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Climatic marginality: a new metric for the susceptibility of tree species to warming exemplified by Fagus sylvatica (L.) and Ellenberg’s quotient

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Abstract

In the face of climate warming, maps of potential tree species distribution can support forest management planning at coarse scales. For evaluating future suitability, conditions at the rear edge, i.e. at the meridional and lower altitudinal limits of species distribution, are of particular importance. Therefore, we present the concept of climatic marginality (distance to the rear edge) as a metric for the susceptibility against climate warming. Using a statistic niche model fitted to observed and potential beech occurrence in ICP Forests Level I monitoring plots and WorldClim data, we evaluate the modelled xeric limit of European beech based on the Ellenberg’s climate quotient involving thresholds suggested by Ellenberg and other authors. The applicability of the marginality index was tested with independent study sites. Despite the limitations of niche modelling, estimated climatic thresholds of beech were well in accordance with textbook knowledge and recent research. The regional patterns of climatic marginality were plausible and more meaningful with respect to the rear edge compared to conventional niche model outputs. In terms of climatic marginality, most regions in Central Europe are far from the xeric limit of beech. Evaluation based on independently sampled sites indicated that inclusion of soil and topography (microclimate) may permit implications at the local scale, e.g. growth potential estimations.

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Acknowledgments

The research within the project MARGINS (http://margins.ecoclimatology.com/index.html) is funded by the Bavarian State Forest Administration, an authority of the Ministry for Nutrition, Agriculture and Forestry. Additionally the research has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC Grant Agreement No. [282250]. We thank ICP Forests and the involved country representatives for providing Level I data. We are deeply indebted to our colleagues Tzvetan Zlatanov, Elitsa Stoyanova and Plamen Mitov (Bulgaria), Bálint Pataki (Hungary), Mario Pellegrini (Italy), Stanislav Lazarov and Maria Teodosiu (Romania), and Tom Levanič (Slovenia) for providing us access, guidance, and support for the sampling of beech stands in their countries. We would like to thank anonymous reviewers for valuable comments to improve the paper.

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Authors and Affiliations

  1. Bavarian State Institute of Forestry, Hans-Carl-von-Carlowitz-Platz 1, 85354, Freising, Germany

    Karl H. Mellert, Matthias Jantsch, Steffen Taeger & Christian Kölling

  2. Faculty of Forestry, University of Applied Sciences Weihenstephan Triesdorf, Hans-Carl-von-Carlowitz-Platz 3, 85354, Freising, Germany

    Jörg Ewald & Daniel Hornstein

  3. Ecoclimatology, Technical University Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354, Freising, Germany

    Isabel Dorado-Liñán, Christian Zang & Annette Menzel

  4. Technical University Munich, Institute for Advanced Study, Lichtenbergstraße 2a, 85748, Garching, Germany

    Annette Menzel

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  1. Karl H. Mellert
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Appendix

Appendix

For a general evaluation of our EQ-model, we tested the questions, whether SDMs including a more physiological predictor (i.e. climatic water balance, CWB) would be superior to the simple EQ-model. Apart from the EQ-model, we included two models involving CWB in this comparison: The CWB-model based on summer precipitation minus potential evapotranspiration (PET) according to Hargreaves and Allen (2003) and the CWB+T01-model using climatic water balance plus temperature of the coldest month (T01). The performance of the three models was tested by common accuracy measures based on the whole data set (Europe, Table 5) and for Central Europe (target area, Table 6).

Table 5 Predictive performance in Europe (7569 ICP Forest level I plots) of tested models using the accuracy measures, proportion classified correct (PCC), sensitivity, specificity, area under the curve (AUC), Cohen’s Kappa and the true skill statistics (TSS)
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Table 6 Predictive performance in Central Europe (3051 ICP Forest level I plots) of tested models using the accuracy measures, proportion classified correct (PCC), sensitivity, specificity, area under the curve (AUC), Cohen’s Kappa and the true skill statistics (TSS)
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Mellert, K.H., Ewald, J., Hornstein, D. et al. Climatic marginality: a new metric for the susceptibility of tree species to warming exemplified by Fagus sylvatica (L.) and Ellenberg’s quotient. Eur J Forest Res 135, 137–152 (2016). https://doi.org/10.1007/s10342-015-0924-9

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