Philippa Kaur

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Lindenmayer, D. B. & Laurance, W. F. The ecology, distribution, conservation and management of large old trees. Biol. Rev. Camb. Phil. Soc. 92, 1434–1458 (2017).Article 

Google Scholar 
Voigt, C. C., Borissov, I. & Kelm, D. H. Bats fertilize roost trees. Biotropica 47, 403–406 (2015).Article 

Google Scholar 
Blicharska, M. & Mikusiński, G. Incorporating social and cultural significance of large old trees in conservation policy. Conserv. Biol. 28, 1558–1567 (2014).Article 

Google Scholar 
Sponsel, L. E. Spiritual Ecology: A Quiet Revolution (ABC-CLIO, 2012).Omura, H. Trees, forests and religion in Japan. Mt. Res. Dev. 24, 179–182 (2004).Article 

Google Scholar 
Heintzman, P. Nature-based recreation and spirituality: a complex relationship. Leis. Sci. 32, 72–89 (2009).Article 

Google Scholar 
Irvine, K. N., Hoesly, D., Bell-Williams, R. & Warber, S. L. in Biodiversity and Health in the Face of Climate Change (eds Marselle, M. R. et al.) 213–247 (Springer, 2019).Millennium Ecosystem Assessment Ecosystems and Human Well-Being: Synthesis (Island Press, 2005).Vihervaara, P., Rönkä, M. & Walls, M. Trends in ecosystem service research: early steps and current drivers. Ambio 39, 314–324 (2010).Article 

Google Scholar 
Brown, J. H. Macroecology (Univ. Chicago Press, 1995).Piovesan, G. & Biondi, F. On tree longevity. New Phytol. 231, 1318–1337 (2021).Article 

Google Scholar 
Matsui, K. Geography of Religion in Japan: Religious Space, Landscape, and Behavior (Springer, 2013).Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

Google Scholar 
Giant Trees Follow-Up Survey Report, the Sixth Census of the National Survey of the Natural Environment (in Japanese) (Biodiversity Center of Japan & Ministry of the Environment, 2001); https://www.biodic.go.jp/reports2/6th/kyojuflup/6_kyojuflup.pdfMakino, K. Folkloristics of Giant Trees (in Japanese) (Kobunsha, 1986).Daniel, T. C. et al. Contributions of cultural services to the ecosystem services agenda. Proc. Natl Acad. Sci. USA 109, 8812–8819 (2012).Article 
CAS 

Google Scholar 
Muthukrishna, M. et al. Beyond Western, Educated, Industrial, Rich, and Democratic (WEIRD) psychology: measuring and mapping scales of cultural and psychological distance. Psychol. Sci. 31, 678–701 (2020).Article 

Google Scholar 
Twigger-Ross, C. L. & Uzzell, D. L. Place and identity processes. J. Environ. Psychol. 16, 205–220 (1996).Article 

Google Scholar 
Mittermeier, R. A., Turner, W. R., Larsen, F. W., Brooks, T. M. & Gascon, C. in Biodiversity Hotspots (eds Zachos, F. E. & Habel, J. C.) 3–22 (Springer, 2011).Watanabe, T., Matsunaga, K., Kanazawa, Y., Suzuki, K. & Rotherham, I. D. Landforms and distribution patterns of giant Castanopsis sieboldii trees in urban areas and western suburbs of Tokyo, Japan. Urban For. Urban Green. 60, 126997 (2021).Article 

Google Scholar 
Uryu, S. jpmesh: Utilities for Japanese Mesh Code. R package version 2.1.0 https://CRAN.R-project.org/package=jpmesh (2022).R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Yamanouchi, T. et al. A Checklist of Japanese Plant Names (Japan Node of Global Biodiversity Information Facility, 2019); https://www.gbif.jp/v2/activities/wamei_checklist.html More

Myxococcota and Bdellovibrionota were active constituents of activated sludge microbiotaTo explore the predating activity and diversity of predatory bacteria in activated sludge, 13C-labeled Escherichia coli and Pseudomonas putida cells (determined as 97.09 and 97.30 atom% 13C, respectively) were added to the sludge microcosms for maximumly eight days of incubation, and 13C incorporation was examined using rRNA-SIP to identify prokaryotic and eukaryotic microorganisms involved in actively consuming the 13C-labeled prey cells. Bacterial 16S rRNA gene amplicon sequencing-based analysis indicated the relative contribution of 47.9% and 42.7% of the obtained sequences by the added biomass upon amendment in the 13C-E. coli (Fig. 1A) and 13C-P. putida (Fig. 1B) microcosms, which dropped below 1.0% after 16 h and eight days of incubation, respectively. The overall bacterial community structure at the steady state was highly comparable to that of the control microcosms (Fig. 1C), indicating that the prey cell amendments did not induce too strong fluctuation in the microbiota structure during the SIP experiment that prevented disentangling the indigenous community dynamics.Fig. 1: The dynamics of the prokaryotic communities and mineralization of the added 13C-biomass during the microcosm experiment.The overall prokaryotic communities were obtained by 16S rRNA gene amplicon sequencing of the total DNA from the activated sludge microcosms amended with 13C-E. coli (A) and 13C-P. putida (B) cells, and the control group (C) without amendment. The structure of the active prokaryotic communities was inferred based on amplicon sequencing of the light rRNA fractions from the microcosms amended with 13C-E. coli (D) and 13C-P. putida (E) cells. The temporal change in the proportion of produced 13CO2 in total CO2 indicated the mineralization of the 13C-labeled cells of E. coli and P. putida in duplicate microcosms (F). Relative sequence abundance of the ten most abundant prokaryotic phyla, together with the genera Escherichia-Shigella and Pseudomonas, was shown.Full size imageThe metabolically active bacterial communities, as inferred by 16S rRNA gene transcripts of the light rRNA fractions from the microcosms, were rather consistent throughout the experiment (Fig. 1D, E), but they showed clear compositional differences compared to the overall prokaryotic communities inferred by 16S rRNA gene amplicon sequences (Fig. 1A, B). Myxococcota and Bdellovibrionota species showed an average relative abundance of 17.5 (±0.7) % and 2.7 (±0.2) % in the 16S rRNA gene transcripts, respectively, which were significantly higher than 5.4 (±0.6) % and 1.3 (±0.1) % in the 16S rRNA genes of bacterial communities (p 1% in the 13C-heavy fractions, strong 13C-labeling was found for the as-yet-uncultivated myxobacterial mle1-27 clade (average EF 2.66 across time and treatments), which contributed to 10.3% to 38.9% of the 16S rRNA gene transcripts in the 13C-heavy fractions, indicating its high metabolic activity in consuming the 13C-labeled biomass of both E. coli and P. putida. Comparatively, Haliangium spp. and uncultured Polyangiaceae belonging to Myxococcota, as well as the as-yet-uncultivated OM27 clade belonging to Bdellovibrionota, also exhibited strong 13C-labeling (maximum EF across time: 2.4–39.5), but almost exclusively in the microcosms amended with 13C-E. coli cells (Fig. 2A). The as-yet-uncultivated myxobacterial VHS-B3-70 clade exhibited the strongest enrichment (average EF 16.67 across time and treatments) but made up only 0.2% to 2.3% of 16S rRNA gene transcripts of the 13C-heavy fraction (Fig. 2A). Overall, our microcosm experiment tracking added 13C-labeled prey bacterial cells with rRNA-SIP suggested prominent predatory activity of Myxococcota and Bdellovibrionota lineages including largely as-yet-uncultivated ones (e.g., the mle1-27, VHS-B3-70, and OM27 clades) in activated sludge.Fig. 2: The enrichment of incorporators of added 13C-biomass in heavy rRNA fractions and the temporal labeling patterns.13C-labeled prokaryotic (A) and micro-eukaryotic (B) genus-level taxa were identified by SIP in the microcosms added with E. coli and P. putida after one, two, and four days of incubation. Enrichment factor (EF) was calculated for microorganisms using heavy and light rRNA gradient fractions of the 13C- and 12C-microcosms to infer 13C-labeling. Taxa with an EF  > 0.1 in at least one of the treatment groups at one sampling time point was considered labeled. The area of circles indicates the relative sequence abundance of the labeled taxa in heavy 13C-rRNA. The negative EFs and positive EFs 1% in the heavy rRNA fractions of at least one of the 13C-E. coli and 13C-P. putida microcosms at a sampling point.Full size imageMyxococcota and Bdellovibrionota predated more selectively than protistsFor the micro-eukaryotes, several taxa belonging to Ciliophora, especially Cyrtophoria spp. and Telotrochidium spp., and also Peritrichia spp., Vaginicola spp., Aspidisca spp., and Epistylis spp., were highly enriched (maximum EF across time and treatments: 0.9–6.7) in the 13C-heavy rRNA fractions (Fig. 3B), in agreement with the dominance of Ciliophora in the micro-eukaryotic rRNA gene transcripts (Fig. 2B). The Candida-Lodderomyces clade and Cyberlindnera-Candida clade within Ascomycota, Magnoliophyta spp. within Phragmoplastophyta, and Poteriospumella spp. and unclassified Chromulinales within Ochrophyta were also strongly labeled (maximum EF: 13.5–242.5, Fig. 2B). Moreover, the 13C-biomass incorporation by micro-eukaryotes was independent of whichever prey bacteria (Fig. 2B, D), revealing no detectable prey preference in the metabolically active micro-eukaryotic predators. On the contrary, differential labeling by 13C-E. coli and 13C-P. putida cells was frequently observed for the predatory bacteria (Fig. 2A, C). The most obvious example was the OM27 clade ASVs belonging to Bdellovibrionota, which were found to incorporate 13C-labeled biomass exclusively of E. coli (Fig. 2C). Comparatively, Haliangium-affiliated ASV27 and ASV63 were labeled only by 13C-E. coli, ASV57 labeled by both 13C-E. coli and 13C-P. putida, while ASV72 and ASV76 were also labeled by 13C-P. putida, but only at a later sampling point (Fig. 2C). These results on the divergent labeling patterns with the tested prey bacteria together strongly implied population-specific predating behaviors of predatory bacteria in activated sludge.Fig. 3: In situ relative abundance of Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge at a local WWTP (WWTP01) based on sampling over two years.The abundance of the abundant genera belonging to Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge were compared according to amplicon sequencing-based analysis of bacterial 16S rRNA gene V3-V4 region. The top 10 abundant genus-level taxa across samples collected from eight samplings are shown, with the putative predators identified by SIP in the microcosm experiment highlighted. The asterisk denotes significant difference in relative abundance between aerobic and anaerobic sludges (p 0.1% in the activated sludge of WWTP01, including the putative predators identified in the microcosm experiment, i.e., Haliangium spp. (2.8 ± 0.7%) which represented the most abundant myxobacterial lineage in the activated sludge, uncultured Polyangiaceae (0.4 ± 0.1%), and the mle1-27 clade (0.2 ± 0.0%; Fig. 3). Moreover, Pajaroellobacter (1.2 ± 0.2%), Nannocystis (0.4 ± 0.1%), Phaselicystis (0.3 ± 0.1%), and several other myxobacterial clades, although not identified as putative predators in the microcosm experiment, were among the abundant myxobacteria in situ in the activated sludge. Although the myxobacterial genera showed comparable relative abundance in the anaerobic tanks, fed by returned activated sludge, to their counterparts in the aerobic tanks, the obligately aerobic myxobacteria were presumably metabolically inactive in the anerobic sludge. Unlike Myxococcota, members of Bdellovibrionota altogether showed significantly higher relative abundance in the aerobic sludge (1.0 ± 0.2%) than in the anaerobic sludge (0.6 ± 0.1%, paired samples Wilcoxon test p  More

Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).
Google Scholar 
Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).
Google Scholar 
Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).
Google Scholar 
Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).ADS 
CAS 

Google Scholar 
Alexander Pyron, R. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280, 1–10 (2013).
Google Scholar 
Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).
Google Scholar 
Wright, S., Keeling, J. & Gillman, L. The road from Santa Rosalia: a faster tempo of evolution in tropical climates. Proc. Natl Acad. Sci. USA 103, 7718–7722 (2006).ADS 
CAS 

Google Scholar 
Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).
Google Scholar 
Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
CAS 

Google Scholar 
Igea, J. & Tanentzap, A. J. Angiosperm speciation speeds up near the poles. Ecol. Lett. 23, 1–40 (2020).
Google Scholar 
Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007).ADS 
CAS 

Google Scholar 
Rabosky, D. L. & Huang, H. A robust semi-parametric test for detecting trait-dependent diversification. Syst. Biol. 65, 181–193 (2016).
Google Scholar 
Hansen, J. et al. Global temperature change. Proc. Natl Acad. Sci. USA 103, 14288–14293 (2006).ADS 
CAS 

Google Scholar 
Huey, R. B. & Kingsolver, J. G. Climate warming, resource availability, and the metabolic meltdown of ectotherms. Am. Nat. 194, E140–E150 (2019).
Google Scholar 
Gerringer, M. E., Linley, T. D., Jamieson, A. J., Goetze, E. & Drazen, J. C. Pseudoliparis swirei sp. Nov.: A newly-discovered hadal snailfish (Scorpaeniformes: Liparidae) from the Mariana Trench. Zootaxa 4358, 161–177 (2017).
Google Scholar 
Childress, J. J. Are there physiological and biochemical adaptations of metabolism in deep-sea animals? Trends Ecol. Evol. 10, 30–36 (1995).CAS 

Google Scholar 
Seibel, B. A. & Drazen, J. C. The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities. Philos. Trans. R. Soc. B Biol. Sci. 362, 2061–2078 (2007).CAS 

Google Scholar 
Eme, D., Anderson, M. J., Myers, E. M. V., Roberts, C. D. & Liggins, L. Phylogenetic measures reveal eco-evolutionary drivers of biodiversity along a depth gradient. Ecography 43, 689–702 (2020).
Google Scholar 
Costello, M. J. & Chaudhary, C. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Curr. Biol. 27, R511–R527 (2017).CAS 

Google Scholar 
Brown, A. & Thatje, S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: Physiological contributions to adaptation of life at depth. Biol. Rev. 89, 406–426 (2014).
Google Scholar 
Zintzen, V., Anderson, M. J., Roberts, C. D., Harvey, E. S. & Stewart, A. L. Effects of latitude and depth on the beta diversity of New Zealand fish communities. Sci. Rep. 7, 1–10 (2017).CAS 

Google Scholar 
Coleman, R. R., Copus, J. M., Coffey, D. M., Whitton, R. K. & Bowen, B. W. Shifting reef fish assemblages along a depth gradient in Pohnpei, Micronesia. PeerJ 2018, 1–30 (2018).
Google Scholar 
Neat, F. C. & Campbell, N. Proliferation of elongate fishes in the deep sea. J. Fish. Biol. 83, 1576–1591 (2013).CAS 

Google Scholar 
Martinez, C. M. et al. The deep sea is a hot spot of fish body shape evolution. Ecol. Lett. 24, 1788–1799 (2021).
Google Scholar 
Webb, P. Introduction to Oceanography (Online OER textbook, 2017).Hanly, P. J., Mittelbach, G. G. & Schemske, D. W. Speciation and the latitudinal diversity gradient: Insights from the global distribution of endemic fish. Am. Nat. 189, 604–615 (2017).
Google Scholar 
Tedesco, P. A., Paradis, E., Lévêque, C. & Hugueny, B. Explaining global-scale diversification patterns in actinopterygian fishes. J. Biogeogr. 44, 773–783 (2017).
Google Scholar 
Cooney, C. R., Seddon, N. & Tobias, J. A. Widespread correlations between climatic niche evolution and species diversification in birds. J. Anim. Ecol. 85, 869–878 (2016).
Google Scholar 
Title, P. O. & Burns, K. J. Rates of climatic niche evolution are correlated with species richness in a large and ecologically diverse radiation of songbirds. Ecol. Lett. 18, 433–440 (2015).
Google Scholar 
Seeholzer, G. F., Claramunt, S. & Brumfield, R. T. Niche evolution and diversification in a Neotropical radiation of birds (Aves: Furnariidae). Evolution 71, 702–715 (2017).
Google Scholar 
Kozak, K. H. & Wiens, J. J. Accelerated rates of climatic-niche evolution underlie rapid species diversification. Ecol. Lett. 13, 1378–1389 (2010).
Google Scholar 
Schnitzler, J., Graham, C. H., Dormann, C. F., Schiffers, K. & Peter Linder, H. Climatic niche evolution and species diversification in the cape flora, South Africa. J. Biogeogr. 39, 2201–2211 (2012).
Google Scholar 
Ghezelayagh, A. et al. Prolonged morphological expansion of spiny-rayed fishes following the end-Cretaceous. Nat. Ecol. Evol. 1–10. https://doi.org/10.1038/s41559-022-01801-3 (2022).Polato, N. R. et al. Narrow thermal tolerance and low dispersal drive higher speciation in tropical mountains. Proc. Natl Acad. Sci. USA 115, 12471–12476 (2018).ADS 
CAS 

Google Scholar 
Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).
Google Scholar 
O’Hara, T. D., Hugall, A. F., Woolley, S. N. C., Bribiesca-Contreras, G. & Bax, N. J. Contrasting processes drive ophiuroid phylodiversity across shallow and deep seafloors. Nature 565, 636–639 (2019).ADS 

Google Scholar 
Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639 (2010).
Google Scholar 
Hulsey, C. D., Roberts, R. J., Loh, Y. H. E., Rupp, M. F. & Streelman, J. T. Lake Malawi cichlid evolution along a benthic/limnetic axis. Ecol. Evol. 3, 2262–2272 (2013).CAS 

Google Scholar 
Woolley, S. N. C. et al. Deep-sea diversity patterns are shaped by energy availability. Nature 533, 393–396 (2016).ADS 
CAS 

Google Scholar 
Pigot, A. L., Owens, I. P. F. & Orme, C. D. L. The environmental limits to geographic range expansion in birds. Ecol. Lett. 13, 705–715 (2010).
Google Scholar 
Gerringer, M. E., Linley, T. D. & Nielsen, J. G. Revision of the depth record of bony fishes with notes on hadal snailfishes (Liparidae, Scorpaeniformes) and cusk eels (Ophidiidae, Ophidiiformes). Mar. Biol. 168, 1–9 (2021).
Google Scholar 
Kolora, S. R. R. et al. Origins and evolution of extreme life span in Pacific Ocean rockfishes. Science 374, 842–847 (2021).ADS 
CAS 

Google Scholar 
Rutschmann, S. et al. Parallel ecological diversification in Antarctic notothenioid fishes as evidence for adaptive radiation. Mol. Ecol. 20, 4707–4721 (2011).
Google Scholar 
Wilson, L. A. B., Colombo, M., Hanel, R., Salzburger, W. & Sánchez-Villagra, M. R. Ecomorphological disparity in an adaptive radiation: opercular bone shape and stable isotopes in Antarctic icefishes. Ecol. Evol. 3, 3166–3182 (2013).
Google Scholar 
Ingram, T. Speciation along a depth gradient in a marine adaptive radiation. Proc. R. Soc. B. 278, 613–618 (2011).
Google Scholar 
Hyde, J. R., Kimbrell, C. A., Budrick, J. E., Lynn, E. A. & Vetter, R. D. Cryptic speciation in the vermilion rockfish (Sebastes miniatus) and the role of bathymetry in the speciation process. Mol. Ecol. 17, 1122–1136 (2008).CAS 

