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Species traits predict the aryl hydrocarbon receptor 1 (AHR1) subtypes responsible for dioxin sensitivity in birds

  • 1.

    White, S. S. & Birnbaum, L. S. An overview of the effects of dioxins and dioxin-like compounds on vertebrates, as documented in human and ecological epidemiology. J. Environ. Sci. Health C Environ. Carcinog. Ecotoxicol. Rev. 27, 197–211 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 2.

    Harris, M. L. & Elliott, J. E. Effects of polychlorinated biphenyls, dibenzo-p-dioxins and dibenzofurans, and polybrominated diphenyl ethers in wild birds. In Environmental Contaminants in Biota: Interpreting Tisue Concentrations (eds Beyer, W. N. & Meador, J. P.) 477–528 (CRC Press, Cambridge, 2011).

    Google Scholar 

  • 3.

    Grasman, K. A., Scanlon, P. F. & Fox, G. A. Reproductive and physiological effects of environmental contaminants in fish-eating birds of the Great Lakes: A review of historical trends. Environ. Monit. Assess. 53, 117–145 (1998).

    CAS  Google Scholar 

  • 4.

    Okey, A. B. An aryl hydrocarbon receptor odyssey to the shores of toxicology: The Deichmann Lecture, International Congress of Toxicology-XI. Toxicol. Sci. 98, 5–38 (2007).

    CAS  PubMed  Google Scholar 

  • 5.

    Denison, M. S., Soshilov, A. A., He, G., DeGroot, D. E. & Zhao, B. Exactly the same but different: Promiscuity and diversity in the molecular mechanisms of action of the aryl hydrocarbon (dioxin) receptor. Toxicol. Sci. 124, 1–22 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 6.

    Tian, J. et al. The aryl hydrocarbon receptor: A key bridging molecule of external and internal chemical signals. Environ. Sci. Technol. 49, 9518–9531 (2015).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • 7.

    Beischlag, T. V., Morales, J. L., Hollingshead, B. D. & Perdew, G. H. The aryl hydrocarbon receptor complex and the control of gene expression. Crit. Rev. Eukaryot. Gene Expr. 18, 207–250 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 8.

    Hahn, M. E. Aryl hydrocarbon receptors: Diversity and evolution. Chem. Biol. Interact. 141, 131–160 (2002).

    CAS  PubMed  ADS  Google Scholar 

  • 9.

    Hwang, J.-H. et al. Ecological factors drive natural selection pressure of avian aryl hydrocarbon receptor 1 genotypes. Sci. Rep. 6, 27526 (2016).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • 10.

    Head, J. A., Hahn, M. E. & Kennedy, S. W. Key amino acids in the aryl hydrocarbon receptor predict dioxin sensitivity in avian species. Environ. Sci. Technol. 42, 7535–7541 (2008).

    CAS  PubMed  ADS  Google Scholar 

  • 11.

    Farmahin, R. et al. Amino acid sequence of the ligand-binding domain of the aryl hydrocarbon receptor 1 predicts sensitivity of wild birds to effects of dioxin-like compounds. Toxicol. Sci. 131, 139–152 (2013).

    CAS  PubMed  Google Scholar 

  • 12.

    Head, J. A. & Kennedy, S. W. Correlation between an in vitro and an in vivo measure of dioxin sensitivity in birds. Ecotoxicology 19, 377–382 (2010).

    CAS  PubMed  Google Scholar 

  • 13.

    Karchner, S. I., Franks, D. G., Kennedy, S. W. & Hahn, M. E. The molecular basis for differential dioxin sensitivity in birds: Role of the aryl hydrocarbon receptor. Proc. Natl. Acad. Sci. U.S.A. 103, 6252–6257 (2006).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • 14.

    Farmahin, R. et al. Sequence and in vitro function of chicken, ring-necked pheasant, and Japanese quail AHR1 predict in vivo sensitivity to dioxins. Environ. Sci. Technol. 46, 2967–2975 (2012).

    CAS  PubMed  ADS  Google Scholar 

  • 15.

    Manning, G. E. et al. A luciferase reporter gene assay and aryl hydrocarbon receptor 1 genotype predict the LD(50) of polychlorinated biphenyls in avian species. Toxicol. Appl. Pharmacol. 263, 390–401 (2012).

    CAS  PubMed  Google Scholar 

  • 16.

    Fujisawa, N. et al. Dioxin sensitivity-related two critical amino acids of aryl hydrocarbon receptor may not correlate with the taxonomy of phylogeny in avian species. J. Vet. Med. Sci. 75, 1577–1583 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 17.

    McGill, B. J., Enquist, B. J., Wiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).

    PubMed  Google Scholar 

  • 18.

    Menezes, S., Baird, D. J. & Soares, A. M. V. M. Beyond taxonomy: A review of macroinvertebrate trait-based community descriptors as tools for freshwater biomonitoring. J. Appl. Ecol. 47, 711–719 (2010).

    Google Scholar 

  • 19.

    Chown, S. L. Trait-based approaches to conservation physiology: Forecasting environmental change risks from the bottom up. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 367, 1615–1627 (2012).

    PubMed  PubMed Central  Google Scholar 

  • 20.

    Baird, D. J., Rubach, M. N. & Van den Brink, P. J. Trait-based ecological risk assessment (TERA): The new frontier?. Integr. Environ. Assess. Manage. 4, 2–3 (2008).

    Google Scholar 

  • 21.

    De Lange, H., Lahr, J., Van der Pol, J. J., Wessels, Y. & Faber, J. H. Ecological vulnerability in wildlife: An expert judgment and multicriteria analysis tool using ecological traits to assess relative impact of pollutants. Environ. Toxicol. Chem. 28, 2233–2240 (2009).

    PubMed  Google Scholar 

  • 22.

    Klaassen, M., Hoye, B. J., Nolet, B. A. & Buttemer, W. A. Ecophysiology of avian migration in the face of current global hazards. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 367, 1719–1732 (2012).

    PubMed  PubMed Central  Google Scholar 

  • 23.

    Reynolds, J. D. Life histories and extinction risk. In Macroecology (eds Blackburn, T. M. & Gaston, K. J.) 195–217 (Blackwell Publishing, Ltd., Hoboken, 2003).

    Google Scholar 

  • 24.

    Bennett, P. M. & Owens, I. P. F. Variation in extinction risk among birds: change or evolutionary predisposition?. Proc. R. Soc. Lond. B 264, 401–408 (1997).

    ADS  Google Scholar 

  • 25.

    Morrow, E. H. & Pitcher, T. E. Sexual selection and the risk of extinction in birds. Proc. R. Soc. Lond. B 270, 1793–1799 (2003).

    Google Scholar 

  • 26.

    Thomas, G. H., Lanctot, R. B. & Székely, T. Can intrinsic factors explain population declines in North American breeding shorebirds? A comparative analysis. Anim. Conserv. 9, 252–258 (2006).

    Google Scholar 

  • 27.

    Nosek, J. A., Craven, S. R., Karasov, W. H. & Peterson, R. E. 2,3,7,8-Tetrachlorodibenzo-p-dioxin in terrestrial environments: Implications for resource management. Wildl. Soc. Bull. 21, 179–187 (1993).

    Google Scholar 

  • 28.

    Borga, K., Fisk, A. T., Hoekstra, P. E. & Muir, D. C. G. Biological and chemical factors of importance in the bioaccumulation and trophic transfer of persistent organochlorine contaminants in Arctic marine food webs. Environ. Toxicol. Chem. 23, 2367–2385 (2004).

    CAS  PubMed  Google Scholar 

  • 29.

    Post, D. M. The long and short of food-chain length. Trends Ecol. Evol. 17, 269–277 (2002).

    Google Scholar 

  • 30.

    Arnoldsson, K., Andersson, P. L. & Haglund, P. Photochemical formation of polybrominated dibenzo-p-dioxins from environmentally abundant hydroxylated polybrominated diphenyl ethers. Environ. Sci. Technol. 46, 7567–7574 (2012).

    CAS  PubMed  ADS  Google Scholar 

  • 31.

    Haglund, P. et al. Brominated dibenzo-p-dioxins: A new class of marine toxins?. Environ. Sci. Technol. 41, 3069–3074 (2007).

    CAS  PubMed  ADS  Google Scholar 

  • 32.

    Gribble, G. W. Naturally Occurring Organohalogen Compounds: A Comprehensive Update (Springer, Wien, 2010).

    Google Scholar 

  • 33.

    Sawada, T., Aono, M., Asakawa, S., Ito, A. & Awano, K. Structure determination and total synthesis of a novel antibacterial substance, AB0022A, produced by a cellular slime mold. J. Antibiot. (Tokyo) 53, 959–966 (2000).

    CAS  Google Scholar 

  • 34.

