Letter | Published:

Threat of climate change on a songbird population through its impacts on breeding

Nature Climate Changevolume 8pages718722 (2018) | Download Citation

Abstract

Understanding global change processes that threaten species viability is critical for assessing vulnerability and deciding on appropriate conservation actions1. Here we combine individual-based2 and metapopulation models to estimate the effects of climate change on annual breeding productivity and population viability up to 2100 of a common forest songbird, the Acadian flycatcher (Empidonax virescens), across the Central Hardwoods ecoregion, a 39.5-million-hectare area of temperate and broadleaf forests in the USA. Our approach integrates local-scale, individual breeding productivity, estimated from empirically derived demographic parameters that vary with landscape and climatic factors (such as forest cover, daily temperature)3, into a dynamic-landscape metapopulation model4 that projects growth of the regional population over time. We show that warming temperatures under a worst-case scenario with unabated climate change could reduce breeding productivity to an extent that this currently abundant species will suffer population declines substantial enough to pose a significant risk of quasi-extinction from the region in the twenty-first century. However, we also show that this risk is greatly reduced for scenarios where emissions and warming are curtailed. These results highlight the importance of considering both direct and indirect effects of climate change when assessing the vulnerability of species.

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References

  1. 1.

    Akçakaya, H. R., Butchart, S. H. M., Watson, J. E. M. & Pearson, R. G. Preventing species extinctions resulting from climate change. Nat. Clim. Change 4, 1048–1049 (2014).

  2. 2.

    Grimm, V. & Railsback, S. F. Individual-Based Modeling and Ecology (Princeton Univ. Press, Princeton, NJ, 2013).

  3. 3.

    Cox, W. A., Thompson, F. R.III, Reidy, J. L. & Faaborg, J. Temperature can interact with landscape factors to affect songbird productivity. Glob. Change Biol. 19, 1064–1074 (2013).

  4. 4.

    Bonnot, T. W., Thompson, F. R.III & Millspaugh, J. J. Dynamic-landscape metapopulation models reveal species-specific population responses to landscapes projected under climate change. Ecosphere 8, e01890 (2017).

  5. 5.

    IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 271–359 (Cambridge Univ. Press, 2014).

  6. 6.

    Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. 40, 677–697 (2009).

  7. 7.

    Iverson, L. R. et al. Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models. Landscape Ecol. 32, 1327–1346 (2016).

  8. 8.

    Moritz, C. & Agudo, R. The future of species under climate change: resilience or decline? Science 341, 504–508 (2013).

  9. 9.

    Ralston, J., DeLuca, W. V., Feldman, R. E. & King, D. I. Realized climate niche breadth varies with population trend and distribution in North American birds. Glob. Ecol. Biogeogr. 25, 1173–1180 (2016).

  10. 10.

    Wang, W. J., He, H. S., Thompson, F. R., Fraser, J. S. & Dijak, W. D. Landscape- and regional-scale shifts in forest composition under climate change in the Central Hardwood Region of the United States. Landsc. Ecol. 31, 149–163 (2016).

  11. 11.

    Willis, S. G. et al. Integrating climate change vulnerability assessments from species distribution models and trait-based approaches. Biol. Conserv. 190, 167–178 (2015).

  12. 12.

    Townsend, A. K. et al. The interacting effects of food, spring temperature, and global climate cycles on population dynamics of a migratory songbird. Glob. Change Biol. 22, 544–555 (2016).

  13. 13.

    Cox, W. A., Thompson, F. R. & Reidy, J. L. The effects of temperature on nest predation by mammals, birds, and snakes. Auk 130, 784–790 (2013).

  14. 14.

    George, A. D., Thompson, F. R. & Faaborg, J. Isolating weather effects from seasonal activity patterns of a temperate North American Colubrid. Oecologia 178, 1251–1259 (2015).

  15. 15.

    Weatherhead, P. J., Sperry, J. H., Carfagno, G. L. F. & Blouin-Demers, G. Latitudinal variation in thermal ecology of North American ratsnakes and its implications for the effect of climate warming on snakes. J. Therm. Biol. 37, 273–281 (2012).

  16. 16.

    Evans, M. E. K., Merow, C., Record, S., McMahon, S. M. & Enquist, B. J. Towards process-based range modeling of many species. Trends Ecol. Evol. 31, 860–871 (2016).

  17. 17.

    Beever, E. A. et al. Improving conservation outcomes with a new paradigm for understanding species’ fundamental and realized adaptive capacity. Conserv. Lett. 9, 131–137 (2016).

  18. 18.

    Lany, N. K. et al. Breeding timed to maximize reproductive success for a migratory songbird: the importance of phenological asynchrony. Oikos 125, 656–666 (2016).

  19. 19.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  20. 20.

    Fordham, D. A. et al. Adapted conservation measures are required to save the Iberian lynx in a changing climate. Nat. Clim. Change 3, 899–903 (2013).