Google Scholar 
Kai, Y., Orr, J. W., Sakai, K. & Nakabo, T. Genetic and morphological evidence for cryptic diversity in the Careproctus rastrinus species complex (Liparidae) of the North Pacific. Ichthyol. Res. 58, 143–154 (2011).
Google Scholar 
Gerringer, M. E. et al. Habitat influences skeletal morphology and density in the snailfishes (family Liparidae). Front. Zool. 18, 1–22 (2021).
Google Scholar 
Saveliev, P. A. & Metelyov, E. A. Species composition and distribution of eelpouts (Zoarcidae, Perciformes, Actinopterygii) in the northwestern Sea of Okhotsk in summer. Prog. Oceanogr. 196, 102605 (2021).
Google Scholar 
Quattrini, A. M. et al. Niche divergence by deep-sea octocorals in the genus Callogorgia across the continental slope of the Gulf of Mexico. Mol. Ecol. 22, 4123–4140 (2013).
Google Scholar 
Zardus, J. D., Etter, R. J., Chase, M. R., Rex, M. A. & Boyle, E. E. Bathymetric and geographic population structure in the pan-Atlantic deep-sea bivalve Deminucula atacellana (Schenck, 1939). Mol. Ecol. 15, 639–651 (2006).CAS 

Google Scholar 
Schüller, M. Evidence for a role of bathymetry and emergence in speciation in the genus Glycera (Glyceridae, Polychaeta) from the deep Eastern Weddell Sea. Polar Biol. 34, 549–564 (2011).
Google Scholar 
Smith, W. L., Everman, E. & Richardson, C. Phylogeny and taxonomy of flatheads, scorpionfishes, sea robins, and stonefishes (Percomorpha: Scorpaeniformes) and the evolution of the lachrymal saber. Copeia 106, 94–119 (2018).
Google Scholar 
Jamon, M., Renous, S., Gasc, J. P., Bels, V. & Davenport, J. Evidence of force exchanges during the six-legged walking of the bottom-dwelling fish,Chelidonichthys lucerna. J. Exp. Zool. 307A, 542–547 (2007).
Google Scholar 
McCune, A. R. & Carlson, R. L. Twenty ways to lose your bladder: common natural mutants in zebrafish and widespread convergence of swim bladder loss among teleost fishes. Evol. Dev. 6, 246–259 (2004).
Google Scholar 
Rabosky, D. L. Speciation rate and the diversity of fishes in freshwaters and the oceans. J. Biogeogr. 47, 1207–1217 (2020).
Google Scholar 
Daane, J. M. et al. Historical contingency shapes adaptive radiation in Antarctic fishes. Nat. Ecol. Evol. 3, 1102–1109 (2019).
Google Scholar 
Mu, Y. et al. Whole genome sequencing of a snailfish from the Yap Trench (~7,000 m) clarifies the molecular mechanisms underlying adaptation to the deep sea. PLoS Genet. 17, e1009530 (2021).CAS 

Google Scholar 
Yancey, P. H., Gerringer, M. E., Drazen, J. C., Rowden, A. A. & Jamieson, A. Marine fish may be biochemically constrained from inhabiting the deepest ocean depths. Proc. Natl Acad. Sci. USA 111, 4461–4465 (2014).ADS 
CAS 

Google Scholar 
Janzen, D. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Google Scholar 
Kozak, K. H. & Wiens, J. J. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. R. Soc. B: Biol. Sci. 274, 2995–3003 (2007).
Google Scholar 
Sheldon, K. S., Huey, R. B., Kaspari, M. & Sanders, N. J. Fifty years of mountain passes: a perspective on Dan Janzen’s classic article. Am. Nat. 191, 553–565 (2018).
Google Scholar 
Muñoz, M. M. & Bodensteiner, B. L. Janzen’s hypothesis meets the bogert effect: connecting climate variation, thermoregulatory behavior, and rates of physiological evolution. Integr. Organ. Biol. 1, oby002 (2019).
Google Scholar 
Santidrián Tomillo, P., Fonseca, L., Paladino, F. V., Spotila, J. R. & Oro, D. Are thermal barriers ‘higher’ in deep sea turtle nests? PLoS ONE 12, 1–14 (2017).
Google Scholar 
Brown, J. H. Why marine islands are farther apart in the tropics. Am. Nat. 183, 842–846 (2014).
Google Scholar 
Jablonski, D. et al. Out of the tropics, but how? Fossils, bridge species, and thermal ranges in the dynamics of the marine latitudinal diversity gradient. Proc. Natl Acad. Sci. USA 110, 10487–10494 (2013).ADS 
CAS 

Google Scholar 
Hattermann, T. Antarctic thermocline dynamics along a narrow shelf with easterly winds. J. Phys. Oceanogr. 48, 2419–2443 (2018).ADS 

Google Scholar 
Robison, B. H. What drives the diel vertical migrations of Antarctic midwater fish? J. Mar. Biol. Ass. 83, 639–642 (2003).
Google Scholar 
Bourgeaud, L. et al. Climatic niche change of fish is faster at high latitude and in marine environments. Preprint at bioRxiv https://doi.org/10.1101/853374 (2019).Pie, M. R. et al. The evolution of latitudinal range limits in tropical reef fishes: heritability, limits, and inverse Rapoport’s rule. J. Biogeogr. 00, 1–12 (2021).
Google Scholar 
Powell, M. G. & Glazier, D. S. Asymmetric geographic range expansion explains the latitudinal diversity gradients of four major taxa of marine plankton. Paleobiology 43, 196–208 (2017).
Google Scholar 
Lawson, A. M. & Weir, J. T. Latitudinal gradients in climatic-niche evolution accelerate trait evolution at high latitudes. Ecol. Lett. 17, 1427–1436 (2014).
Google Scholar 
Boag, T. H., Gearty, W. & Stockey, R. G. Metabolic tradeoffs control biodiversity gradients through geological time. Curr. Biol. 31, 2906–2913.e3 (2021).CAS 

Google Scholar 
Near, T. J. et al. Ancient climate change, antifreeze, and the evolutionary diversification of Antarctic fishes. Proc. Natl Acad. Sci. USA 109, 3434–3439 (2012).ADS 
CAS 

Google Scholar 
Hotaling, S., Borowiec, M. L., Lins, L. S. F., Desvignes, T. & Kelley, J. L. The biogeographic history of eelpouts and related fishes: Linking phylogeny, environmental change, and patterns of dispersal in a globally distributed fish group. Mol. Phylogenet. Evol. 162, 107211 (2021).
Google Scholar 
Thatje, S., Hillenbrand, C.-D., Mackensen, A. & Larter, R. Life hung by a thread: endurance of Antarctic fauna in glacial periods. Ecology 89, 682–692 (2008).
Google Scholar 
Keller, I. & Seehausen, O. Thermal adaptation and ecological speciation. Mol. Ecol. 21, 782–799 (2012).CAS 

Google Scholar 
Deutsch, C., Penn, J. L. & Seibel, B. Metabolic trait diversity shapes marine biogeography. Nature 585, 557–562 (2020).ADS 
CAS 

Google Scholar 
Labeyrie, L. D., Duplessy, J. C. & Blanc, P. L. Variations in mode of formation and temperature of oceanic deep waters over the past 125,000 years. Nature 327, 477–482 (1987).ADS 
CAS 

Google Scholar 
Boag, T. H., Stockey, R. G., Elder, L. E., Hull, P. M. & Sperling, E. A. Oxygen, temperature and the deep-marine stenothermal cradle of Ediacaran evolution. Proc. R. Soc. B: Biol. Sci. 285, 2011724 (2018).
Google Scholar 
Koslow, J. A. Community structure in North Atlantic deep-sea fishes. Prog. Oceanogr. 31, 321–338 (1993).ADS 

Google Scholar 
Brunn, A. The abyssal fauna: its ecology, distribution, and origin. Nature 177, 1105–1108 (1956). Fr.ADS 

Google Scholar 
Gaither, M. R. et al. Depth as a driver of evolution in the deep sea: Insights from grenadiers (Gadiformes: Macrouridae) of the genus Coryphaenoides. Mol. Phylogenet. Evol. 104, 73–82 (2016).
Google Scholar 
Eastman, J. T. Evolution and diversification of Antarctic notothenioid fishes. Am. Zool. 31, 93–110 (1991).
Google Scholar 
Quattrini, A. M., Gómez, C. E. & Cordes, E. E. Environmental filtering and neutral processes shape octocoral community assembly in the deep sea. Oecologia 183, 221–236 (2017).ADS 

Google Scholar 
Stefanoudis, P. V. et al. Depth-dependent structuring of reef fish assemblages from the shallows to the rariphotic zone. Front. Mar. Sci. 6, 1–16 (2019).
Google Scholar 
Zintzen, V., Anderson, M. J., Roberts, C. D. & Diebel, C. E. Increasing variation in taxonomic distinctness reveals clusters of specialists in the deep sea. Ecography 34, 306–317 (2011).
Google Scholar 
Price, S. A., Claverie, T., Near, T. J. & Wainwright, P. C. Phylogenetic insights into the history and diversification of fishes on reefs. Coral Reefs 34, 997–1009 (2015).ADS 

