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A review of habitat thresholds for dead wood: a baseline for management recommendations in European forests

Abstract

In contemporary forest management, also of commercial forests, threshold values are widely used for consideration of biodiversity conservation. Here, we present various aspects of dead-wood threshold values. We review published and unpublished dead-wood threshold data from European lowland beech–oak, mixed-montane, and boreo-alpine spruce–pine forests separately to provide managers of European forests with a baseline for management decisions for their specific forest type. Our review of dead-wood threshold data from European forests revealed 36 critical values with ranges of 10–80 m3 ha−1 for boreal and lowland forests and 10–150 m3 ha−1 for mixed-montane forests, with peak values at 20–30 m3 ha−1 for boreal coniferous forests, 30–40 m3 ha−1 for mixed-montane forests, and 30–50 m3 ha−1 for lowland oak–beech forests. We then expand the focus of dead-wood threshold analyses to community composition. We exemplify the two major statistical methods applied in ecological threshold analysis to stimulate forest researchers to analyze more of their own data with a focus on thresholds. Finally, we discuss further directions of dead-wood threshold analysis. We anticipate that further investigations of threshold values will provide a more comprehensive picture of critical ranges for dead wood, which is urgently needed for an ecological and sustainable forestry.

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Acknowledgments

We thank Karen A. Brune for linguistic revision of the manuscript and Thibault Lachat, Christophe Bouget and an anonymous reviewer for helpful comments on an earlier version of the manuscript.

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Müller, J., Bütler, R. A review of habitat thresholds for dead wood: a baseline for management recommendations in European forests. Eur J Forest Res 129, 981–992 (2010). https://doi.org/10.1007/s10342-010-0400-5

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Keywords

  • Thresholds
  • Conditional inference tree
  • Maximally selected rank statistic
  • Logistic regression
  • Bootstrapping
  • Variable selection