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Unexpected Negative Effect of Available Water Capacity Detected on Recent Conifer Forest Growth Trends Across Wide Environmental Gradients

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Abstract

National Forest Inventories (NFIs) perform systematic forest surveys across space and time. They are hence powerful tools to understand climate controls on forest growth at wide geographical scales and account for the effects of local abiotic and biotic interactions. To investigate the effects of climate change upon growth dynamics of four major European conifer species along elevation and continentality gradients, we herein provide an original harmonization of the French and Austrian NFI datasets. The growth of Norway spruce, Scots pine, silver fir and European larch over the 1996–2016 period was studied in pure and even-aged plots across different ecological regions. We derived climate-driven growth trends from > 65, 000 radial increment series filtered out from major biotic and abiotic influences using statistical modeling. We further identified primary environmental drivers of conifer growth by regressing growth trends against regionally aggregated biotic and abiotic forest attributes. Negative growth trends were observed in continental regions undergoing the most rapid warming and thermal amplitude contraction over the study period. Negative trends were also associated with lower forest structural heterogeneity and, surprisingly, with greater available water capacity. Remarkably, we observed these associations both at the inter- and intra-species levels, suggesting the universality of these primary growth determinants. Our study shows that harmonized NFI data at the transnational level provide reliable information on climate–growth interactions. Here, greater forest structural complexity and greater water resource limitation were highlighted as drivers of greater forest resilience to climate change at large-scale. This result forms crucial bases to implementing climate-smart forest management.

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Acknowledgements

This study was funded by the French National Research Agency (ANR) through the ‘Laboratoire d’Excellence ARBRE’ and the PIA ‘Lorraine Université d’Excellence’ projects (ANR-11-LABX-0002-01 and ANR-15-IDEX-04-LUE) and by the Région Grand Est through a fellowship “Soutien aux jeunes chercheurs” (18_GE5_029). We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu). We are thankful to Francois Morneau for help regarding the extraction of the French NFI data, Ingrid Seynave and Emilien Kunz for the extraction of ground-flora based soil variables and Emeline Chaste for help regarding the extraction of netcdf climate data. We want to thank Georg Kindermann for providing annualized diameter data for Austria and familiarization with the interpolation algorithm. We also acknowledge support from Karl Gartner, Edwin Herzberger and Rainer Reiter from the Department of Forest Ecology and Soils at BFW for the extraction and computation of Austrian soil variables. Finally, we would like to thank the subject-matter editor and the two anonymous reviewers for their thoughtful feedback on this manuscript.

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  1. Institut National de L’Information Géographique Et Forestière, Laboratoire D’Inventaire Forestier, 14 rue Girardet, 54000, Nancy, France

    Clémentine Ols & Jean-Daniel Bontemps

  2. Bundesforschungszentrum Für Wald, Institut Für Waldinventur, Seckendorff-Gudent-Weg 8, 1131, Wien, Austria

    Thomas Gschwantner & Klemens Schadauer

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  1. Clémentine Ols
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Correspondence to Clémentine Ols.

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Author’s Contributions: JDB with the help of TG and KS conceived or designed the study; CO with the help of JDB and TG performed the research; CO analyzed the data; JDB and CO contributed new methods or models; and CO with the help of JDB, TG and KS wrote the paper.

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Ols, C., Gschwantner, T., Schadauer, K. et al. Unexpected Negative Effect of Available Water Capacity Detected on Recent Conifer Forest Growth Trends Across Wide Environmental Gradients. Ecosystems 25, 404–421 (2022). https://doi.org/10.1007/s10021-021-00663-3

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