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    Uropygial gland microbiota differ between free-living and captive songbirds

    Zilber-Rosenberg, I. & Rosenberg, E. Role of microorganisms in the evolution of animals and plants: The hologenome theory of evolution. FEMS Microbiol. Rev. 32, 723–735 (2008).Article 
    PubMed 

    Google Scholar 
    Archie, E. A. & Theis, K. R. Animal behaviour meets microbial ecology. Anim. Behav. 82, 425–436 (2011).Article 

    Google Scholar 
    McFall-Ngai, M. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. 110, 3229–3236 (2013).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cresci, G. A. & Bawden, E. Gut microbiome: What we do and don’t know. Nutr. Clin. Pract. Off. Publ. Am. Soc. Parenter. Enter. Nutr. 30, 734–746 (2015).
    Google Scholar 
    Martin, C. R., Osadchiy, V., Kalani, A. & Mayer, E. A. The brain–gut–microbiome axis. Cell. Mol. Gastroenterol. Hepatol. 6, 133–148 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davidson, G. L., Raulo, A. & Knowles, S. C. Identifying microbiome-mediated behaviour in wild vertebrates. Trends Ecol. Evol. 35, 972–980 (2020).Article 
    PubMed 

    Google Scholar 
    Ushida, K., Kock, R. & Sundset, M. A. Wildlife microbiology. Microorganisms 9, 1968 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ezenwa, V. O., Gerardo, N. M., Inouye, D. W., Medina, M. & Xavier, J. B. Animal behavior and the microbiome. Science 338, 198–199 (2012).Article 
    ADS 
    PubMed 

    Google Scholar 
    Ezenwa, V. O. & Williams, A. E. Microbes and animal olfactory communication: Where do we go from here?. BioEssays 36, 847–854 (2014).Article 
    PubMed 

    Google Scholar 
    Carthey, A. J. R., Gillings, M. R. & Blumstein, D. T. The extended genotype: Microbially mediated olfactory communication. Trends Ecol. Evol. 33, 885–894 (2018).Article 
    PubMed 

    Google Scholar 
    Maraci, Ö., Engel, K. & Caspers, B. A. Olfactory communication via microbiota: What is known in birds?. Genes 9, 387 (2018).Article 
    PubMed Central 

    Google Scholar 
    Hird, S. M. Evolutionary biology needs wild microbiomes. Front. Microbiol. 8, 725 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alberdi, A., Martin Bideguren, G. & Aizpurua, O. Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: A meta-analysis. Sci. Rep. 11, 22660 (2021).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leclaire, S., Nielsen, J. F. & Drea, C. M. Bacterial communities in meerkat anal scent secretions vary with host sex, age, and group membership. Behav. Ecol. 25, 996–1004 (2014).Article 

    Google Scholar 
    Theis, K. R., Schmidt, T. M. & Holekamp, K. E. Evidence for a bacterial mechanism for group-specific social odors among hyenas. Sci. Rep. 2, 615 (2012).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Theis, K. R. et al. Symbiotic bacteria appear to mediate hyena social odors. Proc. Natl. Acad. Sci. 110, 1983219837 (2013).Article 
    ADS 

    Google Scholar 
    Gassett, J. W., Dasher, K. A., Miller, K. V., Osborn, D. A. & Russell, S. M. White-tailed deer tarsal glands: Sex and age-related variation in microbial flora. Mammalia 64, 371–377 (2000).Article 

    Google Scholar 
    Sin, Y. W., Buesching, C. D., Burke, T. & Macdonald, D. W. Molecular characterization of the microbial communities in the subcaudal gland secretion of the European badger (Meles meles). FEMS Microbiol. Ecol. 81, 648–659 (2012).Article 
    PubMed 

    Google Scholar 
    Albone, E. S., Eglinton, G., Walker, J. M. & Ware, G. C. The anal sac secretion of the red fox (Vulpes vulpes); its chemistry and microbiology. A comparison with the anal sac secretion of the lion (Panthera leo). Life Sci. 14, 387–400 (1974).Article 
    PubMed 

    Google Scholar 
    Greene, L. K. et al. The importance of scale in comparative microbiome research: New insights from the gut and glands of captive and wild lemurs. Am. J. Primatol. 81, e22974 (2019).Article 
    PubMed 

    Google Scholar 
    Leclaire, S., Jacob, S., Greene, L. K., Dubay, G. R. & Drea, C. M. Social odours covary with bacterial community in the anal secretions of wild meerkats. Sci. Rep. 7, 3240 (2017).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grieves, L. A., Gloor, G. B., Kelly, T. R., Bernards, M. A. & MacDougall-Shackleton, E. A. Preen gland microbiota of songbirds differ across populations but not sexes. J. Anim. Ecol. 90, 2202–2212 (2021).Article 
    PubMed 

    Google Scholar 
    Whittaker, D. J. et al. Social environment has a primary influence on the microbial and odor profiles of a chemically signaling songbird. Front. Ecol. Evol. 4, 1–15 (2016).Article 

    Google Scholar 
    Grieves, L. A., Gloor, G. B., Bernards, M. A. & MacDougall-Shackleton, E. A. Preen gland microbiota covary with major histocompatibility complex genotype in a songbird. R. Soc. Open Sci. 8, 210936 (2021).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Whittaker, D. J. et al. Experimental evidence that symbiotic bacteria produce chemical cues in a songbird. J. Exp. Biol. 222, jeb202978 (2019).Article 
    PubMed 

    Google Scholar 
    Martín-Vivaldi, M. et al. Antimicrobial chemicals in hoopoe preen secretions are produced by symbiotic bacteria. Proc. R. Soc. B Biol. Sci. 277, 123–130 (2010).Article 

    Google Scholar 
    Whittaker, D. J. et al. Intraspecific preen oil odor preferences in dark-eyed juncos (Junco hyemalis). Behav. Ecol. 22, 1256–1263 (2011).Article 

    Google Scholar 
    Grieves, L. A., Bernards, M. A. & MacDougall-Shackleton, E. A. Behavioural responses of songbirds to preen oil odour cues of sex and species. Anim. Behav. 156, 57–65 (2019).Article 

    Google Scholar 
    Grieves, L. A., Gloor, G. B., Bernards, M. A. & MacDougall-Shackleton, E. A. Songbirds show odour-based discrimination of similarity and diversity at the major histocompatibility complex. Anim. Behav. 158, 131–138 (2019).Article 

    Google Scholar 
    Pearce, D. S., Hoover, B. A., Jennings, S., Nevitt, G. A. & Docherty, K. M. Morphological and genetic factors shape the microbiome of a seabird species (Oceanodroma leucorhoa) more than environmental and social factors. Microbiome 5, 146 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Leclaire, S. et al. Plumage microbiota covaries with the major histocompatibility complex in blue petrels. Mol. Ecol. 28, 833–846 (2019).PubMed 

    Google Scholar 
    Bisson, I.-A., Marra, P. P., Burtt, E. H. Jr., Sikaroodi, M. & Gillevet, P. M. Variation in plumage microbiota depends on season and migration. Microb. Ecol. 58, 212 (2009).Article 
    PubMed 

    Google Scholar 
    Kartzinel, T. R., Hsing, J. C., Musili, P. M., Brown, B. R. & Pringle, R. M. Covariation of diet and gut microbiome in African megafauna. Proc. Natl. Acad. Sci. 116, 23588–23593 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Arcese, P., Sogge, M. K., Marr, A. B. & Patten, M. A. Song sparrow (Melospiza melodia), version 2.0. In The Birds of North America (ed. Rodewald, P. G.) (Cornell Lab of Ornithology, 2002).
    Google Scholar 
    Breton, J. et al. Ecotoxicology inside the gut: Impact of heavy metals on the mouse microbiome. BMC Pharmacol. Toxicol. 14, 62 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ruan, Y. et al. High doses of copper and mercury changed cecal microbiota in female mice. Biol. Trace Elem. Res. 189, 134–144 (2019).Article 
    PubMed 

    Google Scholar 
    Lin, X. et al. Acute oral methylmercury exposure perturbs the gut microbiome and alters gut-brain axis related metabolites in rats. Ecotoxicol. Environ. Saf. 190, 110130 (2020).Article 
    PubMed 

    Google Scholar 
    Grieves, L. A. et al. Food stress, but not experimental exposure to mercury, affects songbird preen oil composition. Ecotoxicology 29, 275–285 (2020).Article 
    PubMed 

    Google Scholar 
    Christian, V. J., Miller, K. R. & Martindale, R. G. Food insecurity, malnutrition, and the microbiome. Curr. Nutr. Rep. 9, 356–360 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Genton, L., Cani, P. D. & Schrenzel, J. Alterations of gut barrier and gut microbiota in food restriction, food deprivation and protein-energy wasting. Clin. Nutr. 34, 341–349 (2015).Article 
    PubMed 

    Google Scholar 
    Noguera, J. C., Aira, M., Pérez-Losada, M., Domínguez, J. & Velando, A. Glucocorticoids modulate gastrointestinal microbiome in a wild bird. R. Soc. Open Sci. 5, 171743 (2018).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wienemann, T. et al. The bacterial microbiota in the ceca of Capercaillie (Tetrao urogallus) differs between wild and captive birds. Syst. Appl. Microbiol. 34, 542–551 (2011).Article 
    PubMed 

    Google Scholar 
    Salgado-Flores, A., Tveit, A. T., Wright, A.-D., Pope, P. B. & Sundset, M. A. Characterization of the cecum microbiome from wild and captive rock ptarmigans indigenous to Arctic Norway. PLoS One 14, e0213503 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hird, S. M., Sánchez, C., Carstens, B. C. & Brumfield, R. T. Comparative gut microbiota of 59 neotropical bird species. Front. Microbiol. 6, 1403 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thomas, R. H. et al. Use of TLC-FID and GC-MS⁄FID to examine the effects of migratory state, diet and captivity on preen wax composition in White-throated Sparrows Zonotrichia albicollis. Ibis 152, 782–792 (2010).Article 

    Google Scholar 
    Xie, Y. et al. Effects of captivity and artificial breeding on microbiota in feces of the red-crowned crane (Grus japonensis). Sci. Rep. 6, 33350 (2016).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    San Juan, P. A., Castro, I. & Dhami, M. K. Captivity reduces diversity and shifts composition of the Brown Kiwi microbiome. Anim. Microbiome 3, 48 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wu, H., Wu, F.-T., Zhou, Q.-H. & Zhao, D.-P. Comparative analysis of gut microbiota in captive and wild oriental white storks: Implications for conservation biology. Front. Microbiol. 12, 649466 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rodríguez-Ruano, S. M. et al. The hoopoe’s uropygial gland hosts a bacterial community influenced by the living conditions of the bird. PLoS One 10, e0139734 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xenoulis, P. G. et al. Molecular characterization of the cloacal microbiota of wild and captive parrots. Vet. Microbiol. 146, 320–325 (2010).Article 
    PubMed 

    Google Scholar 
    Kelly, T. R., Vinson, A. E., King, G. M. & Lattin, C. R. No guts about it: Captivity, but not neophobia phenotype, influences the cloacal microbiome of house sparrows (Passer domesticus). Integr. Org. Biol. 4, obac010 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grond, K., Sandercock, B. K., Jumpponen, A. & Zeglin, L. H. The avian gut microbiota: Community, physiology and function in wild birds. J. Avian Biol. 49, e01788 (2018).Article 

    Google Scholar 
    Videvall, E., Strandh, M., Engelbrecht, A., Cloete, S. & Cornwallis, C. K. Measuring the gut microbiome in birds: Comparison of faecal and cloacal sampling. Mol. Ecol. Resour. 18, 424–434 (2018).Article 
    PubMed 

    Google Scholar 
    McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kohl, K. D., Skopec, M. M. & Dearing, M. D. Captivity results in disparate loss of gut microbial diversity in closely related hosts. Conserv. Physiol. 2, cou009 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Martínez-Mota, R., Kohl, K. D., Orr, T. J. & Denise Dearing, M. Natural diets promote retention of the native gut microbiota in captive rodents. ISME J. 14, 67–78 (2020).Article 
    PubMed 

    Google Scholar 
    Chatelain, M., Frantz, A., Gasparini, J. & Leclaire, S. Experimental exposure to trace metals affects plumage bacterial community in the feral pigeon. J. Avian Biol. 47, 521–529 (2016).Article 