  21. 21.

    Hunter, C. M. et al. Climate change threatens polar bear populations: a stochastic demographic analysis. Ecology 91, 2883–2897 (2010).

  22. 22.

    Selwood, K. E., McGeoch, M. A. & Mac Nally, R. The effects of climate change and land-use change on demographic rates and population viability. Biol. Rev. Camb. Philos. Soc. 90, 837–853 (2015).

  23. 23.

    Tanner, E. P. et al. Extreme climatic events constrain space use and survival of a ground-nesting bird. Glob. Chang Biol. 23, 1832–1846 (2017).

  24. 24.

    Dijak, W. D. et al. Revision and application of the LINKAGES model to simulate forest growth in central hardwood landscapes in response to climate change. Landscape Ecol. 32, 1365–1384 (2016).

  25. 25.

    Jørgensen, P. S. et al. Continent-scale global change attribution in European birds—combining annual and decadal time scales. Glob. Change Biol. 22, 530–543 (2016).

  26. 26.

    Bonnot, T. W., Thompson, F. R. & Millspaugh, J. J. Extension of landscape-based population viability models to ecoregional scales for conservation planning. Biol. Conserv. 144, 2041–2053 (2011).

  27. 27.

    Rushing, C. S. et al. Spatial and temporal drivers of avian population dynamics across the annual cycle. Ecology 98, 2837–2850 (2017).

  28. 28.

    Alberti, M. et al. Global urban signatures of phenotypic change in animal and plant populations. Proc. Natl Acad. Sci. USA 114, 8951–8956 (2017).

  29. 29.

    Bonnot, T. W., Thompson, F. R., Millspaugh, J. J. & Jones-Farrand, D. T. Landscape-based population viability models demonstrate importance of strategic conservation planning for birds. Biol. Conserv. 165, 104–114 (2013).

  30. 30.

    Cox, W. A., Thompson, F. R. III, Root, B. & Faaborg, J. Declining brown-headed cowbird (Molothrus ater) populations are associated with landscape-specific reductions in brood parasitism and increases in songbird productivity. PLoS One 7, e47591 (2012).

  31. 31.

    Millar, R. J. et al. Emission budgets and pathways consistent with limiting warming to 1.5 °C. Nat. Geosci. 10, 741–747 (2017).

  32. 32.

    Cleland D. T. et al. Ecological Subregions: Sections and Subsections for the Conterminous United States. Gen. Tech. Report WO-76D (cartographer Sloan, A. M.) presentation scale 1:3,500,000; coloured (US Department of Agriculture, Forest Service, 2007).

  33. 33.

    Livneh, B. et al. A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. J. Clim. 26, 9384–9392 (2013).

  34. 34.

    Whitehead, D. R. & Taylor, T. Acadian flycatcher (Empidonax virescens) The Birds of North America https://birdsna.org/Species-Account/bna/species/acafly(2002).

  35. 35.

    Askins, R. A., Lynch, J. F. & Greenberg, R. Population declines in migratory birds in eastern North America. Curr. Ornithol. 7, 1–57 (1990).

  36. 36.

    Freemark, K. E. & Collins, B. S. in Ecology and Conservation of Neotropical Migrant Landbirds (eds Hagan, J. M. III & Johnston, D. W.) 443–454 (Smithsonian Institution Press, Washington, DC, 1992).

  37. 37.

    Robinson, S. K., Thompson, F. R. III, Donovan, T. M., Whitehead, D. R. & Faaborg, J. Regional forest fragmentation and the nesting success of migratory birds. Science 267, 1987–1990 (1995).

  38. 38.

    Sauer, J. R. et al. The North American Breeding Bird Survey, Results and Analysis 1966–2015 v.2.07.2017 (USGS Patuxent Wildlife Research Center, 2017).

  39. 39.

    Mattsson, B. J., Cooper, R. J. & Handel, C. Which life-history components determine breeding productivity for individual songbirds? A case study of the Louisiana waterthrush (Seiurus motacilla). Auk 124, 1186–1200 (2007).

  40. 40.

    Powell, L. A., Conroy, M. J., Krementz, D. G. & Lang, J. D. A model to predict breeding-season productivity for multibrooded songbirds. Auk 116, 1001–1008 (1999).

  41. 41.

    Hirsch-Jacobson, R. Population Dynamics of a Migrant Songbird: Do We Need to Monitor the Entire Breeding Season? PhD thesis, Univ. Missouri (2011).

  42. 42.

    Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections: Release of Downscaled CMIP5 Climate Projections, Comparison with Preceding Information, and Summary of User Needs (US Department of the Interior, Bureau of Reclamation, Technical Services Center, 2013).

  43. 43.

    Fry, J. et al. Completion of the 2006 National Land Cover Database for the Conterminous United States. Programm. Eng. Remote Sens. 77, 858–864 (2011).

  44. 44.

    Tirpak, J. M., Jones-Farrand, D. T., Thompson, F. R. III, Twedt, D. J. & Uihlein, W. B. III Multiscale Habitat Suitability Index Models for Priority Landbirds in the Central Hardwoods and West Gulf Coastal Plain/Ouachitas Bird Conservation Regions Report No. WO-NRS-49 (USDA Forest Service, 2009).

  45. 45.

    Dijak, W. D. & Rittenhouse, C. D. in Models for Planning Wildlife Conservation in Large Landscapes (eds Millspaugh, J. J. & Thompson, F. R. III) 367–390 (Elsevier/Academic, Amsterdam, 2009).

  46. 46.

    Tirpak, J. M. et al. Assessing ecoregional-scale habitat suitability index models for priority landbirds. J. Wildlife Manag. 73, 1307–1315 (2009).

  47. 47.

    Wilson, B. T., Lister, A. J. & Riemann, R. I. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. For. Ecol. Manag. 271, 182–198 (2012).

  48. 48.

    Jenness, J. DEM Surface Tools for ArcGIS (surface_area.exe) http://www.jennessent.com/arcgis/surface_area.html(2013).

  49. 49.

    The National Hydrography Dataset: NHDPlus Version 1 (United States Geological Survey, 2010).

  50. 50.

    Johnston, D. W. & Odum, E. P. Breeding bird populations in relation to plant succession on the Piedmont of Georgia. Ecology 37, 50–62 (1956).

  51. 51.

    Howell, C. A., Porneluzi, P. A., Clawson, R. L. & Faaborg, J. Breeding density affects point-count accuracy in Missouri forest birds. J. Field Ornithol. 75, 123–133 (2004).

  52. 52.

    Reidy, J. L., Thompson, F. R. & Bailey, J. W. Comparison of methods for estimating density of forest songbirds from point counts. J. Wildlife Manag. 75, 558–568 (2011).

  53. 53.

    Caswell, H. Matrix Population Models: Construction, Analysis, and Interpretation 2nd edn (Sinauer Associates, Sunderland, MA, 2001).

  54. 54.

    Ausprey, I. J. & Rodewald, A. D. Postfledging survivorship and habitat selection across a rural-to-urban landscape gradient. Auk 128, 293–302 (2011).

  55. 55.

    Jenkins, J. M. A., Thompson, F. R. & Faaborg, J. Contrasting patterns of nest survival and postfledging survival in ovenbirds and Acadian flycatchers in Missouri forest fragments. Condor 118, 583–596 (2016).

  56. 56.

    Rodewald, A. D. & Shustack, D. P. Urban flight: understanding individual and population-level responses of Nearctic–Neotropical migratory birds to urbanization. J. Anim. Ecol. 77, 83–91 (2008).

  57. 57.

    Tittler, R., Fahrig, L. & Villard, M.-A. Evidence of large-scale source-sink dynamics and long-distance dispersal among wood thrush populations. Ecology 87, 3029–3036 (2006).

  58. 58.

    Akçakaya, H. R. RAMAS GIS: Linking Spatial Data with Population Viability Analysis v.5.0 (Applied Biomathematics, 2005).

  59. 59.

    Bayne, E. M. & Hobson, K. A. Annual survival of adult American redstarts and ovenbirds in the southern boreal forest. Wilson Bull. 114, 358–367 (2002).

  60. 60.

    Smith, S. M. The ‘underworld’ in a territorial sparrow: adaptive strategy for floaters. Am. Nat. 112, 571–582 (1978).

  61. 61.

    McGowan, C. P., Runge, M. C. & Larson, M. A. Incorporating parametric uncertainty into population viability analysis models. Biol. Conserv. 144, 1400–1408 (2011).

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Acknowledgements

The authors thank C. Rota, J. S. Fraser and W. D. Dijak for data and input on the study. Funding was provided by the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative, the Department of the Interior USGS Northeast Climate Adaptation Science Center graduate and post-doctoral fellowships, and the USDA Forest Service Northern Research Station. The contents of this Letter are solely the responsibility of the authors and do not necessarily represent the views of the United States Government.

Author information

Affiliations

  1. School of Natural Resources, University of Missouri, Columbia, MO, USA

    • Thomas W. Bonnot
  2. Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, Gainesville, FL, USA

    • W. Andrew Cox
  3. Northern Research Station, United States Forest Service, Columbia, MO, USA

    • Frank R. Thompson
  4. Wildlife Biology Program, Department of Ecosystem and Conservation Sciences, W. A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, USA

    • Joshua J. Millspaugh

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Contributions

All authors contributed to the design of the analysis. T.W.B. and W.A.C. developed the individual-based model. T.W.B. developed the metapopulation model and performed the analysis. All authors contributed to drafting of the manuscript.

Corresponding author

Correspondence to Thomas W. Bonnot.

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    Supplementary Figures 1–7, Supplementary R Code

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DOI

https://doi.org/10.1038/s41558-018-0232-8