, MA: Springer; 1996. pp. 337–49.Kiene RP, Linn LJ. Distribution and turnover of dissolved DMSP and its relationship with bacterial production and dimethylsulfide in the Gulf of Mexico. Limnol Oceanogr. 2000;45:849–61.CAS 

Google Scholar 
Curson AR, Todd JD, Sullivan MJ, Johnston AW. Catabolism of dimethylsulphoniopropionate: microorganisms, enzymes and genes. Nat Rev Microbiol. 2011;9:849–59.CAS 
PubMed 

Google Scholar 
Simó R. Production of atmospheric sulfur by oceanic plankton: biogeochemical, ecological and evolutionary links. Trends Ecol Evol. 2001;16:287–94.PubMed 

Google Scholar 
Charlson RJ, Lovelock JE, Andreae MO, Warren SG. Oceanic phytoplankton, atmospheric sulphur, cloud albedo and climate. Nature. 1987;326:655–61.CAS 

Google Scholar 
Reisch CR, Moran MA, Whitman WB. Bacterial catabolism of dimethylsulfoniopropionate (DMSP). Front Microbiol. 2011;2:172.CAS 
PubMed 
PubMed Central 

Google Scholar 
Varaljay VA, Howard EC, Sun S, Moran MA. Deep sequencing of a dimethylsulfoniopropionate-degrading gene (dmdA) by using PCR primer pairs designed on the basis of marine metagenomic data. Appl Environ Microbiol. 2010;76:609–17.CAS 
PubMed 

Google Scholar 
Varaljay VA, Robidart J, Preston CM, Gifford SM, Durham BP, Burns AS, et al. Single-taxon field measurements of bacterial gene regulation controlling DMSP fate. ISME J. 2015;9:1677.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ledyard KM, DeLong EF, Dacey JW. Characterization of a DMSP-degrading bacterial isolate from the Sargasso Sea. Arch Microbiol. 1993;160:312–8.CAS 

Google Scholar 
Todd JD, Kirkwood M, Newton-Payne S, Johnston AW. DddW, a third DMSP lyase in a model Roseobacter marine bacterium, Ruegeria pomeroyi DSS-3. ISME J. 2012;6:223–6.CAS 
PubMed 

Google Scholar 
Todd JD, Curson AR, Kirkwood M, Sullivan MJ, Green RT, Johnston AW. DddQ, a novel, cupin‐containing, dimethylsulfoniopropionate lyase in marine roseobacters and in uncultured marine bacteria. Environ Microbiol. 2011;13:427–38.CAS 
PubMed 

Google Scholar 
Todd J, Curson A, Dupont C, Nicholson P, Johnston A. The dddP gene, encoding a novel enzyme that converts dimethylsulfoniopropionate into dimethyl sulfide, is widespread in ocean metagenomes and marine bacteria and also occurs in some Ascomycete fungi. Environ Microbiol. 2009;11:1376–85.CAS 
PubMed 

Google Scholar 
Todd JD, Curson AR, Nikolaidou‐Katsaraidou N, Brearley CA, Watmough NJ, Chan Y, et al. Molecular dissection of bacterial acrylate catabolism–unexpected links with dimethylsulfoniopropionate catabolism and dimethyl sulfide production. Environ Microbiol. 2010;12:327–43.CAS 
PubMed 

Google Scholar 
Curson A, Rogers R, Todd J, Brearley C, Johnston A. Molecular genetic analysis of a dimethylsulfoniopropionate lyase that liberates the climate‐changing gas dimethylsulfide in several marine α‐proteobacteria and Rhodobacter sphaeroides. Environ Microbiol. 2008;10:757–67.CAS 
PubMed 

Google Scholar 
Delmont TO, Hammar KM, Ducklow HW, Yager PL, Post AF. Phaeocystis antarctica blooms strongly influence bacterial community structures in the Amundsen Sea polynya. Front Microbiol. 2014;5:646.PubMed 
PubMed Central 

Google Scholar 
Stoica E, Herndl GJ. Bacterioplankton community composition in nearshore waters of the NW Black Sea during consecutive diatom and coccolithophorid blooms. Aquat Sci. 2007;69:413–8.CAS 

Google Scholar 
Giebel HA, Brinkhoff T, Zwisler W, Selje N, Simon M. Distribution of Roseobacter RCA and SAR11 lineages and distinct bacterial communities from the subtropics to the Southern Ocean. Environ Microbiol. 2009;11:2164–78.CAS 
PubMed 

Google Scholar 
Landa M, Blain S, Christaki U, Monchy S, Obernosterer I. Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms. ISME J. 2016;10:39–50.CAS 
PubMed 

Google Scholar 
Wemheuer B, Güllert S, Billerbeck S, Giebel H-A, Voget S, Simon M, et al. Impact of a phytoplankton bloom on the diversity of the active bacterial community in the southern North Sea as revealed by metatranscriptomic approaches. FEMS Microbiol Ecol. 2014;87:378–89.CAS 
PubMed 

Google Scholar 
Alonso-Gutiérrez J, Lekunberri I, Teira E, Gasol JM, Figueras A, Novoa B. Bacterioplankton composition of the coastal upwelling system of ‘Ría de Vigo’, NW Spain. FEMS Microbiol Ecol. 2009;70:493–505.PubMed 

Google Scholar 
Brown MV, Van De Kamp J, Ostrowski M, Seymour JR, Ingleton T, Messer LF, et al. Systematic, continental scale temporal monitoring of marine pelagic microbiota by the Australian Marine Microbial Biodiversity Initiative. Sci Data. 2018;5:180130.PubMed 
PubMed Central 

Google Scholar 
Ajani P, Hallegraeff G, Allen D, Coughlan A, Richardson A, Armand L, et al. Establishing baselines: a review of eighty years of phytoplankton diversity and biomass in southeastern Australia. Oceanogr Mar Biol. 2016;54:387–412.
Google Scholar 
Matear R, Chamberlain M, Sun C, Feng M. Climate change projection of the Tasman Sea from an eddy‐resolving ocean model. J Geophys Res Oceans. 2013;118:2961–76.
Google Scholar 
Ostrowski M, Seymour J, Messer L, Varkey D, Goosen K, Smith M, et al. Status of Australian marine microbial assemblages. In State and Trends of Australia’s Ocean Report, Integrated Marine Observing System (IMOS). 2020. https://doi.org/10.26198/5e16aa3e49e7f.Lynch TP, Morello EB, Evans K, Richardson AJ, Rochester W, Steinberg CR, et al. IMOS National Reference Stations: a continental-wide physical, chemical and biological coastal observing system. PloS ONE. 2014;9:e113652.PubMed 
PubMed Central 

Google Scholar 
Lynch T, Roughan M, Mclaughlan D, Hughes D, Cherry D, Critchley G, et al. A national reference station infrastructure for Australia – Using telemetry and central processing to report multi-disciplinary data streams for monitoring marine ecosystem response to climate change. In: OCEANS 2008. 2008. https://doi.org/10.1109/OCEANS.2008.5151856.Appleyard SA, Abell G, Watson R. Tackling microbial related issues in cultured shellfish via integrated molecular and water chemistry approaches. Clayton: CSIRO Marine and Atmospheric Research; 2013.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 

Google Scholar 
Duarte CM. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol Oceanogr Bull. 2015;24:11–4.
Google Scholar 
Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, et al. Marine microbial metagenomes sampled across space and time. Sci Data. 2018;5:1–7.
Google Scholar 
Logares R, Sunagawa S, Salazar G, Cornejo‐Castillo FM, Ferrera I, Sarmento H, et al. Metagenomic 16S rDNA Illumina tags are a powerful alternative to amplicon sequencing to explore diversity and structure of microbial communities. Environm Microbiol. 2014;16:2659–71.CAS 

Google Scholar 
Dadon-Pilosof A, Conley KR, Jacobi Y, Haber M, Lombard F, Sutherland KR, et al. Surface properties of SAR11 bacteria facilitate grazing avoidance. Nat Microbiol. 2017;2:1608–15.PubMed 

Google Scholar 
Lane D. 16S/23S rRNA sequencing. In: Nucleic acid techniques in bacterial systematics. New York: John Wiley & Sons; 1991, pp. 115–75.Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc Natl Acad Sci. 1985;82:6955–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Piredda R, Tomasino M, D’erchia A, Manzari C, Pesole G, Montresor M, et al. Diversity and temporal patterns of planktonic protist assemblages at a Mediterranean Long Term Ecological Research site. FEMS Microbiol Ecol. 2017;93.Stoeck T, Bass D, Nebel M, Christen R, Jones MD, Breiner HW, et al. Multiple marker parallel tag environmental DNA sequencing reveals a highly complex eukaryotic community in marine anoxic water. Mol Ecol. 2010;19:21–31.CAS 
PubMed 

Google Scholar 
Andrews S. FastQC: a quality control tool for high throughput sequence data. Cambridge, United Kingdom: Babraham Bioinformatics, Babraham Institute; 2010.Magoč T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27:2957–63.PubMed 
PubMed Central 

Google Scholar 
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gordon A, Hannon G. Fastx-toolkit. FASTQ/A short-reads preprocessing tools. 2010;5. http://hannonlab.cshl.edu/fastx_toolkit/.Yilmaz P, Parfrey LW, Yarza P, Gerken J, Pruesse E, Quast C, et al. The SILVA and “all-species living tree project (LTP)” taxonomic frameworks. Nucl Acids Res. 2014;42:D643–8.CAS 
PubMed 

Google Scholar 
Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucl Acids Res. 2012;41:D597–604.PubMed 
PubMed Central 

Google Scholar 
Simon M, Scheuner C, Meier-Kolthoff JP, Brinkhoff T, Wagner-Döbler I, Ulbrich M, et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J. 2017;11:1483–99.PubMed 
PubMed Central 

Google Scholar 
Brinkhoff T, Giebel H-A, Simon M. Diversity, ecology, and genomics of the Roseobacter clade: a short overview. Arch Microbiol. 2008;189:531–9.CAS 
PubMed 

Google Scholar 
Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 

Google Scholar 
Kumar S, Stecher G, Tamura K. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucl Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Felsenstein J. Evolutionary trees from gene frequencies and quantitative characters: finding maximum likelihood estimates. Evolution. 1981:1229–42.Tamura K, Nei M. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol. 1993;10:512–26.CAS 
PubMed 

Google Scholar 
emcparland. emcparland/dmspOTUs: first release. 2021. https://doi.org/10.5281/zenodo.5090864.Keeling PJ, Burki F, Wilcox HM, Allam B, Allen EE, Amaral-Zettler LA, et al. The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing. PLoS Biol. 2014;12:e1001889.PubMed 
PubMed Central 

Google Scholar 
Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29:2933–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Matsen FA, Kodner RB, Armbrust EV. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 2010;11:538.
Google Scholar 
McParland EL, Levine NM. The role of differential DMSP production and community composition in predicting variability of global surface DMSP concentrations. Limnol Oceanogr. 2019;64:757–73.CAS 

Google Scholar 
Reshef DN, Reshef YA, Sabeti PC, Mitzenmacher M. An empirical study of the maximal and total information coefficients and leading measures of dependence. Ann Appl Stat. 2018;12:123–55.
Google Scholar 
Hammer Ø, Harper DA, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4:9.
Google Scholar 
Buchan A, González JM, Moran MA. Overview of the marine Roseobacter lineage. Appl Environ Microbiol. 2005;71:5665–77.CAS 
PubMed 
PubMed Central 

Google Scholar 
Moran MA, González JM, Kiene RP. Linking a bacterial taxon to sulfur cycling in the sea: studies of the marine Roseobacter group. Geomicrobiol J. 2003;20:375–88.CAS 

Google Scholar 
Harris G, Nilsson C, Clementson L, Thomas D. The water masses of the east coast of Tasmania: seasonal and interannual variability and the influence on phytoplankton biomass and productivity. Mar Freshw Res. 1987;38:569–90.CAS 

Google Scholar 
Kiene RP, Linn LJ, González J, Moran MA, Bruton JA. Dimethylsulfoniopropionate and methanethiol are important precursors of methionine and protein-sulfur in marine bacterioplankton. Appl Environ Microbiol. 1999;65:4549–58.CAS 
PubMed 
PubMed Central 

Google Scholar 
Raina J-B, Tapiolas D, Willis BL, Bourne DG. Coral-associated bacteria and their role in the biogeochemical cycling of sulfur. Appl Environ Microbiol. 2009;75:3492–501.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brinkmeyer R, Rappé M, Gallacher S, Medlin L. Development of clade-(Roseobacter and Alteromonas) and taxon-specific oligonucleotide probes to study interactions between toxic dinoflagellates and their associated bacteria. Eur J Phycol. 2000;35:315–29.
Google Scholar 
Töpel M, Pinder MI, Johansson ON, Kourtchenko O, Clarke AK, Godhe A. Complete genome sequence of novel Sulfitobacter pseudonitzschiae Strain SMR1, isolated from a culture of the marine diatom Skeletonema marinoi. J Genomics. 2019;7:7.PubMed 
PubMed Central 

