Sensory pollutants alter bird phenology and fitness across a continent

  • 1.

    Buxton, R. T. et al. Noise pollution is pervasive in U.S. protected areas. Science 356, 531–533 (2017).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 2.

    Kyba, C. C. M. et al. Artificially lit surface of Earth at night increasing in radiance and extent. Sci. Adv. 3, e1701528 (2017).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  • 3.

    Barber, J. R., Crooks, K. R. & Fristrup, K. M. The costs of chronic noise exposure for terrestrial organisms. Trends Ecol. Evol. 25, 180–189 (2010).

    PubMed  Article  Google Scholar 

  • 4.

    Swaddle, J. P. et al. A framework to assess evolutionary responses to anthropogenic light and sound. Trends Ecol. Evol. 30, 550–560 (2015).

    PubMed  Article  Google Scholar 

  • 5.

    Gaston, K. J., Davies, T. W., Nedelec, S. L. & Holt, L. A. Impacts of artificial light at night on biological timings. Annu. Rev. Ecol. Evol. Syst. 48, 49–68 (2017).

    Article  Google Scholar 

  • 6.

    Dominoni, D. M. et al. Why conservation biology can benefit from sensory ecology. Nat. Ecol. Evol. 4, 502–511 (2020).

    PubMed  Article  Google Scholar 

  • 7.

    Visser, M. E. & Gienapp, P. Evolutionary and demographic consequences of phenological mismatches. Nat. Ecol. Evol. 3, 879–885 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 8.

    Francis, C. D. & Barber, J. R. A framework for understanding noise impacts on wildlife: an urgent conservation priority. Front. Ecol. Environ. 11, 305–313 (2013).

    Article  Google Scholar 

  • 9.

    Shannon, G. et al. A synthesis of two decades of research documenting the effects of noise on wildlife. Biol. Rev. Camb. Philos. Soc. 91, 982–1005 (2016).

    PubMed  Article  Google Scholar 

  • 10.

    van Langevelde, F., Ettema, J. A., Donners, M., WallisDeVries, M. F. & Groenendijk, D. Effect of spectral composition of artificial light on the attraction of moths. Biol. Conserv. 144, 2274–2281 (2011).

    Article  Google Scholar 

  • 11.

    Hale, J. D., Fairbrass, A. J., Matthews, T. J., Davies, G. & Sadler, J. P. The ecological impact of city lighting scenarios: exploring gap crossing thresholds for urban bats. Glob. Chang. Biol. 21, 2467–2478 (2015).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  • 12.

    Halfwerk, W., Holleman, L. J. M., Lessells, C. M. & Slabbekoorn, H. Negative impact of traffic noise on avian reproductive success. J. Appl. Ecol. 48, 210–219 (2011).

    Article  Google Scholar 

  • 13.

    Kight, C. R., Saha, M. S. & Swaddle, J. P. Anthropogenic noise is associated with reductions in the productivity of breeding Eastern Bluebirds (Sialia sialis). Ecol. Appl. 22, 1989–1996 (2012).

    PubMed  Article  Google Scholar 

  • 14.

    Injaian, A. S., Poon, L. Y. & Patricelli, G. L. Effects of experimental anthropogenic noise on avian settlement patterns and reproductive success. Behav. Ecol. 29, 1181–1189 (2018).

    Article  Google Scholar 

  • 15.

    Kempenaers, B., Borgström, P., Loës, P., Schlicht, E. & Valcu, M. Artificial night lighting affects dawn song, extra-pair siring success, and lay date in songbirds. Curr. Biol. 20, 1735–1739 (2010).

    CAS  PubMed  Article  Google Scholar 

  • 16.

    Cooper, C. B., Hochachka, W. M., Butcher, G. & Dhondt, A. A. Seasonal and latitudinal trends in clutch size: thermal constraints during laying and incubation. Ecology 86, 2018–2031 (2005).

    Article  Google Scholar 

  • 17.

    Van Renterghem, T., Botteldooren, D. & Verheyen, K. Road traffic noise shielding by vegetation belts of limited depth. J. Sound Vibrat. 331, 2404–2425 (2012).

    ADS  Article  Google Scholar 

  • 18.

    Luginbuhl, C. B. et al. From the ground up II: sky glow and near-ground artificial light propagation in Flagstaff, Arizona. Publ. Astron. Soc. Pacif. 121, 204–212 (2009).

    ADS  Article  Google Scholar 

  • 19.

    Boncoraglio, G. & Saino, N. Habitat structure and the evolution of bird song: a meta-analysis of the evidence for the acoustic adaptation hypothesis. Funct. Ecol. 21, 134–142 (2007).

