Changes in plant community composition lag behind climate warming in lowland forests

Journal name:
Nature
Volume:
479,
Pages:
517–520
Date published:
DOI:
doi:10.1038/nature10548
Received
Accepted
Published online

Climate change is driving latitudinal and altitudinal shifts in species distribution worldwide1, 2, leading to novel species assemblages3, 4. Lags between these biotic responses and contemporary climate changes have been reported for plants and animals5. Theoretically, the magnitude of these lags should be greatest in lowland areas, where the velocity of climate change is expected to be much greater than that in highland areas6. We compared temperature trends to temperatures reconstructed from plant assemblages (observed in 76,634 surveys) over a 44-year period in France (1965–2008). Here we report that forest plant communities had responded to 0.54°C of the effective increase of 1.07°C in highland areas (500–2,600m above sea level), while they had responded to only 0.02°C of the 1.11°C warming trend in lowland areas. There was a larger temperature lag (by 3.1 times) between the climate and plant community composition in lowland forests than in highland forests. The explanation of such disparity lies in the following properties of lowland, as compared to highland, forests: the higher proportion of species with greater ability for local persistence as the climate warms7, the reduced opportunity for short-distance escapes8, 9, and the greater habitat fragmentation. Although mountains are currently considered to be among the ecosystems most threatened by climate change (owing to mountaintop extinction), the current inertia of plant communities in lowland forests should also be noted, as it could lead to lowland biotic attrition10.

At a glance

Figures

  1. Theoretical response of plant communities to climate warming.
    Figure 1: Theoretical response of plant communities to climate warming.

    a, Floristically (green scale; FrT) and climatically (red; CrT) reconstructed temperature trends over time. The green scale (0–100%) describes different hypothetical floristically reconstructed temperature trends corresponding to increasing intensity of the plant community responses and leading to increasing recovery of the climate–flora equilibrium (measured as the actual change in FrT over time relative to the effective change in CrT over the same time period; see Supplementary Methods for complete formula). b, Temperature niche separation among 10 virtual species. The range of temperature requirements for each species is represented by a vertical line. c, Three illustrative cases of increasing recovery of the climate–flora equilibrium in plant community composition (based on the 10 virtual species in b). Bottom, absence of climate–flora equilibrium recovery, corresponding to plant communities composed mainly of cold-demanding species, reflecting temperature conditions before climate warming (T0), and leading to an important temperature lag between FrT and CrT. Middle, partial recovery of the climate–flora equilibrium, corresponding to reshuffled plant communities, leading to a mixed assemblage of cold- and heat-demanding species and to reduced temperature lag between FrT and CrT. Top, complete recovery of the climate–flora equilibrium, corresponding to important reshuffling of the plant community, leading to an assemblage of heat-demanding species reflecting the effective temperature increase (T1T0) and to the absence of temperature lag between FrT and CrT. The small circles represent virtual plant species; the three large green disks each depict a community of plants.

  2. Comparison of floristically (green) and climatically (red) reconstructed temperature trends between 1965 and 2008.
    Figure 2: Comparison of floristically (green) and climatically (red) reconstructed temperature trends between 1965 and 2008.

    a, Trends in lowland forest plant communities (<500m a.s.l.). b, Trends in highland forest plant communities (500–2,600m a.s.l.). The thickness of lines shows the range of reconstructed temperature trends (n = 1,000 trends). Dashed lines indicate the start of the contemporary climate warming period (1987–2008). Breaks in trends are due to no sample convergence for the years 1965 (in highland areas), 1972 and 1974 (in both lowland and highland areas).

  3. Compositional changes in the plant communities of lowland and highland forests according to four different biogeographic groups.
    Figure 3: Compositional changes in the plant communities of lowland and highland forests according to four different biogeographic groups.

    Mean shifts in the proportions of plant communities are shown (data points) with standard deviations (error bars) estimated from 1,000 floristic samples used to reconstruct temperatures. The significance of changes from the null hypothesis of zero shift is displayed (*P<0.05 for more than 95% of the floristic samples; Wilcoxon paired signed-rank test). The number of species analysed in lowland (nLw) and highland (nHl) plant communities are displayed below the figure. Mo, mountainous to alpine species; SMo, sub-mountainous to lowland species; Cos, cosmopolitan species; ThM, thermophilous to Mediterranean species. See Supplementary Methods and Supplementary Table 1 for more details about the different biogeographic groups.

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Author information

Affiliations

  1. AgroParisTech, ENGREF, UMR1092 Laboratoire d’Étude des Ressources Forêt-Bois (LERFoB), 14 rue Girardet, F-54000 Nancy, France

    • Romain Bertrand,
    • Christian Piedallu,
    • Gabriela Riofrío-Dillon,
    • Jean-Claude Pierrat &
    • Jean-Claude Gégout
  2. INRA, Centre de Nancy, UMR1092 Laboratoire d’Étude des Ressources Forêt-Bois (LERFoB), F-54280 Champenoux, France

    • Romain Bertrand,
    • Christian Piedallu,
    • Gabriela Riofrío-Dillon,
    • Jean-Claude Pierrat &
    • Jean-Claude Gégout
  3. Ecoinformatics & Biodiversity Group, Department of Bioscience, Aarhus University, Ny Munkegade 114, DK-8000 Aarhus C, Denmark

    • Jonathan Lenoir
  4. CNRS, Institut de Biologie Moléculaire des Plantes (IBMP), Université de Strasbourg (UDS), 12 rue du Général Zimmer, F-67084 Strasbourg Cedex, France

    • Patrice de Ruffray
  5. Inventaire Forestier National, Château des Barres, F-45290 Nogent-sur-Vernisson, France

    • Claude Vidal

Contributions

R.B. designed the study, methodology and modelling approach, performed all the statistical analysis and wrote the paper; P.d.R. provided the Sophy database; C.V. provided the NFI database; J.-C.G. provided the EcoPlant database, helped to design the methodology and supervised the work; R.B. and G.R. contributed equally to format the floristic database; J.-C.P. advised the use of the Breiman’s random forest regression to infer temperatures from the plant assemblages; R.B. and C.P. contributed equally to compute the climate model of historic temperature prediction; J.L. contributed actively to improve the clarity of the paper. All authors discussed and commented on the results.

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The authors declare no competing financial interests.

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Supplementary information

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  1. Supplementary Information (5M)

    This file contains Supplementary Figures 1-10 with legends, Supplementary Methods, Supplementary Table 1 and additional references.

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