Google Scholar 
Hong Z, Lai Q, Luo Q, Jiang S, Zhu R, Liang J, et al. Sulfitobacter pseudonitzschiae sp. nov., isolated from the toxic marine diatom Pseudo-nitzschia multiseries. Int J Syst Evol Microbiol. 2015;65:95–100.CAS 
PubMed 

Google Scholar 
Yang Q, Ge Y-M, Iqbal NM, Yang X, Zhang X-l. Sulfitobacter alexandrii sp. nov., a new microalgae growth-promoting bacterium with exopolysaccharides bioflocculanting potential isolated from marine phycosphere. Antonie Van Leeuwenhoek. 2021;114:1091–106.CAS 
PubMed 

Google Scholar 
Ankrah NY, Lane T, Budinoff CR, Hadden MK, Buchan A. Draft genome sequence of Sulfitobacter sp. CB2047, a member of the Roseobacter clade of marine bacteria, isolated from an emiliania huxleyi bloom. Genome Announc. 2014;2:e01125–14.PubMed 
PubMed Central 

Google Scholar 
Kwak M-J, Lee J-S, Lee KC, Kim KK, Eom MK, Kim BK, et al. Sulfitobacter geojensis sp. nov., Sulfitobacter noctilucae sp. nov., and Sulfitobacternoctilucicola sp. nov., isolated from coastal seawater. Int J Syst Evol Microbiol. 2014;64:3760–7.PubMed 

Google Scholar 
Zhang F, Fan Y, Zhang D, Chen S, Bai X, Ma X, et al. Effect and mechanism of the algicidal bacterium Sulfitobacter porphyrae ZFX1 on the mitigation of harmful algal blooms caused by Prorocentrum donghaiense. Environ Pollut. 2020;263:114475.CAS 
PubMed 

Google Scholar 
Keller MD. Dimethyl sulfide production and marine phytoplankton: the importance of species composition and cell size. Biol Oceanogr. 1989;6:375–82.
Google Scholar 
McParland EL, Lee MD, Webb EA, Alexander H, Levine NM. DMSP synthesis genes distinguish two types of DMSP producer phenotypes. Environ Microbiol. 2021;23:1656–69.CAS 
PubMed 

Google Scholar 
Galí M, Simó R. A meta‐analysis of oceanic DMS and DMSP cycling processes: disentangling the summer paradox. Glob Biogeochem Cycles. 2015;29:496–515.
Google Scholar 
Carr A, Diener C, Baliga NS, Gibbons SM. Use and abuse of correlation analyses in microbial ecology. ISME J. 2019;13:2647–55.PubMed 
PubMed Central 

Google Scholar 
Amin S, Hmelo L, Van Tol H, Durham B, Carlson L, Heal K, et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015;522:98.CAS 
PubMed 

Google Scholar 
Miller TR, Hnilicka K, Dziedzic A, Desplats P, Belas R. Chemotaxis of Silicibacter sp. strain TM1040 toward dinoflagellate products. Appl Environ Microbiol. 2004;70:4692–701.CAS 
PubMed 
PubMed Central 

Google Scholar 
Seymour JR, Simó R, Ahmed T, Stocker R. Chemoattraction to dimethylsulfoniopropionate throughout the marine microbial food web. Science. 2010;329:342–5.CAS 
PubMed 

Google Scholar  More

Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. The population sizes and global extinction risk of reef-building coral species at biogeographic scales. Nat. Ecol. Evol. 5, 663–669 (2021).Article 

Google Scholar 
Hubbell, S. P. et al. How many tree species are there in the Amazon and how many of them will go extinct? Proc. Natl Acad. Sci. USA 105, 11498–11504 (2008).CAS 
Article 

Google Scholar 
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
Article 

Google Scholar 
Muir, P. R. et al. Conclusions of low extinction risk for most species of reef-building corals are premature. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01659-5 (2022).Richards, Z. T., Syms, C., Wallace, C. C., Muir, P. R. & Willis, B. L. Multiple occupancy–abundance patterns in staghorn coral communities. Divers. Distrib. 19, 884–895 (2013).Article 

Google Scholar 
Zvuloni, A., Artzy-Randrup, Y., Stone, L., van Woesik, R. & Loya, Y. Ecological size–frequency distributions: how to prevent and correct biases in spatial sampling. Limnol. Oceanogr. Methods 6, 144–153 (2008).Article 

Google Scholar  More

Dietzel, A., Bode, M., Connolly, S. R. & Hughes, T. P. The population sizes and global extinction risk of reef-building coral species at biogeographic scales. Nat. Ecol. Evol. 5, 663–669 (2021).Article 

Google Scholar 
Richards, Z. T., van Oppen, M. J., Wallace, C. C., Willis, B. L. & Miller, D. J. Some rare Indo-Pacific coral species are probable hybrids. PLoS ONE 3, e3240 (2008).Article 

Google Scholar 
Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
Article 

Google Scholar 
Richards, Z. T., Syms, C., Wallace, C. C., Muir, P. R. & Willis, B. L. Multiple occupancy–abundance patterns in staghorn coral communities. Divers. Distrib. 19, 84–895 (2013).Article 

Google Scholar 
Hoeksema, B. W. & Cairns, S. World List of Scleractinia (accessed 6 May 2021); http://www.marinespecies.org/scleractiniaHughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 493–496 (2018).Article 

Google Scholar 
Thurba, R. V. et al. Deciphering coral disease dynamics: integrating host, microbiome, and the changing environment. Front. Ecol. Evol. 8, 575927 (2020).Article 

Google Scholar 
Muir, P. R., Marshall, P. A., Abdulla, A. & Aguirre, J. D. Species identity and depth predict bleaching severity in reef building corals: shall the deep inherit the reef? Proc. R. Soc. B 284, 20171551 (2017).Article 

Google Scholar 
van Woesik, R., Sakai, K., Ganase, A. & Loya, Y. Revisiting the winners and the losers a decade after coral bleaching. Mar. Ecol. Prog. Ser. 434, 67–76 (2011).Article 

Google Scholar 
Chen, Y.-H., Shertzer, K. W. & Viehman, T. S. Spatio-temporal dynamics of the threatened elkhorn coral Acropora palmata: implications for conservation. Divers. Distrib. 26, 1582–1597 (2020).Article 