    Article  Google Scholar 

  • 20.

    Francis, C. D. Vocal traits and diet explain avian sensitivities to anthropogenic noise. Glob. Chang. Biol. 21, 1809–1820 (2015).

    ADS  PubMed  Article  Google Scholar 

  • 21.

    Huet des Aunay, G. et al. Negative impact of urban noise on sexual receptivity and clutch size in female domestic canaries. Ethology 123, 843–853 (2017).

    Article  Google Scholar 

  • 22.

    Proppe, D. S., Sturdy, C. B. & St Clair, C. C. Anthropogenic noise decreases urban songbird diversity and may contribute to homogenization. Glob. Chang. Biol. 19, 1075–1084 (2013).

    ADS  PubMed  Article  Google Scholar 

  • 23.

    Kleist, N. J., Guralnick, R. P., Cruz, A., Lowry, C. A. & Francis, C. D. Chronic anthropogenic noise disrupts glucocorticoid signaling and has multiple effects on fitness in an avian community. Proc. Natl Acad. Sci. USA 115, E648–E657 (2018).

    CAS  PubMed  Article  Google Scholar 

  • 24.

    Dominoni, D., Quetting, M. & Partecke, J. Artificial light at night advances avian reproductive physiology. Proc. Biol. Sci. B 280, 20123017 (2013).

    Google Scholar 

  • 25.

    Visser, M. E., Both, C. & Lambrechts, M. M. Global climate change leads to mistimed avian reproduction. Adv. Ecol. Res. 35, 89–110 (2004).

    Article  Google Scholar 

  • 26.

    Winkler, D. W., Dunn, P. O. & McCulloch, C. E. Predicting the effects of climate change on avian life-history traits. Proc. Natl Acad. Sci. USA 99, 13595–13599 (2002).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 27.

    Dunn, P. O. & Winkler, D. W. Climate change has affected the breeding date of tree swallows throughout North America. Proc. Biol. Sci. 266, 2487–2490 (1999).

    CAS  PubMed Central  Article  PubMed  Google Scholar 

  • 28.

    Both, C. & Visser, M. E. Adjustment to climate change is constrained by arrival date in a long-distance migrant bird. Nature 411, 296–298 (2001).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 29.

    Burgess, M. D. et al. Tritrophic phenological match-mismatch in space and time. Nat. Ecol. Evol. 2, 970–975 (2018).

    PubMed  Article  Google Scholar 

  • 30.

    van de Pol, M. & Wright, J. A simple method for distinguishing within- versus between-subject effects using mixed models. Anim. Behav. 77, 753–758 (2009).

    Article  Google Scholar 

  • 31.

    Cornell Laboratory of Ornithology. The Birds of North America Online (Cornell Laboratory of Ornithology, 2015).

  • 32.

    Falchi, F. et al. The new world atlas of artificial night sky brightness. Sci. Adv. 2, e1600377 (2016).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  • 33.

    Mennitt, D. J. & Fristrup, K. M. Influence factors and spatiotemporal patterns of environmental sound levels in the contiguous United States. Noise Control Eng. J. 64, 342–353 (2016).

    Article  Google Scholar 

  • 34.

    Dooling, R. J., Lohr, B. & Dent, M. L. in Comparative Hearing: Birds and Reptiles (eds. Dooling, R. J. et al.) 308–359 (Springer, 2000).

  • 35.

    Arnold, C. L. & Gibbons, C. J. Impervious surface coverage: the emergence of a key environmental indicator. J. Am. Plann. Assoc. 62, 243–258 (1996).

    Article  Google Scholar 

  • 36.

    McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).

    Article  Google Scholar 

  • 37.

    Xian, G. et al. Change of impervious surface area between 2001 and 2006 in the conterminous United States. Photogramm. Eng. Remote Sensing 77, 758–762 (2012).

    Google Scholar 

  • 38.

    United States Census Bureau. 2010 Census (US Census Bureau, 2011).

  • 39.

    Hall, M. I. & Ross, C. F. Eye shape and activity pattern in birds. J. Zool. (Lond.) 271, 437–444 (2007).

    Article  Google Scholar 

  • 40.

    Kirk, E. C. Comparative morphology of the eye in primates. Anat. Rec. A Discov. Mol. Cell. Evol. Biol. 281, 1095–1103 (2004).

    PubMed  Article  Google Scholar 

  • 41.

    Martin, G. R. in Perception and Motor Control in Birds: An Ecological Approach (eds. Davies, M. & Green, P.) 5–34 (Springer, 1994).

  • 42.

    Blackwell, B. F., Fernández-Juricic, E., Seamans, T. W. & Dolan, T. Avian visual system configuration and behavioural response to object approach. Anim. Behav. 77, 673–684 (2009).