    Tanahashi, T., Takenaka, Y., Nagakura, N. & Hamada, N. Dibenzofurans from the cultured lichen mycobionts of Lecanora cinereocarnea. Phytochemistry 58, 1129–1134 (2001).

    CAS  PubMed  Google Scholar 

  • 35.

    Leighton, F. A. The toxicity of petroleum oils to birds. Environ. Rev. 1, 92–103 (1993).

    CAS  Google Scholar 

  • 36.

    Albers, P. H. Birds and polycyclic aromatic hydrocarbons. Avian Poult. Biol. Rev. 17, 125–140 (2006).

    Google Scholar 

  • 37.

    Latimer, J. S. & Zheng, J. The sources, transport, and fat of PAHs in the marine environment. In PAHs: An Ecotoxicological Perspective (ed. Douben, P. E. T.) (Wiley, Hoboken, 2003).

    Google Scholar 

  • 38.

    Machala, M., Vondracek, J., Blaha, L., Ciganek, M. & Neca, J. Aryl hydrocarbon receptor-mediated activity of mutagenic polycyclic aromatic hydrocarbons determined using in vitro reporter gene assay. Mutat. Res. 497, 49–62 (2001).

    CAS  PubMed  Google Scholar 

  • 39.

    Abdel-Shafy, H. I. & Mansour, M. S. M. A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation. Egypt. J. Pet. 25, 107–123 (2016).

    Google Scholar 

  • 40.

    Tanabe, S. Contamination and toxic effects of persistent endocrine disrupters in marine mammals and birds. Mar. Pollut. Bull. 45, 69–77 (2002).

    CAS  PubMed  ADS  Google Scholar 

  • 41.

    Booth, S. et al. Global deposition of airborne dioxin. Mar. Pollut. Bull. 75, 182–186 (2013).

    CAS  PubMed  Google Scholar 

  • 42.

    Rowe, C. L. “The calamity of so long life”: Life histories, contaminants, and potential emerging threats to long-lived vertebrates. Bioscience 58, 623–631 (2008).

    Google Scholar 

  • 43.

    Sutter, G. I. Analyses of Laboratory and Field Studies of Reproductive Toxicity in Birds Exposed to Dioxin-Like Compounds for the Use in Ecological Risk Assessment (2003).

  • 44.

    Rodewald, P. G. (ed.) The Birds of North America (Cornell Laboratory of Ornithology, Itaca, 2015).

    Google Scholar 

  • 45.

    Pitcher, T. E., Dunn, P. O. & Whittingham, L. A. Sperm competition and the evolution of testes size in birds. J. Evol. Biol. 18, 557–567 (2005).

    CAS  PubMed  Google Scholar 

  • 46.

    Robinson, S. A., Lajeunesse, M. J. & Forbes, M. R. Sex differences in mercury contamination of birds: Testing multiple hypotheses with meta-analysis. Environ. Sci. Technol. 46, 7094–7101 (2012).

    CAS  PubMed  ADS  Google Scholar 

  • 47.

    Dunning, J. B. J. CRC Handbook of Avian Body Masses (CRC Press, Boca Raton, 2007).

    Google Scholar 

  • 48.

    Google Maps, North America. (2015). www.google.ca/maps/place/North+America/@2.8138232,163.4417995,2z/data=!3m1!4b1!4m2!3m1!1s0x52b30b71698e729d:0x131328839761a382. Accessed 20 Dec 2015.

  • 49.

    Rubolini, D., Liker, A., Garamszegi, L. Z., Møller, A. P. & Saino, N. Using the birdtree.org website to obtain robust phylogenies for avian comparative studies: A primer. Curr. Zool. 61, 959–965 (2015).

    PubMed  PubMed Central  Google Scholar 

  • 50.

    Jetz, W., Thomas, G. H., Joy, J. B., Harman, K. & Mooers, O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).

    CAS  PubMed  PubMed Central  ADS  Google Scholar 

  • 51.

    Jetz, W. et al. Global distribution and conservation of evolutionary distinctness in birds. Curr. Biol. 24, 919–930 (2014).

    CAS  PubMed  Google Scholar 

  • 52.

    Hackett, S. J. et al. A phylogenomic study of birds reveals their evolutionary history. Science 320, 1763–1768 (2008).

    CAS  PubMed  ADS  Google Scholar 

  • 53.

    Holder, M. T., Sukumaran, J. & Lewis, P. O. A justification for reporting the majority-rule consensus tree in Bayesian phylogenetics. Syst. Biol. 57, 814–821 (2008).

    PubMed  Google Scholar 

  • 54.