Google Scholar 
Sheppard, C., Sheppard, A. & Fenner, D. Coral mass mortalities in the Chagos Archipelago over 40 years: regional species and assemblage extinctions and indications of positive feedbacks. Mar. Poll. Bull. 154, 111075 (2020).CAS 
Article 

Google Scholar 
DeVantier, L. & Turak, E. Species richness and relative abundance of reef-building corals in the Indo-West Pacific. Diversity 9, 25 (2017).Article 

Google Scholar 
Wallace, C. C. Staghorn Corals of the World (CSIRO, 1999).Benzoni, F., Stefani, F., Pichon, M. & Galli, P. The name game: morpho-molecular species boundaries in the genus Psammocora (Cnidaria, Scleractinia). Zool. J. Linn. Soc. 160, 421–456 (2010).Article 

Google Scholar 
Richards, Z. T., Berry, O. & van Oppen, M. J. H. Cryptic genetic divergence within threatened species of Acropora coral from the Indian and Pacific Oceans. Conserv. Genet. 17, 577–591 (2016).Article 

Google Scholar 
Sheets, E. A., Warner, P. A. & Palumbi, S. R. Accurate population genetic measurements require cryptic species identification in corals. Coral Reefs 37, 549–563 (2018).Article 

Google Scholar 
Bongaerts, P. et al. Morphological stasis masks ecologically divergent coral species on tropical reefs. Curr. Biol. 31, 2286–2298 (2021).CAS 
Article 

Google Scholar 
Frankham, R. Effective population size/adult population size ratios in wildlife: a review. Genet. Res. 89, 491–503 (2008).Article 

Google Scholar  More

Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).CAS 

Google Scholar 
Barichivich, J. et al. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob. Change Biol. 19, 3167–3183 (2013).
Google Scholar 
Vitasse, Y. et al. Assessing the effects of climate change on the phenology of European temperate trees. Agr. Forest Meteorol. 151, 969–980 (2011).
Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 

Google Scholar 
Wang, H. et al. Overestimation of the effect of climatic warming on spring phenology due to misrepresentation of chilling. Nat. Commun. 11, 4945 (2020).CAS 

Google Scholar 
Myneni, R. C. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 

Google Scholar 
Piao, S. et al. Leaf onset in the Northern Hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).CAS 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Phil. Trans. R. Soc. B 365, 3227–3246 (2010).
Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
White, A., Cannell, M. G. R. & Friend, A. D. The high-latitude terrestrial carbon sink: a model analysis. Glob. Change Biol. 6, 227–245 (2000).
Google Scholar 
Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–53 (2008).CAS 

Google Scholar 
Fu, Y. H. et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol. 20, 3743–3755 (2014).
Google Scholar 
Yun, J. et al. Influence of winter precipitation on spring phenology in boreal forests. Glob. Change Biol. 11, 5176–5187 (2018).
Google Scholar 
Fu, Y. H. et al. Increased heat requirement for leaf flushing in temperate woody species over 1980–2012: effects of chilling, precipitation and insolation. Glob. Change Biol. 21, 2687–2697 (2015).
Google Scholar 
Wipf, S., Stoeckli, V. & Bebi, P. Winter climate change in alpine tundra: plant responses to changes in snow depth and snowmelt timing. Climatic Change 94, 105–121 (2009).
Google Scholar 
Peñuelas, J. et al. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytol. 161, 837–846 (2004).
Google Scholar 
Paschalis, A. et al. Rainfall manipulation experiments as simulated by terrestrial biosphere models: where do we stand? Glob. Change Biol. 26, 3336–3355 (2020).
Google Scholar 
Green, J. K. et al. Regionally strong feedbacks between the atmosphere and terrestrial biosphere. Nat. Geosci. 10, 410–414 (2017).CAS 

Google Scholar 
Trenberth, K. E., Dai, A., Rasmussen, R. M. & Parsons, D. B. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84, 1205–1217 (2003).
Google Scholar 
Qian, W., Fu, J. & Yan, Z. Decrease of light rain events in summer associated with a warming environment in China during 1961–2005. Geophys. Res. Lett. 34, L11705 (2007).
Google Scholar 
Sun, Y., Solomon, S., Dai, A. & Portmann, R. W. How often will it rain? J. Clim. 20, 4801–4818 (2007).
Google Scholar 
Chou, C. et al. Mechanisms for global warming impacts on precipitation frequency and intensity. J. Clim. 13, 3291–3306 (2012).
Google Scholar 
Held, I. M. & Soden, B. J. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686–5699 (2006).
Google Scholar 
Fowler, M. D., Kooperman, G. J., Randerson, J. T. & Pritchard, M. S. The effect of plant physiological responses to rising CO2 on global streamflow. Nat. Clim. Change 9, 873–879 (2019).CAS 

Google Scholar 
Belnap, J., Phillips, S. L. & Miller, M. E. Response of desert biological soil crusts to alterations in precipitation frequency. Oecologia 141, 306–316 (2004).
Google Scholar 
Knapp, A. K. et al. Consequences of more extreme precipitation regimes for terrestrial ecosystems. Bioscience 58, 811–821 (2008).
Google Scholar 
Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).CAS 

Google Scholar 
De Boeck, H. J., Dreesen, F. E., Janssens, I. A. & Nijs, I. Climatic characteristics of heat waves and their simulation in plant experiments. Glob. Change Biol. 16, 1992–2000 (2010).
Google Scholar 
Shen, M. et al. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).
Google Scholar 
Peaucelle, M. et al. Spatial variance of spring phenology in temperate deciduous forests is constrained by background climatic conditions. Nat. Commun. 10, 5388 (2019).
Google Scholar 
Estiarte, M. & Peñuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).
Google Scholar 
Austin, A. T. et al. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–235 (2004).
Google Scholar 
White, M. A., Thornton, P. E. & Running, S. W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cycles 11, 217–234 (1997).CAS 

Google Scholar 
Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).
Google Scholar 
Ge, Q., Wang, H., Rutishauser, T. & Dai, J. Phenological response to climate change in China: a meta-analysis. Glob. Change Biol. 21, 265–274 (2015).
Google Scholar 
Schwartz, M. D., Betancourt, J. L. & Weltzin, J. F. From Caprio’s lilacs to the USA National Phenology Network. Front. Ecol. Environ. 10, 324–327 (2012).
Google Scholar 
Wu, C. et al. Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn. Glob. Ecol. Biogeogr. 22, 994–1006 (2013).
Google Scholar 
Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).
Google Scholar 
Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agr. Forest Meteorol. 291, 108077 (2020).
Google Scholar 
Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).
Google Scholar 
Shen, M. et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agr. Forest Meteorol. 189, 71–80 (2014).
Google Scholar 
Wu, C. et al. Widespread decline in winds delayed autumn foliar senescence over high latitudes. Proc. Natl Acad. Sci. USA 118, e2015821118 (2021).CAS 