    Google Scholar 
    Jacob, S. et al. Uropygial gland size and composition varies according to experimentally modified microbiome in great tits. BMC Evol. Biol. 14, 134 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jacob, J. & Ziswiler, V. The uropygial gland. In Avian Biology Vol. 6 (eds Farner, D. S. et al.) 199–324 (Academic Press, 1982).Chapter 

    Google Scholar 
    Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143 (2018).Article 
    PubMed 

    Google Scholar 
    Egert, M. & Simmering, R. The microbiota of the human skin. Microbiota Hum. Body 902, 61–81 (2016).Article 

    Google Scholar 
    Grice, E. A. et al. A diversity profile of the human skin microbiota. Genome Res. 18, 1043–1050 (2008).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xu, B. et al. Molecular and biochemical characterization of a novel xylanase from Massilia sp. RBM26 isolated from the feces of Rhinopithecus bieti. J. Microbiol. Biotechnol. 26, 9–19 (2016).Article 
    PubMed 

    Google Scholar 
    Rosenberg, E. The Prokaryotes (Springer, 2014).Book 

    Google Scholar 
    Tang, J., Huang, J., Qiao, Z., Wang, R. & Wang, G. Mucilaginibacter pedocola sp. Nov., isolated from a heavy-metal-contaminated paddy field. Int. J. Syst. Evol. Microbiol. 66, 4033–4038 (2016).Article 
    PubMed 

    Google Scholar 
    Vasconcelos, A. L. et al. Mucilaginibacter sp. strain metal (loid) and antibiotic resistance isolated from estuarine soil contaminated mine tailing from the Fundão dam. Genes 13, 174 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dewi, G. & Kollanoor Johny, A. Lactobacillus in food animal production—A forerunner for clean label prospects in animal-derived products. Front. Sustain. Food Syst. 6, 831195 (2022).Article 

    Google Scholar 
    Dworkin, M. The Prokaryotes Proteobacteria: Alpha and Beta Subclasses (Springer Science & Business Media, 2006).Book 

    Google Scholar 
    Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: A call for the consideration of host-associated microbiota in wildlife management practices. Proc. R. Soc. B 286, 20182448 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).Article 
    PubMed 

    Google Scholar 
    Sherwin, E., Bordenstein, S. R., Quinn, J. L., Dinan, T. G. & Cryan, J. F. Microbiota and the social brain. Science 366, eaar2016 (2019).Article 
    PubMed 

    Google Scholar 
    Bottini, C. L., MacDougall-Shackleton, S. A., Branfireun, B. A. & Hobson, K. A. Feathers accurately reflect blood mercury at time of feather growth in a songbird. Sci. Total Environ. 775, 145739 (2021).Article 
    ADS 
    PubMed 

    Google Scholar 
    Kelly, T. R., Bonner, S. J., MacDougall-Shackleton, S. A. & MacDougall-Shackleton, E. A. Exposing migratory sparrows to Plasmodium suggests costs of resistance, not necessarily of infection itself. J. Exp. Zool. Part Ecol. Integr. Physiol. 329, 5–14 (2018).Article 

    Google Scholar 
    Whittaker, D. J. & Hagelin, J. C. Female-based patterns and social function in avian chemical communication. J. Chem. Ecol. 47, 53–62 (2020).
    Google Scholar 
    Griffiths, R., Double, M. C., Orr, K. & Dawson, R. J. A DNA test to sex most birds. Mol. Ecol. 7, 1071–1075 (1998).Article 
    PubMed 

    Google Scholar 
    Canadian Council on Animal Care (CCAC). Three Rs | Trois R :: About the Three Rs. https://3rs.ccac.ca/.Lane, D. J. et al. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc. Natl. Acad. Sci. 82, 6955–6959 (1985).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).Article 
    ADS 
    PubMed 

    Google Scholar 
    Gloor, G. B. et al. Microbiome profiling by Illumina sequencing of combinatorial sequence-tagged PCR products. PLoS One 5, e15406 (2010).Article 
    ADS 
    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).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bian, G. et al. The gut microbiota of healthy aged Chinese is similar to that of the healthy young. Msphere 2, e00327-e417 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genom. Bioinform. 3, lqab019 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).Article 
    PubMed 

    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).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aitchison, J. The Statistical Analysis of Compositional Data (Chapman and Hall, 1986).Book 
    MATH 

    Google Scholar 
    Gloor, G. B. & Reid, G. Compositional analysis: A valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. 62, 692–703 (2016).Article 
    PubMed 

    Google Scholar 
    Quinn, T. P., Erb, I., Richardson, M. F. & Crowley, T. M. Understanding sequencing data as compositions: An outlook and review. Bioinformatics 34, 2870–2878 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Palarea-Albaladejo, J. & Martin-Fernandez, J. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemom. Intell. Lab. Syst. 143, 85–96 (2015).Article 

    Google Scholar 
    Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Aitchison, J. & Greenacre, M. Biplots of compositional data. J. R. Stat. Soc. Ser. C Appl. Stat. 51, 375–392 (2002).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).Article 

    Google Scholar 
    R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).
    Google Scholar 
    Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Dixon, P. & Palmer, M. W. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).Article 

    Google Scholar 
    Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. & Gloor, G. B. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One 8, e67019 (2013).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nearing, J. T. et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat. Commun. 13, 342 (2022).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Halsey, L. G., Curran-Everett, D., Vowler, S. L. & Drummond, G. B. The fickle P value generates irreproducible results. Nat. Methods 12, 179–185 (2015).Article 
    PubMed 

    Google Scholar  More

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    Spotting hopeful signs for coral health in Barbados’s backyard

    I’m a coral-reef ecologist at the University of the West Indies at Cave Hill in Barbados. Every five years, as often as our funding allows, my team and I survey coral reefs for the government. I was born in Spain and earned my PhD at McGill University in Montreal, Canada. But I decided to work in the Caribbean, where I think I am more useful.We monitor the abundance and diversity of corals, algae, sponges and fish. Barbados no longer has populations of large fish, such as groupers and snappers, because of overfishing. The populations of parrotfish, Barbados’s most important species ecologically and economically, have seemed stable for the past decade.Reefs are under threat globally, and the biggest losses of corals here occurred in the 1970s and 1980s. Since the 1990s, the shallow reefs have stabilized, but the deeper reefs have continued to deteriorate. And numbers of sponges and algae, which can damage corals when too abundant, have gradually increased in the deeper reefs. Still, there are positive signs. Staghorn corals (Acropora cervicornis), which nearly went extinct here in the 1970s, are making a slow comeback.This photo was taken in early September and the water was 28 °C or 29 °C. But I still wore a wetsuit with a hood, because after 90 minutes of scuba diving, you get cold.We survey 43 sites in two months, doing one or two dives a day, three times a week. Four of us dive together; we are like a well-oiled machine.I wish we could do surveys more frequently; in a rapidly changing environment, we need to know what is happening. But there’s not enough money. Still, new technology can model reefs in 3D. Those tools are becoming more affordable, and I think we’ll be using them in the next decade. Then, we could monitor more sites more often with the same resources.I’ve wanted to be a biologist since I was a young boy. And it doesn’t get any better than studying coral reefs in your backyard. More

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    Vegetation type is an important predictor of the arctic summer land surface energy budget

    Surface energy fluxes and componentsIn our study, we focused on the circumpolar land north of 60° latitude, and specifically on the extent of the circumpolar Arctic vegetation map (CAVM20, Supplementary Fig. 1–3). We obtained half-hourly and hourly in situ observations of energy fluxes and meteorological variables from the monitoring networks FLUXNET28 (fluxnet.org; FLUXNET2015 dataset), AmeriFlux29 (ameriflux.lbl.gov), AON31,32 (aon.iab.uaf.edu), ICOS (icos-cp.eu), GEM35,36 (g-e-m.dk), GC-Net33,34 (cires1.colorado.edu/steffen/gcnet) and PROMICE30; (promice.dk; Supplementary Table 3). We did not include observations from the Baseline Surface Radiation Network (BSRN; bsrn.awi.de) and Global Energy Balance Archive (GEBA; geba.ethz.ch) because they typically lack information on non-radiative energy fluxes. Finally, we did not include observations from the European Flux Database Cluster (EFDC, europe-fluxdata.eu) because these data are largely located outside the domain of the CAVM20.We aggregated surface energy fluxes and components (Supplementary Table 1) to daily resolution as follows: (i) we extracted only directly measured data and excluded gap-filled data by filtering according to quality information; (ii) we performed a basic outlier filtering (excluding shortwave and longwave radiation flux values >1400 Wm−2 and in case of incoming/outgoing radiation More

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    Honey compositional convergence and the parallel domestication of social bees

    Allsop, K. A. & Miller, J. B. Honey revisited: A reappraisal of honey in pre-industrial diets. Br. J. Nutr. 75, 513–520 (1996).Article 
    CAS 
    PubMed 

    Google Scholar 
    Dams, M. & Dams, L. Spanish rock art depicting honey gathering during the Mesolithic. Nature 268, 228–230 (1977).Article 
    ADS 

    Google Scholar 
    Bradbear, N. Bees and their role in forest livelihoods: A guide to the services provided by bees and the sustainable harvesting, processing and marketing of their products. Non-Wood Forests Products Series, Vol. 19 (FAO, Rome, 2009).
    Google Scholar 
    Crane, E. The World History of Beekeeping and Honey Hunting (Routledge, 1999).Book 

    Google Scholar 
    Kritsky, G. Beekeeping from Antiquity through the middle ages. Annu. Rev. Entomol. 62, 249–264 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Grüter, C. Stingless Bees: Their Behaviour, Ecology and Evolution (Springer International Publishing, 2020).Book 

    Google Scholar 
    Weaver, N. & Weaver, E. C. Beekeeping with the stingless bee Melipona beecheii, by the Yucatecan Maya. Bee World 62, 7–19 (1981).Article 

    Google Scholar 
    Quezada-Euán, J. J. G. Stingless Bees of Mexico: The Biology, Management and Conservation of an Ancient Heritage (Springer, 2018).Book 

    Google Scholar 
    Medellín Morales, S. Meliponicultura Maya: Perspectivas para su sostenibilidad. Reporte de sostenibilidad Maya no. 2; 67 pp. (1991).González-Acereto, J. A. La meliponicultura yucateca en crisis: Una actividad indígena a punto de desaparecer, 1er Seminario Nacional sobre Abejas sin Aguijón. Boca Río Ver México 9–12 (1999).Russell, P. The History of Mexico: From Pre-conquest to Present (Routledge, 2010).
    Google Scholar 
    Quezada-Euan, J. J., May-Itzá, W. & González-Acereto, J. Meliponiculture in Mexico: Problems and perspective for development. Bee World 82, 160–167 (2001).Article 

    Google Scholar 
    Freitas, B. M. et al. Diversity, threats and conservation of native bees in the Neotropics. Apidologie 40, 332–346 (2009).Article 

    Google Scholar 
    Toledo-Hernández, E. et al. The stingless bees (Hymenoptera: Apidae: Meliponini): A review of the current threats to their survival. Apidologie 53, 8 (2022).Article 

    Google Scholar 
    Guzman-Novoa, E. et al. The process and outcome of the Africanization of honey bees in Mexico: Lessons and future directions. Front. Ecol. Evol. 8, 404 (2020).Article 

    Google Scholar 
    Fletcher, M. et al. Stingless bee honey, a novel source of trehalulose: A biologically active disaccharide with health benefits. Sci. Rep. 10, 12128 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rao, P. V., Krishnan, K. T., Salleh, N. & Gan, S. H. Biological and therapeutic effects of honey produced by honey bees and stingless bees: A comparative review. Rev. Bras. Farmacogn. 26, 657–664 (2016).Article 
    CAS 

    Google Scholar 
    Rattanawannee, A. & Duangphakdee, O. Southeast Asian meliponiculture for sustainable livelihood. In Modern Beekeeping – Bases for Sustainable Production (ed. Ranz, R. E. R.) (IntechOpen, 2019).
    Google Scholar 
    Heard, T. The role of stingless bees in crop pollination. Annu. Rev. Entomol. 44, 183–206 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Slaa, E. J., Chaves, L. A. S., Malagodi-Braga, K. S. & Hofstede, F. E. Stingless bees in applied pollination: Practice and perspectives. Apidologie 37, 293–315 (2006).Article 

    Google Scholar 
    Kendall, L. K., Stavert, J. R., Gagic, V., Hall, M. & Rader, R. Initial floral visitor identity and foraging time strongly influence blueberry reproductive success. Basic Appl. Ecol. https://doi.org/10.1016/j.baae.2022.02.009 (2022).Article 

