Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Increasing climatic decoupling of bird abundances and distributions

Nature Ecology & Evolution volume 6, pages 1299–1306 (2022)Cite this article

Subjects

Abstract

Species abundances and distributions are changing in response to changing climate and other anthropogenic drivers but how this translates into how well species can match their optimal climate conditions as they change is not well understood. Using a continental-scale 30-year time series, we quantified temporal trends in climate matching of North American bird species and tested whether geographical variation in rates of climate and land use change and/or species traits could underlie variation in trends among species. Overall, we found that species abundances and distributions are becoming more decoupled from climate as it changes through time. Species differences in climate matching trends were related to their ecological traits, particularly habitat specialization, but not to average rates of climate and land use change within the species’ ranges. Climatic decoupling through time was particularly prominent for birds that were declining in abundance and occupancy, including threatened species. While we could not discern whether climate decoupling causes or is caused by the negative population trends, higher climatic decoupling in declining species could lead to a feedback as birds experience increasing exposure to suboptimal climatic conditions.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the data and methods used in our study.
Fig. 2: Overall temporal trend in climate matching.
Fig. 3: Species traits underlying the temporal trends of climate matching.
Fig. 4: Association between temporal trends in climate matching and demography or threat status.

Similar content being viewed by others

Data availability

All data are publicly available and cited (see refs. 21,47,60,61,62 and Methods).

Code availability

All computer code is available at https://github.com/duarte-viana/SCAT.

References

  1. Brondizio, E. S., Settele, J., Díaz, S. & Ngo, H. T. IPBES (2019): Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES Secretariat, 2019).

  2. Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5 °C rather than 2 °C. Science 360, 791–795 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl Acad. Sci. USA 109, 8606–8611 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Travis, J. M. J. Climate change and habitat destruction: a deadly anthropogenic cocktail. Proc. Biol. Sci. 270, 467–473 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hill, J. K. et al. Impacts of landscape structure on butterfly range expansion. Ecol. Lett. 4, 313–321 (2001).

    Article  Google Scholar 

  7. Guo, F., Lenoir, J. & Bonebrake, T. C. Land-use change interacts with climate to determine elevational species redistribution. Nat. Commun. 9, 1315 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. McLaughlin, J. F., Hellmann, J. J., Boggs, C. L. & Ehrlich, P. R. Climate change hastens population extinctions. Proc. Natl Acad. Sci. USA 99, 6070–6074 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Mantyka-Pringle, C. S. et al. Climate change modifies risk of global biodiversity loss due to land-cover change. Biol. Conserv. 187, 103–111 (2015).

    Article  Google Scholar 

  11. Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Forister, M. L. et al. Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl Acad. Sci. USA 107, 2088–2092 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Oliver, T. H. & Morecroft, M. D. Interactions between climate change and land use change on biodiversity: attribution problems, risks, and opportunities. Wiley Interdiscip. Rev. Clim. Change 5, 317–335 (2014).

    Article  Google Scholar 

  14. MacLean, S. A. & Beissinger, S. R. Species’ traits as predictors of range shifts under contemporary climate change: a review and meta-analysis. Glob. Change Biol. 23, 4094–4105 (2017).

    Article  Google Scholar 

  15. Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).

    Article  Google Scholar 

  16. Root, T. Energy constraints on avian distributions and abundances. Ecology 69, 330–339 (1988).

    Article  Google Scholar 

  17. Whitfield, M. C., Smit, B., McKechnie, A. E. & Wolf, B. O. Avian thermoregulation in the heat: scaling of heat tolerance and evaporative cooling capacity in three southern African arid-zone passerines. J. Exp. Biol. 218, 1705–1714 (2015).

    Article  PubMed  Google Scholar 

  18. McKechnie, A. E. et al. Avian thermoregulation in the heat: evaporative cooling in five Australian passerines reveals within-order biogeographic variation in heat tolerance. J. Exp. Biol. 220, 2436–2444 (2017).

    PubMed  Google Scholar 

  19. Platts, P. J. et al. Habitat availability explains variation in climate-driven range shifts across multiple taxonomic groups. Sci. Rep. 9, 15039 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Pearson, R. G. Climate change and the migration capacity of species. Trends Ecol. Evol. 21, 111–113 (2006).

    Article  PubMed  Google Scholar 

  21. Partners in Flight. Avian Conservation Assessment Database Version 2021 (accessed 5 February 2021); http://pif.birdconservancy.org/ACAD

  22. Hill, M. J. & Guerschman, J. P. The MODIS global vegetation fractional cover product 2001–2018: characteristics of vegetation fractional cover in grasslands and savanna woodlands. Remote Sens. (Basel) 12, 406 (2020).