    Article  Google Scholar 

  • 43.

    Hall, M. I., Iwaniuk, A. N. & Gutiérrez-Ibáñez, C. Optic foramen morphology and activity pattern in birds. Anat. Rec. (Hoboken) 292, 1827–1845 (2009).

    Article  Google Scholar 

  • 44.

    Moore, B. A., Doppler, M., Young, J. E. & Fernández-Juricic, E. Interspecific differences in the visual system and scanning behavior of three forest passerines that form heterospecific flocks. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 199, 263–277 (2013).

    PubMed  Article  Google Scholar 

  • 45.

    Ritland, S. M. The Allometry of the Vertebrate Eye (Univ. of Chicago, 1983).

  • 46.

    Tyrrell, L. P. & Fernández-Juricic, E. The hawk-eyed songbird: retinal morphology, eye shape, and visual fields of an aerial insectivore. Am. Nat. 189, 709–717 (2017).

    PubMed  Article  Google Scholar 

  • 47.

    Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).

    Article  Google Scholar 

  • 48.

    Uyeda, J. C., Pennell, M. W., Miller, E. T., Maia, R. & McClain, C. R. The evolution of energetic scaling across the vertebrate tree of life. Am. Nat. 190, 185–199 (2017).

    PubMed  Article  Google Scholar 

  • 49.

    Vitousek, M. N. et al. Macroevolutionary patterning in glucocorticoids suggests different selective pressures shape baseline and stress-induced levels. Am. Nat. 193, 866–880 (2019).

    PubMed  Article  Google Scholar 

  • 50.

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

    ADS  CAS  PubMed  Article  Google Scholar 

  • 51.

    Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 1717–2032 (2014).

    Article  Google Scholar 

  • 52.

    Lislevand, T., Figuerola, J. & Székely, T. Avian body sizes in relation to fecundity, mating system, display behavior, and resource sharing. Ecology 88, 1605 (2007).

    Article  Google Scholar 

  • 53.

    Cornell Laboratory of Ornithology. All About Birds (Cornell Laboratory of Ornithology, 2018).

  • 54.

    Rousset, F. & Ferdy, J.-B. Testing environmental and genetic effects in the presence of spatial autocorrelation. Ecography 37, 781–790 (2014).

    Article  Google Scholar 

  • 55.

    Smith, R. J. & Moore, F. R. Arrival timing and seasonal reproductive performance in a long-distance migratory landbird. Behav. Ecol. Sociobiol. 57, 231–239 (2005).

    Article  Google Scholar 

  • 56.

    Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. nlme: linear and nonlinear mixed effects models (R package version 3.1-104, 2012).

  • 57.

    Revell, L. J. Phylogenetic signal and linear regression on species data. Methods Ecol. Evol. 1, 319–329 (2010).

    Article  Google Scholar 

  • 58.

    Ives, A. R., Midford, P. E. & Garland, T. Jr. Within-species variation and measurement error in phylogenetic comparative methods. Syst. Biol. 56, 252–270 (2007).

    PubMed  Article  Google Scholar 

  • 59.

    Garamszegi, L. Z. in Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 157–199 (Springer, 2014).

  • 60.

    Jones, K. E. & Purvis, A. An optimum body size for mammals? Comparative evidence from bats. Funct. Ecol. 11, 751–756 (1997).

    Article  Google Scholar 

  • 61.

    Hurlbert, S. H., Levine, R. A. & Utts, J. Coup de grâce for a tough old bull: “statistically significant” expires. Am. Stat. 73, 352–357 (2019).

    Article  Google Scholar 

  • 62.

    Amrhein, V., Greenland, S. & McShane, B. Scientists rise up against statistical significance. Nature 567, 305–307 (2019).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  • 63.

    Halsey, L. G. The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum? Biol. Lett. 15, 20190174 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  • 64.

    Ware, H. E., McClure, C. J. W., Carlisle, J. D. & Barber, J. R. A phantom road experiment reveals traffic noise is an invisible source of habitat degradation. Proc. Natl Acad. Sci. USA 112, 12105–12109 (2015).

    ADS  CAS  PubMed  Article  Google Scholar 

  • 65.

    R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2019).

  • 66.

    Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).

    Article  Google Scholar 

  • 67.

    Lüdecke, D., Makowski, D. & Waggoner, P. Performance: Assessment of Regression Models Performance (2019).

  • 68.

    Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).

    Article  Google Scholar 

  • 69.

    Yu, G. & Ekstrøm, C. T. emojifont: emoji and font awesome in graphics (R package version 0.5.3, 2019).

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