Ecology

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

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

Denlinger, D. L. Dormancy in tropical insects. Annu. Rev. Entomol. 31, 239–264. https://doi.org/10.1146/annurev.en.31.010186.001323 (1986).CAS 
Article 
PubMed 

Google Scholar 
Moreau, R. E. The breeding seasons of African birds—1. Land birds. Ibis 92, 223–267. https://doi.org/10.1111/j.1474-919X.1950.tb01750.x (1950).Article 

Google Scholar 
Fogden, M. P. L. Seasonality and population dynamics of equatorial forest birds in Sarawak. Ibis 114, 307–343. https://doi.org/10.1111/j.1474-919X.1972.tb00831.x (1972).Article 

Google Scholar 
Karr, J. R. Resource availability, and community diversity in tropical bird communities. Am. Nat. 110, 973–994. https://doi.org/10.1086/283121 (1976).Article 

Google Scholar 
Oppel, S. et al. The effects of rainfall on different components of seasonal fecundity in a tropical forest passerine. Ibis 155, 464–475. https://doi.org/10.1111/ibi.12052 (2013).Article 

Google Scholar 
Shaw, P. Rainfall, leafing phenology and sunrise time as potential Zeitgeber for the bimodal, dry season laying pattern of an African rain forest tit (Parus fasciiventer). J. Ornithol. 158, 263–275. https://doi.org/10.1007/s10336-016-1395-6 (2017).Article 

Google Scholar 
Withers, P. C. & Cooper, C. E. Metabolic depression: A historical perspective. In Aestivation: Molecular and Physiological Aspects, Progress in Molecular and Subcellular Biology (eds Navas, C. A. & Carvalho, J. E.) 1–23 (Springer-Verlag, 2010).
Google Scholar 
Fletcher, B. S., Pappas, S. & Kapatos, E. Changes in ovaries of olive fly (Dacus-oleae-(Gmelin)) during summer, and their relationship to temperature, humidity and fruit availability. Ecol. Entomol. 3, 99–107. https://doi.org/10.1111/j.1365-2311.1978.tb00908.x (1978).Article 

Google Scholar 
Braby, M. F. Reproductive seasonality in tropical satyrine butterflies—Strategies for the dry season. Ecol. Entomol. 20, 5–17. https://doi.org/10.1111/j.1365-2311.1995.tb00423.x (1995).Article 

Google Scholar 
Goehring, L. & Oberhauser, K. S. Effects of photoperiod, temperature, and host plant age on induction of reproductive diapause and development time in Danaus plexippus. Ecol. Entomol. 27, 674–685. https://doi.org/10.1046/j.1365-2311.2002.00454.x (2002).Article 

Google Scholar 
Valera, F., Casas-Crivillé, A. & Hoi, H. Interspecific parasite exchange in a mixed colony of birds. J. Parasitol. 89, 245–250. https://doi.org/10.1645/0022-3395(2003)089[0245:IPEIAM]2.0.CO;2 (2003).Article 
PubMed 

Google Scholar 
Grimaldi, D. The bird flies, genus Carnus: Species revision, generic relationships, and a fossil Meoneura in amber (Diptera: Carnidae). Am. Mus. Novit. 3190, 1–30 (1997).MathSciNet 

Google Scholar 
Valera, F., Casas-Crivillé, A. & Calero-Torralbo, M. A. Prolonged diapause in the ectoparasite Carnus hemapterus (Diptera: Cyclorrapha, Acalyptratae)—How frequent is it in parasites?. Parasitology 133, 179–186. https://doi.org/10.1017/S0031182006009899 (2006).CAS 
Article 
PubMed 

Google Scholar 
Sabrosky, C. W., Bennett, G. F. & Whitworth, T. L. Bird blow flies (Protocalliphora) in North America (Diptera: Calliphoridae), with notes on the Palearctic species. https://library.si.edu/digital-library/book/birdblowfliespro00sabr (Smithsonian Institute Press, 1989).Dodge, H. R. & Aitken, T. H. G. Philornis flies from Trinidad (Diptera: Muscidae). J. Kansas Entomol. Soc. 41, 134–154 (1968).
Google Scholar 
Couri, M. S. Notes and descriptions of Philornis flies (Diptera, Muscidae, Cyrtoneurinininae). Rev. Bras. Entomol. 28, 473–490 (1984).
Google Scholar 
Couri, M. S. Myiasis caused by obligatory parasites. Ia. Philornis Meinert (Muscidae). In Myiasis in Man and Animals in the Neotropical Region (eds Guimaraes, J. H. & Papavero, N.) 44–70 (Editora Pleiade, 1999).
Google Scholar 
Silvestri, L., Antoniazzi, L. R., Couri, M. S., Monje, L. D. & Beldomenico, P. M. First record of the avian ectoparasite Philornis downsi Dodge & Aitken, 1968 (Diptera: Muscidae) in Argentina. Syst. Parasitol. 80, 137–140. https://doi.org/10.1007/s11230-011-9314-y (2011).CAS 
Article 
PubMed 

Google Scholar 
Bulgarella, M. et al. Philornis downsi, an avian nest parasite invasive to the Galápagos Islands, in mainland Ecuador. Ann. Entomol. Soc. Am. 108, 242–250. https://doi.org/10.1093/aesa/sav026 (2015).Article 

Google Scholar 
Kleindorfer, S. & Dudaniec, R. Y. Host-parasite ecology, behavior and genetics: a review of the introduced fly parasite Philornis downsi and its Darwin finch hosts. BMC Zool. 1, 1. https://doi.org/10.1186/s40850-016-0003-9 (2016).Article 

Google Scholar 
Fessl, B., Heimpel, G. E. & Causton, C. E. Invasion of an avian nest parasite, Philornis downsi, to the Galapagos Islands: Colonization history, adaptations to novel ecosystems, and conservation challenges. In Disease Ecology: Social and Ecological Interactions in the Galapagos Islands (ed. Parker, P. G.) 213–266 (Springer, 2018). https://doi.org/10.1007/978-3-319-65909-1_9DO.Chapter 

Google Scholar 
McNew, S. M. & Clayton, D. H. Alien invasion: biology of Philornis flies highlighting Philornis downsi, an introduced parasite of Galapagos birds. Annu. Rev. Entomol. 63, 369–387. https://doi.org/10.1146/annurev-ento-020117-043103 (2018).CAS 
Article 
PubMed 

Google Scholar 
Anchundia, D. & Fessl, B. The conservation status of the Galapagos Martin, Progne modesta: Assessment of historical records and results of recent surveys. Bird Conserv. Int. 31, 129–138. https://doi.org/10.1017/S095927092000009X (2021).Article 

Google Scholar 
Coloma, A., Anchundia, D., Piedrahita, P., Pike, C. & Fessl, B. Observations on the nesting of the Galapagos Dove, Zenaida galapagoensis, in Galapagos, Ecuador. Galapagos Res. 69, 34–38 (2020).
Google Scholar 
Lack, D. Darwin’s Finches (Cambridge University Press, 1947).
Google Scholar 
Grant, P. R. Ecology and Evolution of Darwin’s Finches (Princeton University Press, 1986).
Google Scholar 
Bulgarella, M., Quiroga, M. A. & Heimpel, G. E. Additive negative effects of Philornis nest parasitism and small and declining Neotropical bird populations. Bird Conserv. Int. 29, 339–360. https://doi.org/10.1017/S0959270918000291 (2019).Article 

