Landscape‐scale modeling of reference period forest conditions and fire behavior on heavily logged lands. Issue 3 (24th March 2014)

• Record Type: Journal Article
• Authors: Maxwell, R. Stockton; Taylor, Alan H.; Skinner, Carl N.; Safford, Hugh D.; Isaacs, Rachel E.; Airey, Catherine; Young, Amanda B.
• