Simulating Forest Structure, Timber Production, and Socioeconomic Effects in a Multi-Owner Province

Abstract

Protecting biodiversity has become a major goal in managing coastal forests in the Pacific Northwest--an area in which human activities have had a significant influence on landscape change. A complex pattern of public and private forest ownership, combined with new regulations for each owner group, raises questions about how well and how efficiently these policies achieve their biodiversity goals. To develop a deeper understanding of the aggregate effect of forest policies, we simulated forest structures, timber production, and socioeconomic conditions over time for the mixture of private and public lands in the 2.3-million-ha Coast Range Physiographic Province of Oregon. To make these projections, we recognized both vegetative complexity at the stand level and spatial complexity at the landscape level. We focused on the two major factors influencing landscape change in the forests of the Coast Range: (1) land use, especially development for houses and cities, and (2) forest management, especially clearcutting. Our simulations of current policy suggest major changes in land use on the margins of the Coast Range, a divergence in forest structure among the different owners, an increase in old-growth forests, and a continuing loss of the structural elements associated with diverse young forests. Our simulations also suggest that current harvest levels can be approximately maintained, with the harvest coming almost entirely from private lands. A policy alternative that retained live trees for wildlife would increase remnant structures but at a cost to landowners (5-7% reduction in timber production). Another alternative that precluded thinning of plantations on federal land would significantly reduce the area of very large diameter (>75 cm dbh) conifer forests 100 years into the future

Accounting for a Diverse Forest Ownership Structure in Projections of Forest Sustainability Indicators

“…Our results thus suggest that generalizing forest management for forest owner categories can lead to considerable over-or underestimation of ecosystem service provision in forest resource projections when the diversity of management strategies between and within owner categories is not accounted for. In contrast to technical and environmental constraints affecting forest management, social constraints are rarely taken into account in projections of forest resource development and wood mobilization scenarios [68], even though it has been shown that social constraints could have an important impact on future wood supply and forest development [4,12,16,19]. When the results of forest impact analysis are used as the basis for decisionmaking regarding the long-term use of forests, as is the case in Sweden, it is important that all relevant constraints within forest management are considered.…”

“…The sensitivity of the Diverse scenario to subjective interpretation of the NIPF owner's management strategies from [19] was tested by changing the most influential parameters: rotation length, harvest level and proportion of set-aside, creating two alternatives for the Diverse scenario, one with more intensive management (Diverse_m) and one with less intensive management (Diverse_l) ( Table 7). The applied parameter changes were meant to span a considerable, but still plausible range of parameter settings [49].…”

Climate change effects on vegetation in the Pacific Northwest: a review and synthesis of the scientific literature and simulation model projections

“…Capturing these processes typically requires a spatially explicit landscape model (see below). However, gap models can be linked with landscape models (e.g., Johnson et al 2007), and a new class of individual tree models can operate at landscape scales (Seidl et al 2012).…”

ForestSim Model of Impacts of Smallholder Dynamics: Forested Landscapes of the Upper Peninsula of Michigan

“…Examples of aggregated models for land use and management include Hartter and Boston's [34] model of land conversion to agriculture based on the caloric needs of households and Brown et al 's [35] model of land use change in the Upper Midwest region of the United States. Such models, focusing on the ecological impacts of harvesting and land use conversion, often simulate land use decision-making as a probability of land use transition based on observed transition rates without incorporating the social mechanisms underlying these transition decisions (e.g., [35][36][37][38][39]). Consequently, while forest ecosystem characteristics are disaggregated within these models, ownership decision-making is still aggregated by owner class (e.g., public, industrial, smallholder) and therefore is relatively homogenous within class.…”