Although climate change is expected to drive tree species toward colder and wetter regions of their distribution, broadscale empirical evidence is lacking. One possibility is that past and present human activities in forests obscure or alter the effects of climate. Here, using data from more than two million monitored trees from 73 widely distributed species, we quantify changes in tree species density within their climatic niches across Northern Hemisphere forests. We observe a reduction in mean density across species, coupled with a tendency toward increasing tree size. However, the direction and magnitude of changes in density exhibit considerable variability between species, influenced by stand development that results from previous stand-level disturbances. Remarkably, when accounting for stand development, our findings show a significant change in density toward cold and wet climatic conditions for 43% of the species, compared to only 14% of species significantly changing their density toward warm and arid conditions in both early- and late-development stands. The observed changes in climate-driven density showed no clear association with species traits related to drought tolerance, recruitment and dispersal capacity, or resource use, nor with the temperature or aridity affiliation of the species, leaving the underlying mechanism uncertain. Forest conservation policies and associated management strategies might want to consider anticipated long-term species range shifts alongside the integration of contemporary within-distribution density changes.

Birmingham Institute of Forest Research University of Birmingham Birmingham B15 2TT United Kingdom

Centre de Recerca Ecològica i Aplicacions Forestals Catalonia E08193 Spain

Departamento de Biología Vegetal y Ecología Universidad de Sevilla Sevilla 41012 Spain

Department of Botany and Biodiversity Research University of Vienna Vienna 1030 Austria

Department of Forest Resource and Management Division of Forest Resource Data Swedish University of Agricultural Sciences Umeå 90183 Sweden

Department of Geology Geography and Environment Science Environmental Remote Sensing Research Group Universidad de Alcalá Alcalá de Henares 28801 Spain

Department of Life Sciences Forest Ecology and Restoration Group Universidad de Alcalá Alcalá de Henares 28805 Spain

Department of Physical Geography and Ecosystem Science Lund University Lund S 223 62 Sweden

Department of Policy and Strategy Agency for Nature and Forests Brussels 1000 Belgium

Forest and Natural Resources Research Centre Warsaw 02 491 Poland

Forest Ecology Department of Environmental Systems Science Institute of Terrestrial Ecosystems Federal Institute of Technology Zurich Zurich 8092 Switzerland

Global Change Research Institute CAS Department of Climate Change Impacts on Agroecosystems Brno 603 00 Czech Republic

Institute of Forest Ecosystem Research Research and Science Jilove u Prahy 254 01 Czech Republic

Natural Resources Institute Finland Helsinki 00790 Finland

School of Geography Earth and Environmental Sciences University of Birmingham Birmingham B15 2TT United Kingdom

Taxus IT Warsaw 02 491 Poland

The United States Department of Agriculture Forest Service Northern Research Station Durham NH 03824

Universidad de Alcalá Franklin Institute Alcalá de Henares 28801 Spain

Université Grenoble Alpes Institut National de Recherche pour l'Agriculture l'Alimentation et l'Environnement St Martin d'Heres 38402 France

Wageningen Environmental Research Team Sustainable Forest Ecosystems Wageningen University and Research Wageningen 6708 PB The Netherlands

Zobrazit více v PubMed

McDowell N. G., et al. , Pervasive shifts in forest dynamics in a changing world. Science 368, eaaz9463 (2020). PubMed

Mottl O., et al. , Global acceleration in rates of vegetation change over the past 18,000 years. Science 372, 860–864 (2021). PubMed

Vilà-Cabrera A., et al. , Anthropogenic land-use legacies underpin climate change-related risks to forest ecosystems. Trends Plant Sci. 28, 1132–1143 (2023). PubMed

Pugh T. A. M., et al. , Role of forest regrowth in global carbon sink dynamics. Proc. Natl. Acad. Sci. U.S.A. 116, 43824387 (2019). PubMed PMC

Song X.-P., et al. , Global land change from 1982 to 2016. Nature 560, 639–643 (2018). PubMed PMC

Zhu Z., et al. , Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).

