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Spatial variation in avian phenological response to climate change linked to tree health

Abstract

While there is overwhelming evidence for phenological responses of animal and plant populations to climate change, most studies have been conducted at the level of entire populations, thus neglecting the scale at which much selection operates and at which animals and plants respond to their environments. Here, using data from a 60-year study, we demonstrate marked small-scale spatial variation in the rate of change in timing of egg laying in great tits (Parus major). We show, further, that this variation is linked to changes in the health of a key primary producer, oak (Quercus robur). The existence of small-scale spatial variability in responses to climate change has important implications for understanding the extent to which local adaptation and phenotypic plasticity govern responses to climate change and for the role of behavioural responses such as habitat selection and dispersal in ameliorating challenges due to climate extremes.

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Fig. 1: Great tit laying date has advanced as spring temperatures have warmed, allowing birds to track their caterpillar prey.
Fig. 2: The rate of change in laying date varies across Wytham Woods.
Fig. 3: Local oak health predicts the rate of laying-date change.

Data availability

The data needed to replicate the analyses presented in this paper are available at https://doi.org/10.6084/m9.figshare.14345960.v1.

Code availability

The codes needed to replicate the analyses presented in this paper are available at https://doi.org/10.6084/m9.figshare.14345960.v1. Our analyses relied on the following packages: ‘MCMCglmm’ (version 2.29)95, ‘vegan’ (version 2.5-6)93, ‘climwin’ (version1.2.3)90 and ‘stats’ (version 4.0.3)96.

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Acknowledgements

We thank all those who have contributed to the long-term nestbox study in Wytham Woods and the collection of associated data. The long-term population study has been supported by numerous funding sources, including recently by grants from BBSRC (BB/L006081/1), ERC (AdG250164), and NERC (NE/K006274/1, NE/S010335/1).

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E.F.C. and B.C.S. conceived the study. C.E.R. and E.F.C. performed the analysis. E.F.C. and C.E.R. drafted the manuscript with input from B.C.S.

Corresponding authors

Correspondence to Charlotte E. Regan or Ben C. Sheldon.

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The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Suzanne Bonamour, Michał Glądalski, Eunbi Kwon and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Oak health is non-randomly distributed across Wytham Woods.

(a) The spatial structure of oak crown dieback across Wytham. Shown are all 5,748 mature oak trees, coloured according to the amount of crown dieback (where a score of 1 denotes a tree whose canopy has 0 to 25 % dieback and a value of 5 denotes a tree that is dead) (b) Mantel correlogram showing the direction and strength of spatial autocorrelation in the dieback scores of oak trees in different distance classes.

Extended Data Fig. 2 An oak’s survival probability is predicted by its dieback score.

Survival probability of 394 oaks over a 39-year period (1975–2014) in relation to the degree of local oak dieback.

Extended Data Fig. 3 The estimated relationship between oak dieback score and the rate of laying date change was consistent across buffer sizes.

Shown are posterior means and credible intervals from models of the rate of laying date change with average oak dieback score calculated using buffers of different sizes.

Extended Data Fig. 4 Oak dieback was the only fixed effect whose 95% credible interval did not overlap zero.

Shown are fixed effect posterior distributions from the mixed effects model with rate of laying date change as the response, the fixed effects shown in the figure, a spatial similarity matrix, and each data point weighted according to its standard error.

Extended Data Fig. 5 Oak canopy health was scored on a scale between 1 and 5.

A score of 1 corresponds to a tree with 0–25% dieback, 2 to a tree with 25%–50% dieback, 3 to a tree with 50%–75% dieback, 4 to a tree with 75% to 100% dieback, and 5 to a tree that is dead.

Extended Data Fig. 6 There were no consistent effects of environmental explanatory variables on oak dieback score.

Shown are posterior means and 95% credible intervals from the 100 models exploring the relationships between local environmental factors and oak dieback scores for random samples of 200 trees. Each model included a spatial similarity matrix to account for potential spatial autocorrelation in dieback scores.

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Cole, E.F., Regan, C.E. & Sheldon, B.C. Spatial variation in avian phenological response to climate change linked to tree health. Nat. Clim. Chang. 11, 872–878 (2021). https://doi.org/10.1038/s41558-021-01140-4

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