    Google Scholar 
    Kiatoko, N. et al. Effective pollination of greenhouse Galia musk melon (Cucumis melo L. var. reticulatus ser.) by afrotropical stingless bee species. J. Apic. Res. https://doi.org/10.1080/00218839.2021.2021641 (2022).Article 

    Google Scholar 
    Nkoba, K. et al. African endemic stingless bees as an efficient alternative pollinator to honey bees in greenhouse cucumber (Cucumis sativus L.). J. Apic. Res. https://doi.org/10.1080/00218839.2021.2013421 (2022).Article 

    Google Scholar 
    FAO, A. Good beekeeping practices for sustainable apiculture. (FAO, IZSLT, Apimondia and CAAS, 2020). doi:https://doi.org/10.4060/cb5353en.Patel, V., Pauli, N., Biggs, E., Barbour, L. & Boruff, B. Why bees are critical for achieving sustainable development. Ambio 50, 49–59 (2021).Article 
    PubMed 

    Google Scholar 
    Fuller, D. Q. et al. Convergent evolution and parallelism in plant domestication revealed by an expanding archaeological record. Proc. Natl. Acad. Sci. 111, 6147–6152 (2014).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Purugganan, M. D. An evolutionary genomic tale of two rice species. Nat. Genet. 46, 931–932 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kleisner, K. & Stella, M. Monsters we met, monsters we made: On the parallel emergence of phenotypic similarity under domestication. Σημειωτκή – Sign Syst. Stud. 37, 454–476 (2009).Article 

    Google Scholar 
    Wilkins, A. S., Wrangham, R. W. & Fitch, W. T. The, “Domestication Syndrome” in mammals: A unified explanation based on neural crest cell behavior and genetics. Genetics 197, 795–808 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lecocq, T. Insects: The disregarded domestication histories. In Animal Domestication (ed. Teletchea, F.) (IntechOpen, 2018).
    Google Scholar 
    Pollan, M. The botany of desire: A plant’s-eye view of the world. Econ. Bot. 57(1), 146–147 (2002).
    Google Scholar 
    Chuttong, B., Chanbang, Y., Sringarm, K. & Burgett, M. Physicochemical profiles of stingless bee (Apidae: Meliponini) honey from South East Asia (Thailand). Food Chem. 192, 149–155 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Spivak, M. & Danka, R. G. Perspectives on hygienic behavior in Apis mellifera and other social insects. Apidologie 52, 1–16 (2021).Article 

    Google Scholar 
    Breed, M. D., Guzmán-Novoa, E. & Hunt, G. J. 3. Defensive behavior of honey bees: Organization, genetics, and comparisons with other bees. Annu. Rev. Entomol. 49, 271–298 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hunt, G. J. et al. Behavioral genomics of honeybee foraging and nest defense. Naturwissenschaften 94, 247–267 (2007).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Faegri, K. & van der Pijl,. Principles of Pollination Ecology (Pergamon Press, 1979).
    Google Scholar 
    Nicolson, S. W. & Thornburg, R. W. Nectar chemistry. In Nectaries and Nectar (eds Nicolson, S. W. et al.) (Springer Netherlands, 2007).Chapter 

    Google Scholar 
    Abrahamczyk, S. et al. Pollinator adaptation and the evolution of floral nectar sugar composition. J. Evol. Biol. 30, 112–127 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Parachnowitsch, A. L., Manson, J. S. & Sletvold, N. Evolutionary ecology of nectar. Ann. Bot. 123, 247–261 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Rasmussen, C. & Cameron, S. A. Global stingless bee phylogeny supports ancient divergence, vicariance, and long distance dispersal. Biol. J. Linn. Soc. 99, 206–232 (2010).Article 

    Google Scholar 
    Bantle, J. P. Dietary fructose and metabolic syndrome and diabetes. J. Nutr. 139, 1263S-1268S (2009).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Erejuwa, O. O., Sulaiman, S. A. & Wahab, M. S. A. fructose might contribute to the hypoglycemic effect of honey. Molecules 17, 1900–1915 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kwakman, P. H. S. & Zaat, S. A. J. Antibacterial components of honey. IUBMB Life 64, 48–55 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Viuda-Martos, M., Ruiz-Navajas, Y., Fernández-López, J. & Pérez-Álvarez, J. A. Functional properties of honey, propolis, and royal jelly. J. Food Sci. 73, R117–R124 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Machado De-Melo, A. A., de Almeida-Muradian, L. B., Sancho, M. T. & Pascual-Maté, A. Composition and properties of Apis mellifera honey: A review. J. Apic. Res. 57, 5–37 (2018).Article 

    Google Scholar 
    Nordin, A., Sainik, N. Q. A. V., Chowdhury, S. R., Saim, A. B. & Idrus, R. B. H. Physicochemical properties of stingless bee honey from around the globe: A comprehensive review. J. Food Compos. Anal. 73, 91–102 (2018).Article 
    CAS 

    Google Scholar 
    Viteri, R., Zacconi, F., Montenegro, G. & Giordano, A. Bioactive compounds in Apis mellifera monofloral honeys. J. Food Sci. 86, 1552–1582 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bueno, F. G. B. et al. Stingless bee floral visitation in the global tropics and subtropics. BioRxiv. https://doi.org/10.1101/2021.04.26.440550 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rasmussen, C. & Cameron, S. A. A molecular phylogeny of the Old World stingless bees (Hymenoptera: Apidae: Meliponini) and the non-monophyly of the large genus Trigona. Syst. Entomol. 32, 26–39 (2007).Article 

    Google Scholar 
    Mokaya, H. O., Nkoba, K., Ndunda, R. M. & Vereecken, N. J. Characterization of honeys produced by sympatric species of Afrotropical stingless bees (Hymenoptera, Meliponini). Food Chem. 366, 130597 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Souza, E. C. A., Menezes, C. & Flach, A. Stingless bee honey (Hymenoptera, Apidae, Meliponini): A review of quality control, chemical profile, and biological potential. Apidologie 52, 113–132 (2021).Article 

    Google Scholar 
    Ohmenhaeuser, M., Monakhova, Y. B., Kuballa, T. & Lachenmeier, D. W. Qualitative and quantitative control of honeys using NMR spectroscopy and chemometrics. ISRN Anal. Chem. 2013, 1–9 (2013).Article 

    Google Scholar 
    Mazzoni, V., Bradesi, P., Tomi, F. & Casanova, J. Direct qualitative and quantitative analysis of carbohydrate mixtures using 13C NMR spectroscopy: Application to honey. Magn. Reson. Chem. 35, S81–S90 (1997).Article 
    CAS 

    Google Scholar 
    Consonni, R. & Cagliani, L. R. Geographical characterization of polyfloral and acacia honeys by nuclear magnetic resonance and chemometrics. J. Agric. Food Chem. 56, 6873–6880 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Schievano, E., Peggion, E. & Mammi, S. H1 nuclear magnetic resonance spectra of chloroform extracts of honey for chemometric determination of its botanical origin. J. Agric. Food Chem. 58, 57–65 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    RStudio Team. RStudio: Integrated Development Environment for R. Rstudio, PBC, Boston, MA. URL http://www.rstudio.com (2020).R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2021).Oksanen J., et al. Vegan: Community ecology package. McGlinn lab URL https://CRAN.R-project.org/package=vegan (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, New York, 2016).Book 
    MATH 

    Google Scholar 
    Yu, G. Using ggtree to visualize data on tree-like structures. Curr. Protoc. Bioinforma. 69, e96. https://doi.org/10.1002/cpbi.96 (2020).Article 

    Google Scholar 
    Cáceres, M. D. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574 (2009).Article 
    PubMed 

    Google Scholar  More

  • in

    South African Lagerstätte reveals middle Permian Gondwanan lakeshore ecosystem in exquisite detail

    Lucas, S. G. Permian tetrapod extinction events. Earth Sci. Rev. 170, 31–60 (2017).
    Google Scholar 
    Rampino, M. R. & Shen, S.-Z. The end-Guadalupian (259.8 Ma) biodiversity crisis: the sixth major mass extinction? Hist. Biol. 33, 716–722 (2019).
    Google Scholar 
    Day, M. O. & Rubidge, B. S. The late capitanian mass extinction of terrestrial vertebrates in the Karoo Basin of South Africa. Front. Earth Sci. 9, 631198 (2021).
    Google Scholar 
    Bordy, E. M. & Paiva, F. Stratigraphic architecture of the karoo river channels at the end-capitanian. Front. Earth Sci. 8, 521766 (2021).
    Google Scholar 
    Erwin, D. H., Bowring, S. A. & Yugan, J. In Catastrophic events and mass extinctions: impacts and beyond (eds. Koeberl, C. & MacLeod, K. G.) 363–383 (Geological Society of America, 2002).Fielding, C. R. et al. Age and pattern of the southern high-latitude continental end-Permian extinction constrained by multiproxy analysis. Nat. Commun. 10, 385 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Viglietti, P. A. et al. Evidence from South Africa for a protracted end-Permian extinction on land. Proc. Natl Acad. Sci. USA 118, e2017045118 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rubidge, B. S. Did mammals originate in Africa? South African fossils and the Russian connection. Syd. Haughton Meml. Lect. 4, 1–14 (1995).
    Google Scholar 
    Day, M. O. & Rubidge, B. S. A brief lithostratigraphic review of the Abrahamskraal and Koonap formations of the Beaufort Group, South Africa: towards a basin-wide stratigraphic scheme for the Middle Permian Karoo. J. Afr. Earth Sci. 100, 227–242 (2014).
    Google Scholar 
    Day, M., Ramezani, J., Frazer, R. & Rubidge, B. U-Pb zircon age constraints on the vertebrate assemblages and palaeomagnetic record of the Guadalupian Abrahamskraal Formation, Karoo Basin, South Africa. J. Afr. Earth Sci. 186, 104435 (2022).CAS 

    Google Scholar 
    Koch, N. M., Garwood, R. & Parry, L. Fossils improve phylogenetic analyses of morphological characters. Proc. R. Soc. B Biol. Sci. 288, 1–8 (2021).
    Google Scholar 
    McLoughlin, S. Glossopteris: insights into the architecture and relationships of an iconic Permian Gondwanan plant. J. Bot. Soc. Bengal 65, 93–106 (2011).
    Google Scholar 
    Slater, B. J., McLoughlin, S. & Hilton, J. A high-latitude Gondwanan lagerstätte: the Permian permineralised peat biota of the Prince Charles Mountains, Antarctica. Gondwana Res. 27, 1446–1473 (2015).
    Google Scholar 
    Plumstead, E. P. Three thousand million years of plant life in Africa. (Geological Society of South Africa, 1969).Lacey, W. S., van Dijk, D. E. & Gordon-Gray, K. D. Fossil plants from the Upper Permian in the Mooi River district of Natal, South Africa. Ann. Natal. Mus. 22, 349–420 (1975).
    Google Scholar 
    Anderson, J. M. & Anderson, H. M. Palaeoflora of Southern Africa. Prodomus of South African megafloras. Devonian to Lower Cretaceous. (Balkema, 1985).Bordy, E. M. & Prevec, R. Sedimentology, palaeontology and palaeo-environments of the Middle (?) to Upper Permian Emakwezini Formation (Karoo Supergroup, South Africa). South Afr. J. Geol. 111, 429–458 (2008).Prevec, R. et al. Portrait of a Gondwanan ecosystem: a new late Permian fossil locality from KwaZulu-Natal, South Africa. Rev. Palaeobot. Palynol. 156, 454–493 (2009).
    Google Scholar 
    Mcloughlin, S. & Prevec, R. The architecture of Permian glossopterid ovuliferous reproductive organs. Alcheringa Australas. J. Palaeontol. 43, 480–510 (2019).
    Google Scholar 
    McLoughlin, S. & Prevec, R. The reproductive biology of glossopterid gymnosperms—a review. Rev. Palaeobot. Palynol. 295, 104527 (2021).
    Google Scholar 
    Riek, E. F. New Upper Permian insects from Natal, South Africa. Ann. Natal. Mus. 22, 755–789 (1976).
    Google Scholar 
    Riek, E. F. Fossil insects from the Middle Ecca (Lower Permian) of southern Africa. Palaeontol. Afr. 19, 145–148 (1976).
    Google Scholar 
    Riek, E. F. An entomobryid collembolan (Hexapoda: Collembola) from the Lower Permian of Southern Africa. Palaeontol. Afr. 19, 141–143 (1976).
    Google Scholar 
    McLachlan, I. R. & Anderson, A. M. Fossil insect wings from the Early Permian White Band Formation, South Africa. Palaeontol. Afr. 20, 83–86 (1977).
    Google Scholar 
    Pinto, I. D. & Pinto De Ornellas, L. New fossil insects from the White Band Formation (Permian), South Africa. Pesqui. Zool. 10, 96–104 (1978).
    Google Scholar 
    van Dijk, D. E. & Geertsema, H. Permian insects from the Beaufort Group of Natal, South Africa. Ann. Natal. Mus. 40, 137–171 (1999).
    Google Scholar 
    Geertsema, H., van Dijk, D. E. & van den Heever, A. J. Palaeozoic insects of southern Africa: a review. Palaeontol. Afr. 38, 19–25 (2002).
    Google Scholar 
    Rubidge, B. S., Erwin, D. H., Ramezani, J., Bowring, S. A. & de Klerk, W. J. High-precision temporal calibration of Late Permian vertebrate biostratigraphy: U-Pb zircon constraints from the Karoo Supergroup, South Africa. Geology 41, 363–366 (2013).CAS 