Google Scholar 
Weber, M. G., Wagner, C. E., Best, R. J., Harmon, L. J. & Matthews, B. Evolution in a Community Context: On Integrating Ecological Interactions and Macroevolution. Trends Ecol. Evol. 32, 291–304 (2017).
Google Scholar 
Linley, T. D. et al. Fishes of the hadal zone including new species, in situ observations and depth records of Liparidae. Deep Sea Res. Part I Oceanogr. Res. Pap. 114, 99–110 (2016).ADS 

Google Scholar 
Jamieson, A. J., Linley, T. D., Eigler, S. & Macdonald, T. A global assessment of fishes at lower abyssal and upper hadal depths (5000 to 8000 m). Deep Sea Res. Part I Oceanogr. Res. Pap. 103642. https://doi.org/10.1016/j.dsr.2021.103642 (2021).Boers, N. Observation-based early-warning signals for a collapse of the Atlantic meridional overturning circulation. Nat. Clim. Chang. 11, 680–688 (2021).ADS 

Google Scholar 
Paulus, E. Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front. Mar. Sci. 8, 667048 (2021).Froese, R. & Pauly, D. FishBase. FishBase www.fishbase.org (2019).Boettiger, C., Lang, D. T. & Wainwright, P. C. Rfishbase: exploring, manipulating and visualizing FishBase data from R. J. Fish. Biol. 81, 2030–2039 (2012).CAS 

Google Scholar 
Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 

Google Scholar 
Karstensen, J., Stramma, L. & Visbeck, M. Oxygen minimum zones in the eastern tropical Atlantic and Pacific oceans. Prog. Oceanogr. 77, 331–350 (2008).ADS 

Google Scholar 
Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. Part I: Oceanogr. Res. Pap. 126, 85–102 (2017).ADS 

Google Scholar 
Alfaro, M. E. et al. Explosive diversification of marine fishes at the Cretaceous–Palaeogene boundary. Nat. Ecol. Evol. 2, 688–696 (2018).
Google Scholar 
Magnuson-Ford, K. & Otto, S. P. Linking the investigations of character evolution and species diversification. Am. Nat. 180, 225–245 (2012).
Google Scholar 
Goldberg, E. E. & Igić, B. Tempo and mode in plant breeding system evolution. Evolution 66, 3701–3709 (2012).
Google Scholar 
Rabosky, D. L. & Goldberg, E. E. Model inadequacy and mistaken inferences of trait-dependent speciation. Syst. Biol. 64, 340–355 (2015).CAS 

Google Scholar 
Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
Google Scholar 
Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. (2019).Collyer, M. L. & Adams, D. C. RRPP: An r package for fitting linear models to high-dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).
Google Scholar 
Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).
Google Scholar 
Freckleton, R. P., Phillimore, A. B. & Pagel, M. Relating traits to diversification: a simple test. Am. Nat. 172, 102–115 (2008).
Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
CAS 

Google Scholar 
Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).ADS 
CAS 

Google Scholar 
May, M. R. & Moore, B. R. A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation. Syst. Biol. 69, 530–544 (2020).
Google Scholar 
Höhna. et al. RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Syst. Biol. 65, 726–736 (2016).
Google Scholar 
Burress, E. D. & Muñoz, M. M. Ecological opportunity from innovation, not islands, drove the anole lizard adaptive radiation. Syst. Biol. 0, 1–12 (2021).
Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 

Google Scholar 
Ives, A. R. & Helmus, M. R. Phylogenetic metrics of community similarity. Am. Nat. 176, E128–E142 (2010).
Google Scholar 
Costello, M. J. & Breyer, S. Ocean depths: the mesopelagic and implications for global warming. Curr. Biol. 27, R36–R38 (2017).CAS 

Google Scholar  More

As part of the streamflow data release, NEON released four relevant data products: Gauge Height26, Elevation of Surface Water29, Stage-discharge Rating Curves30, and Continuous Discharge15. Data users are able to download this full suite of information and protocols to inform decisions on data usage and applicability. We evaluated the quality of the Continuous Discharge product using all four relevant NEON data products, considering the validity of model inputs as well as the goodness-of-fit of final streamflow estimates. We analyzed 1) the fit of the regression between manual stage height readings and continuous pressure transducer data used to estimate continuous stream surface elevation, 2) the fit of rating curves transforming stream surface elevation to streamflow, and 3) the proportion of streamflow estimates over the maximum manually-measured streamflow.Stage classificationThe rating curve models predicting streamflow required continuous stream stage estimates as model inputs. NEON predicted continuous gauge height with a two step approach. First, continuous in-stream transducer readings were converted to water height by applying an offset between the transducer elevation and the staff gauge (Eq. 1). This offset is derived from the NEON geolocation database as the difference between the location of the pressure transducer and the staff gauge27. The offset changes only when the location of either the staff gauge or transducer moves.$${h}_{wc}=frac{{P}_{sw}}{p,ast ,g},ast ,1000+{h}_{stage}$$
(1)
Conversion of pressure data to water height used by NEON27 where hwc is the estimated water column height (m), Psw is calibrated surface water pressure (kPa), p is the density of water (999 kg/m3), g is the acceleration due to gravity (9.81 m/s2), and hstage is the offset between the pressure transducer and the staff gauge (m).Then, NEON uses a linear regression between manually-measured reference stage height and the calculated gauge height from Eq. 1, yielding final predictions of continuous stream gauge height27. In an ideal setting, stage and gauge height should correlate perfectly28. In the field, sensor uncertainty, manual reference measurement error, and shifting conditions in the stream can convolute the relationship. We tested the goodness of fit between continuously estimated stream gauge height values and manual stage measurements using the Nash-Sutcliffe model efficiency coefficient (Eq. 2). Nash-Sutcliffe coefficient is a commonly used metric in hydrology used to evaluate how well a model performed relative to observed values (manually measured stage and calculated gauge height). For the purposes of this discussion, manual reference measurements will be referred to as ‘stage’ and automated, sensed readings as ‘gauge height’.$$NSE=1-frac{Sigma {left({Q}_{o}-{Q}_{m}right)}^{2}}{Sigma {left({Q}_{o}-{bar{Q}}_{o}right)}^{2}}$$
(2)
Equation 2 presents Nash-Sutcliffe model efficiency coefficient, where Qo is an observed value (streamflow or stage height), Qm is a modeled value, and ({bar{Q}}_{o}) is the mean of observed values.Stage, gauge height, and regression data were sourced from the NEON Continuous Discharge product, representing what was directly applied to streamflow estimation. Up to 26 stage measurements were available per year. We examined every regression between stage and gauge height (one per site year in which data was available) and classified each as either ‘good’, ‘fair’, or ‘poor’ quality based on their goodness of fit. Regressions with a NSE (Eq. 2) of 0.90 or greater were considered good, those with a NSE of less than 0.90 but greater than or equal to 0.75 were considered fair, and those with an NSE of less than 0.75 were considered poor (Fig. 2).Drift detectionBecause electronic instruments, such as pressure transducers, can have systematic directional drift, referred to as ‘drift’, during deployment, we developed an approach to detect periods of time when NEON’s Elevation of Surface Water product drifted. We used two methods to assess and flag the potential for instrument drift at monthly time steps. First, we flagged any period the manually measured stage fell outside NEON’s uncertainty bound for gauge height made at the same time. From this, we calculated the proportion of stage measurements outside of the gauge height uncertainty bounds per month. This proved to be a relatively lenient filter that missed periods of manually identified drift. We found adding a second filter that flagged any month where the difference between the manually measured stage and gauge height exceeded 6 cm, was effective in catching the majority of periods where drift was identified. Second, we calculated the average differences between stage and gauge height for each month (Fig. 3). To determine appropriate cut-off values to classify areas of potential drift, we manually audited and flagged periods of observable directional drift. Our goal was to set a maximum cut-off difference which retained as much usable data as possible while still capturing 70% of the manually flagged directional drift periods. Applying this method, we determined a cut-off value of 6 cm average monthly deviation between observed and predicted stage values.Using these two filters in combination, we again classified data into three groups: ‘likely no drift’, ‘potential drift’, and ‘not assessed’. Site-months with no more than 50% of stage measurements outside of the gauge height time series uncertainty and an average difference between stage and gauge height less than 6 cm were considered to have ‘likely no drift’. Site-months with either more than 50% of stage readings outside of the gauge height time series uncertainty or an average difference between stage and gauge height more than 6 cm were deemed to have ‘potential drift’. Site-months with no stage measurements could not be evaluated and were considered ‘not assessed’. Although this approach to identify drift is imperfect, in that slight drift could be missed and times without manual measurements are not possible to assess, we believe this is a helpful method given the data available from NEON and the fact drift has been observed when visually inspecting data (Fig. 3).Rating curve classificationTo evaluate how well rating curves predicted streamflow, we assessed each rating curve used to convert stage to discharge. NEON prepares a new rating curve for each site’s water year (beginning on October 1st)27. In cases where NEON reported multiple rating curves for a site’s water year each curve was assessed separately across the time series which it was used. We classified rating curves into three tiers based on two metrics: the Nash-Sutcliffe coefficient (Eq. 2) between observed and predicted streamflow, and the percentage of continuous discharge values above the maximum manually measured gauging used to construct the rating curve.First, we calculated the Nash-Sutcliffe coefficient for each rating curve to estimate how well rating curves captured the variation in the stage-streamflow relationship. We used the reported values for modeled and manually measured streamflow from the ‘Y1simulated’ and ‘Y1observed’ columns in the ‘sdrc_resultsResiduals’ table of the Stage-discharge rating curves product. NEON generally conducts between 12 and 24 manual gaugings per year to build and maintain the stage-discharge relationship.Second, we calculated the percentage of continuous streamflow values outside the range of manually measured estimates of streamflow. This was useful to assess if the stage-discharge relationship is representative of observed flow conditions. The relationship between discharge and stage is often nonlinear, with inflection points around changes in channel morphology making gauging the stream at high and low flow conditions critical to building a reliable rating curve16. A rating curve based on a large number of direct field measurements all taken during a narrow range of baseflows, for example, could generate a rating curve with a high Nash-Sutcliffe coefficient that is unreliable when extrapolated to high or low flow events. Using these two metrics, we were able to classify rating curves into categories of relative quality. To calculate the percentage of values in the continuous streamflow product that fall outside the range of manually gauged streamflow values, we extracted the maximum and minimum gauging values from the ‘sdrc_resultsResiduals’ table in the Stage-discharge Rating Curve product. We then compared the predicted values derived from each rating curve (as reported in the ‘csd_continuousDischarge’ table) to the extracted range and calculated the proportion of values which fell outside of it.We used the Nash-Sutcliffe coefficient and percentage of streamflow values over the maximum observed field measurements to classify rating curves into three categories outlined in Table 1.To integrate stage-gauge regressions, drift detections, and rating curve classification, we produced a summary table with classifications for all three tests and the corresponding metrics used in each classification (Fig. 5). The table is grouped by month and site so users can query sites and determine which months have the appropriate data for their needs. More

