Classifying development stages of primeval European beech forests: is clustering a useful tool?

Area of Science:

• Ecology
• Forestry
• Quantitative Ecology

Background:

• Old-growth and primeval forests naturally progress through development cycles.
• Existing classification methods for forest development stages rely on a priori assumptions about stand structure.
• This study investigates an alternative approach using data-driven clustering.

Purpose of the Study:

• To test if multivariate datasets of primeval beech forest structure exhibit inherent clusters representing development stages.
• To evaluate the effectiveness of clustering algorithms in identifying distinct forest development stages.
• To compare clustering with traditional classification methods.

Main Methods:

• Derived seven ecological stand structural attributes from two mapped primeval beech forests in Albania.
• Utilized K-means clustering on datasets from virtual sampling points at various spatial scales (200–1500 m²).
• Analyzed clustering quality using average silhouette width and performed sensitivity analysis with different kernels.

Main Results:

• Clustering successfully identified areas of homogeneous stand structure ( > 200 m²).
• However, differences between clusters were minimal, indicated by low average silhouette widths (< 0.28).
• The datasets showed a homogeneous configuration with weak clustering trends.

Conclusions:

• Forest development appears to be a continuous scale rather than discrete stages.
• Discriminating between stages involves arbitrarily splitting continuous data.
• Direct quantification of structural features is recommended over classification for analyzing forest development cycles.