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

Nature Climate Change volume 11, pages 872–878 (2021)Cite this article

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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.

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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.

References

  1. Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).

    Article  CAS  Google Scholar 

  2. Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).

    Article  CAS  Google Scholar 

  3. Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).

    Article  CAS  Google Scholar 

  4. Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224–228 (2018).

    Article  Google Scholar 

  5. Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).

    Article  Google Scholar 

  6. Merilä, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).

    Article  Google Scholar 

  7. Chevin, L. M. & Hoffmann, A. A. Evolution of phenotypic plasticity in extreme environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 372, 20160138 (2017).

    Article  Google Scholar 

  8. Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180174 (2019).

    Article  Google Scholar 

  9. Thackeray, S. J. et al. Trophic level asynchrony in rates of phenological change for marine, freshwater and terrestrial environments. Glob. Change Biol. 16, 3304–3313 (2010).

    Article  Google Scholar 

  10. Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).

    Article  CAS  Google Scholar 

  11. Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  13. Kharouba, H. M. & Wolkovich, E. M. Disconnects between ecological theory and data in phenological mismatch research. Nat. Clim. Change 10, 406–415 (2020).

    Article  Google Scholar 

  14. Samplonius, J. M. et al. Strengthening the evidence base for temperature-mediated phenological asynchrony and its impacts. Nat. Ecol. Evol. 5, 155–164 (2021).

    Article  Google Scholar 

  15. Charmantier, A. et al. Adaptive phenotypic plasticity in response to climate change in a wild bird population. Science 320, 800–803 (2008).

    Article  CAS  Google Scholar 

  16. Tomotani, B. M. et al. Climate change leads to differential shifts in the timing of annual cycle stages in a migratory bird. Glob. Change Biol. 24, 823–835 (2018).

    Article  Google Scholar 

  17. Moyes, K. et al. Advancing breeding phenology in response to environmental change in a wild red deer population. Glob. Change Biol. 17, 2455–2469 (2011).

    Article  Google Scholar 

  18. Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).

    Article  CAS  Google Scholar 

  19. Todd, B. D., Scott, D. E., Pechmann, J. H. K. & Whitfield Gibbons, J. Climate change correlates with rapid delays and advancements in reproductive timing in an amphibian community. Proc. R. Soc. Lond. B Biol. Sci. 278, 2191–2197 (2011).

    Google Scholar 

  20. Taylor, S. G. Climate warming causes phenological shift in pink salmon, Oncorhynchus gorbuscha, behavior at Auke Creek, Alaska. Glob. Change Biol. 14, 229–235 (2008).

    Article  Google Scholar 

  21. Mills, L. S. et al. Camouflage mismatch in seasonal coat color due to decreased snow duration. Proc. Natl Acad. Sci. USA 110, 7360–7365 (2013).

    Article  CAS  Google Scholar 

  22. Lameris, T. K. et al. Arctic geese tune migration to a warming climate but still suffer from a phenological mismatch. Curr. Biol. 28, 2467–2473.e4 (2018).

    Article  CAS  Google Scholar 

  23. Singer, M. C. & Parmesan, C. Phenological asynchrony between herbivorous insects and their hosts: signal of climate change or pre-existing adaptive strategy? Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3161–3176 (2010).

    Article  Google Scholar 

  24. Charmantier, A. & Gienapp, P. Climate change and timing of avian breeding and migration: evolutionary versus plastic changes. Evol. Appl. 7, 15–28 (2014).

    Article  Google Scholar 

  25. Keogan, K. et al. Global phenological insensitivity to shifting ocean temperatures among seabirds. Nat. Clim. Change 8, 313–317 (2018).

    Article  Google Scholar 

  26. 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).

    Article  CAS  Google Scholar 

  27. Both, C., van Asch, M., Bijlsma, R. G., van den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations? J. Anim. Ecol. 78, 73–83 (2009).

    Article  Google Scholar 

  28. Cresswell, W. & McCleery, R. How great tits maintain synchronization of their hatch date with food supply in response to long-term variability in temperature. J. Anim. Ecol. 72, 356–366 (2003).

    Article  Google Scholar 

  29. Visser, M. E., Van Noordwijk, A. J., Tinbergen, J. M. & Lessells, C. M. Warmer springs lead to mistimed reproduction in great tits (Parus major). Proc. R. Soc. Lond. B Biol. Sci. 265, 1867–1870 (1998).

    Article  Google Scholar 

  30. Sanz, J. J., Potti, J., Moreno, J., Merino, S. & Frías, O. Climate change and fitness components of a migratory bird breeding in the Mediterranean region. Glob. Change Biol. 9, 461–472 (2003).

    Article  Google Scholar 

  31. Marrot, P., Charmantier, A., Blondel, J. & Garant, D. Current spring warming as a driver of selection on reproductive timing in a wild passerine. J. Anim. Ecol. 87, 754–764 (2018).

    Article  Google Scholar 

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

    Article  Google Scholar 

  33. Visser, M. E., Holleman, L. J. M. & Gienapp, P. Shifts in caterpillar biomass phenology due to climate change and its impact on the breeding biology of an insectivorous bird. Oecologia 147, 164–172 (2006).

    Article  Google Scholar 

  34. Pearce-Higgins, J. W., Yalden, D. W. & Whittingham, M. J. Warmer springs advance the breeding phenology of golden plovers Pluvialis apricaria and their prey (Tipulidae). Oecologia 143, 470–476 (2005).

    Article  CAS  Google Scholar 

  35. Nussey, D. H., Clutton-Brock, T. H., Elston, D. A., Albon, S. D. & Kruuk, L. E. B. Phenotypic plasticity in a maternal trait in red deer. J. Anim. Ecol. 74, 387–396 (2005).

    Article  Google Scholar 

  36. Husby, A. et al. Contrasting patterns of phenotypic plasticity in reproductive traits in two great tit (Parus major) populations. Evolution 64, 2221–2237 (2010).

    Google Scholar 

  37. Matthysen, E., Adriaensen, F. & Dhondt, A. A. Multiple responses to increasing spring temperatures in the breeding cycle of blue and great tits (Cyanistes caeruleus, Parus major). Glob. Change Biol. 17, 1–16 (2011).

    Article  Google Scholar 

  38. Fisher, J. I., Mustard, J. F. & Vadeboncoeur, M. A. Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens. Environ. 100, 265–279 (2006).

    Article  Google Scholar 

  39. Duparc, A. et al. Co-variation between plant above-ground biomass and phenology in sub-alpine grasslands. Appl. Veg. Sci. 16, 305–316 (2013).

    Article  Google Scholar 

  40. Hinks, A. E. et al. Scale-dependent phenological synchrony between songbirds and their caterpillar food source. Am. Nat. 186, 84–97 (2015).

    Article  Google Scholar 

  41. Lambrechts, M. M., Blondel, J., Maistre, M. & Perret, P. A single response mechanism is responsible for evolutionary adaptive variation in a bird’s laying date. Proc. Natl Acad. Sci. USA 94, 5153–5155 (1997).

    Article  CAS  Google Scholar 

  42. Dawson, A. Control of the annual cycle in birds: endocrine constraints and plasticity in response to ecological variability. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 1621–1633 (2008).

    Article  Google Scholar 

  43. Visser, M. E. et al. Phenology, seasonal timing and circannual rhythms: towards a unified framework. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3113–3127 (2010).

    Article  CAS  Google Scholar 

  44. Caro, S. P., Schaper, S. V., Hut, R. A., Ball, G. F. & Visser, M. E. The case of the missing mechanism: how does temperature influence seasonal timing in endotherms? PLoS Biol. 11, e1001517 (2013).

    Article  CAS  Google Scholar 

  45. Bourgault, P., Thomas, D., Perret, P. & Blondel, J. Spring vegetation phenology is a robust predictor of breeding date across broad landscapes: a multi-site approach using the Corsican blue tit (Cyanistes caeruleus). Oecologia 162, 885–892 (2010).

    Article  Google Scholar 

  46. Bison, M. et al. Best environmental predictors of breeding phenology differ with elevation in a common woodland bird species. Ecol. Evol. 10, 10219–10229 (2020).

    Article  Google Scholar 

  47. Bernhardt, J. R., O’Connor, M. I., Sunday, J. M. & Gonzalez, A. Life in fluctuating environments. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190454 (2020).

    Article  Google Scholar 

  48. Gienapp, P., Reed, T. E. & Visser, M. E. Why climate change will invariably alter selection pressures on phenology. Proc. R. Soc. Lond. B Biol. Sci. 281, 20141611 (2014).

    Google Scholar 

  49. Lönnstedt, O. M., McCormick, M. I., Chivers, D. P. & Ferrari, M. C. O. Habitat degradation is threatening reef replenishment by making fish fearless. J. Anim. Ecol. 83, 1178–1185 (2014).

    Article  Google Scholar 

  50. Pellerin, F., Cote, J., Bestion, E. & Aguilée, R. Matching habitat choice promotes species persistence under climate change. Oikos 128, 221–234 (2019).

    Article  Google Scholar 

  51. Firth, J. A., Verhelst, B. L., Crates, R. A., Garroway, C. J. & Sheldon, B. C. Spatial, temporal and individual-based differences in nest-site visits and subsequent reproductive success in wild great tits. J. Avian Biol. 49, e01740 (2018).

    Article  Google Scholar 

  52. Naef-Daenzer, B. & Keller, L. F. The foraging performance of great and blue tits (Parus major and P. caeruleus) in relation to caterpillar development, and its consequences for nestling growth and fledging weight. J. Anim. Ecol. 68, 708–718 (1999).