Google Scholar 
Causton, C. E. et al. Population dynamics of an invasive bird parasite, Philornis downsi (Diptera: Muscidae), in the Galapagos Islands. PLoS ONE 14(10), e0224125. https://doi.org/10.1371/journal.pone.0224125 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hayes, E. J. & Wall, R. Age-grading adult insects: A review of techniques. Physiol. Entomol. 24, 1–10. https://doi.org/10.1046/j.1365-3032.1999.00104.x (1999).Article 

Google Scholar 
Lahuatte, P. F., Lincango, M. P., Heimpel, G. E. & Causton, C. E. Rearing larvae of the avian nest parasite, Philornis downsi (Diptera: Muscidae), on chicken blood-based diets. J. Insect Sci. 16, 84. https://doi.org/10.1093/jisesa/iew064 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Moon, R. D. & Krafsur, E. S. Pterin quantity and gonotrophic stage as indicators of age in Musca autumnalis (Diptera: Muscidae). J. Med. Entomol. 32, 673–684. https://doi.org/10.1093/jmedent/32.5.673 (1995).CAS 
Article 
PubMed 

Google Scholar 
Butler, S. M. et al. Characterization of age and cuticular hydrocarbon variation in mating pairs of house fly, Musca domestica, collected in the field. Med. Vet. Entomol. 23, 426–442. https://doi.org/10.1111/j.1365-2915.2009.00831.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Mail, T. S. & Lehane, M. J. Characterisation of pigments in the head capsule of the adult stablefly Stomoxys calcitrans. Entomol. Exp. Appl. 46, 125–131. https://doi.org/10.1111/j.1570-7458.1988.tb01102.x (1988).Article 

Google Scholar 
Trueman, M. & D’Ozouville, N. Characterizing the Galapagos terrestrial climate in the face of global climate change. Galapagos Res. 67, 26–37 (2010).
Google Scholar 
Stramma, L. et al. Observed El Niño conditions in the eastern tropical Pacific in October 2015. Ocean Sci. 12, 861–873. https://doi.org/10.5194/os-12-861-2016 (2016).ADS 
Article 

Google Scholar 
Martin, N. J. et al. Seasonal and ENSO influences on the stable isotopic composition of Galapagos precipitation. J. Geophys. Res-Atmos. 123, 261–275. https://doi.org/10.1002/2017JD027380 (2018).ADS 
Article 

Google Scholar 
Grant, P. R., Grant, B. R., Keller, L. F. & Petren, K. Effects of El Niño events on Darwin’s finch productivity. Ecology 81, 2442–2457. https://doi.org/10.2307/177466 (2000).Article 

Google Scholar 
Sage, R. et al. Environmentally cued hatching in the bird parasite Philornis downsi (Diptera: Muscidae). Entomol. Exp. Appl. 166, 752–760. https://doi.org/10.1111/eea.12721 (2018).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B 73, 3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x (2011).MathSciNet 
Article 
MATH 

Google Scholar 
Lack, D. Breeding seasons in the Galapagos. Ibis 92, 268–278. https://doi.org/10.1111/j.1474-919X.1950.tb01751.x (1950).Article 

Google Scholar 
Grant, P. R. & Boag, P. T. Rainfall on the Galapagos and the demography of Darwin’s Finches. Auk 97, 227–244 (1980).Article 

Google Scholar 
Peck, S. B. Beetles of the Galápagos Islands, Ecuador: Evolution, Ecology, and Diversity (Insecta: Coleoptera) (NRC Research Press, 2006).
Google Scholar 
Grant, P. R. & Grant, B. R. The breeding and feeding characteristics of Darwin’s Finches on Isla Genovesa, Galapagos. Ecol. Monogr. 50, 381–410. https://doi.org/10.2307/2937257 (1980).Article 

Google Scholar 
Boag, P. T. & Grant, P. R. Darwin Finches (Geospiza) on Isla Daphne Major, Galapagos—Breeding and feeding ecology in a climatically variable environment. Ecol. Monogr. 54, 463–489. https://doi.org/10.2307/1942596 (1984).Article 

Google Scholar 
Schluter, D. Feeding correlates of breeding and social organization in two Galapagos Finches. Auk 101, 59–68. https://doi.org/10.1093/auk/101.1.59 (1984).Article 

Google Scholar 
Hau, M., Wikelski, M., Gwinner, H. & Gwinner, E. 2004 Timing of reproduction in a Darwin’s finch: Temporal opportunism under spatial constraints. Oikos 106, 489–500 (2004).Article 

Google Scholar 
Pike, C. L. et al. Behavior of the avian parasite Philornis downsi (Diptera: Muscidae) in and near host nests in the Galapagos Islands. J. Insect Behav. https://doi.org/10.1007/s10905-021-09789-7 (2021).Article 

Google Scholar 
Heleno, R. H., Olesen, J. M., Nogales, M., Vargas, P. & Traveset, A. Seed dispersal network in the Galapagos and the consequences of alien plant invasions. Proc. R. Soc. B. 280, 20122112. https://doi.org/10.1098/rspb.2012.2112 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Traveset, A., Chamorro, S., Olesen, J. M. & Heleno, R. Space, time and aliens: charting the dynamic structure of Galapagos pollination networks. AoB PLANTS 7, plv068. https://doi.org/10.1093/aobpla/plv068 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ervin, S. Nesting behavior of the large-billed flycatcher on Isla Santa Cruz. Noticias de Galapagos 51, 17–20 (1992).
Google Scholar 
Lincango, P. et al. Interactions between the avian parasite, Philornis downsi (Diptera: Muscidae) and the Galapagos Flycatcher, Myiarchus magnirostris Gould (Passeriformes: Tyrannidae). J. Wildl. Dis. 51, 907–910. https://doi.org/10.7589/2015-01-025 (2015).CAS 
Article 
PubMed 

Google Scholar 
Dudaniec, R. Y., Gardner, M. G., Donnellan, S. & Kleindorfer, S. Genetic variation in the invasive parasite, Philornis downsi (Diptera: Muscidae) on the Galapagos archipelago. BMC Ecol. 8, 13. https://doi.org/10.1186/1472-6785-8-13 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Cimadom, A. et al. Darwin’s finches treat their feathers with a natural repellent. Sci. Rep. 6, 34559. https://doi.org/10.1038/srep34559 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Common, L. K., Dudaniec, R. Y., Colombelli-Negrel, D. & Kleindorfer, S. Taxonomic shifts in Philornis larval behavior and rapid changes in Philornis downsi Dodge & Aitken (Diptera: Muscidae), an invasive avian parasite on the Galapagos Islands. InTech Open https://doi.org/10.5772/intechopen.88854 (2019).Article 