    Sukumaran, J. & Holder, M. T. Sumtrees: Phylogenetic tree summarization, version 4.0.0. https://github.com/jeetsumkamaran/DendroPy. Accessed 31 Jan 2015

  • 55.

    Sukumaran, J. & Holder, M. T. DendroPy: A Python library for phylogenetic computing. Bioinformatics 26, 1569–1571 (2010).

    CAS  PubMed  Google Scholar 

  • 56.

    Python Software Foundation. Python Language Reference (2015).

  • 57.

    Paradis, E., Claude, J. & Strimmer, K. APE: Analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).

    CAS  PubMed  Google Scholar 

  • 58.

    Kembel, S. W. et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26, 1463–1464 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 59.

    R Core Team. R: A Language and Environment for Statistical Computing (2019).

  • 60.

    Mundry, R. Statistical issues and assumptions of phylogenetic generalized least squares. In Modern Phylogenetic Comparative Methods and their Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 131–153 (Springer, New York, 2014).

    Google Scholar 

  • 61.

    De’ath, G. Boosted trees for ecological modeling and prediction. Ecology 88, 243–251 (2007).

    PubMed  Google Scholar 

  • 62.

    Elith, J., Leathwick, J. R. & Hastie, T. A. A working gruide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).

    CAS  PubMed  Google Scholar 

  • 63.

    Harvey, P. H. & Pagel, M. D. The Comparative Method in Evolutionary Biology (Oxford University Press, Oxford, 1991).

    Google Scholar 

  • 64.

    De’ath, G. Multivariate regression trees: A new technique for constrined classification analysis. Ecology 83, 1103–1117 (2002).

    Google Scholar 

  • 65.

    Freckleton, R. P., Harvey, P. H. & Pagel, M. Phylogenetic analysis and comparative data: A test and review of evidence. Am. Nat. 160, 712–726 (2002).

    CAS  PubMed  Google Scholar 

  • 66.

    Freckleton, R. P., Cooper, N. & Jetz, W. Comparative methods as a statistical fix: The dangers of ignoring an evolutionary model. Am. Nat. 178, E10–E17 (2011).

    PubMed  Google Scholar 

  • 67.

    Diniz-Filho, J. A. F., San’Ana, L. M. & Bini, M. An eigenvector method for estimating phylogenetic inertia. Evolution 52, 1247–1262 (1998).

    Google Scholar 

  • 68.

    Covain, R., Dray, S., Fisch-Muller, S. & Monotoya-Burgos, J. I. Assessing phylogenetic dependence of morphological traits using co-inertia prior to investigate character evolution in Loricariinae catfishes. Mol. Phylogenet. Evol. 46, 986–1002 (2008).

    PubMed  Google Scholar 

  • 69.

    Galvan, I. & Moller, A. P. Brain size and the expression of pheomelanin-based colour in birds. J. Evol. Biol. 24, 999–1006 (2011).

    CAS  PubMed  Google Scholar 

  • 70.

    Galvan, I. et al. Long lifespans have evolved with long and monounsaturated fatty acids in birds. Evolution 69, 2776–2784 (2015).

    CAS  PubMed  Google Scholar 

  • 71.

    Nogues-Bravo, D. et al. Phenotypic correlates of potential range size and range filling in European trees. Perspect. Plant Ecol. Evol. Syst. 16, 219–227 (2014).

    Google Scholar 

  • 72.

    Bisson, I.-A., Safi, K. & Holland, R. A. Evidence for repeated independent evolution of migration in the largest family of bats. PLoS ONE 4, e7504 (2009).

    PubMed  PubMed Central  ADS  Google Scholar 

  • 73.

    Diniz-Filho, J. A. F. & Torres, N. M. Phylogenetic comparative methods and the geographic range size–body relationship in new world terrestrial carnivora. Evol. Ecol. 16, 51–67 (2002).

    Google Scholar 

  • 74.

    Diniz-Filho, J. A. F. et al. On the selection of phylogenetic eigenvectors for ecological analyses. Ecography 35, 239–249 (2012).

    Google Scholar 

  • 75.

    Diniz-Filho, J. A. F., Rangel, T. F., Santos, T. & Bini, L. M. Exploring patterns of interspecific variation in quantitative traits using sequential phylogenetic eigenvector regressions. Evolution (N. Y.) 66, 1079–1090 (2012).

    Google Scholar 

  • 76.

    Hijmans, R. J., Phillips, R. J., Leathwick, S. & Elith, J. dismo: Species distrubution modeling (2016).


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