    Article  Google Scholar 

  23. Wiebe, K. L. & Gerstmar, H. Influence of spring temperatures and individual traits on reproductive timing and success in a migratory woodpecker. Auk 127, 917–925 (2010).

    Article  Google Scholar 

  24. Viana, D. S. & Chase, J. M. Ecological traits underlying interspecific variation in climate matching of birds. Glob. Ecol. Biogeogr. 31, 1021–1034 (2022).

    Article  Google Scholar 

  25. Kellermann, V., Van Heerwaarden, B., Sgrò, C. M. & Hoffmann, A. A. Fundamental evolutionary limits in ecological traits drive Drosophila species distributions. Science 325, 1244–1246 (2009).

    Article  CAS  PubMed  Google Scholar 

  26. Devictor, V. et al. Differences in the climatic debts of birds and butterflies at a continental scale. Nat. Clim. Change 2, 121–124 (2012).

    Article  Google Scholar 

  27. Mason, L. R. et al. Population responses of bird populations to climate change on two continents vary with species’ ecological traits but not with direction of change in climate suitability. Clim. Change 157, 337–354 (2019).

    Article  Google Scholar 

  28. Coyle, J. R., Hurlbert, A. H. & White, E. P. Opposing mechanisms drive richness patterns of core and transient bird species. Am. Nat. 181, E83–E90 (2013).

    Article  PubMed  Google Scholar 

  29. Valiela, I. & Martinetto, P. Changes in bird abundance in eastern North America: urban sprawl and global footprint? BioScience 57, 360–370 (2007).

    Article  Google Scholar 

  30. Smith, S. J., Edmonds, J., Hartin, C. A., Mundra, A. & Calvin, K. Near-term acceleration in the rate of temperature change. Nat. Clim. Change 5, 333–336 (2015).

    Article  Google Scholar 

  31. Winkler, K., Fuchs, R., Rounsevell, M. & Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 12, 2501 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Currie, D. J. & Venne, S. Climate change is not a major driver of shifts in the geographical distributions of North American birds. Glob. Ecol. Biogeogr. 26, 333–346 (2017).

    Article  Google Scholar 

  34. Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl Acad. Sci. USA 114, 12976–12981 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Barnagaud, J.-Y. et al. Relating habitat and climatic niches in birds. PLoS ONE 7, e32819 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ponti, R., Arcones, A., Ferrer, X. & Vieites, D. R. Seasonal climatic niches diverge in migratory birds. Ibis 162, 318–330 (2020).

    Article  Google Scholar 

  37. Zurell, D., Gallien, L., Graham, C. H. & Zimmermann, N. E. Do long-distance migratory birds track their niche through seasons? J. Biogeogr. 45, 1459–1468 (2018).

    Article  Google Scholar 

  38. Stephens, P. A. et al. Consistent response of bird populations to climate change on two continents. Science 352, 84–87 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Ralston, J., DeLuca, W. V., Feldman, R. E. & King, D. I. Population trends influence species ability to track climate change. Glob. Change Biol. 23, 1390–1399 (2017).

    Article  Google Scholar 

  40. Magurran, A. E. et al. Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol. Evol. 25, 574–582 (2010).

    Article  PubMed  Google Scholar 

  41. Jarzyna, M. A. & Jetz, W. A near half-century of temporal change in different facets of avian diversity. Glob. Change Biol. 23, 2999–3011 (2017).

    Article  Google Scholar 

  42. van der Bolt, B., van Nes, E. H., Bathiany, S., Vollebregt, M. E. & Scheffer, M. Climate reddening increases the chance of critical transitions. Nat. Clim. Change 8, 478–484 (2018).

    Article  Google Scholar 

  43. Bowler, D. E., Heldbjerg, H., Fox, A. D., O’Hara, R. B. & Böhning-Gaese, K. Disentangling the effects of multiple environmental drivers on population changes within communities. J. Anim. Ecol. 87, 1034–1045 (2018).

    Article  PubMed  Google Scholar 

  44. Zurell, D., Graham, C. H., Gallien, L., Thuiller, W. & Zimmermann, N. E. Long-distance migratory birds threatened by multiple independent risks from global change. Nat. Clim. Change 8, 992–996 (2018).