Google Scholar 
Common, L. K., O’Connor, J. A., Dudaniec, R. Y., Peters, K. J. & Kleindorfer, S. Evidence for rapid downward fecundity selection in an ectoparasite (Philornis downsi) with earlier host mortality in Darwin’s finches. J. Evol. Biol. 33, 524–533. https://doi.org/10.1111/jeb.13588 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Dieckhoff, C., Theobald, J. C., Waeckers, F. L. & Heimpel, G. E. Egg load dynamics and the risk of egg and time limitation experienced by an aphid parasitoid in the field. Ecol. Evol. 4, 1739–1750. https://doi.org/10.1002/ece3.1023 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Papaj, D. R. Ovarian dynamics and host use. Annu. Rev. Entomol. 45, 423–448. https://doi.org/10.1146/annurev.ento.45.1.423 (2000).CAS 
Article 
PubMed 

Google Scholar 
Barton Browne, L., van Gerwen, A. C. M. & Williams, K. L. Oocyte resorption during ovarian development in the blowfly Lucilia cuprina. J. Insect Physiol. 25, 147–153. https://doi.org/10.1016/0022-1910(79)90093-3 (1979).Article 

Google Scholar 
Venkatesh, K. & Morrison, P. E. Some aspects of oogenesis in the stable fly Stomoxys calcitrans (Diptera, Muscidae). J. Insect Physiol. 26, 711–715. https://doi.org/10.1016/0022-1910(80)90045-1 (1980).Article 

Google Scholar 
Spradbery, J. P. & Schweizer, G. Oosorption during ovarian development in the screw-worm fly, Chrysomya bezziana. Entomol. Exp. Appl. 30, 209–214. https://doi.org/10.1111/j.1570-7458.1981.tb03102.x (1981).Article 

Google Scholar 
Curry, R. L. & Grant, P. R. Demography of the cooperatively breeding Galapagos mockingbird, Nesomimus parvulus, in a climatically variable environment. J. Anim. Ecol. 58, 441–463. https://doi.org/10.2307/4841 (1989).Article 

Google Scholar 
Calero-Torralbo, M. A. & Valera, F. Synchronization of host-parasite cycles by means of diapause: Host influence and parasite response to involuntary host shifting. Parasitol. 135, 1343–1352. https://doi.org/10.1017/S0031182008004885 (2008).CAS 
Article 

Google Scholar 
Larimore, R. W. Synchrony of cliff swallow nesting and development of the tick Ixodes baergi. Southwest. Nat. 32, 121–126 (1987).Article 

Google Scholar 
Bulgarella, M. & Heimpel, G. E. Host range and community structure of bird parasites in the genus Philornis (Diptera: Muscidae) on the Island of Trinidad. Ecol. Evol. 5, 3695–3703. https://doi.org/10.1002/ece3.1621 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Common, L. K. et al. Avian vampire fly (Philornis downsi) mortality differs across Darwin’s finch host species. Sci. Rep. 11, 15832. https://doi.org/10.1038/s41598-021-94996-7 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mendonca, E. & Couri, M. S. New associations between Philornis Meinert (Diptera, Muscidae) and Thamnophilidae (Aves, Passeriformes). Revta. Bras. Zool. 16, 1223–1225 (1999).Article 

Google Scholar 
Di Giacomo, A. G. Aves de la Reserva El Bagual. Historia natural y paisaje de la Reserva El Bagual, provincia de Formosa, Argentina. Inventario de la fauna de vertebrados y de la flora vascular de un área del Chaco Húmedo (eds. Di Giacomo, A. G. & Krapovickas, S. F.). Temas de Naturaleza y Conservación 4, 203–465 (Aves Argentinas/AOP, 2005).Koop, J. A. H., Causton, C. E., Bulgarella, M., Cooper, E. & Heimpel, G. E. Population structure of a nest parasite of Darwin’s finches within its native and invasive ranges. Conserv. Genet. 22, 11–22. https://doi.org/10.1007/s10592-020-01315-0 (2021).Article 

Google Scholar 
Kawecki, T. J. & Ebert, D. Conceptual issues in local adaptation. Ecol. Lett. 7, 1225–1241. https://doi.org/10.1111/j.1461-0248.2004.00684.x (2004).Article 

Google Scholar  More

Dall, S. R. X. & Johnstone, R. A. Managing uncertainty: Information and insurance under the risk of starvation. Philos. Trans. R. Soc. Lond. B 357, 1519–1526 (2002).
Article 

Google Scholar 
Balogh, A. C. V., Gamberale-Stille, G. & Leimar, O. Learning and the mimicry spectrum: from quasi-Bates to super-Müller. Anim. Behav. 76, 1591–1599 (2008).Article 

Google Scholar 
Barnett, C. A., Bateson, M. & Rowe, C. Better the devil you know: Avian predators find variation in prey toxicity aversive. Biol. Lett. 10, 20140533 (2014).Article 

Google Scholar 
Ruxton, G. D., Allen, W. L., Sherratt, T. N. & Speed, M. P. Avoiding Attack: The Evolutionary Ecology of Crypsis, Aposematism, and Mimicry 2nd edn. (Oxford University Press, 2018).Book 

Google Scholar 
Sherratt, T. N. State-dependent risk-taking by predators in systems with defended prey. Oikos 103, 93–100 (2003).Article 

Google Scholar 
Sherratt, T. N., Speed, M. S. & Ruxton, G. D. Natural selection on unpalatable species imposed by state-dependent foraging behaviour. J. Theor. Biol. 228, 217–226 (2004).MathSciNet 
Article 
ADS 

Google Scholar 
Gamberale-Stille, G. & Guilford, T. Automimicry destabilizes aposematism: Predator sample-and-reject behaviour may provide a solution. Proc. R. Soc. Lond. B 271, 2621–2625 (2004).Article 

Google Scholar 
Skelhorn, J. & Rowe, C. Avian predators taste-reject aposematic prey on the basis of their chemical defence. Biol. Lett. 2, 348–350 (2006).Article 

Google Scholar 
Skelhorn, J. & Rowe, C. Automimic frequency influences the foraging decisions of avian predators on aposematic prey. Anim. Behav. 74, 1563–1572 (2007).Article 

Google Scholar 
Brower, J. V. Z. Experimental studies of mimicry. IV. The reactions of starlings to different proportions of models and mimics. Am. Nat. 94, 271–282 (1960).Article 

Google Scholar 
Huheey, J. E. Studies in warning coloration and mimicry VIII. Further evidence for a frequency-dependent model of predation. J. Herpetol. 14, 223–230 (1980).Avery, M. L. Application of mimicry theory to bird damage control. J. Wildl. Manag. 49, 1116–1121 (1985).Article 

Google Scholar 
Nonacs, P. Foraging in a dynamic mimicry complex. Am. Nat. 126, 165–180 (1985).Article 

Google Scholar 
Rowland, H. M., Ihalainen, E., Lindström, L., Mappes, J. & Speed, M. P. Co-mimics have a mutualistic relationship despite unequal defences. Nature 448, 64–67 (2007).CAS 
Article 
ADS 

Google Scholar 
Skelhorn, J. & Rowe, C. Predators’ toxin burdens influence their strategic decisions to eat toxic prey. Curr. Biol. 17, 1479–1483 (2007).CAS 
Article 

Google Scholar 
Jones, R. S., Davis, S. C. & Speed, M. P. Defence cheats can degrade protection of chemically defended prey. Ethology 119, 52–57 (2013).Article 