Google Scholar 
Elmore, A. J. et al. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Change Biol. 18, 656–674 (2012).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
Google Scholar 
Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
Google Scholar 
New, M., Hulme, M. & Jones, P. D. Representing twentieth‐century space–time climate variability. Part I: development of a 1961–90 mean monthly terrestrial climatology. J. Clim. 12, 829–856 (1999).
Google Scholar 
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F. A trend-preserving bias correction—the ISI-MIP approach. Earth Syst. Dynam. 4, 219–236 (2013).
Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).
Google Scholar 
Vicenteserrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).
Google Scholar 
Barr, A. G. et al. Inter‐annual variability in the leaf area index of a boreal aspen–hazelnut forest in relation to net ecosystem production. Agr. Forest Meteorol. 126, 237–255 (2004).
Google Scholar 
Chen, J., Chen, W., Liu, J., Cihlar, J. & Gray, S. Annual carbon balance of Canada’s forests during 1895–1996. Glob. Biogeochem. Cycles 14, 839–849 (2000).CAS 

Google Scholar 
Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere–biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).
Google Scholar  More

Let (y_{itk}) denote the WCR count observed for trap i in week t in year k, and assume it to follow a Poisson distribution with parameter (mu _{itk})$$begin{aligned} y_{itk} | mu _{itk}, sim Poisson(mu _{itk}) end{aligned}$$
(1)
The intensity parameter (mu _{itk}) represents the rate of emergence for a given time period. Instead of allowing it to depend purely on time t, a phenological variable of growing degree days (GDD) is used, as warmer temperatures are required for WCR development25,26,27,28. GDDs reflect the heat accumulation and are defined as an integral of warmth above a base temperature after a given start date:$$begin{aligned} GDD = int (T(t)-T_{base})dt. end{aligned}$$
(2)
The above integral can be approximated by$$begin{aligned} GDD = max left( frac{T_{max} – T_{min}}{2} – T_{base}, 0 right) . end{aligned}$$
(3)
Here (T_{min}) is the minimum daily temperature, (T_{max}) is the maximum daily temperature, and (T_{base}) is a set base temperature. In this study, the base temperature was set to (10,^{circ })C, and the starting date was the beginning of April, which marks the start of the growing season in Austria.The rate of cumulative emergence of the WCR beetle can be described by a Gompertz function. The Gompertz function is a sigmoidal function which describes growth as being slowest at the beginning and the end of a given period and is defined as$$begin{aligned} f(z_t) = alpha exp (-beta exp (-gamma z_t)). end{aligned}$$
(4)
where (alpha) is the upper asymptote, (beta) is a relative starting value, (gamma) is a growth rate coefficient which affects the slope, and (z_t) are the cumulative growing degree days. In this study, one can consider the asymptote as proxy to the saturation level of WCR population growth. Lower values of (beta) suggest an earlier first emergence in the season, while lower values of (gamma) indicate a longer emergence period. To investigate whether there is an association between climate variables and the emergence dynamics, the Gompertz curve parameters were assumed to linearly depend on climate covariates. In this regression modelling framework, a spatially correlated residual structure can be added in either (alpha), (beta), and/or (gamma) if there is evidence to do so.To reflect the nature of the emergence dynamics and to preserve the shape of the increasing Gompertz curve, the parameters of the model were restricted to positive values such that (alpha >0), (beta >0), and (gamma >0). The time at inflection or period of highest growth can be obtained by solving Eq. (4) for the value of t at which the concavity of the function changes. The time at inflection is described as:$$begin{aligned} T_z^* = frac{log (beta )}{gamma } end{aligned}$$
(5)
The Gompertz function describes cumulative emergence. Thus to describe the marginal emergence rate, the derivative of the Gompertz function can be used instead. Consequently, as the WCR trapping data consisted of weekly counts, the rate of emergence (mu _{itk}) is better described by the log of the derivative of the Gompertz function$$begin{aligned} log (mu _{itk}) = log (alpha _{ik}) + log (gamma _{ik}) + log (beta _{ik}) + gamma _i z_{itk} – beta _{ik} exp (-gamma z_{itk}). end{aligned}$$
(6)
The parameters (alpha _{ik}), (beta _{ik}) and (gamma _{ik}) are site and year specific such that:$$begin{aligned}&alpha _{ik} sim N(mu _{alpha _{ik}}, tau _{alpha }) end{aligned}$$
(7)
$$begin{aligned}&gamma _{ik} sim N(mu _{gamma _{ik}}, tau _{gamma }) end{aligned}$$
(8)
$$begin{aligned}&beta _{ik} sim N(mu _{beta _{ik}}, tau _{beta }). end{aligned}$$
(9)
Here, (tau _{alpha }), (tau _{beta }), and (tau _{gamma }) are the precision (inverse variance) parameters of the prior distributions for (alpha), (beta) and (gamma) respectively. Moreover, the means of the distributions (mu _{alpha _{ik}}), (mu _{beta _{ik}}), and (mu _{gamma _{ik}}) can be expressed as functions of known covariates:$$begin{aligned} mu _{alpha _{ik}}= & {} a_{0} + {mathbf {w}}^T X_{alpha _{ik}}, end{aligned}$$
(10)
$$begin{aligned} mu _{beta _{ik}}= & {} b_{0}, end{aligned}$$
(11)
$$begin{aligned} mu _{gamma _{ik}}= & {} g_{0} + {mathbf {u}}^T X_{gamma _{ik}}. end{aligned}$$
(12)
Here (a_{0}) is the intercept, ({mathbf {w}}) is a vector of the regression coefficients, and (X_{alpha _{ik}}) are the location and year specific covariates. The predictors used in the regression of (mu _{alpha _{ik}}) are the average winter temperature, the precipitation sum during winter, the year, the percentage of the agricultural area per Austrian municipality used for cultivating maize crops (maize), and the corresponding centred coordinates of the trap locations; x, y, and their functions (x^2), (y^2), and xy. The parameter (g_{0}) is the intercept for the regression of (mu _{gamma _{ik}}), and u is the corresponding regression coefficient. The predictor used for (mu _{gamma _ik}) is the average yearly spring temperature.The intercepts and regression coefficients ((mathbf {w}) and (mathbf {u})) were given non-informative normal priors N(0, 0.01). The precision parameters (tau _{alpha }), (tau _{beta }) and (tau _{gamma }) were assigned prior distributions Gamma(0.01, 0.01).The model was fitted using WinBUGS through the R2WinBUGS package in R29,30,31. The model was run for 20000 iterations, with a burn-in of 10000 iterations, and a thinning rate of five. Convergence was determined by visual assessments of trace plots and marginal posterior densities. More

