Future Development on Private Forests in Three States
Privately owned forests provide many public benefits, including clean water and air, wildlife habitat, and recreational opportunities. By 2030, 44.2 million acres of rural private forest land across the conterminous United States are projected to experience substantial increase in residential development. As housing density increases, the public benefits provided by private forests can be permanently altered. In this report, we examine factors behind projected patterns of residential development and conversion of private forest land by 2030 in northwestern Washington, southern Maine, and northwestern Georgia. These areas were selected for detailed study because the first Forests on the Edge study (Stein et al. 2005) revealed that they each contain watersheds that were nationally ranked as high in terms of the percentage of watersheds containing private forests projected to be developed.
- Population growth from in migration is a key factor in Washington and Georgia while much of the new residential development in Maine appears to be related to demand for second homes.
- Ownership of forest land is changing in all three areas. Based on past patterns, forest lands owned by nonindustrial private forestland owners will likely undergo the greatest conversions to developed uses. It is not clear what impact divestment of forest land by the forest industry may have on forestland conversion rates.
- The legacy of different patterns of historical settlement continues to influence current development trends. In all three regions, future development is projected along existing transportation networks. However, in Maine and Georgia, these networks are much more extensive than in Washington, thus supporting more dispersed development in some areas of those states. The amount of federal land in Washington, and the state’s topography, also influence the pattern of residential development there.
- Land-use planning mechanisms and forest conservation efforts in the three states will influence the pattern of housing density in each study area.