    Article  Google Scholar 

  45. Northrup, J. M., Rivers, J. W., Yang, Z. & Betts, M. G. Synergistic effects of climate and land-use change influence broad-scale avian population declines. Glob. Change Biol. 25, 1561–1575 (2019).

    Article  Google Scholar 

  46. Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Pardieck, K. L., Ziolkowski, D. J. Jr, Lutmerding, M., Aponte, V. & Hudson, M.-A. R. North American Breeding Bird Survey Dataset 1966–2018 Version 2018.0. (US Geological Survey, 2019); https://www.sciencebase.gov/catalog/item/5d65256ae4b09b198a26c1d7

  48. Harris, D. J., Taylor, S. D. & White, E. P. Forecasting biodiversity in breeding birds using best practices. PeerJ 6, e4278 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Wickham, H., Francois, R., Henry, L. & Müller, K. dplyr: a Grammar of Data Manipulation. R package version 1.0.0 https://cran.r-project.org/web/packages/dplyr/index.html (2020).

  50. Wickham, H. & Henry, L. tidyr: Tidy Messy Data. R package version 1.1.0 https://cran.r-project.org/web/packages/tidyr/index.html (2020).

  51. Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.0-12 https://cran.r-project.org/web/packages/raster/index.html (2015).

  52. Bivand, R., Pebesma, E. J. & Gómez-Rubio, V. Applied Spatial Data Analysis with R (Springer, 2013).

  53. Hijmans, R. J. geosphere: Spherical Trigonometry. R package version 1.5–10 https://cran.r-project.org/web/packages/geosphere/index.html (2019).

  54. Hart, E. M. & Bell, K. prism. R package version 0.0.6 https://github.com/ropensci/prism (2015).

  55. Senyondo, H. et al. rdataretriever: R interface to the data retriever. J. Open Source Softw. 6, 2800 (2021).

    Article  Google Scholar 

  56. Morris, B. D. & White, E. P. The EcoData retriever: improving access to existing ecological data. PLoS ONE 8, e65848 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Senyondo, H. et al. Retriever: data retrieval tool. J. Open Source Softw. 2, 451 (2017).

    Article  Google Scholar 

  58. Hurlbert, A. H. & White, E. P. Disparity between range map- and survey-based analyses of species richness: patterns, processes and implications. Ecol. Lett. 8, 319–327 (2005).

    Article  Google Scholar 

  59. Harris, D. J. Generating realistic assemblages with a joint species distribution model. Methods Ecol. Evol. 6, 465–473 (2015).

    Article  Google Scholar 

  60. Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 2463 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Eyres, A., Böhning-Gaese, K. & Fritz, S. A. Quantification of climatic niches in birds: adding the temporal dimension. J. Avian Biol. 48, 1517–1531 (2017).

    Article  Google Scholar 

  62. Martin, A. E. & Fahrig, L. Habitat specialist birds disperse farther and are more migratory than habitat generalist birds. Ecology 99, 2058–2066 (2018).

    Article  PubMed  Google Scholar 

  63. Sauer, J. R. & Link, W. A. Analysis of the North American Breeding Bird Survey using hierarchical models. Auk 128, 87–98 (2011).

    Article  Google Scholar 

  64. García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an R package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evol. 10, 2195–2202 (2019).

    Article  Google Scholar 

  65. Krenek, S., Berendonk, T. U. & Petzoldt, T. Thermal performance curves of Paramecium caudatum: a model selection approach. Eur. J. Protistol. 47, 124–137 (2011).

    Article  PubMed  Google Scholar 

  66. Bahn, V. & McGill, B. J. Can niche-based distribution models outperform spatial interpolation? Glob. Ecol. Biogeogr. 16, 733–742 (2007).

    Article  Google Scholar 

  67. Dobson, L. L., La Sorte, F. A., Manne, L. L. & Hawkins, B. A. The diversity and abundance of North American bird assemblages fail to track changing productivity. Ecology 96, 1105–1114 (2015).

    Article  PubMed  Google Scholar 

  68. Roberts, D. R. et al. Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929 (2017).

    Article  Google Scholar 

  69. Tikhonov, G. et al. Joint species distribution modelling with the R-package HMSC. Methods Ecol. Evol. 11, 442–447 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Greenwell, B., Boehmke, B., Cunningham, J. & GBM Developers. gbm: Generalized boosted regression models. R package version 2.1.5 https://cran.r-project.org/web/packages/gbm/index.html (2019).

  71. Wood, S. N. Generalized Additive Models: an Introduction with R (CRC Press/Taylor & Francis Group, 2017).

  72. 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).

