Abstract
Research Highlights: We used airborne lidar to assess the multi-scalar patterns of forest structure across a large (471,000 hectare), multi-owner landscape of the Oregon Coast Range, USA. The results of this study can be used in the development and evaluation of conservation strategies focused on forest management. Background and Objectives: Human management practices reflect policy and economic decisions and shape forest structure through direct management and modification of disturbance regimes. Previous studies have found that land ownership affects forest cover, patch dynamics, structure, and ecosystem function and services. However, prior assessments of forest structure across landscapes and ownerships have been limited by a lack of high-fidelity forest structure measurements across a large spatial extent. We addressed three research questions: (1) What distinct classes of forest structure exist across our study area? (2) How does the distribution and pattern of forest structure vary among types of owners at scales of patches, ownership types, and subregion, and is this independent of property size? and (3) What implications do the fine and sub-regional scale patterns have for landscape configuration goals under recent updates to the Northwest Forest Plan? Materials and Methods: We examined forest structure patterns by identifying six statistically distinct classes of forest structure and then examining their distribution across and within ownership types. We used these structure classes to examine their area within each ownership class, mean patch size, and intermixing at multiple scales. Results and Conclusion: We found that the six different forest structure classes in the study area can be interpreted as two assemblages: production-style forests, principally on private lands, and structurally complex forests, principally on public lands. We found that land ownership objectives resulted in distinct landscape patterns of forest structure as measured by mean structure class patch size and intermixing of different structure class patches. Finally, we found that forest structure differed between public and private lands but differed comparatively little among ownership types within those two broad categories.
Highlights
The physical structure of forests results from their environmental setting, history of disturbances, and human management practices [1,2,3,4,5,6]
We identified forest structure classes, which use the combination of the four selected lidar metrics to rank forests by structural complexity
Median values and interquartile ranges for three of the four input lidar metrics—P95, P25, and rumple—increased approximately linearly from the structurally simple to structurally complex classes, while canopy cover reached its maximum in the Pre-Harvest structure class (3)
Summary
The physical structure of forests results from their environmental setting, history of disturbances, and human management practices [1,2,3,4,5,6]. Forest structure is quantifiable and can be manipulated, allowing it to be the focus of forest policy and management actions [12]. Human management impacts the types and patterns of forest structure through direct management (e.g., harvest), modification of disturbance regimes (e.g., wildfire suppression), and increasingly, climate change [15,16,17,18,19,20]. The resulting combination of landowner objectives, management practices, and the constraints placed upon them creates a spatial pattern of forest structure dependent on anthropogenic decision-making and actions
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