Orchard site selectionThe survey was conducted from 2018 to 2019 in the Circum-Bohai Bay region, which included Shandong, Hebei, and Liaoning provinces and Beijing, and the Loess Plateau region, which included Shanxi and Shaanxi provinces. Five typical production counties were selected in each province or city. Representative orchards were selected according to the production of the main varieties in each county (orchard area was greater than 1.0 ha; the pear trees were 15 to 25 years old; and the yield of orchards ranged from 40 to 60 t ha−1). A total of 225 orchards were investigated (Fig. 1), including 150 in the Circum-Bohai Bay region and 75 in the Loess Plateau region (Table 1).Fig. 1The locations of the 225 pear orchards.Full size imageTable 1 Numbers of pear orchard and main cultivated varieties investigated in Circum-Bohai Bay and Loess Plateau.Full size tableSample collection and pretreatmentSoil and leaf samples were collected at the stage in which the growth of new shoots ceased, from July 1 to July 1510. Eleven sampling sites were determined in each orchard according to an “S” shape sampling method (Fig. 2), and soil samples from the 0–20 cm, 20–40 cm and 40–60 cm layers were collected. The soil samples of the same soil layer at each sampling site were mixed into one sample. Then, the soil samples were air-dried, ground and sifted with a nylon sieve for determination of nutrient concentrations.Fig. 2The “S” shape sampling method. The red dots are the sampling locations.Full size imageTen to fifteen pear trees in each orchard of the same size and vigour and 5 to 10 mature leaves from the middle of a long shoot from the periphery of each tree were selected for leaf sampling11. Then, all the leaves from the same orchard were mixed into one leaf sample. The leaves were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the leaf samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Fruit samples were collected at the ripening stage. Pear trees from which leaf samples were collected from each orchard were selected for fruit sample collection. Three to five peripheral fruits of the same size were collected from each tree, and fruit samples from the same orchard were mixed into one sample. The fruits were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again, cut into slices and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the fruit samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Sample determinationVarious indicators of soil and plant samples were determined according to the method of Cui et al.12 and Bao13.Soil pH determinationA potentiometric method was used to measure soil pH. Carbon dioxide-free water was added to soil that had been passed through a 2 mm sieve at a water-soil ratio of 2.5:1. The soil solution was stirred for 1 min and left undisturbed for 30 min. Each soil sample was measured more than three times with a pH meter (FE20K PLUS PH, Mettler-Toledo, Switzerland), and the difference in the parallel determination results was less than 0.2 pH units. The electrode was washed with deionized water and dried with filter paper after each sample measurement. A calibration solution was used to calibrate the electrode between measurements after every 10 soil samples.Soil organic matter determinationSoil organic matter was measured according to the Schollenberger method using chromic acid redox titration. Five millilitres of a 0.8 M 1/6 K2Cr2O7 solution was added to a test tube with approximately 0.5000 g of soil that had been passed through a 0.25 mm sieve. The mixture was then added to 5 mL concentrated sulfuric acid and shaken gently to disperse the soil. The tube was placed in a phosphoric acid bath, heated to 170 °C and boiled for 5 min. To condense the water vapour that escaped during the heating process, a small funnel was placed on the top of the test tube. The substances in the test tube and funnel were transferred to a conical flask after cooling. Then, the solution was added to 1,10-phenanthroline hydrate and titrated with 0.2 M FeSO4 until it turned maroon. A blank experiment was performed when each batch of samples was measured. The soil organic matter content was calculated according to the following formula:$${rm{omega }}left({rm{OM}}right)=frac{left({rm{V}}-{rm{V}}0right)times {rm{c}}times 3times 1.724times {rm{f}}}{{rm{m}}}$$
(1)
ω(OM): soil organic matter content; c: standard FeSO4 solution concentration; V: volume of the standard FeSO4 used in titration; V0: volume of standard FeSO4 used in titrating control sample; 3: molar mass of a quarter of carbon; 1.724: the conversion factor from organic carbon to organic matter; f: oxidation correction coefficient (the value was 1.1); m: mass of oven-dried soil sample.Soil total N determinationTotal N was determined by the semitrace Kjeldahl method. Approximately 1.0000 g of air-dried soil that had been passed through a 0.25 mm sieve was added to a digestion tube. Meanwhile, the soil moisture content was measured to calculate the mass of the oven-dried soil. Two grams of accelerator and 5 mL of concentrated sulfuric acid were added to the tube. The tube was then covered with a small funnel, and the sample was digested at 360 °C for 15–20 min. The mixture was digested for 1 h until the colour changed from brown to greyish green or greyish white. Two digested soilless samples were used as controls. After the digestion tube cooled, it was placed in a distiller, and a small amount of deionized water was added. Five millilitres of a 2% boric acid indicator was added to a 150 mL conical flask, and the flask was placed at the end of the condenser tube. Then, the digestion solution was distilled until the distillate volume was approximately 75 mL. The distillate was titrated with 0.01 M standard hydrochloric acid to a purplish red colour endpoint. The soil total N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
(2)
ω(N): soil total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried soil sample.Soil alkaline hydrolysable N determinationApproximately 2.00 g of air-dried soil that have been passed through a 2 mm sieve was placed in the outer chamber of a diffuser. The diffuser was gently rotated to evenly distribute the soil in the outer chamber. Two millilitres of H3BO3 indicator was placed in the inner chamber of the diffusion dish. The edge of the frosted glass surface of the diffuser was coated with alkaline glycerin and covered with frosted glass. The diffuser was covered tightly and secured with rubber bands after 10.00 mL of 1 M NaOH was injected into the diffuser through a hole in the frosted glass. The diffuser was placed in a 40 °C incubator for alkaline hydrolysis diffusion for 24 h. Then, the mixture was titrated with 0.01 M standard hydrochloric acid until it turned purplish red. A blank test was performed at the same time as the samples. The soil alkaline hydrolysable N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
(3)
ω(N): soil alkaline hydrolysable N concentration; c: standard acid solution concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of air-dried soil sample.Soil available P determinationApproximately 2.50 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle and 50 mL of 0.5 M NaHCO3 was added. After the bottle was shaken for 30 min, the mixture was immediately filtered with phosphorus-free filter paper. Ten millilitres of the filtrate was accurately measured into a conical flask, and 5.00 mL of Mo-Sb-Vc colour developer and 10 mL of deionized water were added. The absorbance of the mixture was measured at approximately 700 nm after 30 min using a UV-Vis spectrophotometer (UV1900PC, AuCy Instrument, Shanghai, China). Finally, the P concentration was calculated according to a standard curve prepared with solutions of different P concentrations. A blank test was performed at the same time that the samples were determined.Soil available K determinationApproximately 5.00 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle, and 50 mL of 1.0 M NH4OAc was added. After the sample was shaken for 30 min, the mixture was immediately filtered with dry filter paper. The concentration of K in the filtrate was determined directly by a flame photometer (LM12-FP6430, Haifuda, China) according to a standard curve prepared with solutions of different K concentrations. A blank test was performed at the same time that the samples were determined.Leaf and fruit N determinationApproximately 0.3000 g of plant powder that had been passed through a 0.5 mm sieve was placed into a digestion tube and 5 mL concentrated sulfuric acid was added. Then, the digestion tube was placed onto a digestion stove at 360 °C after two doses of 2 mL H2O2, and the sample was digested until the mixture turned brown. After the tube cooled, 2 mL H2O2 was added, and the digestion was continued for 5 min. This process was repeated until the mixture turned clear. The mixture was diluted to 100 mL in a volumetric flask for testing after it cooled. Then, 5 to 10 mL of the liquid to be tested was accurately measured into a distiller for distillation. The distillation and titration processes were the same as those used for ammonium in the Soil total N determination section. A blank test was performed at the same time as sample measurement. The leaf or fruit N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14times {rm{V}}1}{{rm{m}}times {rm{V}}2}$$
(4)
ω(N): total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried sample; V1: volume of the digestion solution after constant volume; V2: measured volume of digestion solution after constant volume.Leaf and fruit P, K, Ca, Fe, Mn, Cu, Zn, B determinationApproximately 0.5000 g of plant powder that had been passed through a 0.5 mm sieve was placed in a digestion tube and a 10 mL mixture of concentrated nitric acid and hypochlorous acid (4:1) was added. After the sample was left undisturbed for more than 4 h, it was placed onto a digestion stove and heated to 150 °C so that NO2 could volatilize slowly. Then, the temperature was appropriately increased to a temperature not higher than 250 °C until the digestive solution was transparent and approximately 2 mL remained. The solution was transferred into a volumetric flask after cooling and adjusted to a constant volume of 50 mL. The solution was then filtered, and the concentration of each element in the solution was determined by a plasma emission spectrometer (ICP-OES, OPTIMA 3300 DV, 75 Perkin-Elmer, USA). A blank test was performed at the same time as sample measurement. The leaf or fruit P, K, Ca, Fe, Mn, Cu, Zn, and B concentrations were calculated according to the following formula:$${rm{omega }}({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})=frac{rho ({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})times {rm{V}}times {rm{f}}}{{rm{m}}}$$
(5)
ω(P, K, Ca, Fe, Mn, Cu, Zn, B): P, K, Ca, Fe, Mn, Cu, Zn, B concentration in leaf or fruit; ρ(P, K, Ca, Fe, Mn, Cu, Zn, B): the concentration of P, K, Ca, Fe, Mn, Cu, Zn or B in the liquid to be measured; V: volume of the liquid to be measured after constant volume; f: dilution ratio of the liquid to be measured; m: mass of oven-dried sample. More

