Factors affecting the fruiting of bilberries: an analysis of categorical data set

The present study is an application of categorical data analysis in ecological research. The approach is based on logistic regression following an exploratory graphical analysis. The material was collected in an extensive forest inventory in which a set of observations was made by eye in a stratified random sample of 262 mature upland forest stands in South Finland. The problem was to interpret the variation in the fertility of the tillers of bilberry (Vaccinium myrtillus). In the field, the fertility was recorded as a three-class ordinal variable. The information available for the interpretation included the visually estimated density of the tree crowns, the soil fertility class determined using Cajander's forest site types and the percent cover of V. myrtillus.The GLM framework was employed in successive stages of the data analysis in order to find a model to fit the data. For this, the three-class ordinal response variable was reduced to two classes: stands characterized by (a) sterile and (b) fertile bilberry tillers. Successful prediction of the distribution of these two types of forest stands was achieved with a logistic-regression model by using canopy coverage and soil fertility classes as predictor variables. The generalized linear modelling framework is suitable for studying many ecological problems even when only rough categorical estimates of environmental scalars are available.