Au T. F., et al. , Younger trees in the upper canopy are more sensitive but also more resilient to drought. Nat. Clim. Change 12, 1168–1174 (2022).

Bradford J. B., Bell D. M., A window of opportunity for climate-change adaptation: Easing tree mortality by reducing forest basal area. Front. Ecol. Environ. 15, 11–17 (2017).

Fernández-de-Uña L., Martínez-Vilalta J., Poyatos R., Mencuccini M., McDowell N. G., The role of height-driven constraints and compensations on tree vulnerability to drought. New Phytol. 239, 2083–2098 (2023). PubMed

Searle E. B., Chen H. Y. H., Paquette A., Higher tree diversity is linked to higher tree mortality. Proc. Natl. Acad. Sci. U.S.A. 119, e2013171119 (2022). PubMed PMC

Babst F., et al. , Site- and species-specific responses of forest growth to climate across the European continent. Global Ecol. Biogeogr. 22, 706717 (2013).

Hampe A., Petit R. J., Conserving biodiversity under climate change: The rear edge matters. Ecol. Lett. 8, 461467 (2005). PubMed

Lenoir J., Svenning J.-C., Climate-related range shifts a global multidimensional synthesis and new research directions. Ecography 38, 1528 (2015).

Fei S., et al. , Divergence of species responses to climate change. Sci. Adv. 3, e1603055 (2017). PubMed PMC

Rabasa S. G., et al. , Disparity in elevational shifts of European trees in response to recent climate warming. Global Change Biol. 19, 24902499 (2013). PubMed

Rubenstein M. A., et al. , Climate change and the global redistribution of biodiversity: Substantial variation in empirical support for expected range shifts. Environ. Evidence 12, 7 (2023).

Wason J. W., Dovciak M., Tree demography suggests multiple directions and drivers for species range shifts in mountains of Northeastern United States. Global Change Biol. 23, 33353347 (2017). PubMed

Zhu K., Woodall C. W., Ghosh S., Gelfand A. E., Clark J. S., Dual impacts of climate change: Forest migration and turnover through life history. Global Change Biol. 20, 251264 (2014). PubMed

Jump A. S., et al. , Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Global Change Biol. 23, 37423757 (2017). PubMed

Kelly A. E., Goulden M. L., Rapid shifts in plant distribution with recent climate change. Proc. Natl. Acad. Sci. U.S.A. 105, 11823–11826 (2008). PubMed PMC

Jump A. S., Mátyás C., Peñuelas J., The altitude-for-latitude disparity in the range retractions of woody species. Trends Ecol. Evol. 24, 694–701 (2009). PubMed

Maggini R., et al. , Are Swiss birds tracking climate change? Detecting elevational shifts using response curve shapes. Ecol. Modell. 222, 21–32 (2011).

Woodward F. I., The impact of low temperatures in controlling the geographical distribution of plants. Philos. Trans. R. Soc. London B, Biol. Sci. 326, 585–593 (1990).

Hawkins B. A., Rueda M., Rangel T. F., Field R., Diniz-Filho J. A. F., Community phylogenetics at the biogeographical scale: Cold tolerance, niche conservatism and the structure of North American forests. J. Biogeogr. 41, 23–38 (2014). PubMed PMC

Gaston K. J., Geographic range limits: Achieving synthesis. Proc. R Soc. B, Biol. Sci. 276, 1395–1406 (2009). PubMed PMC

Engelbrecht B. M. J., et al. , Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007). PubMed

McDowell N. G., Mechanisms linking drought, hydraulics, carbon metabolism, and vegetation mortality. Plant Physiol. 155, 1051–1059 (2011). PubMed PMC

Westoby M., “The self-thinning rule” in Advances in Ecological Research, MacFadyen A., Ford E. D., Eds. (Academic Press, 1984), pp. 167–225.

Pugh T. A. M., et al. , The anthropogenic imprint on temperate and boreal forest demography and carbon turnover. Global Ecol. Biogeogr. 33, 100–115 (2024). PubMed PMC

Astigarraga J., et al. , Evidence of non-stationary relationships between climate and forest responses: Increased sensitivity to climate change in Iberian forests. Global Change Biol. 26, 50635076 (2020). PubMed

Talluto M. V., Boulangeat I., Vissault S., Thuiller W., Gravel D., Extinction debt and colonization credit delay range shifts of eastern North American trees. Nat. Ecol. Evol. 1, 1–6 (2017). PubMed

Bennett A. C., McDowell N. G., Allen C. D., Anderson-Teixeira K. J., Larger trees suffer most during drought in forests worldwide. Nat. Plants 1, 15139 (2015). PubMed

McIntyre P. J., et al. , Twentieth-century shifts in forest structure in California: Denser forests, smaller trees, and increased dominance of oaks. Proc. Natl. Acad. Sci. U.S.A. 112, 1458–1463 (2015). PubMed PMC

Woodall C. W., et al. , Classifying mature federal forests in the United States: The forest inventory growth stage system. For. Ecol. Manage. 546, 121361 (2023).

Pedersen E. J., Miller D. L., Simpson G. L., Ross N., Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ 7, e6876 (2019). PubMed PMC

Gelman A., Pardoe I., Average predictive comparisons for models with nonlinearity, interactions, and variance components. Sociol. Methodol. 37, 23–51 (2007).

Sharma S., et al. , North American tree migration paced by climate in the West, lagging in the East. Proc. Natl. Acad. Sci. U.S.A. 119, e2116691118 (2022). PubMed PMC

Suvanto S., et al. , Understanding Europe’s forest harvesting regimes (2023), https://eartharxiv.org/repository/view/5858/. Accessed 28 November 2023.

Cartereau M., Leriche A., Médail F., Baumel A., Tree biodiversity of warm drylands is likely to decline in a drier world. Global Change Biol. 29, 3707–3722 (2023). PubMed

Anderegg W. R. L., Anderegg L. D. L., Kerr K. L., Trugman A. T., Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Global Change Biol. 25, 3793–3802 (2019). PubMed

Martínez-Vilalta J., García-Valdés R., Jump A., Vilà-Cabrera A., Mencuccini M., Accounting for trait variability and coordination in predictions of drought-induced range shifts in woody plants. New Phytol. 240, 23–40 (2023). PubMed

Kunstler G., et al. , Demographic performance of European tree species at their hot and cold climatic edges. J. Ecol. 109, 1041–1054 (2021).

Breshears D. D., Huxman T. E., Adams H. D., Zou C. B., Davison J. E., Vegetation synchronously leans upslope as climate warms. Proc. Natl. Acad. Sci. U.S.A. 105, 11591–11592 (2008). PubMed PMC

Lyu S., Alexander J. M., Competition contributes to both warm and cool range edges. Nat. Commun. 13, 2502 (2022). PubMed PMC

Greenwood S., et al. , Tree mortality across biomes is promoted by drought intensity, lower wood density and higher specific leaf area. Ecol. Lett. 20, 539–553 (2017). PubMed

Reich P. B., et al. , Geographic range predicts photosynthetic and growth response to warming in co-occurring tree species. Nat. Clim. Change 5, 148–152 (2015).

Valladares F., et al. , The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014). PubMed

Liang Y., et al. , What is the role of disturbance in catalyzing spatial shifts in forest composition and tree species biomass under climate change? Global Change Biol. 29, 1160–1177 (2023). PubMed

Alexander J. M., Diez J. M., Levine J. M., Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015). PubMed

Dyderski M. K., Paź S., Frelich L. E., Jagodziński A. M., How much does climate change threaten European forest tree species distributions? Global Change Biol. 24, 1150–1163 (2018). PubMed

Wolodzko T., extraDistr: Additional univariate and multivariate distributions (R Package, Version 1.10.0, 2020). https://CRAN.R-project.org/package=extraDistr. Accessed 28 November 2023.

Fick S. E., Hijmans R. J., WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

Trabucco A., Zomer R., Global aridity index and potential evapotranspiration (ET0) climate database v2. FigShare Fileset (2019), 10.6084/m9.figshare.7504448.v3. Deposited 18 January 2019. PubMed DOI PMC

Caudullo G., Welk E., San-Miguel-Ayanz J., Chorological data for the main European woody species. Mendeley Data, V14. 10.17632/hr5h2hcgg4.14. Accessed 22 September 2022. PubMed DOI PMC

Prasad A. M., Iverson L. R., Little’s Range and FIA Importance Value Database for 135 Eastern US Tree Species (Northeastern Research Station, USDA Forest Service, Delaware, Ohio, 2003), https://web.archive.org/web/20170127094624/ and https://www.fs.fed.us/nrs/atlas/littlefia/. Accessed 14 May 2022.

Conservation Biology Institute, Data basin. https://databasin.org/. Accessed 08 July 2022.

Kattge J., et al. , TRY plant trait database—Enhanced coverage and open access. Global Change Biol. 26, 119–188 (2020). PubMed

Liu D., et al. “Climate-driven variations in functional strategies of temperate forest ecosystems” in EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12540 (2023), 10.5194/egusphere-egu23-12540. DOI

Ruiz-Benito P., et al. , Climatic stress during stand development alters the sign and magnitude of age-related growth responses in a subtropical mountain pine. PLoS ONE 10, e0126581 (2015). PubMed PMC

Berendse F., Aerts R., Nitrogen-use-efficiency: A biologically meaningful definition? Funct. Ecol. 1, 293–296 (1987).

Poggio L., et al. , SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. Soil 7, 217240 (2021).

R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

Kaufman L., Rousseeuw P. J., Finding Groups in Data: An Introduction to Cluster Analysis (Wiley, 1990).

Bates D., Maechler M., Bolker B., Walker S., lme4: Linear mixed-effects models using eigen and S4 (2022). https://github.com/lme4/lme4/. Accessed 28 November 2023.

Hijmans R. J., terra: Spatial data analysis (2022). https://rspatial.org/index.html. Accessed 28 November 2023.

Wickham H., et al. , Welcome to the Tidyverse. J. Open Source Software 4, 1686 (2019).

Wood S. N., Generalized Additive Models: An Introduction with R (Chapman Hall/CRC, ed. 2, 2017).

Wood S., mgcv: Mixed GAM computation vehicle with automatic smoothness estimation (2022). https://cran.r-project.org/web/packages/mgcv/index.html. Accessed 28 November 2023.

Simpson G. L., gratia: Graceful ggplot-based graphics and other functions for GAMs fitted using mgcv (2022). https://gavinsimpson.github.io/gratia/. Accessed 28 November 2023.

Hartig F., DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models (2022). https://florianhartig.github.io/DHARMa/. Accessed 28 November 2023.

Breheny P., Burchett W., visreg: Visualization of regression models (2020). https://pbreheny.github.io/visreg/. Accessed 28 November 2023.

Lüdecke D., et al. , performance: Assessment of regression models performance (2022). https://easystats.github.io/performance/. Accessed 28 November 2023.

Kay M., ggdist: Visualizations of distributions and uncertainty (2022). https://cran.r-project.org/web/packages/ggdist/index.html. Accessed 28 November 2023. PubMed

Vickers A. J., The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: A simulation study. BMC Med. Res. Methodol. 1, 6 (2001). PubMed PMC

Harrell F., “Change from baseline” in Statistical Thinking—Statistical Errors in the Medical Literature (2017), https://www.fharrell.com/post/errmed/#change-from-baseline. Accessed 28 November 2023.

Lüdecke D., ggeffects: Create tidy data frames of marginal effects for ggplot from model outputs (2022). https://strengejacke.github.io/ggeffects/. Accessed 28 November 2023.

Astigarraga J., Julenasti/tree_species_density: Data and code species density changes (v0.1.2). Zenodo. 10.5281/zenodo.10882718. Deposited 26 March 2024. DOI