Wilson, E. O. The Insect Societies (Oxford University Press, 1971).
Google Scholar 
Beshers, S. N. & Fewell, J. H. Models of division of labor in social insects. Annu. Rev. Entomol. 46, 413–440 (2001).CAS 

Google Scholar 
Seeley, T. D. Adaptive significance of the age polyethism schedule in honeybee colonies. Behav. Ecol. Sociobiol. 4, 287–293 (1982).
Google Scholar 
Tallamy, D. W. Insect parental care. Bioscience 34, 20–24. https://doi.org/10.2307/1309421 (1984).Article 

Google Scholar 
Queller, D. C. Extended parental care and the origin of eusociality. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 256, 105–111. https://doi.org/10.1098/rspb.1994.0056 (1994).Article 
ADS 

Google Scholar 
Bigley, W. S. & Vinson, S. B. Characterization of a brood pheromone isolated from the sexual brood of the imported fire ant, Solenopsis invicta 1,2. Ann. Entomol. Soc. Am. 68, 301–304 (1975).CAS 

Google Scholar 
Endler, A. et al. Surface hydrocarbons of queen eggs regulate worker reproduction in a social insect. Proc. Natl. Acad. Sci. USA 101, 2945–2950. https://doi.org/10.1073/pnas.0308447101 (2004).Article 
ADS 
CAS 

Google Scholar 
Maisonnasse, A., Lenoir, J. C., Beslay, D., Crauser, D. & Le Conte, Y. E-beta-ocimene, a volatile brood pheromone involved in social regulation in the honey bee colony (Apis mellifera). PLoS ONE 5, e13531. https://doi.org/10.1371/journal.pone.0013531 (2010).Article 
ADS 
CAS 

Google Scholar 
Schultner, E., Oettler, J. & Helantera, H. The role of brood in eusocial hymenoptera. Q. Rev. Biol. 92, 39–78. https://doi.org/10.1086/690840 (2017).Article 

Google Scholar 
Amdam, G. V., Hartfelder, K., Norberg, K., Hagen, A. & Omholt, S. W. Altered physiology in worker honey bees (Hymenoptera: Apidae) infested with the mite Varroa destructor (Acari: Varroidae): A factor in colony loss during overwintering? J. Econ. Entomol. 97, 741–747 (2004).
Google Scholar 
Calabi, P. & Traniello, J. F. Behavioral flexibility in age castes of the ant Pheidole dentata. J. Insect Behav. 2, 663–677 (1989).
Google Scholar 
Gordon, D. W. Dynamics of task switching in harvester ants. Anim. Behav. 38, 194–204 (1989).
Google Scholar 
Robinson, G. E. Regulation of division of labor in insect societies. Annu. Rev. Entomol. 37, 637–665. https://doi.org/10.1146/annurev.en.37.010192.003225 (1992).Article 
CAS 

Google Scholar 
Robinson, E. J., Feinerman, O. & Franks, N. R. Flexible task allocation and the organization of work in ants. Proc. R. Soc. B: Biol. Sci. 276, 4373–4380 (2009).
Google Scholar 
Nijhout, H. F. & Wheeler, D. E. Juvenile-hormone and the physiological-basis of Insect polymorphisms. Q. Rev. Biol. 57, 109–133. https://doi.org/10.1086/412671 (1982).Article 
CAS 

Google Scholar 
Herb, B. R. et al. Reversible switching between epigenetic states in honeybee behavioral subcastes. Nat. Neurosci. 15, 1371–1373. https://doi.org/10.1038/nn.3218 (2012).Article 
CAS 

Google Scholar 
Kensuke, N. Age polyethism, idiosyncrasy and behavioural flexibility in the queenless ponerine ant, Diacamma sp. J. Ethol. 13, 113–123 (1995).
Google Scholar 
Kensuke, N. Does behavioral flexibility compensate or constrain colony productivity? Relationship among age structure, labor allocation, and production of workers in ant colonies. J. Insect Behav. 9, 557–569 (1996).
Google Scholar 
Shimoji, H., Kasutani, N., Ogawa, S. & Hojo, M. K. Worker propensity affects flexible task reversion in an ant. Behav. Ecol. 74, 1–8 (2020).
Google Scholar 
Bernadou, A., Busch, J. & Heinze, J. Diversity in identity: Behavioral flexibility, dominance, and age polyethism in a clonal ant. Behav. Ecol. Sociobiol. 69, 1365–1375 (2015).
Google Scholar 
Kohlmeier, P., Feldmeyer, B. & Foitzik, S. Vitellogenin-like A—Associated shifts in social cue responsiveness regulate behavioral task specialization in an ant. PLoS Biol. 16, e2005747 (2018).
Google Scholar 
Tripet, F. & Nonacs, P. Foraging for work and age-based polyethism: The roles of age and previous experience on task choice in ants. Ethology 110, 863–877 (2004).
Google Scholar 
Kohlmeier, P., Alleman, A. R., Libbrecht, R., Foitzik, S. & Feldmeyer, B. Gene expression is more strongly associated with behavioural specialisation than with age or fertility in ant workers. Mol. Ecol. https://doi.org/10.1111/mec.14971 (2018).Article 

Google Scholar 
Levenbook, L. & Bauer, A. C. The fate of the larval storage protein calliphorin during adult development of Calliphora vicina. Insect Biochem. 14, 77–86 (1984).CAS 

Google Scholar 
Zhou, X., Oi, F. M. & Scharf, M. E. Social exploitation of hexamerin: RNAi reveals a major caste-regulatory factor in termites. Proc. Natl. Acad. Sci. 103, 4499–4504 (2006).ADS 
CAS 

Google Scholar 
Zhou, X., Tarver, M. R., Bennett, G., Oi, F. & Scharf, M. Two hexamerin genes from the termite Reticulitermes flavipes: Sequence, expression, and proposed functions in caste regulation. Gene 376, 47–58 (2006).CAS 

Google Scholar 
Hawkings, C., Calkins, T. L., Pietrantonio, P. V. & Tamborindeguy, C. Caste-based differential transcriptional expression of hexamerins in response to a juvenile hormone analog in the red imported fire ant (Solenopsis invicta). PLoS ONE 14, e0216800 (2019).CAS 

Google Scholar 
Hoffman, E. A. & Goodisman, M. A. Gene expression and the evolution of phenotypic diversity in social wasps. BMC Biol. 5, 1–9 (2007).
Google Scholar 
Hunt, J. H., Buck, N. A. & Wheeler, D. E. Storage proteins in vespid wasps: Characterization, developmental pattern, and occurrence in adults. J. Insect Physiol. 49, 785–794 (2003).CAS 

Google Scholar 
Colgan, T. J. et al. Polyphenism in social insects: Insights from a transcriptome-wide analysis of gene expression in the life stages of the key pollinator, Bombus terrestris. BMC Genom. 12, 1–20 (2011).
Google Scholar 
Cremer, S., Armitage, S. A. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, R693–R702 (2007).CAS 

Google Scholar 
Cremer, S., Pull, C. D. & Fuerst, M. A. Social immunity: Emergence and evolution of colony-level disease protection. Annu. Rev. Entomol. 63, 105–123 (2018).CAS 

Google Scholar 
Danihlík, J., Aronstein, K. & Petřivalský, M. Antimicrobial peptides: A key component of honey bee innate immunity: Physiology, biochemistry, and chemical ecology. J. Apic. Res. 54, 123–136 (2015).
Google Scholar 
Koch, S. I. et al. Caste-specific expression patterns of immune response and chemosensory related genes in the leaf-cutting ant, Atta vollenweideri. PLoS ONE 8, e81518 (2013).ADS 

Google Scholar 
Chardonnet, F. et al. Food searching behaviour of a Lepidoptera pest species is modulated by the foraging gene polymorphism. J. Exp. Biol. 217, 3465–3473 (2014).
Google Scholar 
Scheiner, R., Page, R. E. Jr. & Erber, J. Responsiveness to sucrose affects tactile and olfactory learning in preforaging honey bees of two genetic strains. Behav. Brain Res. 120, 67–73 (2001).CAS 

Google Scholar 
Wang, Z. et al. Visual pattern memory requires foraging function in the central complex of Drosophila. Learn. Mem. 15, 133–142 (2008).
Google Scholar 
Zhou, Y., Lei, Y., Lu, L. & He, Y. Temperature-and food-dependent foraging gene expression in foragers of the red imported fire ant Solenopsis invicta Buren (Hymenoptera: Formicidae). Physiol. Entomol. 45, 1–6 (2020).
Google Scholar 
Ingram, K. K. et al. Context-dependent expression of the foraging gene in field colonies of ants: The interacting roles of age, environment and task. Proc. R. Soc. B: Biol. Sci. 283, 20160841 (2016).
Google Scholar 
Ingram, K. K., Oefner, P. & Gordon, D. M. Task-specific expression of the foraging gene in harvester ants. Mol. Ecol. 14, 813–818 (2005).CAS 