Google Scholar 
Guilford, T. “Go-slow” signalling and the problem of automimicry. J. Theor. Biol. 170, 311–316 (1994).Article 
ADS 

Google Scholar 
Skelhorn, J. & Rowe, C. Taste-rejection by predators and the evolution of unpalatability in prey. Behav. Ecol. Sociobiol. 60, 550–555 (2006).Article 

Google Scholar 
Chatelain, M., Halpin, C. G. & Rowe, C. Ambient temperature influences birds’ decisions to eat toxic prey. Anim. Behav. 86, 733–740 (2013).CAS 
Article 

Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).Article 
ADS 

Google Scholar 
Yamazaki, Y., Pagani-Núñez, E., Sota, T. & Barnett C. R. A. The truth is in the detail: predators attack aposematic prey less intensely than other prey types. Biol. J. Linn. Soc. 131, 332–343 (2020).Valkonnen, J. K. et al. Variation in predator species abundance can cause variable selection pressure on warning signalling prey. Ecol. Evol. 2, 1971–1976 (2011).Article 

Google Scholar 
Nokelainen, O., Valkonen, J., Lindstedt, C. & Mappes, J. Changes in predator community structure shifts the efficacy of two warning signals in Arctiid moths. J. Anim. Ecol. 83, 598–605 (2014).Article 

Google Scholar 
Bibby, C. J., Burgess, N. D., Hill, D. A. &. Mustoe S. H. Bird Census Techniques (2nd Edition). (Academic Press, London, 2000).Tsujimoto, D., Lin, C.-H., Kurihara, N. & Barnett, C. R. A. Citizen science in the class-room: the consistency of student collected data and its value in ecological hypothesis testing. Ornithological Sci. 18, 39–47 (2019).Article 

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

Google Scholar 
Rainey, C. Dealing with separation in logistic regression models. Polit. Anal. 24, 339–355 (2016).Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Meth. Ecol. Evol. 4, 133–142 (2012).Article 

Google Scholar 
Hothorn, T,. Bretz, F. & Westfall, P. Simultaneous Inference in General Parametric Models. Biometrical. J. 50, 346–363 (2008).Paradis, E. & Schliep, K. Ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).Barnett, C. R. A., Ringhofer, M. & Suzuki, T. N. Differences in predatory behavior among three bird species when attacking chemically defended and undefended prey. J. Ethol. 39, 29–37 (2021).Article 

Google Scholar 
Carroll, J. & Sherratt, T. N. A direct comparison of the effectiveness of two anti-predator strategies under field conditions. J. Zool. 291, 279–285 (2013).Article 

Google Scholar 
Krebs, C. J. Ecological Methodology (2nd Edition). (Benjamin/Cummings, Menlo Park, CA, 1999).Oksanen, J. vegan: Community Ecology Package. (2020).R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. URL http://www.Rproject.org (2017).Marples, N. M., Speed, M. P. & Thomas, R. J. An individual-based profitability spectrum for understanding interactions between predators and their prey. Biol. J. Linn. Soc. 125, 1–13 (2018).Article 

Google Scholar 
Boyden, T. C. Butterfly palatability and mimicry: experiments with anolis lizards. Evolution 30, 73–81 (1976).Article 

Google Scholar 
Järvi, T., Sillén-Tullberg, B. & Wiklund, C. The cost of being aposematic. An experimental study of predation on larvae of Papilio machaon by the Great Tit Parus major. Oikos 36, 267–272 (1981).Wiklund, C. & Järvi, T. Survival of distasteful insects after being attacked by naïve birds: a reappraisal of aposematic coloration evolving through individual selection. Evolution 36, 998–1002 (1982).Article 

Google Scholar 
Pinheiro, C. E. G. & Campos, V. C. Do rufous-tailed jacamars (Galbula ruficauda) play with aposematic butterflies. Ornitol. Neotrop. 24, 1–3 (2013).
Google Scholar 
Halpin, C. G. & Rowe, C. The effect of distastefulness and conspicuous coloration on post-attack rejection behaviour of predators and survival of prey. Biol. J. Linn. Soc. 120, 236–244 (2017).
Google Scholar 
Sillén-Tullberg, B. Higher survival of an aposematic than of a cryptic form of a distasteful bug. Oecologia 67, 411–415 (1985).Article 
ADS 

Google Scholar 
Fisher, R. A. The Genetical Theory of Natural Selection (Clarenden Press, 1930).Book 

Google Scholar 
Chai, P. Field observations and feeding experiments on the responses of rufous-tailed jacamars butterflies in a tropical rainforest. Biol. J. Linn. Soc. 29, 161–189 (1986).Article 

Google Scholar 
Wang, L.-Y., Huang, W.-S., Tang, H.-C., Huang, L.-C. & Lin, C.-P. Too hard to swallow: A secret secondary defence of an aposematic insect. J. Exp. Biol. 221, jeb172486 (2018).PubMed 

Google Scholar 
Summers, K., Speed, M. P., Blount, J. D. & Stuckert, A. M. M. Are aposematic signals honest? A review. J. Evol. Biol. 28, 1583–1599 (2015).CAS 
Article 

Google Scholar 
Holen, Ø. H. Disentangling taste and toxicity in aposematic prey. Proc. R. Soc. B 280, 20122588 (2013).Article 

Google Scholar 
Speed, M. P. & Franks, D. W. Antagonistic evolution in an aposematic predator-prey system. Evolution 68, 2996–3007 (2014).Article 

Google Scholar  More

Bulgarelli, D., Schlaeppi, K., Spaepen, S., Ver Loren van Themaat, E. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807–838 (2013).CAS 
PubMed 

Google Scholar 
Leach, J. E., Triplett, L. R., Argueso, C. T. & Trivedi, P. Communication in the phytobiome. Cell 169, 587–596 (2017).CAS 
PubMed 

Google Scholar 
Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Muller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).CAS 
PubMed 

Google Scholar 
Jiang, Y. et al. Plant cultivars imprint the rhizosphere bacterial community composition and association networks. Soil Biol. Biochem. 109, 145–155 (2017).CAS 

Google Scholar 
Garbeva, P., van Elsas, J. D. & van Veen, J. A. Rhizosphere microbial community and its response to plant species and soil history. Plant Soil 302, 19–32 (2008).CAS 

Google Scholar 
Li, Y. et al. Rhizobacterial communities of five co-occurring desert halophytes. PeerJ 6, e5508 (2018).PubMed 
PubMed Central 

Google Scholar 
Matthews, A., Pierce, S., Hipperson, H. & Raymond, B. Rhizobacterial community assembly patterns vary between crop species. Front. Microbiol. 10, 581 (2019).PubMed 
PubMed Central 

Google Scholar 
Perez-Jaramillo, J. E., Mendes, R. & Raaijmakers, J. M. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90, 635–644 (2016).CAS 
PubMed 

Google Scholar 
Xu, J. et al. The structure and function of the global citrus rhizosphere microbiome. Nat. Commun. 9, 4894 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant-microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).CAS 
PubMed 

Google Scholar 
Howard, M. M., Munoz, C. A., Kao-Kniffin, J. & Kessler, A. Soil microbiomes from fallow fields have species-specific effects on crop growth and pest resistance. Front. Plant Sci. 11, 1171 (2020).PubMed 
PubMed Central 

