Heterogeneity-constrained random resampling of phytosociological databases
| Authors | |
|---|---|
| Year of publication | 2011 |
| Type | Article in Periodical |
| Magazine / Source | Journal of Vegetation Science |
| MU Faculty or unit | |
| Citation | |
| Web | Fulltext on Wiley Online Library |
| Doi | http://dx.doi.org/10.1111/j.1654-1103.2010.01225.x |
| Field | Botany |
| Keywords | Data representativeness; Point pattern; Releve; Ripley's K function; Sample plot; Selection; Stratification; Vegetation survey |
| Description | Aim: Phytosociological databases often contain unbalanced samples of real vegetation, which should be carefully resampled before any analyses. We propose a new resampling method based on species composition, called heterogeneity-constrained random (HCR) resampling. Method: Many subsets of the source vegetation database are selected randomly. These subsets are sorted by decreasing mean dissimilarity between pairs of the vegetation plots, and then sorted again by increasing variance of these dissimilarities. Ranks from both sortings are summed for each subset, and the subset with the lowest summed rank is considered as the most representative. Results: Both stratified and HCR resampling yielded selection patterns more similar to the reference than resampling without these tools. Outcomes from the resampling that combined these two methods were the most similar to the reference. The efficiency of the HCR resampling method varied with different levels of aggregation in the database. Conclusions: This new method is efficient for resampling phytosociological databases. |
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