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Increasing climatic decoupling of bird abundances and distributions

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.

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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.

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.

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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.

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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.

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The authors declare no competing interests.

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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.

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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.

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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

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