Publication details
The role of vernal species in vegetation classification: a case study on deciduous forests and dry grasslands of Central Europe
| Authors | |
|---|---|
| Year of publication | 2016 |
| Type | Article in Periodical |
| Magazine / Source | Phytocoenologia |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1127/phyto/2016/0034 |
| Field | Botany |
| Keywords | geophytes; partition similarity; response curves; spring flowers; temperate region; therophytes |
| Description | The very short lifespan of some therophytes and geophytes growing in Central European deciduous forests and dry grasslands may be a source of inconsistencies in vegetation data analyses. Based on data provided by the Czech National Phytosociological Database (CNPD) we identified frequently occurring vernal species, using species life-form strategies and species response curves. We also studied vernal species richness in respect to particular vegetation units. Using two data sets of permanent plots (Deciduous Forests and Dry Grasslands; each recorded in spring and summer), we tested whether partitions of spring releves have a higher similarity to partitions of summer releves if we exclude the vernal species. The same question was addressed using large data sets compiled from the CNPD. We found 21 frequent vernal species in forests and 36 in dry grasslands. Many of them are included in the Red List of the Czech Republic. Richest in vernal species were the phytosociological class of mesic and wet forests (Carpino-Fagetea), pioneer vegetation of the Koelerio-Corynephoretea and dry grassland vegetation of the Festuco-Brometea. When we excluded the vernal species before classification of spring releves, we got a significant increase in partition similarity of the summer data. We conclude that exclusion of vernal species helps to get more consistent and comparable results, except of data analyses focused on the spring period and vernal species as vegetation indicators. Application of intra-seasonal stratification might be a way to obtain more balanced data sets suitable for comparison, analyses and classification. |
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