How do we measure cultural services?

The Millenium Ecosystem Assessment (2005) defined cultural services as those services "...that provide recreational, aesthetic, and spiritual benefits." Although many provisioning and cultural services may provide some incidental cultural benefits, FEST methodologies currently include only one service that is primarily cultural: the aesthetic benefits associated with fall foliage and its viewing.

Anecdotal information suggests that sugar maple (Acer saccharum Marshall) is more highly valued for its fall foliage than many other tree species, but research to substantiate this social preference is lacking. In the absence of any formal quantification of species foliage preferences, we calculate a fall foliage metric based on a range of theoretical weights (0-1) for sugar maple as well as for all other hardwoods (including deciduous larch, Larix spp.) combined. The metric is calculated as the proportion of stand basal area represented by sugar maple multiplied by a preference weight, plus the proportion of basal area represented by other hardwoods multiplied by a second preference weight. Non-deciduous conifers are assumed to provide no benefits for fall foliage viewing. Possible values of the metric range from 0 in the situation where no foliage is valued (both preference weights = 0) to 1, where the entire stand consists of sugar maple and/or hardwoods and all corresponding preference weights = 1. As a default, we adopt a preference weight of 1 for sugar maple and a preference weight of 0.5 for other hardwoods; i.e., we assume that sugar maple foliage was twice as preferable as that of other deciduous trees (the red-shaded cell in Figure 1).

FEST case studies involving this service include the simulation of multiple services in managed northern hardwoods stands in the Adirondacks.

Recreational fishing is another important cultural service. Using logistic regression, we can derive models that estimate the likelihood of a pond containing trout or other game fish given the pond's pH and whether or not it had ever been stocked with trout - as well as other variables. In an example from 52 Adirondack Lakes (Figure 2), we can see that at lower values of pH (<6.3) trout are less likely to be present than other game fish in ponds or lakes that have not been stocked with trout. When ponds are stocked, on the other hand, they are not only more likely to contain trout than unstocked ponds, they are also more likely to contain trout than any other type of game fish. Curves like these can be used to estimate the expected value of recreational fishing under a range of scenarios - as in this case study.

Figure 1

Figure 2