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Microclimate and forest density drive plant population dynamics under climate change

Nature Climate Change volume 13, pages 840–847 (2023)Cite this article

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Abstract

Macroclimatic changes are impacting ecosystems worldwide. However, a large portion of terrestrial species live under conditions where impacts of macroclimate change are buffered, such as in the shade of trees, and how this buffering impacts future below-canopy biodiversity redistributions at the continental scale is unknown. Here we show that shady forest floors due to dense tree canopies mitigate severe warming impacts on forest biodiversity, while canopy opening amplifies macroclimate change impacts. A cross-continental transplant experiment in five contrasting biogeographical areas combined with experimental heating and irradiation treatments was used to parametize 25-m resolution mechanistic demographic distribution models and project the current and future distributions of 12 common understorey plant species, considering the effects of forest microclimate and forest cover density. These results highlight microclimates and forest density as powerful tools for forest managers and policymakers to shelter forest biodiversity from climate change.

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Fig. 1: Overview of the study.
Fig. 2: Plant vital rate responses.
Fig. 3: Current demographic distribution model predictions.
Fig. 4: Future demographic distribution model predictions.
Fig. 5: Predicted changes in future population growth.

Data availability

Macroclimate data are available through the global climate archive WorldClim (https://www.worldclim.org/data/index.html). Spatial temperature offset data between forest and open field conditions are available at https://doi.org/10.6084/m9.figshare.14618235.v4 (ref. 20). Tree cover density data are available at https://land.copernicus.eu/pan-european/high-resolution-layers/forests/tree-cover-density/status-maps/2015. Georeferenced observation records used for the continental-scale validation of the Demographic Distribution Models (DDMs) are available at https://doi.org/10.15468/dl.3nvzc8. All experimental plant demographic data and site-level environmental data are available at FigShare https://figshare.com/s/35b070818230ce5795d3 (ref. 60).

Code availability

All scripts to reproduce the methods, analyses and figures are available at FigShare https://figshare.com/s/35b070818230ce5795d3 (ref. 60).

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Acknowledgements

We thank K. Ceunen, H. R. Schaffner, U. Johansson, S. Vaneenooghe, J. Van Loo, S. Van Bouchaute, L. G. Lundin, T. Augustijns and S. Haesen for fieldwork and technical assistance. This work was supported by the European Research Council (ERC) (ERC Starting Grant FORMICA 757833, 2018, http://www.formica.ugent.be), the Research Foundation Flanders (FWO) (K.D.P. ASP035-19, S.G. G0H1517N) and the FWO Scientific research network FLEUR (http://www.fleur.ugent.be).

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Authors and Affiliations

  1. Forest and Nature Lab, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Melle-Gontrode, Belgium

    Pieter Sanczuk, Karen De Pauw, Emiel De Lombaerde, Camille Meeussen, Sanne Govaert, Thomas Vanneste, Leen Depauw, Kris Verheyen, Pieter Vangansbeke & Pieter De Frenne

  2. Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland

    Miska Luoto

  3. Southern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, Lomma, Sweden

    Jörg Brunet, Per-Ola Hedwall & Jaime Uria-Diez

  4. Landscapes, Environment and Geomatics, Department of Physical Geography, Stockholm University, Stockholm, Sweden

    Sara A. O. Cousins

  5. Department of Agriculture, Food, Environment and Forestry, University of Florence, Florence, Italy

    Cristina Gasperini, Giovanni Iacopetti & Federico Selvi

  6. NBFC, National Biodiversity Future Center, Palermo, Italy

    Cristina Gasperini & Federico Selvi

  7. UMR CNRS 7058 ‘Ecologie et Dynamique des Systèmes Anthropisés’ (EDYSAN), Université de Picardie Jules Verne, Amiens, France

    Jonathan Lenoir & Fabien Spicher

  8. IVL Swedish Environmental Institute, Stockholm, Sweden

    Jan Plue

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  1. Pieter Sanczuk
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  2. Karen De Pauw
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Contributions

P.S., K.D.P., E.D.L., M.L., K.V., P.V. and P.D.F. conceived the ideas and designed methodology; all authors collected data; P.S. analysed data in collaboration with E.D.L., M.L., P.V. and P.D.F.; and P.S. led the writing of the manuscript in collaboration with K.D.P., E.D.F., P.V. and P.D.F. All authors contributed critically to the draft and gave final approval for publication.

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Correspondence to Pieter Sanczuk.

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Nature Climate Change thanks Romain Bertrand, Ilya Maclean and Luiz Magnago for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Overview of the experimental set up.

(top) Picture of the experimental site of the dense forest core plot in south Sweden with the full factorial heating and irradiation treatment. (bottom) Details of the plant protocol and design. ©Pieter Sanczuk.

Extended Data Fig. 2 Flowchart of the data.

Analytical framework of the study, data flow and modelling techniques used. Demographic data collected in the period 2019 – 2021 and plot-level environmental data was used to analyses the effects of the environment on all vital rates with (generalized) linear mixed-effect models (that is the vital rate models). The vital rate models were integrated in an integral projection modelling (IPM) approach to estimate population growth rates (λ). The final Demographic Distribution Models (DDM) resulted from projecting the IPM across the study area under the current (baseline) climate conditions and forest density, and under all combination of projected future climate conditions and simulated changes in forest density. For all future DDM predictions, temperature offsets were kept constant.

Extended Data Fig. 3 Plant vital rate responses.

Mixed-effect model coefficient estimates [± standard error] for the number of seeds (# seeds) and seedling size (recruit size) for the study species on which these vital rates could be measured in the experiment. Values are (generalized) linear mixed-effect model (LMM) coefficient estimates [± standard error] (after model selection; presented in horizontal way). Species are ranked following the Colonization Capacity Index (CCI). Significant effects (p < .05; obtained from the LMMs, not corrected) are in bold. Non-significant effects (p ≥ .05) are faded. Sample sizes for each species and vital rates are presented in Extended Data Fig. 4. See Supplementary Tables for model details.

Extended Data Fig. 4 Species-specific sample sizes.

Overview of species-specific sample sizes for the four main vital rates. (left) Species specific total sample size for each vital rate. (right) Annual sample sizes for each species and vital rate across the four experimental treatments (control [C], heating [H], illumination [L] and heating-illumination [LH]). The species are Anemone nemorosa (AneNem), Oxalis acetosella (OxaAce), Carex sylvatica (CarSyl), Vinca minor (VinMin), Poa nemoralis (PoaNem), Allium ursinum (AllUrs), Deschampsia cespitosa (DesCes), Geranium sylvaticum (GerSyl), Geranium robertianum (GerRob), Geum urbanum (GeuUrb), Alliaria petiolata (AllPet) and Urtica dioica (UrtDio).

Extended Data Fig. 5 Scenario analyses.

Predicted change in future population growth rate (λ) due to climate change under different simulated changes in forest density (from minus 50 % decrease to plus 50 % increase in forest density relative to the baseline conditions). Plotted are the continental-scale average % change [± Standard Deviation (SD)] in λ relative to the baseline climate and forest density predictions for 10,000 random locations across the study area. The grey dashed line represents no change.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Methods 1–6.

Reporting Summary

Supplementary Tables

Supplementary tables related to the study design, study area, study species and modelling decisions, and Bayesian linear regressions model output.

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Sanczuk, P., De Pauw, K., De Lombaerde, E. et al. Microclimate and forest density drive plant population dynamics under climate change. Nat. Clim. Chang. 13, 840–847 (2023). https://doi.org/10.1038/s41558-023-01744-y

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