Current issue: 56(4)
Under compilation: 57(1)
Different sampling methods (the percentage cover scale, the graphical method, two-point quadrat methods, the five-, nine- and twelve-class cover scales, and the biomass harvesting) were used in estimating abundance of ground vegetation in clear-cut areas and on an abandoned field in Southern and Central Finland. The results are examined with the help of DCA ordinations. In addition, the species numbers and diversity indices obtained by different sampling methods are compared.
There were no large differences in DCA configurations between the sampling methods. According to all the sampling methods, a complex soil fertility-moisture gradient (a forest site type) was interpreted as the main ordination gradient in the vegetation data for clear-cut areas. However, different sampling methods did not give similar estimates of species numbers and diversity indices.
The PDF includes a summary in Finnish.
The coverage of bilberry (Vaccinium myrtillus L.) was modelled as a function of site and stand characteristics using the permanent sample plots of the National Forest Inventory (NFI) (Model 1). The sample sites consisted of mineral soil forests as well as fells and peatland sites. Annual variation in the bilberry yield (Model 2) was analysed based on measurements over 2001–2014 in the permanent sample plots (so-called MASI plots) in various areas of Finland. We derived annual bilberry yield indices from the year effects of Model 2 and investigated whether these indices could be used to estimate annual variation in bilberry crops in Finland. The highest bilberry coverage was found in mesic heath forests and fell forests. On peatlands the coverage was, on average, lower than on mineral soil sites; the peatland sites with most bilberry coverage were meso-oligotrophic and oligotrophic spruce mires and oligotrophic pine mires. Our bilberry yield indices showed similar variation to those derived from the mean annual berry yields reported and calculated earlier using the MASI plots; the correlation between the indices was 0.795. This approach to calculating annual berry yield indices is a promising way for estimating total annual bilberry yields over a given period of time. Models 1 and 2 can be used in conjunction with the Miina et al.’s (2009) bilberry yield model when bilberry coverage, average annual yield and annual variation in the yield are to be predicted in forest planning.