Google Scholar 
Lucas, C. & Sokolowski, M. B. Molecular basis for changes in behavioral state in ant social behaviors. Proc. Natl. Acad. Sci. 106, 6351–6356 (2009).ADS 
CAS 

Google Scholar 
Ben-Shahar, Y. The foraging gene, behavioral plasticity, and honeybee division of labor. J. Comp. Physiol. A. 191, 987–994 (2005).CAS 

Google Scholar 
Daugherty, T., Toth, A. & Robinson, G. Nutrition and division of labor: Effects on foraging and brain gene expression in the paper wasp Polistes metricus. Mol. Ecol. 20, 5337–5347 (2011).CAS 

Google Scholar 
Morrison, L. W., Porter, S. D., Daniels, E. & Korzukhin, M. D. Potential global range expansion of the invasive fire ant, Solenopsis invicta. Biol. Invasions 6, 183–191 (2004).
Google Scholar 
Valles, S. M., Wetterer, J. K. & Porter, S. D. The red imported fire ant (Hymenoptera: Formicidae) in the West Indies: Distribution of natural enemies and a possible test bed for release of self-sustaining biocontrol agents. Fls. Entomol. 98, 1101–1105 (2015).
Google Scholar 
Greenberg, L., Vinson, S. & Ellison, S. Nine-year study of a field containing both monogyne and polygyne red imported fire ants (Hymenoptera: Formicidae). Ann. Entomol. Soc. Am. 85, 686–695 (1992).
Google Scholar 
Keller, L. & Ross, K. G. Selfish genes: A green beard in the red fire ant. Nature 394, 573–575 (1998).ADS 
CAS 

Google Scholar 
Vinson, S. B. Impact of the invasion of the imported fire ant. Insect Sci. 20, 439–455 (2013).
Google Scholar 
Tschinkel, W. R. The Fire Ants (Harvard University Press, 2006).
Google Scholar 
Cassill, D. L. & Tschinkel, W. R. Task selection by workers of the fire ant Solenopsis invicta. Behav. Ecol. Sociobiol. 45, 301–310 (1999).
Google Scholar 
Mirenda, J. T. & Vinson, S. B. Division of labour and specification of castes in the red imported fire ant Solenopsis invicta Buren. Anim. Behav. 29, 410–420 (1981).
Google Scholar 
Wilson, E. O. Division of labor in fire ants based on physical castes (Hymenoptera: Formicidae: Solenopsis). J. Kansas Entomol. Soc. 51, 615–636 (1978).
Google Scholar 
Sorensen, A., Busch, T. M. & Vinson, S. B. Behavioral flexibility of temporal subcastes in the fire ant, Solenopsis invicta in response to food. Psyche 91, 319–331 (1984).
Google Scholar 
Bigley, W. S. & Vinson, S. B. Characterization of a brood pheromone isolated from the sexual brood of the imported fire ant, Solenopsis invicta. Ann. Entomol. Soc. Am. 2, 301–304 (1975).
Google Scholar 
Bajracharya, P., Lu, H. L. & Pietrantonio, P. V. The red imported fire ant (Solenopsis invicta Buren) kept Y not F: Predicted sNPY endogenous ligands deorphanize the short NPF (sNPF) receptor. PLoS ONE 9(10), e109590 (2014).ADS 

Google Scholar 
Castillo, P. Short neuropeptide F receptor in the worker brain of the red imported fire ant (Solenopsis invicta Buren) and methodology for RNA interference M.S. thesis, Texas A&M University (2015).Castillo, P. & Pietrantonio, P. V. Differences in sNPF receptor-expressing neurons in brains of fire ant (Solenopsis invicta Buren) worker subcastes: Indicators for division of labor and nutritional status? PLoS ONE 8, e83966 (2013).ADS 

Google Scholar 
Cassill, D. L. & Tschinkel, W. R. Allocation of liquid food to larvae via trophallaxis in colonies of the fire ant, Solenopsis invicta. Anim. Behav. 3, 801–813 (1995).
Google Scholar 
Cassill, D. L., Stuy, A. & Buck, R. G. Emergent properties of food distribution among fire ant larvae. J. Theor. Biol. 3, 371–381 (1998).ADS 

Google Scholar 
Dussutour, A. & Simpson, S. J. Communal nutrition in ants. Curr. Biol. 19, 740–744. https://doi.org/10.1016/j.cub.2009.03.015 (2009).Article 
CAS 

Google Scholar 
Petralia, R. S. & Vinson, S. B. Feeding in the larvae of the imported fire ant, Solenopsis invicta: Behavior and morphological adaptations. Ann. Entomol. Soc. Am. 71, 643–648 (1978).
Google Scholar 
Petralia, R. S. & Vinson, S. B. Developmental morphology of larvae and eggs of the imported fire ant, Solenopsis invicta. Ann. Entomol. Soc. Am. 72, 472–484 (1979).
Google Scholar 
Chen, J. Advancement on techniques for the separation and maintenance of the red imported fire ant colonies. Insect Sci. 14, 1–4 (2007).
Google Scholar 
Banks, W. A. et al. (Agricultural Research (Southern Region), Science and Education…, 1981).Valles, S. M. & Porter, S. D. Identification of polygyne and monogyne fire ant colonies (Solenopsis invicta) by multiplex PCR of Gp-9 alleles. Insectes Soc. 2, 199–200 (2003).
Google Scholar 
Schmittgen, T. D. & Livak, K. J. Analyzing real-time PCR data by the comparative CT method. Nat. Protoc. 3, 1101 (2008).CAS 

Google Scholar 
Cheng, D., Zhang, Z., He, X. & Liang, G. Validation of reference genes in Solenopsis invicta in different developmental stages, castes and tissues. PLoS ONE 8, e57718. https://doi.org/10.1371/journal.pone.0057718 (2013).Article 
ADS 
CAS 

Google Scholar 
Qiu, H.-L., Zhao, C.-Y. & He, Y.-R. On the molecular basis of division of labor in Solenopsis invicta (Hymenoptera: Formicidae) workers: RNA-seq analysis. J. Insect Sci. 17, 48 (2017).
Google Scholar 
Chen, J. et al. Role of the foraging gene in worker behavioral transition in the red imported fire ant, Solenopsis invicta (Hymenoptera: Formicidae). Pest Manag. Sci. https://doi.org/10.1002/ps.6921 (2022).Article 

Google Scholar 
Shorter, J. R. & Tibbetts, E. A. The effect of juvenile hormone on temporal polyethism in the paper wasp Polistes dominulus. Insectes Soc. 56, 7–13 (2009).
Google Scholar 
Pankiw, T., Page, R. E. Jr. & Kim Fondrk, M. Brood pheromone stimulates pollen foraging in honey bees (Apis mellifera). Behav. Ecol. Sociobiol. 44, 193–198. https://doi.org/10.1007/s002650050531 (1998).Article 

Google Scholar 
Smedal, B., Brynem, M., Kreibich, C. D. & Amdam, G. V. Brood pheromone suppresses physiology of extreme longevity in honeybees (Apis mellifera). J. Exp. Biol. 212, 3795–3801. https://doi.org/10.1242/jeb.035063 (2009).Article 
CAS 

Google Scholar 
Solis, C. R. & Strassmann, J. E. Presence of brood affects caste differentiation in the social wasp, Polistes exclamans Viereck (Hymenoptera, Vespidae). Funct. Ecol. 4, 531–541. https://doi.org/10.2307/2389321 (1990).Article 

Google Scholar 
Traynor, K. S. Decoding Brood Pheromone: The Releaser and Primer Effects of Young and Old Larvae on Honey Bee (Apis mellifera) Workers (Arizona State University, 2014).
Google Scholar 
Wagoner, K. M., Spivak, M. & Rueppell, O. Brood affects hygienic behavior in the honey bee (Hymenoptera: Apidae). J. Econ. Entomol. 111, 2520–2530. https://doi.org/10.1093/jee/toy266 (2018).Article 
CAS 

Google Scholar 
Nijhout, H. F. & Wheeler, D. E. Juvenile hormone and the physiological basis of insect polymorphisms. Q. Rev. Biol. 57, 109–133 (1982).CAS 