Google Scholar 
Yan, Y., Kuramae, E. E., de Hollander, M., Klinkhamer, P. G. & van Veen, J. A. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J. 11, 56–66 (2017).PubMed 

Google Scholar 
Bakker, P. A., Berendsen, R. L., Doornbos, R. F., Wintermans, P. C. & Pieterse, C. M. The rhizosphere revisited: root microbiomics. Front. Plant Sci. 4, 165 (2013).PubMed 
PubMed Central 

Google Scholar 
Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).PubMed 
PubMed Central 

Google Scholar 
de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).ADS 
PubMed 

Google Scholar 
Hamonts, K. et al. Field study reveals core plant microbiota and relative importance of their drivers. Environ. Microbiol. 20, 124–140 (2018).CAS 
PubMed 

Google Scholar 
Xu, Q. et al. Long-term chemical-only fertilization induces a diversity decline and deep selection on the soil bacteria. mSystems 5, e00337–20 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. Regional environmental conditions shape microbial community structure stronger than local forest management intensity. Ecol. Manag. 409, 250–259 (2018).
Google Scholar 
Bais, H. P., Weir, T. L., Perry, L. G., Gilroy, S. & Vivanco, J. M. The role of root exudates in rhizosphere interactions with plants and other organisms. Annu. Rev. Plant Biol. 57, 233–266 (2006).CAS 
PubMed 

Google Scholar 
Wallenstein, M. D. Managing and manipulating the rhizosphere microbiome for plant health: a systems approach. Rhizosphere 3, 230–232 (2017).
Google Scholar 
Kuzyakov, Y. & Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. N. Phytol. 198, 656–669 (2013).CAS 

Google Scholar 
Roller, B. R., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, L. et al. Microbial functional trait of rRNA operon copy numbers increases with organic levels in anaerobic digesters. ISME J. 11, 2874–2878 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 14, 999–1014 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fan, K., Weisenhorn, P., Gilbert, J. A. & Chu, H. Wheat rhizosphere harbors a less complex and more stable microbial co-occurrence pattern than bulk soil. Soil Biol. Biochem. 125, 251–260 (2018).CAS 

Google Scholar 
Fan, K. et al. Rhizosphere-associated bacterial network structure and spatial distribution differ significantly from bulk soil in wheat crop fields. Soil Biol. Biochem. 113, 275–284 (2017).CAS 

Google Scholar 
Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl Acad. Sci. USA 110, 6548–6553 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baudoin, E., Benizri, E. & Guckert, A. Impact of artificial root exudates on the bacterial community structure in bulk soil and maize rhizosphere. Soil Biol. Biochem. 35, 1183–1192 (2003).CAS 

Google Scholar 
Kuzyakov, Y. & Razavi, B. S. Rhizosphere size and shape: temporal dynamics and spatial stationarity. Soil Biol. Biochem. 135, 343–360 (2019).CAS 

Google Scholar 
Ren, Y. et al. Functional compensation dominates the assembly of plant rhizospheric bacterial community. Soil Biol. Biochem. 150, 107968 (2020).CAS 

Google Scholar 
Chen, Y. et al. Organic amendments shift the phosphorus-correlated microbial co-occurrence pattern in the peanut rhizosphere network during long-term fertilization regimes. Appl. Soil Ecol. 124, 229–239 (2018).ADS 

Google Scholar 
Atulba, S. L. et al. Evaluation of rice root oxidizing potential using digital image analysis. J. Korean Soc. Appl. Bi 58, 463–471 (2015).CAS 

Google Scholar 
Schmidt, H., Eickhorst, T. & Tippkötter, R. Monitoring of root growth and redox conditions in paddy soil rhizotrons by redox electrodes and image analysis. Plant Soil 341, 221–232 (2011).CAS 

Google Scholar 
Pausch, J., Zhu, B., Kuzyakov, Y. & Cheng, W. Plant inter-species effects on rhizosphere priming of soil organic matter decomposition. Soil Biol. Biochem. 57, 91–99 (2013).CAS 

Google Scholar 
Finn, D., Kopittke, P. M., Dennis, P. G. & Dalal, R. C. Microbial energy and matter transformation in agricultural soils. Soil Biol. Biochem. 111, 176–192 (2017).CAS 

Google Scholar 
Jones, R. T. et al. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. ISME J. 3, 442–453 (2009).CAS 
PubMed 

Google Scholar 
Zhao, S. et al. Biogeographical distribution of bacterial communities in saline agricultural soil. Geoderma 361, 114095 (2020).ADS 
CAS 

Google Scholar 
Eiler, A., Heinrich, F. & Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6, 330–342 (2012).CAS 
PubMed 

Google Scholar 
Zhou, J. et al. Generation of arbitrary two-point correlated directed networks with given modularity. Phys. Lett. A 374, 3129–3135 (2010).ADS 
CAS 
MATH 

Google Scholar 
Herron, P. M., Gage, D. J., Arango Pinedo, C., Haider, Z. K. & Cardon, Z. G. Better to light a candle than curse the darkness: illuminating spatial localization and temporal dynamics of rapid microbial growth in the rhizosphere. Front. Plant Sci. 4, 323 (2013).PubMed 
PubMed Central 

Google Scholar 
Blagodatskaya, E., Blagodatsky, S., Anderson, T. H. & Kuzyakov, Y. Microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9, e93282 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
PubMed 

Google Scholar 
Mendes, L. W., Kuramae, E. E., Navarrete, A. A., van Veen, J. A. & Tsai, S. M. Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J. 8, 1577–1587 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hinsinger, P. Bioavailability of soil inorganic P in the rhizosphere as affected by root-induced chemical changes: a review. Plant Soil 237, 173–195 (2001).CAS 

Google Scholar 
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: Concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 

Google Scholar 
Loeppmann, S., Blagodatskaya, E., Pausch, J. & Kuzyakov, Y. Substrate quality affects kinetics and catalytic efficiency of exo-enzymes in rhizosphere and detritusphere. Soil Biol. Biochem. 92, 111–118 (2016).CAS 

Google Scholar 
Ma, X. et al. Spatial patterns of enzyme activities in the rhizosphere: Effects of root hairs and root radius. Soil Biol. Biochem. 118, 69–78 (2018).CAS 

Google Scholar 
Kroener, E., Zarebanadkouki, M., Kaestner, A. & Carminati, A. Nonequilibrium water dynamics in the rhizosphere: How mucilage affects water flow in soils. Water Resour. Res. 50, 6479–6495 (2014).ADS 

Google Scholar 
Carminati, A. Rhizosphere wettability decreases with root age: a problem or a strategy to increase water uptake of young roots? Front. Plant Sci. 4, 298 (2013).PubMed 
PubMed Central 

Google Scholar 
Holz, M., Zarebanadkouki, M., Kaestner, A., Kuzyakov, Y. & Carminati, A. Rhizodeposition under drought is controlled by root growth rate and rhizosphere water content. Plant Soil 423, 429–442 (2018).CAS 

Google Scholar 
Tripathi, B. M. et al. Trends in taxonomic and functional composition of soil microbiome along a precipitation gradient in Israel. Microb. Ecol. 74, 168–176 (2017).PubMed 

