iForest - Biogeosciences and Forestry (Jun 2020)

Multi-aged micro-neighborhood patches challenge the forest cycle model in primeval European beech

  • Zenner EK,
  • Peck JE,
  • Trotsiuk V

DOI
https://doi.org/10.3832/ifor3309-013
Journal volume & issue
Vol. 13, no. 1
pp. 209 – 214

Abstract

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As currently framed, the forest cycle model that underlies close-to-nature management in temperate beech forests throughout the globe specifies an orderly sequence of temporal development within even-aged patches comprising the forest mosaic. Although this model has been widely applied to European beech (Fagus sylvatica L.) forests, the underlying assumptions of disturbance-induced even-agedness (i.e., within-patch age homogeneity) and competition-induced size differentiation (i.e., within-patch size heterogeneity) have not been tested in natural beech forests due to prohibitions on tree coring in primeval forest reserves. In a rare and unprecedented test dataset of spatially explicit tree ages in an old-growth European beech forest, we employed triangulated irregular networks of Delaunay triangles to objectively identify natural tree neighborhoods to determine if neighboring (i.e., within-patch) trees were even- or, at most, two-aged. Age differences among neighboring trees (summarized in 25-yr age classes) were rarely 50 yrs, while the few "even-aged" patches were very small (100 m2) and relatively young (<150 yrs). In this first assessment of the assumptions underlying the forest cycle model in European beech, we observed neither the even-aged cohorts expected for disturbance-induced patches in different phases of development, nor the size differentiation among similarly aged trees that should arise from the neighborhood dynamics of competition, self-thinning, and growth. The lack of patches indicating demographic turnover is fundamentally inconsistent with the forest cycle model as it is currently framed. We call for further exploration of spatially-explicit tree age datasets to determine the generality of these observations.

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