    Google Scholar 
    Mcloughlin, S., Prevec, R. & Slater, B. J. Arthropod interactions with the Permian Glossopteris flora. J. Palaeosciences 70, 43–133 (2021).
    Google Scholar 
    Shcherbakov, D. E. On Permian and Triassic insect faunas in relation to biogeography and the Permian-Triassic crisis. Paleontol. J. 42, 15–31 (2008).
    Google Scholar 
    Nel, A. et al. The earliest known holometabolous insects. Nature 503, 257–261 (2013).CAS 
    PubMed 

    Google Scholar 
    Nicholson, D. B., Mayhew, P. J. & Ross, A. J. Changes to the fossil record of insects through fifteen years of discovery. PLoS ONE 10, 1421–1435 (2015).
    Google Scholar 
    Glenister, B. F., Wardlaw, B. R., Lambert, L. L., Spinosa, C. & Bowring, S. A. Proposal of Guadalupian and component Roadian. Wordian Capitanian Stages Int. Stand. middle Permian Ser. Permophiles 34, 3–11 (1999).
    Google Scholar 
    Allison, P. A. Konservat-Lagerstätten: cause and classification. Paleobiology 14, 331–344 (1988).
    Google Scholar 
    Grimaldi, D. & Engel, M. S. Evolution of the Insects. (Cambridge University Press, 2005).Tian, Q. et al. Experimental investigation of insect deposition in lentic environments and implications for formation of Konservat Lagerstätten. Palaeontology 63, 565–578 (2020).
    Google Scholar 
    McCurry, M. R. et al. A Lagerstätte from Australia provides insight into the nature of Miocene mesic ecosystems. Sci. Adv. 8, 1–11 (2022).
    Google Scholar 
    Beckemeyer, R. J. & Hall, J. D. The entomofauna of the Lower Permian fossil insect beds of Kansas and Oklahoma, USA. Afr. Invertebr. 48, 17 (2007).
    Google Scholar 
    Jell, P. A. The fossil insects of Australia. Mem. Qld. Mus. 50, 1–124 (2004).
    Google Scholar 
    Wickens, H., de, V. & Cole, D. I. Lithostratigraphy of the Skoorsteenberg Formation (Ecca Group, Karoo Supergroup), South Africa. South Afr. J. Geol. 120, 433–446 (2017).
    Google Scholar 
    Rubidge, B. S., Hancox, P. J. & Catuneaunu, O. Sequence analysis of the Ecca–Beaufort contact in the southern Karoo of South Africa. South Afr. J. Geol. 103, 81–96 (2000).
    Google Scholar 
    Lanci, L., Tohver, E., Wilson, A. & Flint, S. Upper Permian magnetic stratigraphy of the lower Beaufort Group, Karoo Basin. Earth Planet. Sci. Lett. 375, 123–134 (2013).CAS 

    Google Scholar 
    Belica, M. E. et al. Refining the chronostratigraphy of the Karoo Basin, South Africa: magnetostratigraphic constraints support an early Permian age for the Ecca Group. Geophys. J. Int. 211, 1354–1374 (2017).CAS 

    Google Scholar 
    Rubidge, B. S. & Day, M. O. Biostratigraphy of the Eodicynodon Assemblage Zone (Beaufort Group, Karoo Supergroup), South Africa. South Afr. J. Geol. 123, 141–148 (2020).
    Google Scholar 
    Nel, A., Garrouste, R. & Prevec, R. The first Permian Gondwanan damselfly-like Protozygoptera (Insecta, Odonatoptera). Hist. Biol. https://doi.org/10.1080/08912963.2022.2067996 (2022).Cawood, R. et al. The first ‘Grylloblattida’ of the family Liomopteridae from the Middle Permian in the Onder Karoo, South Africa (Insecta: Polyneoptera). Comptes Rendus Palevol. https://doi.org/10.5852/cr-palevol2022v21a22 (2022).Surange, K. R. & Chandra, S. Morphology of the gymnospermous fructifications of the Glossopteris flora and their relationships. Palaeontogr. B 149, 153–180 (1975).
    Google Scholar 
    White, M. E. Reproductive structures of the Glossopteridales in the plant fossil collection of the Australian Museum. Rec. Aust. Mus. 31, 473–504 (1978).
    Google Scholar 
    Nishida, H., Pigg, K. B. & DeVore, M. L. In Transformative Paleobotany, Ch. 8 (eds. Krings, M., Harper, C. J., Cúneo, N. R. & Rothwell, G. W.) 145–154 (Academic Press, 2018).McLoughlin, S. New records of Bergiopteris and glossopterid fructifications from the Permian of Western Australia and Queensland. Alcheringa Australas. J. Palaeontol. 19, 175–192 (1995).
    Google Scholar 
    McLoughlin, S. In Gondwana Eight (eds. Findlay, R. H., Unrug, R., Banks, M. R. & Veevers, J. J.) 253–264 (Balkema, 1993).Nishida, H., Pigg, K. B., Kudo, K. & Rigby, J. F. New evidence of the reproductive organs of Glossopteris based on permineralized fossils from Queensland, Australia. II: pollen-bearing organ Ediea gen. nov. J. Plant Res. 127, 233–240 (2014).PubMed 

    Google Scholar 
    Tomescu, A. M. F., Bomfleur, B., Bippus, A. C. & Savoretti, A. In Transformative Paleobotany (eds. Krings, M., Harper, C. J., Cuneo, N. R. & Rothwell, G. W.) 375–416 (Elsevier Academic Press, 2018).Bomfleur, B. et al. Diverse bryophyte mesofossils from the Triassic of Antarctica. Lethaia 47, 120–132 (2014).
    Google Scholar 
    Nel, A., Bechly, G., Prokop, J., Béthoux, O. & Fleck, G. Systematics and evolution of Paleozoic and Mesozoic damselfly-like Odonatoptera of the ‘protozygopteran’ grade. J. Paleontol. 86, 81–104 (2012).
    Google Scholar 
    Riek, E. F. Fossil insects from the Upper Permian of Natal, South Africa. Ann. Natal. Mus. 21, 513–532 (1973).
    Google Scholar 
    Gallego, O. F. et al. The most ancient Platyperlidae (Insecta, Perlida= Plecoptera) from early Late Triassic deposits in southern South America. Ameghiniana 48, 447–461 (2011).
    Google Scholar 
    Martins-Neto, R. G., Gallego, O. F. & Melchor, R. N. The Triassic insect fauna from South America (Argentina, Brazil and Chile): a checklist (except Blattoptera and Coleoptera) and descriptions of new taxa. Acta Zool. Cracoviensia 46, 229–256 (2003).
    Google Scholar 
    van Dijk, D. E. & Geertsema, H. A new genus of Permian Plecoptera (Afroperla) from KwaZulu-Natal, South Africa. Palaeontogr. B 12, 268–270 (2004).
    Google Scholar 
    Béthoux, O., Cui, Y., Kondratieff, B., Stark, B. & Ren, D. At last, a Pennsylvanian stem-stonefly (Plecoptera) discovered. BMC Evol. Biol. 11, 248 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Schubnel, T., Perdu, L., Roques, P., Garrouste, R. & Nel, A. Two new stem-stoneflies discovered in the Pennsylvanian Avion locality, Pas-de-Calais, France (Insecta: ‘Exopterygota’). Alcheringa Australas. J. Palaeontol. 43, 1–6 (2019).
    Google Scholar 
    Sharov, A. G. In Fundamentals of Paleontology: Arthropoda, Tracheata, Chelicerata. (eds. Rohdendorf, B. B. & Davis, D. R.) vol. 9 173–179 (Smithsonian Institution Libraries and NSCF, 1991).Sinitshenkova, N. D. In History of insects. (eds. Rasnitsyn, A. P. & Quicke, D. L. J.) Ch. 3.3, 388–426 (Kluwer Academic Publishers, 2002).Hayes, P. A. & Collinson, M. E. The Flora of the insect limestone (latest Eocene) from the Isle of Wight, southern England. Earth Environ. Sci. Trans. R. Soc. Edinb. 104, 245–261 (2014).
    Google Scholar 
    Zhang, Q. et al. Mayflies as resource pulses in Jurassic lacustrine ecosystems. Geology 50, 1043–1047 (2022).CAS 

    Google Scholar 
    Prokop, J. et al. Ecomorphological diversification of the Late Palaeozoic Palaeodictyopterida reveals different larval strategies and amphibious lifestyle in adults. R. Soc. Open Sci. 6, 190460 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Prokop, J., Nel, A., Engel, M. S., Pecharová, M. & Hörnschemeyer, T. New Carboniferous fossils of Spilapteridae enlighten postembryonic wing development in Palaeodictyoptera. Syst. Entomol. 41, 178–190 (2016).
    Google Scholar 
    Dos Santos, T. B., de Souza Pinheiro, E. R. & Iannuzzi, R. First evidence of seed predation by arthropods from Gondwana and its early Paleozoic history (Rio Bonito Formation, Paraná Basin, Brazil). PALAIOS 35, 292–301 (2020).
    Google Scholar 
    Nel, A., Garrouste, R. & Prokop, J. The first African Anthracoptilidae (Insecta: Paoliida) near the Permian—Triassic boundary in Kenya. Zootaxa 3925, 145 (2015).PubMed 

    Google Scholar 
    Riek, E. F. An unusual immature insect from the Upper Permian of Natal. Ann. Natal. Mus. 22, 271–274 (1974).
    Google Scholar 
    Dunlop, J. A., Penney, D., Tetlie, O. E. & Anderson, L. I. How many species of fossil arachnids are there? J. Arachnol. 36, 267–272 (2008).
    Google Scholar 
    Rasnitsyn, A. P. et al. Sequence and scale of changes in the terrestrial biota during the Cretaceous (based on materials from fossil resins). Cretac. Res. 61, 234–255 (2016).
    Google Scholar 
    Manum, S. B., Bose, M. N. & Sawyer, R. T. Clitellate cocoons in freshwater deposits since the Triassic. Zool. Scr. 20, 347–366 (1991).
    Google Scholar 
    Struck, T. H. et al. Phylogenomic analyses unravel annelid evolution. Nature 471, 95–98 (2011).CAS 
    PubMed 

    Google Scholar 
    Parry, L., Tanner, A. & Vinther, J. The origin of annelids. Palaeontology 57, 1091–1103 (2014).
    Google Scholar 
    Mikulic, D. G., Briggs, D. E. G. & Kluessendorf, J. A Silurian soft-bodied biota. Science 228, 715–717 (1985).CAS 
    PubMed 

    Google Scholar 
    Prokop, J., Szwedo, J., Lapeyrie, J., Garrouste, R. & Nel, A. New Middle Permian insects from Salagou Formation of the Lodève Basin in southern France (Insecta: Pterygota). Ann. Soci.été Entomol. Fr. NS 51, 14–51 (2015).
    Google Scholar 
    Cai, C. et al. Integrated phylogenomics and fossil data illuminate the evolution of beetles. R. Soc. Open Sci. 9, 211771 (2022).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Srivastava, A. K. & Agnihotri, D. Dilemma of late Palaeozoic mixed floras in Gondwana. Palaeogeogr. Palaeoclimatol. Palaeoecol. 298, 54–69 (2010).
    Google Scholar 
    Raff, R. A. Written in stone: fossils, genes and evo–devo. Nat. Rev. Genet. 8, 911–920 (2007).CAS 
    PubMed 