Google Scholar 
Harms, A., Brodersen, D. E., Mitarai, N. & Gerdes, K. Toxins, targets, and triggers: an overview of toxin-antitoxin biology. Mol. Cell 70, 768–784 (2018).CAS 
PubMed 

Google Scholar 
Kearns, P. J. & Shade, A. Trait-based patterns of microbial dynamics in dormancy potential and heterotrophic strategy: case studies of resource-based and post-press succession. ISME J. 12, 2575–2581 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microb. 66, 1328–1333 (2000).ADS 
CAS 

Google Scholar 
Schoeps, R. et al. Land-use intensity rather than plant functional identity shapes bacterial and fungal rhizosphere communities. Front. Micro. 9, 2711 (2018).
Google Scholar 
Nemergut, D. R. et al. Decreases in average bacterial community rRNA operon copy number during succession. ISME J. 10, 1147–1156 (2016).CAS 
PubMed 

Google Scholar 
Cui, J. et al. Carbon and nitrogen recycling from microbial necromass to cope with C:N stoichiometric imbalance by priming. Soil Biol. Biochem. 142, 107720 (2020).CAS 

Google Scholar 
Blagodatskaya, E. V., Blagodatsky, S. A., Anderson, T. H. & Kuzyakov, Y. Priming effects in chernozem induced by glucose and N in relation to microbial growth strategies. Appl. Soil Ecol. 37, 95–105 (2007).
Google Scholar 
Lecomte, S. M. et al. Diversifying anaerobic respiration strategies to compete in the rhizosphere. Front. Environ. Sci. 6, 139 (2018).
Google Scholar 
Herz, K. et al. Drivers of intraspecific trait variation of grass and forb species in German meadows and pastures. J. Veg. Sci. 28, 705–716 (2017).
Google Scholar 
Ravenek, J. M. et al. Linking root traits and competitive success in grassland species. Plant Soil 407, 39–53 (2016).CAS 

Google Scholar 
Larsen, J., Jaramillo-López, P., Nájera-Rincon, M. & González-Esquivel, C. Biotic interactions in the rhizosphere in relation to plant and soil nutrient dynamics. J. Soil Sci. Plant Nutr. 15, 449–463 (2015).
Google Scholar 
Raaijmakers, J. M., Paulitz, T. C., Steinberg, C., Alabouvette, C. & Moënne-Loccoz, Y. The rhizosphere: a playground and battlefield for soilborne pathogens and beneficial microorganisms. Plant Soil 321, 341–361 (2009).CAS 

Google Scholar 
Ma, H.-K. et al. Steering root microbiomes of a commercial horticultural crop with plant-soil feedbacks. Appl. Soil Ecol. 150, 103468 (2020).
Google Scholar 
Hannula, S. E. et al. Persistence of plant-mediated microbial soil legacy effects in soil and inside roots. Nat. Commun 12, 5686 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill, T. C., Walsh, K. A., Harris, J. A. & Moffett, B. F. Using ecological diversity measures with bacterial communities. FEMS Microbiol. Ecol. 43, 1–11 (2003).CAS 
PubMed 

Google Scholar 
Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 6237 (2015).
Google Scholar 
Noble, W. S. How does multiple testing correction work? Nat. Biotechnol. 27, 1135–1137 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Luo, F., Zhong, J., Yang, Y., Scheuermann, R. H. & Zhou, J. Application of random matrix theory to biological networks. Phys. Lett. A 357, 420–423 (2006).ADS 
CAS 

Google Scholar 
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. ICWSM 8, 361–362 (2009).
Google Scholar 
Peng, G. S. & Wu, J. Optimal network topology for structural robustness based on natural connectivity. Phys. A 443, 212–220 (2016).MathSciNet 

Google Scholar 
Ruan, Y., Wang, T., Guo, S., Ling, N. & Shen, Q. Plant grafting shapes complexity and co-occurrence of rhizobacterial assemblages. Microb. Ecol. 80, 643–655 (2020).CAS 
PubMed 

Google Scholar 
Newman, M. E. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinforma. 13, 113 (2012).
Google Scholar 
Guimerà, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).ADS 
PubMed 
PubMed Central 

Google Scholar 
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl Acad. Sci. USA 104, 19891 (2007).ADS 
CAS 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Ling, N. et al. Insight into how organic amendments can shape the soil microbiome in long-term field experiments as revealed by network analysis. Soil Biol. Biochem. 99, 137–149 (2016).CAS 

Google Scholar 
Louca, S., Parfrey Laura, W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).CAS 
PubMed 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
Google Scholar 
Rosenberg, M. S., Adams, D. C. & Gurevitch, J. MetaWin: Statistical software for meta-analysis. Version 2.0. Sinauer (2000).Viechtbauer, W. Conducting meta-analyses in R with the metafor Package. J. Stat. Softw. 36, 1–48 (2010).
Google Scholar 
Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629 (1997).CAS 
PubMed 
PubMed Central 

Google Scholar 
Calcagno, V. & de Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, 1–29 (2010).
Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 