Study subjects and measurement of skin physiological parametersWe analyzed skin microbiome and mycobiome from cheeks and foreheads of healthy younger (19–28 years old, Y-group) and older (60–63 years old, O-group) Korean women who were free from cutaneous disorders (Table 1 and Supplementary Table S1). All 61 subjects had been living in Seoul, Korea, for more than 3 years with normal skin conditions. We preferentially selected those who had sebum secretion greater than 30 arbitrary units and moisture greater than 50 arbitrary units in both groups. Among the measurements of moisture content, pH, sebum content, and transepidermal water loss (TEWL), only sebum and TEWL decreased significantly in the O-group compared to the Y-group in the cheeks (P = 2.25e−06, Wilcoxon rank-sum test; P = 0.019, Welch two-sample t test) and forehead (P = 1.33e−06, Wilcoxon rank-sum test; P = 0.003, Welch two-sample t test). Whereas no significant differences were found in the average values for moisture (cheeks: Y-group, 59.9; O-group, 56.6; forehead: Y-group, 61.1; O-group, 58.7) and pH (cheeks: Y-group, 6.0; O-group, 5.8; forehead: Y-group, 6.0; O-group, 5.6) between the two age groups.Table 1 Characteristics of subjects for aged related skin microbiome and mycobiome study.Full size tableComparisons in cheek and forehead microbiome and mycobiome between the two age groupsWe analyzed bacterial communities from 27 Y-group samples (cheeks, n = 13; forehead, n = 14) and 24 O-group samples (cheeks, n = 12; forehead, n = 12) and fungal communities from 28 Y-group samples (cheeks, n = 15; forehead, n = 13) and 32 O-group samples (cheeks, n = 16; forehead, n = 16), except for samples that were eliminated from the Illumina Mi-Seq sequencing due to low sequence reads (bacteria,  3. 0) (Fig. 4). Pathways belonging to the metabolism category were dominant in each age group. In the cheek of the Y-group, pathways involved in energy metabolism by bacteria, such as glycolysis/gluconeogenesis, citrate cycle, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-alanine metabolism, and thiamine metabolism, were predominant, whereas in the cheek of the O-group, degradation-related pathways, such as fatty acid degradation, synthesis and degradation of ketone bodies, benzoate degradation, and chloroalkane and chloroalkene degradation, were predominant. In the forehead of the Y-group, glycolysis/gluconeogenesis, pentose phosphate pathway, fructose and mannose metabolism, galactose metabolism, d-glutamine and d-glutamate metabolism, d-alanine metabolism, and thiamine metabolism pathway were significantly more abundant, whereas in the forehead of the O-group, fatty acid degradation, synthesis and degradation of ketone bodies, valine/leucine and isoleucine degradation, and limonene/pinene degradation pathway were significantly more abundant.Figure 4Heat map for significantly different predicted functional pathways on (a) cheeks and (b) foreheads of Korean women by age based on LEfSe analysis (LDA score  > 3.0).Full size imageThe metabolism pathway for biotin, a water-soluble vitamin that is effective for skin health and essential for keratin production15, was more prevalent in the cheek and forehead of the Y-group. Interestingly, the metabolism pathway for lipoic acid, which is known to possess beneficial effects against skin aging and is used widely in cosmetic and dermatological products16,17, was significantly higher in the foreheads of the Y-group. We tracked the specific ASVs possessing these pathways, in both biotin metabolism and lipoic acid metabolism, Cutibacterium sp. (ASV2136 and ASV2130) and Staphylococcus sp. (ASV3008) were predicted to have the top three relative abundances in KOs. The relative abundances in biotin metabolism and lipoic acid metabolism of Cutibacterium sp. (ASV2136) were 24.9% and 26.1%, respectively. The relative abundances for each pathway for Staphylococcus sp. (ASV3008) were 10.2% and 18.7%, and for Cutibacterium sp. (ASV2130), they were 9.3% and 10.0%, respectively. We confirmed these two pathways in the genome of skin bacteria, C. acnes (Supplementary Fig. S2). These additional analyses support the reliability of the function in the skin environment of Cutibacterium. Interestingly, from the LEfSe result, Cutibacterium sp. (ASV2136) had a significantly higher abundance in the cheek and forehead microbiome of the Y-group. The pathway of biosynthesis of lipopolysaccharide, also known as bacterial endotoxins, showed higher abundance in the cheek and forehead microbiome of the O-group. The ASVs that contribute to inferring the LPS biosynthesis pathway were identified as Paraburkholderia sp. (ASV5030) and B. vesicularis (ASV4155). Also, pathways related to antibiotic biosynthesis (biosynthesis of vancomycin group antibiotics) and bacterial motility (bacterial chemotaxis and flagellar assembly; both belonging to the cellular processes category) were prominent in the cheek and forehead of the O-group. PICRUSt2 analysis implied that, regardless of skin site differences, the potential functions of the microbial community that compose the skin microbiome were similar according to age.Network analysis on cheek and forehead microbiome and mycobiomeWe performed SParse InversE Covariance estimation for Ecological Association Inference (SPIEC-EASI) analysis to evaluate the overall network of the skin microbes. The results of network density (D) on 81 cheek and 87 forehead ASVs, calculated using the ratio of the number of edges, showed higher network density in the skin microbiome of the Y-group (D = 0.015 and D = 0.001, in cheek and forehead, respectively) than the O-group (D = 0.007 and D = 0.007, respectively) (Fig. 5). To examine network correlation between bacteria and fungi, network density for Bacteria–Fungi (DBF) was calculated by the actual number of edges and a potential number of edges in a correlation ([bacterial nodes × fungal nodes]/2). We confirmed higher network density in the cheek of the Y-group (DBF = 0.008) than the O-group (DBF = 0) and edges of the major bacterial and fungal taxa, such as Staphylococcus sp. (ASV3008)—M. sympodialis (ASV500) and Roseomonas sp. (ASV4088)—M. restricta (ASV482), were observed in the cheek of the Y-group. In the forehead, edges of Methylobacterium sp. (ASV4314)—M. globosa (ASV454), Methylobacterium sp. (ASV4314)—Zygosaccharomyces rouxii (ASV208), and Venionella sp. (ASV3575)—M. sympodialis (ASV500) were observed in the Y-group, and edges of Cutibacterium sp. (ASV2107)—M. globosa (ASV461), Staphylococcus sp. (ASV3024)—M. arunalokei (ASV446), and Methylobacterium (ASV4314)—M. dermatis (ASV448) were observed in the O-group (DBF = 0.004). We found a network between bacteria and fungi with different kingdom levels in the skin microbiome, and especially, we confirmed that different genus or species level microbe was involved in the microbial network according to skin location and Y-, O-group.Figure 5Network analysis of the ASVs on (a) cheeks and (b) forehead of Korean women. Each node represents the ASV and the size of the node is based on relative abundance of each ASV. Color markings indicate the major taxa except for unidentified bacteria or fungi. Shapes represent the level of kingdom, Bacteria (bold) and Fungi (dotted line). The ASVs were selected for bacterial ASVs found in more than half of all samples on the cheeks and forehead, respectively, and for the fungal ASVs with a relative abundance of more than 0.1% in each of the cheeks and forehead samples. The D value is network density calculated using the ratio of the number of edges.Full size image More

