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Phenological shifts of abiotic events, producers and consumers across a continent

Abstract

Ongoing climate change can shift organism phenology in ways that vary depending on species, habitats and climate factors studied. To probe for large-scale patterns in associated phenological change, we use 70,709 observations from six decades of systematic monitoring across the former Union of Soviet Socialist Republics. Among 110 phenological events related to plants, birds, insects, amphibians and fungi, we find a mosaic of change, defying simple predictions of earlier springs, later autumns and stronger changes at higher latitudes and elevations. Site mean temperature emerged as a strong predictor of local phenology, but the magnitude and direction of change varied with trophic level and the relative timing of an event. Beyond temperature-associated variation, we uncover high variation among both sites and years, with some sites being characterized by disproportionately long seasons and others by short ones. Our findings emphasize concerns regarding ecosystem integrity and highlight the difficulty of predicting climate change outcomes.

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Fig. 1: Illustration of the study design and the extent of data.
Fig. 2: Explanatory power and variance partitioning of the HMSC model with mean annual temperature as explanatory variable.
Fig. 3: Responses of the events to the fixed effects of mean temperature and year.
Fig. 4: Residual associations among events related to random effects.

Data availability

The data that support the findings of this study are available in refs. 38,39, with the exact subset of the data used in the present analyses available at https://doi.org/10.5281/zenodo.3774386.

Code availability

The code needed to replicate the current analyses, from data extraction to parameter estimates presented, is available at https://doi.org/10.5281/zenodo.3774386.

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Acknowledgements

The field work was conducted as part of the monitoring programme of nature reserves, Chronicles of Nature. The work was financially supported by the Academy of Finland, grants 250243 (O.O.), 284601 (O.O.), 309581 (O.O.); the European Research Council, ERC Starting Grant 205905 (O.O.) and Synergy Grant 856506 – LIFEPLAN (to O.O. and T.R.); Nordic Environment Finance Corporation Grant (O.O.); Jane and Aatos Erkko Foundation Grant (O.O., T.R., M.H., L.A.); University of Helsinki HiLIFE Fellow Grant 2017–2020 (O.O.); and the Research Council of Norway through its Centres of Excellence Funding Scheme (223257) to O.O. via Centre for Biodiversity Dynamics; the Kone Foundation 44-6977 (M.D.) and 55-14839 (G.T.); a Spanish Ramon y Cajal grant RYC-2014-16263 (M.D.); the Federal Budget for the Forest Research Institute of Karelian Research Centre Russian Academy of Sciences 220-2017-0003, 0220-2017-0005 (L.V., S.S. and J.K.); the Russian Foundation for Basic Research Grant 16-08-00510 (L.K.), and the Ministry of Education and Science of the Russian Federation 0017-2019-0009 (Keldysh Institute of Applied Mathematics, Russian Academy of Sciences) (N.I., M. Shashkov). We also thank additional colleagues contributing to data collection, especially A. Beshkarev, G. Bushmakova, T. Butorina, L. Chrevova, A. Esipov, N. Gordienko, E. Kireeva, V. Koltsova, I. Kurakina, V. Likhvar, I. Likhvar, D. Mirsaitov, M. Nanynets, L. Ovcharenko, L. Rassohina, E. Romanova, A. Shelekhov, N. Shirshova, D. Sizhko, I. Sorokin, H. Subota, V. Syzhko, G. Talanova, P. Valizer and A. Zakusov.

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Authors

Contributions

The data were collected by the 195 authors starting from M.A. and ending with T.Z. in the author list. J.K., E.M., C.L., G.T. and E.G. contributed to the establishment and coordination of the collaborative network and to the compilation and curation of the resulting dataset. T.R., O.O., L.A., M.H. and M.d.M.D. conceived the idea behind the current study and wrote the first draft of the paper, with O.O. conducting the analyses. All authors provided useful comments on earlier drafts.

Corresponding author

Correspondence to Tomas Roslin.

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The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Hideyuki Doi, Amanda Gallinat and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Variance partitioning of alternative HMSC-models.

Plots show the partitioning of the overall variance of the data into the model components identified in the figure legend. Individual panels show results for models with identical structure but using alternative climatic descriptors of the sampling sites; note that the top-left panel corresponds to Fig. 2b of the main text. Silhouettes adapted from https://thenounproject.com.

Extended Data Fig. 2 Explanatory power of alternative HMSC-models.

Plots show the degree of determination (R2) as a function of the timing of the event (mean day of the year when the event occurs) and the trophic level of the organism expressing the event (different colours). Curves show second-order models fitted to groups with at least 5 events; continuous lines show mean model prediction and dashed lines ± one standard error. Individual panels show results for models with identical structure but using alternative climatic descriptors of the sampling sites; note that the top-left panel corresponds to Fig. 2a of the main text. Silhouettes adapted from https://thenounproject.com.

Extended Data Fig. 3 The sign of responses of phenological events to the fixed effects included in the HMSC model.

Plots show cases for which the response is positive (red) or negative (blue) with at least 95% posterior probability. Events have been ordered according to their mean date (increasing from top to bottom). The covariates have been normalized to have zero mean, so that the main effect of the climatic descriptor relates to a data point collected at the middle of the study period, and the main effect of the year relates to a site with an average value of the climatic descriptor. Individual panels show results for models with identical structure but using alternative climatic descriptors of the sampling sites; note that the top-left panel corresponds to Fig. 3a of the main text. Silhouettes adapted from https://thenounproject.com.

Extended Data Fig. 4 Dependency of event-specific responses on phenological timing and on the trophic level of the organism expressing the event.

Individual sections show results for models with identical structure but using alternative climatic descriptors of the sampling sites; note that the top-left section corresponds to Fig. 3b–e of the main text. Within each section, that is for each model, individual panels show the dependency of event-specific responses on phenological timing (mean day of the year when the event occurs) and on the trophic level of the organism expressing the event (shown by curves in different colours for those groups with at least 5 events). The covariates have been normalized to have zero mean, so that the main effect of the climatic descriptor relates to a data point collected at the middle of the study period, and the main effect of the year relates to a site with an average value of the climatic descriptor. In the bottom-right figure within each quadrat, we show the dependency of the response to year × temperature on the response to year; here, the four quadrats within the panel correspond to events that have shifted to earlier especially at cold sites (EC), shifted to earlier especially at warm sites (EW), shifted to later especially at cold sites (LC), and shifted to later especially at warm sites (LW). Filled symbols indicate cases that are either positive or negative with at least 95% posterior probability. Silhouettes adapted from https://thenounproject.com.

Extended Data Fig. 5 Estimated shift in the phenological timing of events occurring in the spring versus autumn as functions of the average climate descriptors of the site.

Plots show the estimated shift in the phenological timing (days per year) among events occurring in the spring (solid line, showing predictions for Day of Year (DOY) 100, that is April 10) versus autumn (dotted line, showing DOY250, that is September 7), plotted against the average climate descriptors of the site. The colours of the lines identify the trophic level of the organism expressing the event. Silhouettes adapted from https://thenounproject.com.

Extended Data Fig. 6 Residual associations among events related to the random effects of the site.

Plots show the estimates of associations among events measured by residual correlation at the site level. The events have been ordered according to their mean date (increasing from left to right, and from top to bottom). Event-to-event association matrices identify pairs showing a positive (red) or negative (blue) association, shown only if association has either sign with at least 95% posterior probability (the remaining cases are shown in white). Note that the top-left panel corresponds to Fig. 4a of the main text. Silhouettes adapted from https://thenounproject.com.

Extended Data Fig. 7 Residual associations among events related to the random effects of the year.

Plots show the estimates of associations between events measured by residual correlation at the year level. The events have been ordered according to their mean date (increasing from left to right, and from top to bottom). Event-to-event association matrices identify pairs showing a positive (red) or negative (blue) association, shown only if association has either sign with at least 95% posterior probability (the remaining cases are shown in white). Note that the top-left panel corresponds to Fig. 4b of the main text. Silhouettes adapted from https://thenounproject.com.

Extended Data Fig. 8 Residual associations among events related to the random effects of the year-site pair.

Plots show the estimates of associations among events measured by residual correlation at the level of samples, that is year×site combinations. The events have been ordered according to their mean date (increasing from left to right, and from top to bottom). Event-to-event association matrices identify pairs showing a positive (red) or negative (blue) association, shown only if association has either sign with at least 95% posterior probability (the remaining cases are shown in white). Note that the top-left panel corresponds to Fig. 4c of the main text. Silhouettes adapted from https://thenounproject.com.

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Roslin, T., Antão, L., Hällfors, M. et al. Phenological shifts of abiotic events, producers and consumers across a continent. Nat. Clim. Chang. 11, 241–248 (2021). https://doi.org/10.1038/s41558-020-00967-7

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