Relationships between site index of Sitka spruce (Picea sitchensis(Bong.) Carr.) and measures of ecological site quality in the Eastern Queen Charlotte Islands

• Main Author: Pearson, Audrey F.
• Language: English
• Published: 2008
• Online Access: http://hdl.handle.net/2429/3042

Relationships between measures of ecological site quality and height growth of Sitka spruce [Picea sitchensis (Bong.) Carr), measured as site index (metres at 50 years at breast height), were investigated for the eastern Queen Charlotte Islands using multiple linear regression techniques. Temperature and light were assumed to be similar in a region of similar climate, expressed as biogeoclimatic subzone, and the investigation focused on measures of moisture and nutrients. This study tested the hypothesis that the classification units of biogeoclimatic ecosystem classification system are good indirect estimates of ecological site quality so are able to predict forest growth. Fifty-five plots were chosen for study to represent a wide range i n ecological site quality and geographic variation. Individual measures of moisture and nutrients were represented as categorical variables; soil moisture and nutrient regime; and continuous variables; soil nutrients and predicted moisture deficit in combination with soil physical measures. Soil moisture and nutrient regimes were represented as spectra of indicator species groups. The synoptic effect of individual measures of moisture and nutrients were expressed as site associations and assumed to be represented by plant associations. The relationship between the categorical and continuous variables for soil nutrients was also examined. The most successful models used the BEC classification units; soil moisture regime, soil nutrient regime and site association. Multiple linear regression models using either soil moisture plus soil nutrient regimes or site associations were equally successful in explaining variation in (ln) site index (adjusted R2 = 0.80; I2 = 0.79). However, the former should be considered the superior model for predictive purposes since the variance of the latter model was not homogeneous. The soil nutrient regime model was as successful in explaining variation in site index as the best continuous model (mineral soil mineralizable N plus forest floor mineralizable N, extractable calcium and potassium) (adjusted = 0.40 versus 0.42). The soil moisture regime model was more successful than those using continuous variables (adjusted = 0.45 versus no relationship). The lack of relationship was attributed to inaccuracy of the water balance models due to their lack of calibration for this location and forest type. There was a moderate relationship with site index and plant associations (adjusted R2 = 0.49); however vegetation is subject to change with succession over time so would be difficult to apply for predictive purposes. Indicator species groups showed no relationship with site index, likely due to the paucity of understorey species from canopy closure and intense browsing by introduced deer. The soil nutrient regime classification was well supported by a discriminant analysis using soil chemical nutrients which correctly classified the plots in 83% of the cases, on average. The soil moisture regime classification could not be tested since the models used to estimate moisture deficit in this study were considered too inaccurate. Continuous synoptic versus categorical models could also not be compared for the same reason. It was concluded that the units of the biogeoclimatic ecosystem classification were good indirect measures of ecological site quality and good predictors of forest growth. The equations generated in this study need to be tested with independent data and further investigation is necessary to determine moisture relationships for Sitka spruce.