Analytical targetsTwo species of undulation motion rays with different pectoral fin shapes bred in KAIYUKAN were analyzed: sharpnose stingray Dasyatis acutirostra and pitted stingray Dasyatis matsubarai (Fig. 1a,b). Blender 2.7925 was used to construct stingray models from pictures26,27 as accurately as possible; Blender is a free and open-source 3D creation suite used to make realistic characters for movies, etc. Detailed information on how to construct models using Blender is provided in our previous paper28. To focus on the effects of pectoral fin movements, we did not consider the body’s shape as in the previous studies12,29. The height and disk width (WD) of all models were set to 0.01 m and 0.44 m, respectively, considering the previous studies30,31. The disk length of each model was determined from WD, referring to the aspect ratio of the rays’ photographs26,27; the disk length (LD) of D. acutirostra and D. matsubarai are 0.348 m and 0.344 m, respectively.Figure 1Analytical targets and description of motion. (a) Analytical model of D. acutirostra. (b) Analytical model of D. matsubarai. (c) Description of motion, (d) the relationship between any two points on the surface before and after the deformation.Full size imageMotionThe motion was given to satisfy the following equations:$$z = left{ {begin{array}{*{20}l} {A;sin left( {omega left( {t – kTleft( {frac{{angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} }}{{All;angl{text{e}}}} – theta } right)} right)h_{1} h_{2} } right.} hfill & {left( {10^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 170^{{text{o}}} } right)} hfill \ {A;sin left( {omega left( {t – kTleft( {frac{{350^{{text{o}}} – left( {angl{text{e}}left( {x_{i} ,y_{i} } right) – 10^{{text{o}}} } right)}}{{All;angl{text{e}}}}} right)} right)h_{1} h_{2} } right.} hfill & {left( {190^{{text{o}}} le angl{text{e}}left( {x_{i} ,y_{i} } right) le 350^{{text{o}}} } right)} hfill \ end{array} } right.$$
(1)
$$begin{array}{c}{h}_{1}=a{r}_{i}^{3}+b{r}_{i}^{2}+c{r}_{i}end{array}$$
(2)
$$h_{2} = left{ {begin{array}{*{20}l} {dleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)^{2} + eleft( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} hfill & {left( {10^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 170^{ circ } } right)} hfill \ {dleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)^{2} + eleft( {350^{ circ } – left( {angleleft( {x_{i} ,y_{i} } right) – 10^{ circ } } right)} right)} hfill & {left( {190^{ circ } le angleleft( {x_{i} ,y_{i} } right) le 350^{ circ } } right)} hfill \ end{array} } right.$$
(3)
$$begin{array}{c}{left({r}_{i}-{r}_{i-1}right)}^{2}+{left({z}_{i}-{z}_{i-1}right)}^{2}={left({r}_{i}^{mathrm{^{prime}}}-{r}_{i-1}^{mathrm{^{prime}}}right)}^{2}+{left({z}_{i}^{mathrm{^{prime}}}-{z}_{i-1}^{mathrm{^{prime}}}right)}^{2}end{array}$$
(4)
$$begin{array}{c}angleleft({x}_{i},{y}_{i}right)= angleleft({x}_{i}^{^{prime}},{y}_{i}^{^{prime}}right).end{array}$$
(5)
Equation (1) represents the amount of movement of the model surface in the z-axis direction, where (A) is the amplitude of the pectoral fin tip, (omega) is the angular velocity, (t) is time, (k) is the wavenumber, (T) is the period, angle(({x}_{i},{y}_{i})) is the angle made by the line connecting the center of rotation and any point (({x}_{i},{y}_{i})) on the model surface with the x-axis, Allangle is the range where the motion is given (160°), and (theta) is the phase difference between the movements of the right and left pectoral fins (Fig. 1c). ({h}_{1}) is the weighting from the center to the radial direction: it is necessary to set the amplitude at the ray’s center to zero and increase the amplitude toward the pectoral fin tip (a = 119.786, b = -7.957, c = 0.498). ({h}_{2}) is the weighting in the circumferential direction: it is necessary to increase the amplitude from the anterior to the tip of the pectoral fin and decrease the amplitude from the tip of the pectoral fin to the posterior (d = − 1.563 × 10–4, e = 0.025). Equation (4) is the condition in which the distance between two neighboring points in the same radial direction is equal before and after the movement (Fig. 1d). (r) is the distance between the center of rotation and any point (({x}_{i},{y}_{i})), defined as (sqrt{{x}_{i}^{2}+{y}_{i}^{2}}). Equation (5) defines angle(({x}_{i},{y}_{i})) as being constant before and after the move (Fig. 1c). The variables after the move are marked with ‘. Variables used in the analysis are A = 0.089 m, T = 0.499, k = 1.270, and ω = 12.599 rad/s. Videos of the created motion from the front and the side are shown in the “Supplement” (Supplement Movies 3, 4).Analytical conditionsAnalysis cases were conducted with eight conditions: two types of pectoral fin shape (Fig. 1a,b) and four types of phase difference (0 (T), 0.25 (T), 0.5 (T), and 0.75 (T)). These conditions were set for investigating the effects of phase differences between left and right pectoral fin movements on swimming and how these effects vary with pectoral fin shape.Numerical methodsA CFD simulation of the ray models in the water flow was performed using OPENFOAM, an open-source finite volume method CFD toolbox32, to calculate the forces acting on the rays in each axial direction and the moment around each axis. The governing equations were the continuity equation and the three-dimensional incompressible Reynolds-averaged Navier–Stokes equation, expressed by:$$begin{array}{*{20}c} {nabla cdot u = 0} \ end{array}$$
(6)
$$begin{array}{*{20}c} {frac{partial u}{{partial {text{t}}}} + nabla cdot left( {uu} right) = – nabla p + nabla cdot left( {vnabla u} right) + nabla cdot left[ {nu left{ {left( {nabla u} right)^{T} – frac{1}{3}nabla cdot uI} right}} right], } \ end{array}$$
