Abstract
Climate warming is shifting biological communities, with warmth-demanding species being favoured at the expense of cold-adapted species in a process referred to as thermophilization1,2,3,4. Because biodiversity responses often lag behind climate warming, climatic debts are accumulating in many ecosystems across the world5,6,7. Although we might expect that thermophilization and climatic debts will vary among habitats, standardized quantification across ecosystems is lacking. Here we analysed multidecadal data from 6,067 resurveyed vegetation plots over 12–78 years in forests, grasslands and on alpine summits across Europe. We demonstrate that forest understory and grassland plant communities experienced positive thermophilization, although not significantly different from zero. By contrast, alpine summit vegetation showed much stronger (up to five times) and significant thermophilization. Thermophilization was driven largely by increases in warmth-demanding species in grasslands, by declines in cold-adapted species on alpine summits and by both processes in forests. Significant climatic debts have accumulated in forests and alpine summits, but less so in grasslands, with debts positively correlated with macroclimate temperature changes. Our findings uncover divergent thermophilization trajectories and increasing climatic debts across ecosystems. Moreover, we highlight the mechanisms that enable some communities to track climate change more closely than others and provide a basis for projecting future shifts in plant communities under accelerating climate warming.
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Data availability
The raw data used in this study are available at Figshare (https://doi.org/10.6084/m9.figshare.28368743.v5)79. Vegetation survey data include the forestREplot database (v.2.3; https://forestreplot.ugent.be) for forests, the GRACE database (https://doi.org/10.1111/jvs.12727)57 for grasslands and the GLORIA database (https://gloria.ac.at) for alpine summits. Other publicly available datasets used in the study include climate data from CRU TS (v.4.07; https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07) and WorldClim (v.2; https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.5086)64, topography data from the R package elevatr (https://cran.r-project.org/web/packages/elevatr), species occurrence records from GBIF (https://doi.org/10.15468/dl.hffjcf) and snow cover data from CHELSA-bioclim (v.2.1; https://chelsa-climate.org).
Code availability
The R code used in this study is available at Figshare (https://doi.org/10.6084/m9.figshare.28368743.v5)79.
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Acknowledgements
K.Y. thanks Y. Peng for her personal and professional support and K. Guo for his help with data analyses. K.Y. acknowledges support from the National Natural Science Foundation of China (32271633 and 32011530426), the National Key Research and Development Program of China (2023YFF1305500), the Central-guided Local Science and Technology Development Fund Projects of Fujian Province (2023L3005) and the Alexander von Humboldt Research Fellowship for Experienced Researchers. P.D.F. and P. Vangansbeke acknowledge support by the European Research Council (ERC) under the Horizon 2020 research and innovation program (ERC Starting Grant FORMICA 757833 and ERC Consolidator Grant CanopyChange 101124948) and from the Research Foundation–Flanders (FWO) by funding the scientific research network FLEUR (W000322N, www.fleur.ugent.be). I.H.M.-S. was funded by the Canada Excellence Research Chairs Program, the Natural Sciences and Engineering Research Council of Canada (NSERC) and the European Union’s Horizon Europe–European Research Council programme under ERC-2022-SYG grant agreement number 101071417–RESILIENCE. A.D.B. was supported by the Knut and Alice Wallenberg Foundation (WAF KAW 2019.0202). M.K. and M. Macek were supported by the Czech Science Foundation (project GACR 23-06614S). R.H., M. Chudomelová and O.V. were supported by the Czech Science Foundation (project 21-11487S). M.K., M. Macek., R.H., M. Chudomelová, P.P. and O.V. were funded by the Czech Academy of Sciences long-term development project (RVO 67985939). A.B.-S. was supported by the European Union: Agricultural Fund for Rural Development (EAFRD), the Conseil Départemental de La Réunion and the Centre de Coopération internationale en Recherche agronomique pour le Développement (CIRAD). T.-M.U. acknowledges support from the Ministry of Research, Innovation and Digitization of Romania through the Core Project BioClimpact no. 7/30.12.2022, code 23020401. P.D.T. was funded by the GreenMount project PN-IV-P2-2.1-TE-2023-0726 from UEFISCDI Romania. F. Máliš was supported by project VEGA 1/0245/25. This study is an outcome of the sREplot working group supported by sDiv, the Synthesis Centre of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig (DFG FZT 118).