    Article  CAS  PubMed  Google Scholar 

  73. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

  74. Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).

    Article  CAS  PubMed  Google Scholar 

  75. Bürkner, P.-C. brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

    Article  Google Scholar 

  76. Stan Development Team. Stan Modeling Language Users Guide and Reference Manual (2020); https://mc-stan.org/users/documentation/

Download references

Acknowledgements

We thank the Biodiversity Synthesis group at iDiv for insightful discussions and S. Blowes for statistical advice. We also thank all the volunteers that participated in the North American BBS. The work was supported by the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT 118, 202548816). D.S.V. was supported by sDiv, the Synthesis Centre of iDiv.

Author information

Authors and Affiliations

  1. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Duarte S. Viana & Jonathan M. Chase

  2. Leipzig University, Leipzig, Germany

    Duarte S. Viana

  3. Estación Biológica de Doñana, Consejo Superior de Investigaciones Científicas (CSIC), Sevilla, Spain

    Duarte S. Viana

  4. Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle (Saale), Germany

    Jonathan M. Chase

Authors
  1. Duarte S. Viana
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jonathan M. Chase
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.S.V. conceived the research through discussion with J.M.C. D.S.V. conducted the data analyses. Both authors wrote the paper.

Corresponding author

Correspondence to Duarte S. Viana.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Stephen Murphy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Phylogenetic relatedness of climate matching trends.

Phylogeny of the bird species (tips) included in the study where the colour scale represents the direction and magnitude of the temporal trends of climate matching.

Extended Data Fig. 2 Associations between species traits and climate matching trends.

Relationships between the temporal trend in climate matching and the species’ Hand-Wing Index (a; Pearson’s correlation r = 0.08, p = 0.42), and migratory status (b; ANOVA F = 1.25, p = 0.29; N = 94, 3, 17 for the ‘directional migratory’, ‘dispersive migratory’ and ‘resident’ categories, respectively), and main habitat preference (c; F = 0.34, p = 0.72; n = 42, 43, 29 species for the ‘dense’, ‘semi-open’ and ‘open’ categories, respectively). The box widths represent the interquartile range (IQR), the median is shown as a vertical thick line within each box and the whiskers extend to the largest (upper) and smallest (lower) value no further than 1.5 × IQR. Data beyond the end of the whiskers are represented by dots.

Extended Data Fig. 3 Associations between species traits and baseline climate matching.

Relationships between the baseline levels (mean across years) of climate matching and the considered species traits, including body mass (a; Pearson’s correlation r = −0.13, p = 0.17), habitat specialisation index (b; Pearson’s correlation r = −0.24, p = 0.00), Hand-Wing Index (c; Pearson’s correlation r = −0.41, p = 0.00), migratory status (d; ANOVA F = 2.10, p = 0.13; n = 94, 3, 17 species for the ‘directional migratory’, ‘dispersive migratory’ and ‘resident’ categories, respectively), and main habitat preference (e; ANOVA F = 2.44, p = 0.09; n = 42, 43, 29 species for the ‘dense’, ‘semi-open’ and ‘open’ categories, respectively). The box widths represent the interquartile range (IQR), the median is shown as a horizontal thick line within each box and the whiskers extend to the largest (upper) and smallest (lower) value no further than 1.5 × IQR. Data beyond the end of the whiskers are represented by dots.

Extended Data Fig. 4 Comparison of our abundance trends estimates with those of two other studies.

Consistency of our estimations of abundance trends with those of Sauer et al.63 (a) and those of ACAD21 (b) across species (n = 25, 26, 5, 40, 18 species for the ‘1’, ‘2’, ‘3’, ‘4’ and ‘5’ categories, respectively). The box widths represent the interquartile range (IQR), the median is shown as a horizontal line within each box and the whiskers extend to the largest (upper) and smallest (lower) value no further than 1.5 × IQR. Data beyond the end of the whiskers are represented by dots.

Extended Data Fig. 5 Relationship between error and mean count in a Poisson model.

How the magnitude (in proportion) of Poisson random error around the mean count (or the predicted value in a Poisson model) varies with the mean count. As the error diminishes, the R2 of a statistical model (e.g. GLM) with a Poisson error distribution increases, even if it is the true model.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Viana, D.S., Chase, J.M. Increasing climatic decoupling of bird abundances and distributions. Nat Ecol Evol 6, 1299–1306 (2022). https://doi.org/10.1038/s41559-022-01814-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-022-01814-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing