Forest growth services (FGR) refer broadly to the ability of a forest ecosystem to maintain growth of trees, shrubs, and other types of vegetation. A number of important ecosystem services are dependent upon this important supporting service – including future provision of building and energy products, carbon sequestration and storage, and wildlife habitat. We primarily focused on two components of forest growth regulation. Standing biomass and tree species composition serve as indicators of the future potential of the forest as a source of wood products. Standing biomass stock also serves as an important indicator of a number of other services that require an intact stand (e.g. some forms of recreation, some wildlife habitat, etc.). The primary ecological datasets used to assess forest growth regulation services were periodic tree inventories, principally measures of tree diameter, density, and species. Tree diameters were converted to estimates of standing biomass using the following formula for mixed hardwoods (Jenkins et al. 2003
): biomass = 2.718282-2.48+(2.4835*ln(diameter))
. Tree composition was assessed on a simple [0,1] scale, in which a value of 1.0 would indicate that all of the stems present were of the most valuable tree species in the region. This index was calculated by multiplying the relative density of each tree species by the relative importance of that species in the region, and summing these individual values to obtain a single index value. Relative importance was approximated using the stumpage value of each species as a measure of demand. To arrive at this value, we took the mean of the most recent stumpage values for each species (or species group) as reported by the states of New York, New Hampshire, and Maine, USA (Figure 1
). These three states were selected as proxies for the region based on data availability. Values for each species were scaled by the value of the most valuable tree in the data set (in this case, black walnut [Juglans nigra
Whereas water quality and water flow services were assessed on an annual time-step, forest inventory data was collected on a larger and less consistent time-step, with individual data points often many years apart. This scale mismatch is problematic, as it makes it difficult to compare forest growth with other services at a specific point in time. Therefore, when comparing multiple services across time, we used linear interpolation to create an annual time-series for biomass and tree composition index with the same scale and range as those associated with the other services.
FEST case studies involving this service include quantification of FQR at Hubbard Brook and Turkey Lakes, and the inclusion of FQR metrics in a tradeoff analysis at Hubbard Brook.