Spatial models of site index based on climate and soil properties for two boreal tree species in Ontario, Canada

Abstract We examined the spatial distribution of site productivity for jack pine and black spruce across a 556,000 km2 area of Ontario, Canada. Our analysis employed data from 1140 forest ecosystem classification (FEC) plots distributed across this region. Using tree-based regression, we related soil, topographic, and climatic conditions at the plots to site index (i.e. estimated height at 50 years of age) of the target species. Both species grew better on deeper mineral soils in the southern arm of the province where wetter, warmer conditions prevail. Models were tested for accuracy and bias using withheld data. Models for both species were unbiased. The jack pine model had a square root of the mean squared prediction error (root-MSPR) of 2.55 m, while that for black spruce was 2.84 m. These error levels are comparable to other rigorously tested results reported in the literature. A map was produced for each species showing the spatial distribution of site index across the study area as predicted by the regression tree models. By necessity, coarse-scale soils data were used to produce the site index maps and this increased root-MSPR errors ∼20–30% as compared to errors obtained using actual soil data recorded in the field. These maps, despite their moderate accuracy, provide a picture of relative productivity that may be used in broad-scale planning initiatives. As well, the tree-based regression models can be used to obtain point estimates of site index at locations where the required soil data are available.