Anticipating Cascading Change: Developing Foresight for Central Hardwood Forest Futures

by

David N. Bengston, Michael J. Dockry, and Stephen R. Shifley

Research Social Scientist (DNB), USDA Forest Service, Northern Research Station, 1992 Folwell Ave., St. Paul, MN 55108; Research Forester (MJD), USDA Forest Service, Northern Research Station, 1992 Folwell Ave., St. Paul, MN 55108; Research Forester (SRS), USDA Forest Service, Northern Research Station, 202 Natural Resources Building, Columbia, MO 65211. DNB is corresponding author. To contact, call 651-649-5162 or email dbengston@fs.fed.us

Abstract – As an artifact of past disturbance, nearly 60% of Central Hardwood forestland is clustered in age classes spanning 40-80 years. Young forests (age 20 years or less) comprise 10% of all forests in the region; forests older than 100 years comprise 4%. Within the region, this unimodal (bell-shaped) pattern of clustered age classes is repeated at smaller spatial scales for individual states and for individual forest-type groups. The unimodal age-class distributions common throughout the Central Hardwood region differ from those observed for other regions of the United States. Because of built-in inertia, the uniform aging of these forests will continue for decades. This study used a participatory “smart group” brainstorming process called the Futures Wheel to identify and evaluate the direct and higher-order implications of the trend: Forests lack age-class diversity and will uniformly grow old. Five first-order consequences of this trend were identified: Continued significant decrease in early-successional forest; Continued significant increase in late-successional forest; Decreased resilience to many types of forest disturbances; Decrease in carbon sequestration rates; Increase in the popular perception that this is the way all forests are and should be. Almost 70 forestry professionals participated in Futures Wheel exercises to identify second- and third-order implications of the five first-orders. Participants identified 66 second-order and 290 third-order implications, and scored them for likelihood and desirability. Analysis of the second- and third-order implications revealed two types of implications relevant for policy and management interventions: high likelihood but highly negative implications, and low likelihood but highly positive implications.