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K.Y. and P.D.F. conceived, designed and supervised the project. P. Vangansbeke, I.H.M.-S., D.M.W., K.V., M.B.-R., L.B., I.R.S., A.D.B., R.H., C.A., E.B., T.B., A.B.-S., J.-L.B.-A., J. Bennie, I.B., V.B., J. Brunet, J.M.B., H.V.C., M. Carbognani, M. Chudomelová, D.C.-K., P.D.T., G.N.D., G.D., J.D., M.D., T. Dirnböck, T. Durak, O.E., B.E., B.J.G., T.H., M.H., P.H., U.J., B.J., R.K., J.K., M.K., T.K., A.L., J.L., M. Macek, M. Malicki, F. Máliš, O.M., F. Mitchell, T.N., T.A.N., M.N., A.C.N., L.N., L.O., A.O., S.O., A.O.-A., J.d.O., H.P., G.P., P.P., R.P., M.P., C. Randin, K.R., C. Rixen, F.H.S., W.S., J.Š., A.S.-S., T.S., K.Ś., B.T., J.-P.T., T.-M.U., T.V., M.V., P. Vergeer, O.V., L.V., P. Vittoz, M.W., S.W. and P.D.F. are the members of the data-related working groups and/or contributed data. K.Y., P. Vangansbeke, F.W., S.Z. and P.D.F. performed the statistical analyses. K.Y. developed the manuscript, with contributions and editing from all co-authors.
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Extended data figures and tables
Extended Data Fig. 1 Frequency distributions of data for survey plots, climate, and plant species.
a, Frequency distribution of plot at baseline and resurvey years in forest, grasslands, and alpine summits. b, Frequency distribution of growing season (April to September) mean temperature (Tmean) change rate across the duration of plant surveys in forests, grassland, and alpine summits. The rate of Tmean change for each plot was calculated as the difference between the average mean temperature during the 5 years preceding the baseline survey and the resurvey, divided by the time interval between the two surveys. The medians of the growing season Tmean change rate across forests, grasslands, and alpine summits were 0.31, 0.22, and 0.31 °C per decade, respectively. c, Frequency distribution of the number of grid cells representing species range sizes quantified by area of occupancy (AOO). Across the species pool in our study, 36.4% of the species have AOO < 1000, among which 77.5% of the species have AOO < 500. The green and blue dashed line indicate an AOO of 500 and 1000, respectively.
Extended Data Fig. 2 Vegetation thermophilization rate and climatic debts (°C per decade) in forests, grasslands, and alpine summits estimated using different datasets.
a, Vegetation thermophilization rate and climatic debt quantified as the loss of cold-adapted species (5th percentile shift), overall (median shift), and gain of warmth-demanding species (95th percentile shift) using plant presence/absence data excluding rare species. Circles represent means (n = 4372, 1209, and 457 plots for forests, grasslands, and alpine summits, respectively) with 80% (thick line) and 95% (thin line) credible intervals (CIs) and posterior distributions obtained from Bayesian mixed-effects models. These results are similar with those estimated using plant abundance data excluding rare species in Fig. 2. b, Vegetation thermophilization rate and climatic debt quantified as the decline/loss of cold-adapted species (5th percentile shift), overall (median shift), and increase/gain of warmth-demanding species (95th percentile shift) using plant abundance and presence/absence data including rare species. Circles represent means (n = 4372, 1209, and 486 plots for forests, grasslands, and alpine summits, respectively) with 80% (thick line) and 95% (thin line) CIs and posterior distributions obtained from Bayesian mixed-effects models. The overall patterns for thermophilization rate and climatic debt are in line with those estimated using plant data excluding rare species.
Extended Data Fig. 3 Impacts of topography, snow cover and slope aspect on vegetation thermophilization rates on alpine summits.
a, Relationships between thermophilization rates and terrain ruggedness index (TRI) and slope. Each point represents a plot (n = 457), and shaded bands around fitted lines indicate 95% confidence intervals (CIs) from linear mixed-effects models with “site” as a random effect. Thermophilization rates were calculated using the median of the floristic temperature distributions that were estimated with vegetation abundance data excluding rare species. TRI and slope were derived from a 30-meter resolution digital elevation model (DEM) using the terrain function from the terra R package. b, Impacts of snow cover days and slope aspect on vegetation thermophilization rate. Thermophilization rates were estimated using vegetation abundance data excluding rare species, and data for snow cover were extracted from CHELSA version 2.1 (30 arc-sec resolution, around1 km). Box plots show the median (centre line), the 25th and 75th percentiles (bounds of box), and the minimum and maximum values (whiskers). Each point represents a plot (n = 457), and a slight jitter was applied to the points to better visualize overlap, where applicable. Snow cover change rate was calculated as the difference in mean snow cover days between the five years preceding the baseline survey and the five years preceding the resurvey, divided by the time span (in decades) between the baseline and resurvey surveys. Statistical differences were assessed using linear mixed-effects models with “site” as a random effect. Different lowercase letters denote significant differences among slope aspects, based on Tukey’s post-hoc tests (two-sided, adjusted for multiple comparisons at p < 0.05). Shaded bands around fitted lines indicate 95% CIs from the linear mixed-effects models.
Extended Data Fig. 4 Thermophilization explained by the mechanisms of species gains, losses, or abundance shifts in different ecosystems.
a, Proportions of plots in which thermophilization was primarily driven by mechanisms of species gains, losses, or abundance shifts. The dominant mechanism was identified for each plot as the one contributing the most (in absolute terms) to the observed shift in community thermal affinity. These results indicate that thermophilization was mainly driven by mechanisms of gains in grasslands, gains and losses in forests, and losses and abundance shifts on alpine summits. b, Relationship between observed versus estimated community-weighted mean temperature (ΔTCWM) for different ecosystem types without or with rare species. The sum of gains, losses, and abundance shifts of persisting species was used to estimate ΔTCWM per plot and compared with the observed ΔTCWM. Each point represents a plot (n = 4372, 1209, and 457 for forests, grasslands, and alpine summits, respectively), coloured by ecosystem type. Solid lines represent linear regression per ecosystem with 95% confidence intervals, and the dashed line denotes the 1:1 relationship. The significant linear relationships between estimated and observed ΔTCWM indicate that the three mechanisms collectively provide a reasonable explanation for observed thermophilization patterns.
Extended Data Fig. 5 Changes in the probability density of species thermal suitability and the proportion of thermally mismatched species in different ecosystems.
a, Changes in the probability density of thermal suitability under local climate (Tclim) inferred from plot-level floristic temperature (Tplant) distributions. Boxplots showing changes in probability density of Tplant between baseline survey and resurvey (Δ = resurvey – baseline survey), with the centre line indicating the median, the box representing the 25th and 75th percentiles, and the whiskers showing the minimum and maximum values. These results show that the probability density at Tclim significantly and marginally decreased from baseline to resurvey in forest and grassland ecosystems, respectively, suggesting increasing climatic mismatch. b, Changes in the proportion of thermally mismatched species (Δ = resurvey – baseline survey). Mismatch is defined as background macroclimate falling outside of the 2.5–97.5% range of a species’ thermal distribution. These results suggest that the proportion of mismatched species significantly increased in forests, but not in grasslands or on alpine summits. In both panels a and b, each point represents a plot (n = 4372, 1209, and 457 for forests, grasslands, and alpine summits, respectively), and the black circles with error bars indicate means with 95% confidence intervals estimated using linear mixed-effects models with “site” as a random-effects factor, and the p values indicate whether change differs significantly from zero (linear mixed-effects models; two-sided).
Extended Data Fig. 6 Quantile-quantile (QQ) plots of vegetation thermophilization rates and climatic debts (°C per decade).
Each plot (n = 6038 and 6067 for estimations excluding and including rare species, respectively) compares the sample quantiles of the observed data with the theoretical quantiles of a normal distribution. The degree of alignment with the 1:1 reference line indicates how well each variable approximates normality, informing the suitability of linear modelling assumptions. GS05, GSMedian, and GS95 indicate the 5th, median, and 95th percentiles of the floristic temperature distribution estimated using mean growing season temperature, respectively, and ab and pa indicate the abundance and presence/absence vegetation data, respectively. These plots show an approximately linear relationship between theoretical and observed quantiles, indicating the deviations from normality were minimal and not severe enough to invalidate the Gaussian assumption.
Extended Data Fig. 7 Climate warming patterns and vegetation thermophilization rate/climatic debt.
a, Relationship between baseline temperature and temperature change rate of the growing season across ecosystems and the associated residual plot. Each point represents a plot (n = 6038), with the red line indicating the linear regression fit and the shaded red area showing the 95% confidence interval around the fitted relationship. These visual and statistical diagnostics suggest that a linear baseline temperature term provides a reasonable adjustment in the estimation of thermophilization rate and climatic debt, with no strong evidence that a more complex or non-linear formulation is necessary in our analysis. b, Vegetation thermophilization rates and climatic debts (°C per decade) in different ecosystems as estimated using the 20% and 80% percentiles of the floristic temperature distribution. Thermophilization rate and climatic data were estimated using vegetation abundance data excluding rare species. Circles represent means (n = 4372, 1209, and 457 plots for forests, grasslands, and alpine summits, respectively) with 80% (thick line) and 95% (thin line) credible intervals and posterior distributions obtained from Bayesian mixed-effects models.
Extended Data Fig. 8 Impacts of climate data resolution and spatial autocorrelation on vegetation thermophilization.
a, Relationship between thermophilization rates estimated using fine (30 arc-sec, around 1 × 1 km) and coarse (10 arc-min, around 20 × 20 km) resolution climate data. Each point represents a plot (n = 457). The dashed lines represent the 1:1 relationship, while solid black lines show the fitted linear regression, with shaded bands indicating the 95% confidence interval around the fitted relationship. b, Overall thermophilziation rates estimated from Bayesian mixed-effects models using fine and coarse resolution climate data. Circles represent means (n = 457 plots) with 80% (thick line) and 95% (thin line) credible intervals (CIs) and posterior distributions obtained from Bayesian mixed-effects models. Thermophilization rates were quantified as the declines of cold-adapted species (5th percentile shift), overall (median), and increases of warmth-demanding species (95th percentile shift) using vegetation abundance data excluding rare species. c, Comparison of estimated thermophilization rates (°C per decade) with and without accounting for spatial autocorrelation using a Gaussian Process (GP). Circles represent means (n = 4372, 1209, and 457 plots for forests, grasslands, and alpine summits, respectively) with 80% (thick line) and 95% (thin line) CIs and posterior distributions obtained from Bayesian mixed-effects models. Thermophilization rates were quantified as the declines of cold-adapted species (5th percentile shift), overall (median), and increases of warmth-demanding species (95th percentile shift) using vegetation abundance data excluding rare species. The overlapping 95% CIs across models suggests that spatial autocorrelation does not confound the observed ecosystem differences in thermophilization, and that the ecosystem effects are robust to spatial structure.
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Yue, K., Vangansbeke, P., Myers-Smith, I.H. et al. Contrasting thermophilization among forests, grasslands and alpine summits. Nature (2026). https://doi.org/10.1038/s41586-025-09622-7
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DOI: https://doi.org/10.1038/s41586-025-09622-7