(7)
where (u) is the velocity vector, t is the time, p is the static pressure divided by the reference density, (nu) is the kinematic viscosity, and I is the unit tensor. The Reynolds number was defined regarding the previous studies3 as:$$begin{array}{c}{R}_{e}=frac{U{L}_{D}}{nu },end{array}$$
(8)
where (U) (/ms) is the given flow speed3 (1.5 × LD/ms), ({L}_{D}) (m) is the length of the ray models, and (nu) is the kinematic viscosity of water at 20 °C (1.0 × 10–6 m2/s). The Reynolds number in this study is 1.8 × 105; considering this, we used the k–ω shear stress turbulence model33,34. The k–ω shear stress turbulence model is a type of Reynolds-averaged Navier–Stokes equation (RANS) turbulence model that is widely used to calculate for the fish swimming flow35,36,37. The overset grid method was used in this study; it is a generic implementation of overset meshes. For both static and dynamic cases, cell-to-cell mapping between multiple, disconnected mesh regions is employed to generate a composite domain38,39. This method permits complex mesh motions and interactions without the penalties associated with deforming meshes. The process is described in detail by Noack40. The calculation volume was 5.4 WD in length, 5.4 WD in height, and 5.4 WD in width (Fig. 2a,b). A hexahedral volume mesh was created using the snappyHexMesh of OPENFOAM. The fluid region was divided into two parts: the overset region and the background region (Fig. 2a,b). The overset region moves and transforms to match the motion of the ray and was made with fine meshes around the analysis target and coarse meshes in the outlying areas; a one-layer boundary layer mesh was created around the analysis target. The overset region shape is an ellipsoid (Fig. 2a,b). The minimum mesh volume is 7.3 × 10–10 (m3), and the maximum mesh volume is 2.6 × 10–2 (m3). The total number of meshes was 9.0 × 105. At the outlet boundary, the average static relative pressure was set to 0 Pa. The surfaces of the fish model were formed into non-slip surfaces.Figure 2Meshes for CFD simulation and differences in force between different meshes. (a) Meshes at the coronal plane of the whole fluid region. (b) Frontal cross-section of the fluid region at the green line in (a). The red region is the overset region. (c,d) Comparison of the instantaneous drag coefficient and the moment coefficient around the y-axis of D. matsubarai between the coarse, fine, and dense mesh.Full size imageThe drag coefficient ({C}_{D}left(tright)), the lateral force coefficient ({C}_{l}left(tright)), the lift coefficient ({C}_{L}left(tright)), the moment coefficient around the x-axis ({C}_{mx}left(tright)), the moment coefficient around the y-axis ({C}_{my}left(tright)) and the moment coefficient around the z-axis ({C}_{mz}left(tright)) were calculated as:$$begin{array}{c}{C}_{D}left(tright)=frac{Dleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(9)
$$begin{array}{c}{C}_{l}left(tright)=frac{lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(10)
$$begin{array}{c}{C}_{L}left(tright)=frac{Lleft(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}{W}_{D}}end{array}$$
(11)
$$begin{array}{c}{C}_{mx}left(tright)=frac{{M}_{psi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
(12)
$$begin{array}{c}{C}_{my}left(tright)=frac{{M}_{phi }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}}end{array}$$
(13)
$$begin{array}{c}{c}_{mz}left(tright)=frac{{M}_{theta }left(tright)}{frac{1}{2}rho {U}^{2}{L}_{D}^{2}{W}_{D}},end{array}$$
(14)
where (Dleft(tright)) is the calculated drag, (lleft(tright)) is the calculated lateral force, (Lleft(tright)) is the calculated lift, ({M}_{psi }left(tright)) is the calculated moment around the x-axis, ({M}_{phi }left(tright)) is the calculated moment around the y-axis, ({M}_{theta }left(tright)) is the calculated moment around the z-axis, and (rho) (kg/m3) is the density of water at 20 °C (998 kg/m3). As shown in a previous study41. the propulsive efficiency (eta) is defined as the ratio of output power ({P}_{o}) to input power ({P}_{e}) which can be written as:$$begin{array}{c}{P}_{o}left(tright)=frac{1}{T}{int }_{0}^{T}Dleft(tright)Udtend{array}$$
(15)
$$begin{array}{c}{P}_{e}left(tright)=frac{1}{T}{int }_{0}^{T}left[Dleft(tright)dot{x}left(tright)+lleft(tright)dot{y}left(tright)+Lleft(tright)dot{z}left(tright)right]dtend{array}$$
(16)
$$begin{array}{c}eta =frac{{P}_{o}}{{P}_{e}}.end{array}$$
(17)
An in-house program calculated the forces acting on rays in each axial direction and the moment around each axis. The numerical method’s validity and reliability were verified by comparing previous experimental and numerical analytical studies of heaving and pitching on airfoil naca001341. A high degree of similarity to previous studies was confirmed; the mean difference in the propulsive efficiency from the previous study of analysis was 5%, and the difference from the previous study of the experiment was 9%. Detailed information such as mesh, length, and velocity, of this analysis method’s verification is provided in the “Supplement”.A grid sensitivity study was conducted using three meshes: coarse, fine, and dense. The coarse mesh has 8.1 × 105 elements, the fine mesh has 9.0 × 105 elements, and the dense mesh has 9.9 × 105 elements. The analysis was conducted using a condition with no phase difference of D. matsubarai. As shown in Fig. 2c,d, the drag coefficient and the moment coefficient around the y-axis are almost the same when the mesh is fine and when the mesh is dense. The mean drag and propulsive efficiency error of fine and coarse meshes are 2.7% and 3.5%, respectively. The fine mesh was used in all simulation cases considering accuracy. We used the same meshes for all cases. More