Google Scholar  More

Flash recordingAll field recording and experiments were performed at the paddy field in the Northern Chita Peninsula, Aichi Prefecture, central Japan, in June and July between 2003 and 2016. The ambient temperature at the firefly’s active period was measured using a thermometer. The flashes were recorded with a digital video camera (NV-GS-400, Panasonic, Japan) mounted on a tripod at a height of 30–50 cm from ground and a distance of 1.0–1.5 m away from the specimen. Isolated specimens were selected for recording to exclude the background light from other nontarget specimens. When another specimen appeared near the target specimen, the video recording was cancelled. When a female copulated during video recording in the field, her flashes until 1 min before copulation were regarded as those of a ‘receptive female’. To record the flashes of a ‘mated female’, the female specimens already mated were prepared in aquariums (because virgin and mated females cannot be distinguished in the field): the eggs were obtained from wild female specimens collected one year before at the same field and reared to adults; immediately after emergence the virgin female was confined in a small container with two cultured males for two nights to facilitate copulation. As the parents of the reared specimens were collected from the observation field (same genetic background), the rearing temperature was almost the same as that of the natural field, the emergence period of the cultured specimens overlapped with that of the natural population, the adult body sizes of the reared and natural specimens were indistinguishable, and the flash pattern of the cultured mated females was indistinguishable from that of the wild (potentially) mated females. Thus, we believe that there was no influence of different rearing environments, i.e., the flash behavior of the cultured mated female specimens is expected to be substantially the same as that of wild mated female specimens. To distinguish them from wild (potentially) mated females, the elytra of cultured mated females were marked with colored ink before placing them in the field, and after three days, the flashes of ink-marked specimens were recorded. Of note, we never observed male attraction and copulation in any of the mated females used for field observation; thus, the mated females were unreceptive.Waveform analysisSequential still images were captured from video files at 30 frames per second using VirtualDub (GPL), and then the light intensities in the images were qualified (8-bit linear gray scaling from black to white at 0–255) using ImageJ software. In this study, we defined ‘flash’ as a luminescent waveform from baseline to baseline and ‘flickering’ as fluctuation above baseline in a single flash. The waveforms containing a saturated signal (255, white) were omitted. The waveforms of the maximum signal value lower than 50 were also omitted because of the difficulty in separating signal and noise. Approximately 10–90 waveforms per individual were analyzed; thus, the effect of the occasional interruption of the flash recording by the specimen’s movement and/or vegetation swinging between the specimen and the video camera is statistically ignorable. FD is defined as the time interval between the beginning and the end of a flash (Fig. S1). Flicker intensity (FI) was defined as$${text{FI}} = left{ {begin{array}{*{20}l} {mathop {max }limits_{1 le i le n} left( {frac{{{text{min}}left( {p_{i} ,p_{i + 1} } right) – t_{i} }}{{min left( {p_{i} , p_{i + 1} } right) + t_{i} }}} right)} hfill & {{text{if}} , n ge 1} hfill \ 0 hfill & {{text{if}} , n = 0} hfill \ end{array} } right.$$where p, t, and n denote the peak and the trough (local extrema) in the waveform of a flash and the number of toughs in the flash, respectively (Fig. S1). In total, we measured the FD and FI values of 347, 94, and 355 waveforms from 13 sedentary males, 7 receptive females, and 8 mated females, respectively. We did not consider the flash brightness as a factor because the measured value of the light intensity depends largely on the distance between the light source and the detector; thus, the actual brightness of the lantern cannot be practically measured in the field.e-FireflyFor male attraction experiments, we built an electronic LED device, the e-firefly, to generate patterned flashes with various FDs and FIs using a chip LED (green type, λmax = 568 nm, Everlight Electronics, Taiwan; Figs. S2 and S3) with a microcontroller PIC16F628A (Microchip Technology, USA) (see Figs. S4-S5). An example of the program for the microcontroller is shown in Supplementary Data S1. The brightness was constant in all programs. Flickering frequency ranged between 5–12 Hz, which corresponds to that of sedentary male flashes (approximately 10 Hz)15. To prevent direct access of the attracted specimen to the light source, the chip LED was covered by a steel net painted green (see Fig. S2). For flying male attraction experiments, when the male landed within a 100-mm distance from the e-firefly, we judged the attraction to be a success; otherwise, it was a failure. For sedentary male attraction experiments, the e-firefly was placed 200–300 mm away from the sedentary male. When the approaching male touched the steel net covering the e-firefly, to warrant a positive approach, we measured the time the male remained on the net. If the male did not move away from the net for more than 2 min, we judged the attraction to be a success (strict criterion for judgment); otherwise, it was a failure.Spectral measurementThe luminescence spectra of e-firefly and A. lateralis were measured using a Flame-S spectrophotometer (Ocean Insight, USA). The living A. lateralis specimens were anesthetized on ice and frozen at − 20 °C until use. The lantern started luminescence by thawing at room temperature, and the spectrum was measured during luminescence (within 5 min).Statistical analysisFirst, we considered a discriminant analysis using a logistic regression model that discriminates between receptive females and others in the observational data. We fitted several models with combinations of FD and FI, quadratic terms of FD and FI (FD2, FI2), interaction of FD and FI (FD (times) FI), and temperature (T) as explanatory variables. Based on Akaike’s information criteria (AIC) values and model simplicity, we chose the logistic regression model with FD, FI, FD2 and T as explanatory variables. Let (p)(({varvec{x}})) denote the conditional probability that a flash is from a receptive female given ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and (widehat{p})(({varvec{x}})) denote its estimate. The coefficients of the logistic regression model are estimated as follows.
[Model for the observational data with temperature (T)]
$$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 32.26 + 69.69 times FD – 43.47 times FI – 76.63 times FD^{2} + 0.87 times T} hfill \ {~quad left( {6.50} right)quad quad left( {15.37} right)quad quad quad left( {8.56} right)quad quad quad quad left( {17.44} right)quad quad quad left( {0.19} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 84}}{text{.75}} hfill \ end{gathered}$$[Model for the observational data without temperature (T)]$$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 7.69~ + 47.57 times FD~ – 38.29 times FI~ – 52.86 times FD^{2} ~} hfill \ {~;left( {1.86} right)quad quad left( {9.68} right)quad quad quad left( {7.08} right)quad quad quad quad left( {11.38} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 114}}{text{.89}} hfill \ end{gathered}$$where values in parentheses indicate standard deviations. The same applies hereafter. Temperature (T) is included in the model not because it affects the occurrence of receptive females but because it affects the FD and/or FI of receptive females. The AIC value increased by 30, which is substantial, when temperature was excluded from the model.Figure 2 shows the FD and FI of each flash from receptive females, mated females and males with the discriminant boundaries of receptive females from others for (p=0.5).We next considered a discriminant analysis for the experimental data. Let ({q}^{f}({varvec{x}})) denote the conditional probability that a flying male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and lands, and ({widehat{q}}^{f}({varvec{x}})) denote its estimate. Among several models we fit, the smallest AIC value is attained by the logistic regression model with FD, FI and T as explanatory variables, but the AIC is not much different from the model with FD and FI only.
[Model for flying males with temperature (T)]
$$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 0.74~~ – 2.42 times FD – 16.82 times FI + 0.31 times T} hfill \ {~;left( {4.01} right)quad quad left( {0.83} right)quad quad quad left( {4.88} right)quad quad quad quad left( {0.20} right)~} hfill \ end{array} hfill \ quad {text{AIC}}:66.96 hfill \ end{gathered}$$

[Model for flying males without temperature (T)]
$$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 5.36~ – 1.72 times FD – 13.69 times FI} hfill \ {~;left( {1.49} right)quad quad left( {0.63} right)~quad quad left( {4.09} right)~~} hfill \ end{array} hfill \ quad {text{AIC}}:67.61 hfill \ end{gathered}$$
For sedentary males, the model with the smallest AIC value includes all the quadratic terms of FI and FD but not temperature. Let ({q}^{s}({varvec{x}})) denote the conditional probability that a sedentary male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and ({widehat{q}}^{s}left({varvec{x}}right)) denote its estimate. The logistic regression model for ({q}^{s}({varvec{x}})) with the best AIC value is given as follows.
[Model for sedentary males]
$${text{log}}frac{{hat{q}~^{s} }}{{1 – hat{q}~^{s} }} = begin{array}{*{20}l} { – 0.68~ + 7.84 times FD~ + 48.17 times FI – 5.35 times FD^{2} – 166.70 times FI^{2} – 65.67 times FD times FI} hfill \ {;left( {0.97} right)quad quad quad left( {2.99} right)quad quad quad left( {17.74} right)quad quad quad left( {1.74} right)quad quad quad quad left( {72.34} right)quad quad quad quad left( {17.67} right)~} hfill \ end{array}$$
Figure 3 shows successes and failures of attraction of flying males on the left and sedentary males on the right with estimated discriminant boundaries.Let us now estimate probabilities that a flying male is attracted and lands or a sedentary male is attracted to a flash when a flash is from a receptive female or when a flash is either from a sedentary male or mated female. The probability that a flying male is attracted and lands when a flash is from a receptive female is a conditional probability and is expressed as follows.$$begin{aligned} Pleft(left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} right) & = frac{{Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) }}{{Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right)}}, \ Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} = mathop int_{Omega }pleft( {varvec{x}} right) fleft( {varvec{x}} right)d{varvec{x}} hspace{5mm}{text{and}} \ Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|varvec{x} right)Pleft(left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} \ & = mathop int_{Omega } pleft( varvec{x} right)q^{f} left( {varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}}mathbf{.} \ end{aligned}$$Integrals are taken over the domain (Omega) of ({varvec{x}}=(FD, FI, T)) of all females and males, and (f({varvec{x}})) is the joint density function of ({varvec{x}}.) Because (f({varvec{x}})) is unknown, we use the empirical distribution of the observational data, and conditional probabilities given ({varvec{x}}) are replaced with their estimates by logistic regression models. Let ({{varvec{x}}}_{i}=left(F{D}_{i}, F{I}_{i}, {T}_{i}right), i=mathrm{1,2},dots N) denote the (i) th observation in the observational data. The estimates of probabilities are given as follows:$$begin{aligned} hat{P}left( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} }right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hspace{15mm} {text{and}} \ hat{P}left( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right). \ end{aligned}$$Thus,$$hat{P}left( left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right) = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n}hat{p}left(varvec{x}_i right)}}.$$Similarly, we have$$begin{aligned} hat{P}left( left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right)) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right))}} \ hat{P}left( left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right)& = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{s} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} hat{p}left( varvec{x}_{i} right)}}hspace{15mm} {text{ and}} \hat{P}left(left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right) hat{q}^{s} left( {varvec{x}_{i} } right)}}{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right)} . \ end{aligned}$$The estimated probabilities are shown in Table 1.Table 1 Estimated probabilities of a flying male and a sedentary male being attracted to flashes from a receptive female and from others.Full size table More