Empirical models for the abundance and berry yield of V. myrtillus were constructed using generalized linear mixed model (GLMM) techniques. The percentage coverage of bilberry was predicted as a function of site and stand characteristics using the permanent sample plots of the National Forest Inventory (NFI) in 1995. The number of bilberries was predicted as a function of percentage coverage and stand characteristics using a sub-sample of the NFI plots in North Karelia. The between-year variation in the bilberry yield was analysed using the permanent experimental plots (MASI) established in different areas of Finland and measured in [2001][2002][2003][2004][2005][2006][2007]. The highest coverage of bilberry was found on mesic heath sites; on sub-xeric and herb-rich heath sites the values were 62% of that for mesic sites. The decreasing effect of deciduous trees (compared to spruce) was significant only on herb-rich heath sites. The coverage increased along with stand development up to certain stand ages and basal areas. The bilberry yields were higher in pine-dominated stands than in spruce-dominated ones. In spruce stands, the coverage of bilberry and stand basal area significantly affected the number of berries, whereas in pine stands only the coverage was a significant predictor. In the MASI data, the bilberry yield of pine stands was two times higher than that of spruce stands; however, the between-year variation in bilberry yield was higher in the spruce than in the pine stands. The estimated models were used to predict the bilberry coverage and yield along with stand development. On mesic heath sites in southern Finland (1200 dd.), the predicted annual yield of bilberry was about 25 kg ha -1 (95% confidence interval 9-73 kg ha -1 ) in a mature pine stand and about 10 kg ha -1 (3-35 kg ha -1 ) in a mature spruce stand. The models can be included in stand simulators, where they would facilitate the prediction of bilberry abundance and yields for silvicultural and forest planning purposes.

No abstract

No abstract

-A distance-independent diameter growth model, a static height model, an ingrowth model and a survival model for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. in Catalonia (north-east Spain) were developed. Separate models were developed for P. sylvestris and P. nigra. These models enable stand development to be simulated on an individual tree basis. The models are based on 922 permanent sample plots established in 1989 and 1990 and remeasured in 2000 and 2001 by the Spanish National Forest Inventory. The diameter growth models are based on 8058 and 5695 observations, the height models on 8173 and 5721 observations, the ingrowth models on 716 and 618 observations, and the survival models on 7823 and 5244 observations, respectively, for P. sylvestris and P. nigra. The relative biases for the height models are 6.7% for P. sylvestris and 3.3% for P. nigra. The biases for the diameter growth models are zero due to the applied Snowdon correction. The biases of the ingrowth models are zero due to the applied fitting method. The relative RMSE values for the P. sylvestris and P. nigra models, respectively, are 56.4% and 48.6% for diameter growth, 24.0% and 21.7% for height, and 224.3% and 257.3% for ingrowth.

Multiple-use forestry requires comprehensive planning to maximize the utilization and sustainability of many forest resources whose growth and productivity are interconnected. Forest fungi represent an economically important nonwood forest resource that provides food, medicine, and recreation worldwide. A vast majority of edible and marketed forest mushrooms belong to fungi that grow symbiotically with forest trees. To respond to the need for planning tools for multiple-use forestry, we developed empirical models for predicting the production of wild mushrooms in pine forests in the South-Central Pyrenees using forest stand and site characteristics as predictors. Mushroom production and species richness data from 45 plots were used. A mixed modelling technique was used to account for between-plot and between-year variation in the mushroom production data. The most significant stand structure variable for predicting mushroom yield was stand basal area. The stand basal area associated with maximum mushroom productivity (15–20 m2·ha–1) coincides with the peak of annual basal area increment in these pine forests. Other important predictors were slope, elevation, aspect, and autumn rainfall. The models are aimed at supporting forest management decisions and forecasting mushroom yields in forest planning.