    Article  Google Scholar 

  53. Naef-Daenzer, B. Patch time allocation and patch sampling by foraging great and blue tits. Anim. Behav. 59, 989–999 (2000).

    Article  CAS  Google Scholar 

  54. Bouwhuis, S., Sheldon, B. C., Verhulst, S. & Charmantier, A. Great tits growing old: selective disappearance and the partitioning of senescence to stages within the breeding cycle. Proc. R. Soc. Lond. B Biol. Sci. 276, 2769–2777 (2009).

    CAS  Google Scholar 

  55. Cole, E. F. & Sheldon, B. C. The shifting phenological landscape: within- and between-species variation in leaf emergence in a mixed-deciduous woodland. Ecol. Evol. 7, 1135–1147 (2017).

    Article  Google Scholar 

  56. Wint, W. The role of alternative host-plant species in the life of a polyphagous moth, Operophtera brumata (Lepidoptera: Geometridae). J. Anim. Ecol. 52, 439–450 (1983).

    Article  Google Scholar 

  57. Keller, L. F. & van Noordwijk, A. J. Effects of local environmental conditions on nestling growth in the great tit Parus major L. Ardea 82, 349–362 (1994).

    Google Scholar 

  58. Wilkin, T. A., Garant, D., Gosler, A. G. & Sheldon, B. C. Density effects on life-history traits in a wild population of the great tit Parus major: analyses of long-term data with GIS techniques. J. Anim. Ecol. 75, 604–615 (2006).

    Article  Google Scholar 

  59. Wilkin, T. A. & Sheldon, B. C. Sex differences in the persistence of natal environmental effects on life histories. Curr. Biol. 19, 1998–2002 (2009).

    Article  CAS  Google Scholar 

  60. Gagen, M. et al. The tree ring growth histories of UK native oaks as a tool for investigating chronic oak decline: an example from the Forest of Dean. Dendrochronologia 55, 50–59 (2019).

    Article  Google Scholar 

  61. Sturrock, R. N. et al. Climate change and forest diseases. Plant Pathol. 60, 133–149 (2011).

    Article  Google Scholar 

  62. MacColl, A. D. C. The ecological causes of evolution. Trends Ecol. Evol. 26, 514–522 (2011).

    Article  Google Scholar 

  63. Grant, P. R. & Price, T. D. Population variation in continuously varying traits as an ecological genetics problem. Integr. Comp. Biol. 21, 795–811 (1981).

    Google Scholar 

  64. Hereford, J. A quantitative survey of local adaptation and fitness trade-offs. Am. Nat. 173, 579–588 (2009).

    Article  Google Scholar 

  65. Hadfield, J. D. The spatial scale of local adaptation in a stochastic environment. Ecol. Lett. 19, 780–788 (2016).

    Article  Google Scholar 

  66. Porlier, M. et al. Variation in phenotypic plasticity and selection patterns in blue tit breeding time: between- and within-population comparisons. J. Anim. Ecol. 81, 1041–1051 (2012).

    Article  Google Scholar 

  67. Hidalgo Aranzamendi, N., Hall, M. L., Kingma, S. A., van de Pol, M. & Peters, A. Rapid plastic breeding response to rain matches peak prey abundance in a tropical savanna bird. J. Anim. Ecol. 88, 1799–1811 (2019).

    Article  Google Scholar 

  68. Caro, S. P., Lambrechts, M. M., Balthazart, J. & Perret, P. Non-photoperiodic factors and timing of breeding in blue tits: impact of environmental and social influences in semi-natural conditions. Behav. Process. 75, 1–7 (2007).

    Article  CAS  Google Scholar 

  69. Bourret, A., Bélisle, M., Pelletier, F. & Garant, D. Multidimensional environmental influences on timing of breeding in a tree swallow population facing climate change. Evol. Appl. 8, 933–944 (2015).

    Article  Google Scholar 

  70. Nussey, D. H., Wilson, A. J. & Brommer, J. E. The evolutionary ecology of individual phenotypic plasticity in wild populations. J. Evol. Biol. 20, 831–844 (2007).

    Article  CAS  Google Scholar 

  71. Morris, D. W. Toward an ecological synthesis: a case for habitat selection. Oecologia 136, 1–13 (2003).

    Article  Google Scholar 

  72. Long, R. A. et al. Linking habitat selection to fitness-related traits in herbivores: the role of the energy landscape. Oecologia 181, 709–720 (2016).

    Article  Google Scholar 

  73. Morris, D. W. Spatial scale and the cost of density-dependent habitat selection. Evol. Ecol. 1, 379–388 (1987).

    Article  Google Scholar 

  74. Patten, M. A. & Kelly, J. F. Habitat selection and the perceptual trap. Ecol. Appl. 20, 2148–2156 (2010).

    Article  Google Scholar 

  75. Ponchon, A., Garnier, R., Grémillet, D. & Boulinier, T. Predicting population responses to environmental change: the importance of considering informed dispersal strategies in spatially structured population models. Divers. Distrib. 21, 88–100 (2015).

    Article  Google Scholar 

  76. Nilsson, A. L. K. et al. Hydrology influences breeding time in the white-throated dipper. BMC Ecol. 20, 70 (2020).

    Article  Google Scholar 

  77. Nilsson, A. L. K. et al. Location is everything, but climate gets a share: analyzing small-scale environmental influences on breeding success in the white-throated dipper. Front. Ecol. Evol. 8, 542846 (2020).

    Article  Google Scholar 

  78. Martin, R. O., Cunningham, S. J. & Hockey, P. A. R. Elevated temperatures drive fine-scale patterns of habitat use in a savanna bird community. Ostrich 86, 127–135 (2015).

    Article  Google Scholar 

  79. Bailey, L. D. et al. Habitat selection can reduce effects of extreme climatic events in a long-lived shorebird. J. Anim. Ecol. 88, 1474–1485 (2019).

    Article  Google Scholar 

  80. Kirby, K. J. et al. Changes in the tree and shrub layer of Wytham Woods (southern England) 1974–2012: local and national trends compared. Forestry 87, 663–673 (2014).

    Article  Google Scholar 

  81. Perrins, C. & McCleery, R. Laying dates and clutch size in the great tit. Wilson Bull. 101, 236–253 (1989).

    Google Scholar 

  82. Wilkin, T. A., Perrins, C. M. & Sheldon, B. C. The use of GIS in estimating spatial variation in habitat quality: a case study of lay-date in the great tit Parus major. Ibis 149, 110–118 (2007).

    Article  Google Scholar 

  83. Perrins, C. M. Population fluctuations and clutch-size in the great tit, Parus major L. J. Anim. Ecol. 34, 601–647 (1965).

    Article  Google Scholar 

  84. Wesołowski, T. & Rowiński, P. Timing of bud burst and tree-leaf development in a multispecies temperate forest. For. Ecol. Manage. 237, 387–393 (2006).

    Article  Google Scholar 

  85. Gibson, C. W. D. in Woodland Conservation and Research in the Clay Vale of Oxfordshire and Buckinghamshire (eds Kirby, K. J. & Write, F. J.) 32–40 (JNCC, 1988).

  86. Dawkin, H. C. & Field, D. R. B. A Long-Term Surveillance System for British Woodland Vegetation. Commonwealth Forestry Institute, Oxford, Occasional Paper No. 1. (1978).

  87. Horsfall, A. S. & Kirby, K. J. The Use of Permanent Quadrats to Record Changes in the Structure and Composition of Wytham Woods, Oxfordshire Research and Survey in Nature Conservation No. 1 (JNCC, 1992).

  88. Wilkin, T. A., King, L. E. & Sheldon, B. C. Habitat quality, nestling diet, and provisioning behaviour in great tits Parus major. J. Avian Biol. 40, 135–145 (2009).

    Article  Google Scholar 

  89. Van Noordwijk, M. & Purnomosidhi, P. Root architecture in relation to tree–soil–crop interactions and shoot pruning in agroforestry. Agrofor. Syst. 30, 161–173 (1995).

    Article  Google Scholar 

  90. Bailey, L. D. & van de Pol, M. climwin: an R toolbox for climate window analysis. PLoS ONE 11, e0167980 (2016).

    Article  CAS  Google Scholar 

  91. van de Pol, M. et al. Identifying the best climatic predictors in ecology and evolution. Methods Ecol. Evol. 7, 1246–1257 (2016).

    Article  Google Scholar 

  92. Simmonds, E. G., Cole, E. F. & Sheldon, B. C. Cue identification in phenology: a case study of the predictive performance of current statistical tools. J. Anim. Ecol. 88, 1428–1440 (2019).

    Article  Google Scholar 

  93. Oksanen, J. et al. vegan: Community Ecology Package: R Package v.2.5-6 (2019); https://CRAN.R-project.org/package=vegan

  94. Sturges, H. A. The choice of a class interval. J. Am. Stat. Assoc. 21, 65–66 (1926).

    Article  Google Scholar 

  95. Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. https://doi.org/10.18637/jss.v033.i02 (2010).

  96. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020); http://www.R-project.org/

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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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  1. These authors contributed equally: Ella F. Cole, Charlotte E. Regan.

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  1. Edward Grey Institute, Department of Zoology, University of Oxford, Oxford, UK

    Ella F. Cole, Charlotte E. Regan & Ben C. Sheldon

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Contributions

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.

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

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The authors declare no competing interests.

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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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