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Spatiotemporal differences in tree spatial patterns between alluvial hardwood and mountain fir–beech forests: do characteristic patterns exist?
Author(s) -
Janik David,
Adam Dušan,
Hort Libor,
Král Kamil,
Šamonil Pavel,
Unar Pavel,
Vrška Tomáš,
Horal David
Publication year - 2013
Publication title -
journal of vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 115
eISSN - 1654-1103
pISSN - 1100-9233
DOI - 10.1111/jvs.12018
Subject(s) - beech , floodplain , alluvium , alluvial plain , forestry , hardwood , ecology , geography , geology , biology , geomorphology , cartography
Questions What are the differences between the tree spatial patterns ( TSP ) of various recruit and mortality waves in alluvial hardwood forests and mountain fir–beech forests? Are there any statistically significant differences between the mean TSP of these forest types? Are these differences stable over time? Location Alluvial floodplain forests at the confluence of the M orava and D yje rivers, and mountain fir–beech forests in the O uter W estern C arpathians, C zech R epublic. Methods In both forest types, seven 2‐ha rectangular plots were analysed. The pair correlation function g ( r ) was used to describe tree density variability of trees with DBH ≥ 10 cm. The analyses were carried out for data sets from the 1970s, 1990s and 2000s. A bootstrap method was used to test for significant differences between the mean values of g ( r ) from alluvial forests and from fir–beech forests. Results Recruits in mountain fir–beech forests revealed consistent clustering to at least 5 m. In alluvial hardwood forests, recruits also showed random distribution as well as occasional regular distribution at distances over 20 m. Bootstrap significance tests revealed significant differences between the mean values of g ( r ) for alluvial forests and fir–beech forests. Alluvial floodplain forests showed statistically significant stronger clustering up to a distance of 4 m in all study periods. At distances over 20 m, mountain fir–beech forests demonstrated stronger clustering. In the 1970s, this was statistically significant only at a distance of 32 m, but in the 2000s, it was at intervals of 22–30 and 34–38 m. Conclusions The methods of data analysis in this study enabled us to find significant features of TSP at finer resolution than the common resulting trichotomy of univariate analysis: clustering, randomness and regularity. We believe that, on the basis of detailed spatial analyses, it is possible to create a TSP model that reflects the typical features of particular forest types.