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

Google Scholar 
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Ant experimentsAnt coloniesSeven colonies of the garden ant L. niger collected from the Soka University and a nearby park were used in this study (Extended Data Table S1). They were placed in plastic cases (35 × 25 × 6 cm). Water was provided ad libitum. They were fed a sucrose solution, and were starved for 2–5 days before the start of the experiment. The colonies were queen-less colonies with 200–700 workers. Aqueous sucrose solution was used as a food resource (bait) in the experiments. The laboratory room where the experiments were performed and the ant colonies were kept was maintained at a temperature of 25–27 °C and a humidity of 60–70%. Artificial lights were also installed in this room.ApparatusWe used an apparatus, called “the main apparatus,” with two paths from the nest to the feeding site (length: 30 cm, width: 2 cm, height: 12 cm for the outward path and 15 cm for the return path) (Fig. 1). This apparatus could separate the outward path (bridge) from the inward path (bridge). Here, the outward path refers to that taken by ants from the nest to the feeding site, whereas the inward path refers to the path taken by ants from the feeding site to the nest.Figure 1The main apparatus used in the three experiments (the main experiment and the comparison experiments 1 and 2). Nests are connected to the experimental apparatus by a slope. In the main experiment, on the outward path, there is ant traffic from the nest to the feeding site on a pheromone trail, and on the inward path, there is ant traffic from the feeding site to the nest on a pheromone trail. In the comparison experiment 1, only a pheromone trail is present on both the outward and inward paths. In the comparison experiment 2, no pheromone trail or ant traffic is present on both the outward and inward paths.Full size imageTwo important features of this apparatus were as follows: firstly, it allowed ants to only enter the outward path from the nest. A rat-guard structure at the end of the inward path prevented the ants on the outward path from entering the inward path (Extended Data Fig. S1A). Secondly, we installed a vertical structure at the end of the outward path (height: 4 cm). After climbing the vertical structure, ants were not allowed to return to the outward path (Extended Data Fig. S1B). Moreover, we installed partitions on the feeding site, which also prevented ants from returning to the outward path after reaching the feeding site (Extended Data Fig. S1C). After entering the feeding site, ants had to pass through a narrow gap (width: 0.5 cm) created by the partition. No visual cues were offered as the apparatus was surrounded on all four sides by plastic walls.In this experiment, we made another apparatus for a single ant (target ant), which would be joining the ant trail on the main bridges (Extended Data Fig. S2). This apparatus, called “the confluence device,” was a detachable device that could be connected at right angles to the outward and inward bridges of the main apparatus. To connect this device to the outward bridge, we made the confluence path of this device under the inward bridge of the main apparatus, since the outward bridge was lower than the inward bridge. Thus, we made a slope on the outward confluence path connected to the outward bridge of the main apparatus. Further, because placing the ants directly on the sidewalk sometimes caused them to fall off the sidewalk owing to panic, we constructed a free space and a wall (height: 5 cm) in the middle of the confluence device on which the ants were placed calmly. Owing to this modification, we could let each target ant calm down and then access the main bridge whenever they wanted to. The apparatus used in this experiment was made of white plastic plates.Pheromone trail with ant trafficThis main experiment was limited to once a day for each colony. A sucrose solution was dripped into the feeding site. Target ants, which were walking on a plastic case as foragers, had been moved from their nests to another case immediately before a trail of (nontarget) ants was formed. Thus, dozens of ants were moved in advance to the case to be used as target ants. Subsequently, a trail of (nontarget) ants was formed from the nest to the main apparatus. Considering that it took some time for the ants that had finished foraging and returned to the nest to recruit their mates, the ants were left for approximately 40 min to an hour until a permanent ant trail was formed. It was difficult to form an ant trail immediately after the start of the experiment since no foraging pheromones could be produced in the first foraging trip on the outward path and since experienced foraging ants may make foraging pheromones on the outward path2,21,22. The target ants were allowed to enter bridges of the main apparatus after the establishment of a permanent ant trail. At that time, trails of individual target ants were started. Target ants were allowed to join at right angles to the path on the apparatus, one by one from the confluence device. Individual target ants were allowed to enter the main apparatus at four different points: (1) Left-Left (LL), located at the left side of the center of the outward path. The outward path was on the left side, whereas the inward path was on the right side for the experimenter when seen from the nest. (2) Left–Right (LR), located at the right side of the center of the outward path. (3) and (4) Right-Left (RL) and Right-Right (RR), located at the left and right sides of the center of the inward path, respectively (Fig. 2). We had set these four points to check if target ants tended to turn their body to a certain direction when entering the main bridges, regardless of the movement direction of the other ants. A video camera (Panasonic, AVCHD 30fps) was used to record the migration of ants to the feeding site or nest. Videos were taken from above, and target ants were used only once.Figure 2Four joining points (LL, LR, RL, and RR) and the confluence device (joining device). The confluence (joining) device was connected at right angles to the center of the outward and inward bridges of the main apparatus. Here, the LR version is shown as an example.Full size imageThe goal lines were set at 15 cm from the center of the main paths. We checked the side (nest side or feeding site side) from which a target ant passed the goal line.Pheromone trail with no ant trafficThis comparison experiment 1 was limited to once a day for each colony. Dozens of ants were moved in advance to another case to be used as target ants in a similar manner to the main experiment. The (nontarget) ants were left for about 40 min to an hour until a permanent ant trail was formed. Subsequently, we removed all the ants from the device. Then, target ants were allowed to enter on the side path one by one. In this case, we left the bait in place to control this experiment under the same condition as the main experiment. As the pheromone trail was created on the outward path as well as on the inward path, it was the only decision-making factor for the ants to join at the main path (outward/inward paths). We checked the side (nest side or feeding site side) from which a target ant passed the goal line in a similar manner to the main experiment.No pheromone trail or ant trafficThis comparison experiment 2 was limited to once a day for each colony. Dozens of ants were moved in advance to another case to be used as target ants in a similar manner to the main experiment. This experiment was conducted to investigate ant behavior under the following two conditions: (1) no ant trails and (2) no pheromones trails. The bait was in place in the same manner. We checked the side (nest side or feeding site side) from which a target ant passed the goal line in a similar manner to the main experiment. After each trial (the target ant passed the goal line), we wiped the apparatus with ethanol solution before the next target ant was allowed to enter the main paths.AnalysisThe goal lines were set at 15 cm from the center of the main paths. We checked which goal side the target ants reached the goal line on each trial. A reverse run referred to the goal to the nest on the outward path and the goal to the feeding site on the inward path. A normal run referred to the goal to the feeding site on the outward path and the goal to the nest on the inward path.In some cases of the main experiment, foraging (nontarget) ants that could not reach the feeding site on their outward path or could not return to the nest on their inward path would be against the ant flows. On the outward path, we considered that the ants conducted a “reverse flow” if the position of their heads was on the nest side compared with the position of their stomach. If not, we defined that the ants conducted a “normal flow” (Extended Data Fig. S3). On the inward path, we defined that the ants conducted a “reverse flow” if the position of their head was on the feeding site side compared with the position of their stomach. If not, we defined that the ants conducted a “normal flow” (Extended Data Fig. S3). We focused on the target ants that came in contact with ants with normal flow. Therefore, if an ant with reverse flow was located within 10 cm of the target ant, that trial was excluded from the analysis.Furthermore, we also evaluated if target ants coming in contact with foraging (nontarget) ants immediately after entering the trail would affect the goal choice. Therefore, we conducted an analysis focusing on the contact using the data from the main experiment. We examined whether or not the target ant made contact with other foraging ants until it passed a point 2 cm from the center of the path. As already mentioned, if the target ant came in contact with another ant moving against the normal flow of the ant trail, this contact was excluded from the counts. Moreover, we also excluded cases in which the body of target ants was on a point 2 cm from the center of the path by visual evaluation. Thus, we examined the goal choice of target ants by focusing on whether or not they came in contact with other ants immediately after joining the main bridges.We also conducted a preliminary experiment using a