    Google Scholar 
    Cunningham, J. A., Liu, A. G., Bengtson, S. & Donoghue, P. C. J. The origin of animals: can molecular clocks and the fossil record be reconciled? BioEssays 39, 1–12 (2017).PubMed 

    Google Scholar 
    McCulloch, G. A., Wallis, G. P. & Waters, J. M. A time-calibrated phylogeny of southern hemisphere stoneflies: Testing for Gondwanan origins. Mol. Phylogenet. Evol. 96, 150–160 (2016).PubMed 

    Google Scholar 
    Cui, Y. et al. Rhythms of Insect Evolution. (John Wiley & Sons, Ltd, 2019).Letsch, H. et al. Combining molecular datasets with strongly heterogeneous taxon coverage enlightens the peculiar biogeographic history of stoneflies (Insecta: Plecoptera). Syst. Entomol. 46, 952–967 (2021).
    Google Scholar 
    Raja, N. B. et al. Colonial history and global economics distort our understanding of deep-time biodiversity. Nat. Ecol. Evol. 6, 145–154 (2022).PubMed 

    Google Scholar 
    Beattie, R. The geological setting and palaeoenvironmental and palaeoecological reconstructions of the Upper Permian insect beds at Belmont, New South Wales, Australia. Afr. Invertebr. 48, 18 (2007).
    Google Scholar 
    Bernardi, M. et al. Late Permian (Lopingian) terrestrial ecosystems: a global comparison with new data from the low-latitude Bletterbach Biota. Earth Sci. Rev. 175, 18–43 (2017).
    Google Scholar 
    Jackson, S. E., Pearson, N. J., Griffin, W. L. & Belousova, E. A. The application of laser ablation-inductively coupled plasma-mass spectrometry to in situ U–Pb zircon geochronology. Chem. Geol. 211, 47–69 (2004).CAS 

    Google Scholar 
    Sláma, J. et al. Plešovice zircon—a new natural reference material for U–Pb and Hf isotopic microanalysis. Chem. Geol. 249, 1–35 (2008).
    Google Scholar 
    Wiedenbeck, M. et al. Three natural zircon standards for U‐Th‐Pb, Lu‐Hf, trace element and REE analyses. Geostand. Newsl. 19, 1–23 (2007).
    Google Scholar 
    Horstwood, M. S. A. et al. Community‐derived standards for LA ‐ ICP ‐ MS U‐(Th‐)Pb geochronology—uncertainty propagation, age interpretation and data reporting. Geostand. Geoanal. Res. 40, 311–332 (2016).CAS 

    Google Scholar 
    Paton, C., Hellstrom, J., Paul, B., Woodhead, J. & Hergt, J. Iolite: freeware for the visualisation and processing of mass spectrometric data. J. Anal. Spectrom. 26, 2508–2518 (2011).CAS 

    Google Scholar 
    Petrus, J. A. & Kamber, B. S. VizualAge: a novel approach to laser ablation ICP-MS U-Pb geochronology data reduction. Geostand. Geoanal. Res. 36, 247–280 (2012).CAS 

    Google Scholar 
    Rees, P. Mc. A., Gibbs, M. T., Ziegler, A. M., Kutzbach, J. E. & Behling, P. J. Permian climates: evaluating model predictions using global paleobotanical data. Geology 27, 891 (1999).
    Google Scholar 
    Walter, H. Vegetation of the Earth and ecological systems of the geo-biosphere. (Springer-Verlag, 1985).Lucas, S. G., Schneider, J. W. & Cassinis, G. Non-marine Permian biostratigraphy and biochronology: an introduction. Geol. Soc. Lond. Spec. Publ. 265, 1–14 (2006).
    Google Scholar 
    Scotese, C. In Atlas of Permo-Triassic Paleogeographic Maps (Mollweide Projection), Maps 43–52, Volumes 3 & 4 of the PALEOMAP Atlas for ArcGIS. (PALEOMAP Project, 2014). More

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    Origin, structure and functional transition of sex pheromone components in a false widow spider

    Experimental spidersExperimental spiders were maintained as previously reported37. Briefly, spiders were the F1 to F4 offspring of mated females collected from hallways of the Burnaby campus of Simon Fraser University (Burnaby, BC, CA). Upon hatching, juvenile spiders were housed individually in petri dishes (100 mm × 20 mm) and provisioned with the vinegar flies Drosophila melanogaster. Subadult spiders were fed with larvae of the mealworm beetle Tenebrio molitor. Each adult female spider was kept in a separate translucent 300-mL plastic cup (Western Family, CA) maintained at 22 °C under a reversed light cycle (12:12 h). Adult males and females were fed with black blow flies, Phormia regina. All spiders had access to water in cotton wicks. Water and food were provided once per week. Laboratory experiments were run during a reversed scotophase (0900 to 1700).Identification of contact pheromone components: Preparation of web extracts (summer 2017; spring and summer 2018)Each of the 100 spiders was allowed to build her web for three days on a wooden triangular prism scaffold (30 cm × 25 cm × 22 cm)44 of bamboo skewers (GoodCook, CA, USA) (Fig. 1b). After the spiders were removed from the scaffold, their webs were reeled up with a glass rod (10 cm × 0.5 cm) and deposited in a 1.5-mL glass vial. Per web, 50 µL of methanol (99.9% HPLC grade, Fisher Chemical, ON, Canada) were added and the silk was extracted for 24 h at room temperature. Prior to analysis, the silk was removed and the sample was concentrated under a steady nitrogen stream to the desired concentration.Identification of contact pheromone components: analyses of web extracts by gas chromatography–mass spectrometry (GC-MS)Aliquots (2 µL) of pooled and concentrated web extract (100 webs in 400 µL of solvent) were analysed by GC–MS, using a Varian Saturn Ion trap 2000 (Varian Inc., now Agilent Technologies Inc., Santa Clara, CA 95051, USA) and an Agilent 7890B GC coupled to a 5977 A MSD, both fitted with a DB-5 GC-MS column (30 m × 0.25 mm ID, film thickness 0.25 µm). The injector port was set to 250 °C, the MS source to 230 °C, and the MS quadrupole to 150 °C. Helium was used as a carrier gas at a flow rate of 35 cm s−1, with the following temperature programme: 50 °C held for 5 min, 10 °C min−1 to 280 °C (held for 10 min). Compounds were identified by comparing their mass spectra and retention indices (relative to aliphatic alkanes67) with those of authentic standards that were purchased or synthesised in our laboratory (Supplementary Table 1).Identification of contact pheromone components: high-performance liquid chromatography (HPLC) of web extractsWeb extract of virgin adult female S. grossa was fractionated by high-performance liquid chromatography (HPLC), using a Waters HPLC system (Waters Corporation, Milford, MA, USA; 600 Controller, 2487 Dual Absorbance Detector, Delta 600 pump) fitted with a Synergy Hydro Reverse Phase C18 column (250 mm × 4.6 mm, 4 µ; Phenomenex, Torrance, CA, USA). The column was eluted with a 1-mL/min flow of a solvent gradient, starting with 80% water (HPLC grade, EMD Millipore Corp., Burlington, MA, USA) and 20% acetonitrile (99.9% HPLC grade, Fisher Chemical, Ottawa, CA) and ending with acetonitrile after 10 min. A 60-web-equivalent extract was injected and 20 1-min fractions were collected. Each HPLC fraction (containing 20 web-equivalents) was tested in T-rod bioassays (Fig. 1c) for the presence of contact pheromone components. All eight fractions that elicited courtship responses by males (Supplementary Fig. 1) were analysed by HPLC-tandem MS/MS.Identification of contact pheromone components: HPLC-tandem MS/MS of bioactive HPLC fractionsThe bioactive HPLC fractions were analysed on a Bruker maXis Impact Quadrupole Time-of-Flight HPLC/MS System. The system consists of an Agilent 1200 HPLC fitted with a spursil C18 column (30 mm × 3.0 mm, 3 µ; Dikma Technologies, Foothill Ranch, CA, USA) and a Bruker maXis Impact Ultra-High Resolution tandem TOF (UHR-Qq-TOF) mass spectrometer. The LC-MS conditions were as follows: The mass spectrometer was set to positive electrospray ionisation (+ESI) with a gas temperature of 200 °C and a gas flow of 9 L/min. The nebuliser was set to 4 bar and the capillary voltage to 4200 V. The column was eluted with a 0.4-mL/min flow of a solvent gradient, starting with 80% water and 20% acetonitrile and ending with 100% acetonitrile after 4 min. The solvent contained 0.1% formic acid to improve peak shape.Identification of contact pheromone components: 1H NMR analyses of a bioactive fractionIn HPLC-MS analyses, a single bioactive fraction (9–10 min) appeared to contain only a single compound. This fraction was then further investigated using 1H NMR spectroscopy. The 1H NMR spectrum was recorded on a Bruker Advance 600 equipped with a QNP (600 MHz) using CDCl3. Signal positions (δ) are given in parts per million from tetramethylsilane (δ 0) and were measured relative to the signal of the solvent (1H NMR: CDCl3: δ 7.26).Identification of contact pheromone components: syntheses of candidate pheromone componentsThe syntheses of candidate pheromone components and synthetic intermediates are reported in the SI.Identification of contact pheromone components: T-rod bioassays (general procedures)The T-rod apparatus37 (Fig. 1c) consisted of a horizontal beam (25 cm × 0.4 cm) and a vertical beam (30 cm × 0.4 cm) held together by labelling tape (3 cm × 1.9 cm, Fisher Scientific, Ottawa, ON, CA). A piece of filter paper (2 cm2) was attached to each distal end of the horizontal beam. For each bioassay, an aliquot of web extract (in methanol), or a blend of synthetic candidate pheromone components, was applied to the randomly assigned treatment filter paper, whereas methanol was applied to the control filter paper. The solvent was allowed to evaporate for 1 min before the onset of a 15-min bioassay. A randomly selected naïve male spider was placed at the base of the vertical beam and the time he spent courting on each filter paper was recorded. In response to the presence of female-produced or synthetic pheromone on a filter paper, the male engaged in courtship, pulling silk with his hindlegs from his spinnerets and adding it to the paper. Sensing contact pheromone, the male essentially behaves as if he were courting on the web of a female. On a web, the male engages in web reduction prior to copulation, a behaviour that entails cutting sections of the female’s web with his chelicerae and wrapping the dismantled web bundle with his own silk pulled from his spinnerets41,56. Each T-rod apparatus was used only once. Replicates of experiments as part of specific research objectives were run in parallel to eliminate day effects on the responses of spiders. The sample size for each experiment was set to 20 unless otherwise stated.Identification of contact pheromone components: T-rod bioassays (specific experiments) (fall 2017; spring and summer 2018)Experiment 1 (fall 2017) tested a synthetic blend of volatile compounds 5–11 unique to mature S. grossa females (Fig. 1c and Supplementary Table 1) vs a solvent control. Parallel experiment 2 tested one web equivalent of virgin female web extract, followed by testing each of the 20 HPLC fractions in six replicates for the occurrence of courtship (spring 2018).Parallel experiments 3–6 (summer 2018) tested web extract at one female web equivalent (1 FWE) (Exp. 3), a ternary blend of the candidate contact pheromone components 12, 16 and 17 (Fig. 2d, Exp. 4), the same ternary blend (12, 16 and 17) in combination with the volatile compounds 5–11 (Exp. 5), and 5–11 on their own (Exp. 6).Parallel dose-response experiments 7–11 (summer 2018) tested the ternary blend of 12, 16 and 17 at five FWEs: 0.001 (Exp. 7); 0.01 (Exp. 8); 0.1 (Exp. 9); 1.0 (Exp. 10); and 10 (Exp. 11).Parallel experiments 12–15 tested the ternary blend, and all possible binary blends, of 12, 16 and 17. Parallel experiments 16–18 tested 12 and 16 in binary combination (Exp. 16) and singly (Exps. 17, 18).Origin of contact pheromone components (fall 2020)To trace the origin of contact pheromone component 12 (and coeluting 16), cold-euthanized female spiders were dissected in saline solution55 (25 mL of water and 25 mL of methanol, 160 mM NaCl, 7.5 mM KCl, 1 mM MgCl2, 4 mM NaHCO3, 4 mM CaCl2, 20 mM glucose, pH 7.4). Samples were homogenised (Kimble Pellet Pestle Motor, Kimble Kontes, USA) in methanol for 1 min, kept 24 h at room temperature for pheromone extraction, and then centrifuged (12,500 rpm, 3 °C for 20 min; Hermle Z 360 K refrigerated centrifuge; B. Hermle AG, Wehingen, DE) to obtain the supernatant for HPLC-MS analyses (see above) for the presence of 12 and 16. Three sequential sets of dissections aimed to determine (1) the pheromone-containing body tagma, (2) the pheromone-containing tissues or glands in that tagma and (3) the specific gland or tissue producing 12 & 16.To identify the pheromone-containing tagma, 11 spiders were severed at the pedicel, generating two tagmata: the cephalothorax with four pairs of legs and the abdomen. Each tagma was then extracted separately in 100 µL of methanol. Eight of 11 abdomen samples contained 12 and 16, whereas only one of 11 thorax samples contained 12 and 16 (Exp. 19), albeit at only trace amounts. With 12 and 16 being present in the abdomen, 20 additional abdomens were dissected68 to obtain separate samples of (i) haemolymph (25 µL), (ii) ventral cuticle (~0.5 cm2 near the pedicel, (iii) the ovaries, (iv) all silk glands combined, and (v) the gut (with anus, cloaca and Malpighian tubules). The remaining spider tissues (vi) were pooled as one sample, and 20 µL of the dissection buffer solution (vii) was obtained to detect potential pheromone bleeding. To each tissue sample, 50 µL of methanol were added. Only silk gland samples contained 12 and 16 (Exp. 20). Having established that only silk gland samples contained 12 and 16, the silk glands of 30 additional spiders were excised in the following order: (i) major ampullate gland, (ii) minor ampullate gland, (iii) anterior aggregate gland, (iv) posterior aggregate gland, (v) tubuliform, (vi) aciniform and flagelliform glands combined and (vii) pyriform gland. The glands from three spiders were combined in each sample and extracted in 30 µL methanol. Seven of ten posterior aggregate gland samples contained 12 and 16, with other silk gland samples not containing 12 and 16 or in only trace amounts (Exp. 21).Transition of contact pheromone components to mate attractant pheromone components: evidence for hydrolysis of contact pheromone components (12, 16 and 17) (spring 2021)To test for the hydrolysis of the contact pheromone components 12, 16 and 17, we compared their breakdown ratio (18/(12 + 16 + 17 + 18) on independent webs aged 0 days and 14 days (Exp. 24). Each of 140 spiders was allowed to spin a web on bamboo scaffolds for three days. Then, the spiders were removed and webs—by random assignment—were extracted immediately (0-day-old webs) or after 14 days of aging (14-day-old webs). On each web, the amount of contact pheromone components 12, 16 and 17, and of amide 18 as a breakdown product, was quantified using HPLC–MS, with 12 and 18 at 25 and 50 ng/µL as external standards.Transition of contact pheromone components to mate attractant pheromone components: Y-tube olfactometer bioassays (general procedures)The attraction of male spiders to web extracts and to candidate mate attractant pheromone components was tested in Y-tube olfactometers56 (Fig. 4a) lined with bamboo sticks to provide grip for the bioassay spider. Test stimuli were presented in translucent oven bags (30 cm × 31 cm; Toppits, Mengen, DE) secured to the orifice of side-arms. Test stimuli consisted of a triangular bamboo prism scaffold (each side 8.5 cm long) bearing a spider’s web, or bearing artificial webbing30 (40 ± 2 mg; Bling Star, CN) that was treated with web extract or synthetic chemicals in methanol (100 µL) as the treatment stimulus or with methanol (100 µL) as the control stimulus. For each experimental replicate, a male spider was introduced into a glass holding tube and allowed 2 min to acclimatise. Then, the holding tube was attached via a glass joint to the Y-tube olfactometer and an air pump was connected to the holding tube, drawing air at 100 mL/min through the olfactometer. Air entered the olfactometer through a glass tube secured to the oven bags’ second opening. A male that entered the olfactometer within the 5-min bioassay period was classed a responder and his first choice of oven bag (the oven bag he reached first) was recorded. Whenever a set of 30 replicates was completed by the same observer, using 30 separate Y-tubes, the Y-tubes were cleaned with hot water and soap (Sparkleen, Thermo Fisher Scientific, MA, United States) and dried in an oven at 100 °C for 3 h, whereas the bamboo sticks and the oven bags were discarded.Transition of contact pheromone components to mate attractant pheromone components: Y-tube olfactometer bioassays (specific experiments) (summer 2018)In experiments 22, 23 and 25–27, males were offered a choice between a solvent control stimulus and a treatment stimulus. The treatment stimulus consisted of (i) virgin female web-extract (1 web-equivalent) (Exp. 22, N = 24), (ii) the volatile compounds 5–11 unique to sexually mature females (Fig. 1d) (Exp. 23, N = 24), (iii) all breakdown products of the contact pheromone components 12, 16 and 17, consisting of the amide N-4-methylvaleroyl-l-serine (18) and the corresponding carboxylic acids 19, 20 and 21 (Exp. 25, N = 30), (iv) a blend of the acids 19, 20 and 21 (Exp. 26, N = 30) and (v) the amide 18 as a single compound (Exp. 27, N = 30). Compounds were tested at quantities as determined in virgin female web extract (50 webs in 150 μL of dichloromethane), following silyl-ester derivatization69 of acids in the extract, with valeric acid (200 ng; ≥99%, Sigma Aldrich, St. Louis, USA) added as an internal standard. Per web equivalent, there were 103 ng of 19, 3 ng of 20 and 54 ng of 21. The amide 18 was present at 200 ng per web equivalent, as determined using N-3-methylbutnaoyl-l-serine methyl ester as an external standard.Transition of contact pheromone components to mate attractant pheromone components: hallway of buildings experiment (fall 2018)As the ternary blend of the carboxylic acids 19, 20 and 21 attracted male spiders in Y-tube olfactometers (see Results), we aimed to confirm their functional role as mate attractant pheromone components also in ‘field’ settings (Exp. 28). To this end, we set up ten replicates of paired traps in building hallways on the Burnaby campus of Simon Fraser University. Adhesive-coated traps (Bell Laboratories Inc., Madison, WI, USA) were spaced 0.5 m within pairs and 20 m between pairs. By random assignment, one trap in each pair was baited with the carboxylic acids 19, 20 and 21 formulated in 200 µL of mineral oil (Anachemia, Montreal, CA; 2.8 mg of 19, 0.112 mg of 20 and 1.52 mg of 21), whereas the control trap received mineral oil only. Test stimuli were disseminated from a 400-μL microcentrifuge tube (Evergreen Scientific, Ranco Dominguez, CA, USA) with a hole in its lid punctured by a No. 3 insect pin (Hamilton Bell, Montvale, NJ, USA). Every week for 4 months (September to December 2018), traps were checked, lures were replaced, and the position of the treatment and the control trap within each trap pair was re-randomised.Communication function of amide breakdown product 18 (fall 2018)As the amide 18 did not attract males in Y-tube olfactometer experiments (see Results), we tested its alternate potential function as a contact pheromone component which, if active, would induce courtship by males. Using the T-rod apparatus (Fig. 1c), we treated one piece of filter paper with a solvent control and the other with a blend comprising both the contact pheromone components 12, 16 and 17 and the amide 18 (Exp. 29), a blend of 12, 16 and 17 (Exp. 30), and 18 alone (Exp. 31).Mechanisms underlying the transition of contact pheromone components to mate attractant pheromone components: relationship between web pH and breakdown rates of contact pheromone components (summer 2020)We allowed each of the 70 spiders to spin two webs, using one web to quantify the amide breakdown product (18) of the contact pheromone components (see above), and the other web to determine its pH according to the slurry method57 (Exp. 32). To this end, we first measured the pH of 50 µL water (HPLC grade, EMD Millipore Corp., Burlington, MA, USA) and then of a web with the water functioning as a conductor for the pH metre (LAQUAtwin pH 22 (Horiba, Kyoto, JP). Between web measurements, the pH metre was rinsed with water and regularly re-calibrated using a pH 7 and a pH 4 buffer (Horiba, Kyoto, JP).Mechanisms underlying the transition of contact pheromone components to mate attractant pheromone components: testing for pH-dependent saponification of contact pheromone components (12, 16 and 17) (summer 2021)To test whether pH alone catalyses saponification of the ester bond of contact pheromone components (12, 16 and 17), synthetic 12 was added to a 40% aqueous pH 7 buffer solution (Exp. 34), a pH 4 buffer solution (Exp. 34), and to acetonitrile (Exp. 35) as a polar aprotic solvent control (N = 12; 100 ng/µL each). pH-Dependent breakdown of 12 over time was assessed by analysing (HPLC-MS) diluted aqueous aliquots (2.5 ng/µl) of each sample at day 0 and after 14 days of storage at room temperature.Mechanisms underlying the transition of contact pheromone components to mate attractant pheromone components: testing for the presence of a carboxylesterhydrolase (CEH) (summer 2021)To test for the presence of a carboxylesterhydrolase (CEH), for each of three replicates we extracted (i) five webs of adult virgin female L. hesperus (positive control, known to have a CEH45), (ii) 20 webs of subadult S. grossa (deemed to have not yet produced a CEH) and (iii) ten webs of adult virgin female S. grossa, accounting for the different amounts of silk produced by these three groups of spiders. For each replicate, webs were extracted in 200 µL 0.05 M Sørensen buffer58 and analysed by Bioinformatics Solutions (Waterloo, ON, CA). After web samples were incubated for 20 min at 60 °C in 2× sample volumes of 10% SDS (lauryl sulfate; protein-denaturing anionic detergent), they were sonicated for 20 min. Then, the supernatant was withdrawn, reduced with dithiothreitol (DTT), and alkylated with iodoacetamide (IAA). Alkylated samples were treated further with a protein solvent (S-Trap kit; Protifi, Farmingdale, NY, USA). Briefly, samples were acidified by phosphoric acid to pH ≤1. Then 6× of sample volume S-trap buffer was mixed in. The mixture was loaded by centrifugation onto an S-Trap Micro Spin Column and washed 3× with S-trap buffer. Using the serine protease trypsin, protein digestions were carried out at 47 °C for 1 h in 50 mM triethylamonium bicarbonate (TEAB) buffer within the S-Trap Micro Spin column. Digestion products were eluted sequentially with 40 µL 50 mM TEAB and 0.2% formic acid. Eluates were dried and re-suspended in 0.1% formic acid.Eluates were analysed by HPLC-MS/MS in positive ion mode on a Thermo Scientific Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher, San Jose, CA, USA), equipped with a nanospray ionisation source and a Thermo Fisher Ultimate 3000 RSLCnano HPLC System (Thermo Fisher). Peptide mixtures were loaded onto a PEPMAP100 C18 trap column (75 µm × 20 mm, 5 µm particle size; Thermo Fisher) at a constant flow of 30 μL/min and 60 °C isothermal. Peptides were eluted at a rate of 0.2 μL/min and separated using a Reprosil C18 analytical column (75 μm × 15 mm, 1.9 μm particle size; PepSep, DK) with a 60-min solvent gradient: 0–45 min: 4–35% acetonitrile + 0.1% formic acid; 45–55 min: 90% acetonitrile + 0.1% formic acid; 55–60 min: 4% acetonitrile + 0.1% formic acid.MS data were acquired in data-dependent mode with a cycle time of 3 s. MS1 scan data were acquired with the Orbitrap mass analyser, using a mass range of 400–1600 m/z, with the resolution set to 120,000. The automatic gain control (AGC) was set to 4e5, with a maximum ion injection time of 50 ms, and the radio frequency (RF) lens was set to 30%. Isolations for MS2 scans were run using a quadrupole mass analyser, with an isolation window of 0.7. MS2 scan data were acquired with the Orbitrap mass analyser at a resolution of 15,000 m/z, with a maximum ion injection time of 22 ms, and the AGC target set to 5e4. Higher energy collisional dissociation (HCD; fixed normalised collision energy: 30%) was used for generating MS2 spectra, with the number of microscans set to 1.Statistics and reproducibilityData (Supplementary Table 2) were analysed statistically using R70. Data of experiments 1–18 and 29–31 (testing courtship by male spiders in response to contact pheromone components) were analysed with a Wilcoxon test or Kruskal–Wallis two-tailed rank-sum test with Benjamini–Hochberg correction to adjust for multiple comparison. Data of experiments 19–21 (revealing the presence of contact pheromone components in the abdomen, silk glands, and posterior aggregate silk gland) were analysed with two-tailed, rather than one-tailed, Wilcoxon test or Kruskal–Wallis rank tests because we had no strong assumption as to whether or not pheromone would be present in any of these potential pheromone sources. The p values were adjusted for multiple comparison using the Benjamini–Hochberg method. Y-tube olfactometer data of experiments 22, 23 and 25–27, as well as the hallway experiment 28 (revealing attraction of male spiders to volatile pheromone components) were analysed using an one-tailed71 binomial test, anticipating attraction of spiders to volatile mate attractant pheromone components rather than to solvent control stimuli. Data of experiment 32 (revealing a correlation between web pH and breakdown of web-borne contact pheromone components) were analysed using generalised linear models. Data of experiments 33–35 (showing pH-dependent breakdown of synthetic contact pheromone) were compared using a two-tailed Kruskal–Wallis test with Benjamini–Hochberg correction.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    Contributions of distemper control and habitat expansion to the Amur leopard viability

    Ceballos, G. & Ehrlich, P. R. Mammal population losses and the extinction crisis. Science 296, 904–907 (2002).CAS 
    PubMed 

    Google Scholar 
    Morrison, J. C., Sechrest, W., Dinerstein, E., Wilcove, D. S. & Lamoreux, J. F. Persistence of large mammal faunas as indicators of global human impacts. J. Mammal. 88, 1363–1380 (2007).
    Google Scholar 
    Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 

    Google Scholar 
    Finnegan, S. P. et al. Reserve size, dispersal and population viability in wide ranging carnivores: the case of jaguars in Emas National Park, Brazil. Anim. Conserv. 24, 3–14 (2021).
    Google Scholar 
    Wang, T. et al. Amur tigers and leopards returning to China: direct evidence and a landscape conservation plan. Landsc. Ecol. 31, 491–503 (2016).
    Google Scholar 
    Gilbert, M. et al. Distemper, extinction, and vaccination of the Amur tiger. Proc. Natl. Acad. Sci. 117, 31954–31962 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Smith, K. F., Acevedo-Whitehouse, K. & Pedersen, A. B. The role of infectious diseases in biological conservation. Anim. Conserv. 12, 1–12 (2009).
    Google Scholar 
    Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Courchamp, F. et al. Inverse density dependence and the Allee effect. Trends Ecol. Evol. 14, 405–410 (1999).CAS 
    PubMed 

    Google Scholar 
    Wittmann, M. J., Stuis, H. & Metzler, D. Genetic Allee effects and their interaction with ecological Allee effects. J. Anim. Ecol. 87, 11–23 (2018).PubMed 

    Google Scholar 
    Estes, J. A. et al. Trophic Downgrading of Planet Earth. Science 333, 301–306 (2011).CAS 
    PubMed 

    Google Scholar 
    Stein, A. B. et al. IUCN Red List of Threatened Species: Panthera pardus. IUCN Red List Threat. Species (2020).Vitkalova, A. V. et al. Transboundary cooperation improves endangered species monitoring and conservation actions: A case study of the global population of Amur leopards. Conserv. Lett. 11, e12574 (2018).
    Google Scholar 
    Wang, T. et al. A science-based approach to guide Amur leopard recovery in China. Biol. Conserv. 210, 47–55 (2017).
    Google Scholar 
    Lewis, J. et al. Assessing the health risks of reintroduction: The example of the Amur leopard, Panthera pardus orientalis. Transbound. Emerg. Dis. 67, 1177–1188 (2020).PubMed 

    Google Scholar 
    Terio, K. A. & Craft, M. E. Canine distemper virus (CDV) in another big cat: should CDV be renamed carnivore distemper virus? mBio 4, e00702–e00713 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Adhikari, R. B., Shrestha, M., Puri, G., Regmi, G. R. & Ghimire, T. R. Canine Distemper Virus (CDV): an emerging threat to Nepal’s wildlife. Appl. Sci. Technol. Ann. 1, 149–154 (2020).
    Google Scholar 
    Roelke-Parker, M. E. et al. A canine distemper virus epidemic in Serengeti lions (Panthera leo). Nature 379, 441–445 (1996).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mulia, B. H. et al. Exposure of Wild Sumatran Tiger (Panthera tigris sumatrae) to Canine Distemper Virus. J. Wildl. Dis. 57, 464–466 (2021).PubMed 

    Google Scholar 
    Gordon, C. H. et al. Canine distemper in endangered Ethiopian wolves. Emerg. Infect. Dis. 824–832 (2015) https://doi.org/10.3201/eid2105.141920.Timm, S. F. et al. A suspected canine distemper epidemic as the cause of a catastrophic decline in Santa Catalina Island foxes (Urocyon littoralis catalinae). J. Wildl. Dis. 45, 333–343 (2009).PubMed 

    Google Scholar 
    Sulikhan, N. S. et al. Canine distemper virus in a wild Far Eastern leopard (Panthera pardus orientalis). J. Wildl. Dis. 54, 170–174 (2018).PubMed 

    Google Scholar 
    Gilbert, M. et al. Canine distemper virus as a threat to wild tigers in Russia and across their range. Integr. Zool. 10, 329–343 (2015).PubMed 

    Google Scholar 
    Almberg, E. S., Cross, P. C. & Smith, D. W. Persistence of canine distemper virus in the Greater Yellowstone Ecosystem’s carnivore community. Ecol. Appl. 20, 2058–2074 (2010).PubMed 

    Google Scholar 
    Cleaveland, S. et al. The conservation relevance of epidemiological research into carnivore viral diseases in the Serengeti. Conserv. Biol. 21, 612–622 (2007).PubMed 

    Google Scholar 
    Haydon, D. T. et al. Low-coverage vaccination strategies for the conservation of endangered species. Nature 443, 692–695 (2006).CAS 
    PubMed 

    Google Scholar 
    Hebblewhite, M., Miquelle, D. G., Murzin, A. A., Aramilev, V. V. & Pikunov, D. G. Predicting potential habitat and population size for reintroduction of the Far Eastern leopards in the Russian Far East. Biol. Conserv. 144, 2403–2413 (2011).
    Google Scholar 
    Jiang, G. et al. New hope for the survival of the Amur leopard in China. Sci. Rep. 5, 15475 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Licht, D. S., Moen, R. A. & Romanski, M. Modeling viability of a potential Canada lynx reintroduction to Isle Royale national park. Nat. Areas J. 37, 170–177 (2017).
    Google Scholar 
    Menges, E. S. Population viability analysis for an endangered plant. Conserv. Biol. 4, 52–62 (1990).
    Google Scholar 
    Beissinger, S. R. & McCullough, D. R. Population viability analysis. J. Wildl. Manag. 67, 481–506 (2003).
    Google Scholar 
    Aresu, M. et al. Assessing the effects of different management scenarios on the conservation of small island vulture populations. Bird. Conserv. Int. 31, 111–128 (2021).
    Google Scholar 
    Benson, J. F. et al. Extinction vortex dynamics of top predators isolated by urbanization. Ecol. Appl. 29, e01868 (2019).PubMed 

    Google Scholar 
    Franklin, A. D., Lacy, R. C., Bauman, K. L., Traylor-Holzer, K. & Powell, D. M. Incorporating drivers of reproductive success improves population viability analysis. Anim. Conserv. 24, 386–400 (2021).
    Google Scholar 
    McCallum, H. Models for managing wildlife disease. Parasitology 143, 805–820 (2016).PubMed 

    Google Scholar 
    Bradshaw, C. J. A. et al. Novel coupling of individual-based epidemiological and demographic models predicts realistic dynamics of tuberculosis in alien buffalo. J. Appl. Ecol. 49, 268–277 (2012).
    Google Scholar 
    Shoemaker, K. T. et al. Effects of prey metapopulation structure on the viability of black-footed ferrets in plague-impacted landscapes: a metamodelling approach. J. Appl. Ecol. 51, 735–745 (2014).
    Google Scholar 
    Shaffer, M. L. Minimum population sizes for species conservation. BioScience 31, 131–134 (1981).
    Google Scholar 
    Seimon, T. A. et al. Canine distemper virus: an emerging disease in wild endangered Amur tigers (Panthera tigris altaica). mBio 4, e00410–e00413 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Wang, T. et al. An introduction to Long-term Tiger-Leopard Observation Network based on camera traps in Northeast China. Biodivers. Sci. 28, 1059 (2020).
    Google Scholar 
    Gilbert, M. et al. Estimating the potential impact of canine distemper virus on the Amur tiger population (Panthera tigris altaica) in Russia. PLOS ONE 9, e110811 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Fahrig, L. How much habitat is enough? Biol. Conserv. 100, 65–74 (2001).
    Google Scholar 
    Thatte, P., Joshi, A., Vaidyanathan, S., Landguth, E. & Ramakrishnan, U. Maintaining tiger connectivity and minimizing extinction into the next century: Insights from landscape genetics and spatially-explicit simulations. Biol. Conserv. 218, 181–191 (2018).
    Google Scholar 
    Hostetler, J. A., Onorato, D. P., Jansen, D. & Oli, M. K. A cat’s tale: the impact of genetic restoration on Florida panther population dynamics and persistence. J. Anim. Ecol. 82, 608–620 (2013).PubMed 

    Google Scholar 
    Johnson, W. E. et al. Genetic restoration of the Florida panther. Science 329, 1641–1645 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sankar, K. et al. Monitoring of reintroduced tigers in Sariska Tiger Reserve, Western India: preliminary findings on home range, prey selection and food habits. Trop. Conserv. Sci. 3, 301–318 (2010).
    Google Scholar 
    Kelly, P., Stack, D. & Harley, J. A review of the proposed reintroduction program for the Far Eastern leopard (Panthera pardus orientalis) and the role of conservation organizations, veterinarians, and zoos. Top. Companion Anim. Med. 28, 163–166 (2013).PubMed 

    Google Scholar 
    Hayward, M. W. & Somers, M. J. Reintroduction of top-order predators: using science to restore one of the drivers of biodiversity. in Reintroduction of Top-Order Predators 1–9 (John Wiley & Sons, Ltd, 2009). https://doi.org/10.1002/9781444312034.ch1.Pujol, B., Zhou, S.-R., Sanchez Vilas, J. & Pannell, J. R. Reduced inbreeding depression after species range expansion. Proc. Natl Acad. Sci. 106, 15379–15383 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    He, C., Du, J., Zhu, D. & Zhang, L. Population viability analysis of small population: a case study for Asian elephant in China. Integr. Zool. 15, 350–362 (2020).PubMed 

    Google Scholar 
    Sugimoto, T., Aramilev, V. V., Nagata, J. & McCullough, D. R. Winter food habits of sympatric carnivores, Amur tigers and Far Eastern leopards, in the Russian Far East. Mamm. Biol. 81, 214–218 (2016).
    Google Scholar 
    Athreya, V., Odden, M., Linnell, J. D. C., Krishnaswamy, J. & Karanth, K. U. A cat among the dogs: leopard Panthera pardus diet in a human-dominated landscape in western Maharashtra, India. Oryx 50, 156–162 (2016).
    Google Scholar 
    Steinmetz, R., Seuaturien, N., Intanajitjuy, P., Inrueang, P. & Prempree, K. The effects of prey depletion on dietary niches of sympatric apex predators in Southeast Asia. Integr. Zool. 16, 19–32 (2021).CAS 
    PubMed 

    Google Scholar 
    Appel, M. J. G. et al. Canine distemper epizootic in lions, tigers, and leopards in North America. J. Vet. Diagn. Invest. 6, 277–288 (1994).CAS 
    PubMed 

    Google Scholar 
    Coltman, D. W., Pilkington, J. G., Smith, J. A. & Pemberton, J. M. Parasite-mediated selection against inbred Soay sheep in a free-living, island population. Evolution 53, 1259–1267 (1999).PubMed 

    Google Scholar 
    Fox, C. W. & Reed, D. H. Inbreeding depression increases with environmental stress: an experimental study and meta-analysis. Evolution 65, 246–258 (2011).PubMed 

    Google Scholar 
    Feng, L. et al. Collaboration brings hope for the last Amur leopards. Cat. N. 65, 20 (2017).
    Google Scholar 
    Lacy, R. C., Pollak, J. P., Miller, P. S., Hungerford, L. & Bright, P. Outbreak. Version 2.10. (2020).Lacy, R. C. & Pollak, J. P. Vortex: A stochastic simulation of the extinction process. Version 10.4. (2021).Pollak, J. P. & Lacy, R. C. Metamodel manager. Version 1.0.6. (2020).Pacioni, C., Sullivan, S., Lees, C. M., Miller, P. S. & Lacy, R. C. Outbreak user’s manual. Version 1.1. (2020).Roscoe, D. E. Epizootiology of canine-distemper in new-jersey raccoons. J. Wildl. Dis. 29, 390–395 (1993).CAS 
    PubMed 

    Google Scholar 
    Odden, M. & Wegge, P. Spacing and activity patterns of leopards Panthera pardus in the Royal Bardia National Park, Nepal. Wildl. Biol. 11, 145–152 (2005).
    Google Scholar 
    Stander, P. E., Haden, P. J., Kaqece, I. & Ghau, I. The ecology of asociality in Namibian leopards. J. Zool. 242, 343–364 (1997).
    Google Scholar 
    Huisman, J., Kruuk, L. E. B., Ellis, P. A., Clutton-Brock, T. & Pemberton, J. M. Inbreeding depression across the lifespan in a wild mammal population. Proc. Natl Acad. Sci. 113, 3585–3590 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Morton, N. E., Crow, J. F. & Muller, H. J. An estimate of the mutational damage in man from data on consanguineous marriages. Proc. Natl Acad. Sci. 42, 855–863 (1956).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Balme, G. A., Slotow, R. & Hunter, L. T. B. Edge effects and the impact of non-protected areas in carnivore conservation: leopards in the Phinda–Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).
    Google Scholar 
    Kumbhojkar, S., Yosef, R., Mehta, A. & Rakholia, S. A camera-trap home-range analysis of the Indian leopard (Panthera pardus fusca) in Jaipur, India. Animals 10, 1600 (2020).PubMed Central 

    Google Scholar 
    Rozhnov, V. V. et al. Home range structure and space use of a female Amur leopard, Panthera pardus orientalis (Carnivora, Felidae). Biol. Bull. 42, 821–830 (2015).
    Google Scholar 
    Ralls, K., Ballou, J. D. & Templeton, A. Estimates of lethal equivalents and the cost of inbreeding in mammals. Conserv. Biol. 2, 185–193 (1988).
    Google Scholar 
    Hammersley, J. M. & Handscomb, D. C. General principles of the Monte Carlo method. in Monte Carlo Methods (eds. Hammersley, J. M. & Handscomb, D. C.) 50–75 (Springer Netherlands, 1964). https://doi.org/10.1007/978-94-009-5819-7_5.Kenney, J. S., Allendorf, F. W., McDougal, C. & Smith, J. L. D. How much gene flow is needed to avoid inbreeding depression in wild tiger populations? Proc. R. Soc. B Biol. Sci. 281, 20133337 (2014).
    Google Scholar 
    O’Grady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133, 42–51 (2006).
    Google Scholar 
    Miller, P. S., Lacy, R. C., Medina-Miranda, R., López-Ortiz, R. & Díaz-Soltero, H. Confronting the invasive species crisis with metamodel analysis: An explicit, two-species demographic assessment of an endangered bird and its brood parasite in Puerto Rico. Biol. Conserv. 196, 124–132 (2016).
    Google Scholar  More

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    Increases in multiple resources promote competitive ability of naturalized non-native plants

    Study speciesTo increase our ability to generalize the results, we conducted two multispecies experiments34. The experiments were designed independently, but, as they used similar treatments, we analyzed them jointly to further increase generalizability. For the experiment in China, we selected eight species that are either native or non-native in China (Supplementary Table 1). For the experiment in Germany, we selected 16 species that are either native or non-native in Germany (Supplementary Table 1). All 24 species, representing seven families, are herbaceous, mainly occur in grasslands, and are common in the respective regions. To control for phylogenetic non-independence of species, we selected at least one non-native and one native species in each of the seven families. All non-native species are fully established (i.e. naturalized sensu Richardson et al.35) in the country where the respective experiment was conducted, and, as they are common, most of them could be considered invasive36,37. We classified the species as naturalized non-native or native to China or Germany based on the following databases: (1) “The Checklist of the Alien Invasive Plants in China”38, (2) the Flora of China (www.efloras.org), and (3) BiolFlor (www.ufz.de/biolflor). Seeds or stem fragments of the study species were obtained from local botanical gardens, local commercial seed companies, or from wild populations (Supplementary Table 1).The experiment in ChinaFrom 21 May to 27 June 2020, we planted or sowed the eight study species into plastic trays filled with potting soil (Pindstrup Plus, Pindstrup Mosebrug A/S, Denmark). We sowed the species at different times (Supplementary Table 1) because they were known to require different times until germination. Three species were grown from stem fragments because they mainly rely on clonal propagation, and the others were propagated from seeds (Supplementary Table 1).On 13 July 2020, we transplanted the cuttings or seedlings into 2.5-L circular plastic pots filled with a mixture of sand and vermiculite (1:1 v/v). Three competition treatments were imposed: (1) competition-free, in which plants were grown alone; (2) intraspecific competition, in which two individuals of the same species were grown together; (3) interspecific competition, in which two individuals, each from a different species were grown together. We grew all eight species without competition, in intraspecific competition, and in all 28 possible pairs of interspecific competition. For the competition-free and intraspecific-competition treatments, we replicated each species seven times (i.e. we had seven technical replicates). For the interspecific-competition treatment, for which we had many pairs of species (i.e. biological replicates), we replicated each pair two times.The experiment took place in a greenhouse at the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences (Changchun, China). The greenhouse had a transparent plastic film on the top, which reduced the ambient light intensity by 12%. It was open on the sides so that insects and other organisms could enter. To vary nutrient availability, we applied to each pot either 5 g (low-nutrient treatment) or 10 g (high-nutrient treatment) of a slow-release fertilizer (Osmocote® Exact Standard, Everris International B.V., Geldermalsen, The Netherlands; 15% N + 9% P2O5 + 12% K2O + 2% MgO + trace elements). To vary light availability, we used two cages (size: 9 m × 4.05 m × 1.8 m). One of them was covered with two layers of black netting material, which reduced the light intensity by 71% (low light-intensity treatment, where the light intensity was on average 233.5 μmol m−2 s−1, measured on a sunny day). The other was left uncovered (high light-intensity treatment, where the light intensity was on average 826.7 μmol m−2 s−1).The experiment included a total of 672 pots ([8 no-competition × 7 replicates + 8 intraspecific-competition × 7 replicates + 28 interspecific-competition × 2 replicates]×2 nutrient treatments × 2 light treatments). The pots were randomly assigned to positions and were randomized once on 15 August within each block (i.e. the low or high light-intensity treatment). The initial height of each plant was measured on 15 July 2020, two days after the transplanting. We watered the plants daily to avoid water limitations. On 1 September 2020, we harvested the aboveground biomass of all plants. The biomass was dried at 65°C for 72 h to constant weight and then weighed to the nearest mg.The experiment in GermanyOn 15 June 2020, we sowed seeds of the 16 species into plastic trays filled with potting soil (Topferde, Einheitserde Co). On 6 July 2020, we transplanted the seedlings into 1.5-L pots filled with a mixture of potting soil and sand (1:1 v/v). Like the experiment in China, we imposed three competition treatments: competition-free, intraspecific competition, and interspecific competition. However, in this experiment, which had two times more species than the experiment in China, we only included 24 randomly chosen species pairs for the interspecific-competition treatment, and all of these pairs consisted of one naturalized non-native and one native species. For the competition-free treatment, we replicated each species two times (i.e. we had two technical replicates). For the competition treatments, we did not use technical replicates for any of the species combinations for logistic reasons. However, as we had a large number of species pairs in the inter-specific competition treatment, we had many biological replicates.The experiment took place outdoors in the Botanical Garden of the University of Konstanz (Konstanz, Germany). To vary nutrient availability, we applied to each pot once a week either 100 ml of a low-concentration liquid fertilizer (low-nutrient treatment; 0.5‰ Universol ® Blue oxide fertilizer, 18% N + 11% P + 18% K + 2.5% MgO + trace elements) or 100 ml of a high-concentration of the same liquid fertilizer (high-nutrient treatment; 1‰). In total, pots in the low- and high-nutrient treatment received 0.4 and 0.8 g fertilizer, respectively. To vary light availability, we used eight metal wire cages (size: 2 m × 2 m × 2 m). Four of the cages were covered with one layer of white and one layer of green netting material, which reduced the ambient light intensity by 84% (low light-intensity treatment; where the light intensity was on average 219.0 μmol m−2 s−1, measured on a sunny day). The remaining four cages were covered only with one layer of the white netting material, which served as a positive control for the effect of netting and reduced light intensity by 53% (high light-intensity treatment; where the light intensity was on average 678.4 μmol m−2 s−1). In other words, the low light-intensity treatment received 34% (66% reduction) of the light intensity in the high light-intensity treatment.The experiment included a total of 320 pots ([16 no-competition × 2 replicates + 16 intraspecific-competition + 32 interspecific-competition]×2 nutrient treatments × 2 light treatments). The eight cages were randomly assigned to fixed positions in the botanical garden. The pots were randomly assigned to the eight cages (40 pots in each cage) and were re-randomized once within and across cages of the same light treatment on 3 August 2020. Besides the weekly fertilization, we watered the plants two or three times a week to avoid water limitations. On 7 and 8 September 2020, we harvested the aboveground biomass of all plants. The biomass was dried at 70 °C for 96 h to constant weight and then weighed to the nearest 0.1 mg.Statistical analysesAll analyses were performed using R version 3.6.139. To test whether resource availability affected competitive outcomes between native and non-native species, we applied linear mixed-effects models to analyze the biomass of the plants in the two experiments jointly and separately, using the nlme package40. For the model used to analyze the two experiments jointly, we excluded interspecific competition between two non-natives and interspecific competition between two natives from the experiment in China, because non-native-non-native and native-non-native combinations were not included in the experiment in Germany. When we analyzed each experiment separately, the results were overall similar to the results of the joint analysis. Therefore, we focus in the manuscript on the joint analysis and present the results of the separate analyses in Supplementary Note 2.Because plant mortality was low and mainly happened after transplanting, we excluded pots in which plants had died. The final dataset contained 1180 individuals from 871 pots. In the model, we included the aboveground biomass of individuals as the response variable. We included the origin of the species (non-native or native), competition treatment (see below for details), nutrient treatment, light treatment and their interactions as fixed effects; study site (China or Germany), and identity and family of the species as random effects. In addition, we allowed each species to respond differently to the nutrient and light treatments (i.e. we included random slopes). To account for pseudoreplication41, we also included pots as random effects and cages (ten cages, eight from Germany and two from China) as random block effects. In the competition treatment, we had three levels: (1) no competition, (2) intraspecific competition, and (3) interspecific competition between native and non-native species. To split them into two contrasts, we created two variables42 testings (1) the effect of the presence of competitors, and (2) the difference between intra- and interspecific competition (see Supplementary Note 3 for details). To improve the normality of the residuals, we natural-log-transformed aboveground biomass. To improve the homoscedasticity of the residuals, we allowed the species and competition treatment to have different variances by using the varComb and varIdent functions43. Significances of the fixed effects were assessed with likelihood-ratio tests (type II) with the car package44.To determine the ‘competitive outcome’, i.e. which species will exclude or dominate over the other species at the endpoint for the community45,46, one should ideally conduct a long-term study. Alternatively, one could vary the density of each species, which mimics the dynamics of species populations across time (see refs. 47,48 for examples). However, applying this space-for-time-substitution method would have largely increased the size of the experiment, especially when combined with the light and nutrient treatments. Still, by growing plants alone, in intraspecific competition and in interspecific competition, our experiments meet the minimal requirement for measuring competitive outcome, at least in terms of short-term biomass production46,49.In the linear mixed-effects model of individual biomass, a significant effect of origin would indicate that native and naturalized non-native species differed in their biomass production, across all competition and resource-availability (light and nutrients) treatments. This would tell us the competitive outcome between non-natives and natives across different resource availabilities. For example, an overall higher level of biomass production of non-native species would indicate that non-natives would dominate when competing with natives. A significant interaction between a resource-availability treatment and the origin of the species would indicate that resource availability affects the biomass production of native and non-native species differently, averaged across all competition treatments. In other words, it would indicate that resource availability affects the competitive outcome between natives and non-natives. A significant interaction between a resource-availability treatment and the competition treatment would indicate that resource availabilities modify the effect of competition (e.g. no competition vs. competition). Other studies frequently have inferred competitive outcomes from the effect of competition by calculating the relative interaction intensity50. However, while the competitive outcome and effect of competition are often related, they are not equivalent45. This is because the competitive outcome is both determined by the effect of competition and intrinsic growth rate48,49. For example, a plant species that strongly suppress other species but has a low intrinsic growth rate still cannot dominate the community.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More