single path apparatus to investigate bi-directional trail behaviour. Please see the Extended Data File S1.Model descriptionThe models were coded using the C programming language. The model description follows the Overview, Design concepts, and Details protocol23,24.PurposeThe purpose of the model was to examine the mechanistic understanding of our findings. We adopted an action of target agents obtained from our ant experiments and compared it with another action of target agents on a trail that was contrary to the fact. To be more precise, target agents were allowed to obey an alignment rule in which they tended to move in the same direction with other agents. We named the former model as the reverse-rule model and the latter model as the alignment-rule model. By doing so, we could find the significance of our findings from ant experiments.Entities, state variables, and scalesWe developed two different models (reverse-rule model and alignment-rule model) that included two types of entities: agents and cells. The agent has the state variable Navigational state, which has two values: Navigational state = {wandering, foraging}. The cell has the state variable Pheromone; this value represents the amount of pheromones in each cell. We used a 2D lattice field and set a straight bridge with 61 cells × 5 cell sizes. We also set goal lines at x-coordinate =  − 30 and 30. If the agents reached coordinates satisfying their x-coordinate =  − 30 or 30, they were removed from the system. If the agents reached y-axis boundaries, their movement direction was restricted. Each trial continued until the target agent reached one of the two goal lines. However, trials were forcibly finished if the target agent never reached any goal line by t = 500-time steps. In total, we conducted 1000 trials.Process overview and schedulingAt the beginning of each trial, an artificial target ant (Navigational state = wandering) was introduced at the center of an artificial simulation field. Foraging agents (Navigational state = foraging) were randomly distributed on the simulation field in advance.Agents on the simulation field selected one direction from two directions (+ x and − x) on each time step and updated their positions. Briefly, an agent at coordinate (x, y) selected one direction from two directions (+ x and − x) and updated its position with one of the three coordinates—(x − 1, y), (x − 1, y + 1), or (x − 1, y − 1)—if it selected the − x direction, or—(x + 1, y), (x + 1, y + 1), or (x + 1, y − 1)—if it selected the + x direction by scanning pheromones on these three coordinates. For example, if an agent at coordinate (0, 2) decided to move in + x direction at one time, the position of this agent was replaced with one of (1, 3), (1, 2) and (1, 1) from (0, 2) by scanning pheromones on these three coordinates. The target agent selected the − x/ + x direction with equal probability on each time step until it met the foragers. In contrast, foraging agents tended to decide to move in the − x direction on each time step with a high probability and therefore they tended to select the − x direction for position updating. Foraging agents deposited pheromones before leaving the current cell (see submodel entitled “Position updating” and submodel entitled “Pheromone updating”). In contrast, the target agents did not deposit pheromones.Using above submodels, artificial ants sometimes met other agents. If the target agent (Navigational state = wandering) met the foragers (Navigational state = foraging), the target agent tended to select one direction from two directions (+ x and − x) on each time step thereafter with a high probability, which was dependent on which direction the met foragers came from. More strictly, in the reverse-rule model, the target agent tended to move in an opposite direction from the foragers if it met the foragers coming from the opposite direction. On the contrary, the target agent in the alignment-rule model tended to move in the same direction with foragers if it met the foragers moving in the same direction (see submodel entitled “The interaction between the target agent and foragers”). For example, in the reverse-rule model, if the target agent at coordinate (x, y), whose previous coordinate was (x − 1, y), met the forager coming from the opposite direction, whose previous coordinate was (x + 1, y), the target agent decided to move in + x direction on each time step thereafter with a high probability until similar events occurred.Design conceptThe mean goal time was the emergent property of the model. Sensing was important as the agents scanned the pheromone concentrations. Stochasticity was used to determine in which direction the agent moved and to select one cell using the pheromone concentrations.InitializationWe set a single agent (target agent) on the coordinate (0, 2) and its Navigational state was set to wandering (Extended Data Fig. S5A). We also set N foraging agents on the bridge whose Navigational state was set to foraging. Therefore, N + 1 agents were on the test field at the beginning of each trial. A target agent was the agent k = 0, whereas foraging agents were agents k = 1, 2, …, N. These foragers were randomly distributed on the bridge. Thus, x(k) (in) {n |− 30 ≤ n ≤ 30, n is an integer} and y(k) (in) {n | 0 ≤ n ≤ 4, n is an integer} for k  > 0.Foraging agents were set to move in the -x direction (Direction(k) for k  > 0 = − x). On the other hand, the target agent randomly chose one direction from two directions at the beginning of each trial (Direction(0) was set to + x or − x with equal probability). Herein, Direction(k) can be − x or + x, which implies bias in the movement direction. The parameter prob(k) indicates the probability of moving in Direction(k). The target agent selected the − x/+ x direction with equal probability on each time step until it met the foragers. Therefore, the parameter prob was set to 0.50 for the target ant (prob(0) = 0.50), whereas prob was set to 0.80 for foraging agents (prob(k) = 0.80 for k  > 0). The amount of pheromones on each cell was set to 1 at the beginning of each trial (pheromone(x, y) = 1) and the pheromone evaporation rate q was set to 0.99.The model descriptions are explained using submodels. A Submodel: the interaction between the target agent and foragers causes differences between two rules (the reverse-rule model and the alignment-rule model).SubmodelsSubmodel: the interaction between the target agent and foragersThe parameters Direction(0) and prob(0) were replaced with new ones whenever the following events occurred.In the reverse-rule model, for any agent k (k  > 0),Herein, (xt(k), yt(k)) indicates the x–y-coordinate for the agent k at time t. Furthermore, (xt(0), yt(0)) = (xt(k), yt(k)) means that the target agent and the agent k occupy the same cell at time t while (xt(0) − xt−1(0)) × (xt(k) − xt−1(k)) =  − 1 indicates that the target agent meets the agent k came from the opposite direction. The target agent replaces Direction(0) with an opposite direction from the forager k (see Extended data Fig. S5B).In the alignment-rule model, for any agent k (k  > 0),(xt(0) − xt−1(0)) × (xt(k) − xt−1(k)) = 1 indicates that the target agent meets the agent k came from the same direction. The target agent replaces Direction(0) with a same direction with the forager k (See Extended Data Fig. S5B).In the reverse-rule model, these events are driven from the experimental observations of real ants. Target ants appear to move against the trail and seem to move straight by contacting those other nestmates that come from the opposite direction. Also, target ants seem to select the reverse goal even if physical contact with ant nestmates does not occur immediately after entering the bridge. So, regarding parameter replacements, we did not consider the position at which the target agent met another agent. Note that foraging agents did not change these parameters until the end of each trial. Further, Direction(0) can be replaced with − x from + x and vice versa whenever the target agent meets foragers that come from the opposite direction.In the alignment-rule model, the target agent tends to move in the same direction with other agents. This is contrary to the experimental observations of real ants.Submodel: position updatingFor all k agents (k = 0–N), the movement direction and position updates are shown as follows (Extended Data Fig. S5C);Here, rnt(k) indicates a random number. Thus, rnt(k) (in) [0.00, 1.00].Prob(0) for the target agent is initially set to 0.50. Therefore, the target agent selects one direction from the two (− x and + x) on each time step randomly before the condition described in submodels—the interaction between the target agent and foragers is satisfied. On the other hand, foraging agents select − x direction with a high probability (= Prob(k)) on each time step. After selecting one direction from two (− x and + x), agents scan three cells in the direction of movement. Using pheromone concentrations on those three cells, they update their positions.If agents reach coordinates satisfying their y-coordinate = 4 or 0, those agents update their position by selecting not three but two coordinates since they are located on the edges of the bridge.Submodel: pheromone updatingForaging agents (k  > 0) deposited pheromones on the current cell when leaving that cell.Then, pheromones are evaporated using the evaporation rate q.For each time iteration, these submodels operated in the following order.STEP 1: The interaction between the target agent and foragers.STEP 2: Position updating.STEP 3: Pheromone updating.AnalysisTo check the accuracy of our model, we counted which goal side the target agent entered the goal line from using the reverse-rule model by setting N = 9. If the target agent passed the goal line at x-coordinate =  − 30 (30), we considered that it reached the normal (reverse) goal. Note that trials in which the target agent never reached any goal lines by t = 500 were excluded from this analysis. Furthermore, to investigate the adaptability of the reverse run mechanism, we examined the time until the target agent reached the goal lines using the reverse-rule model and the alignment-rule model. Herein, we set two different conditions with respect to the number of foraging agents (N = 4 and 9). More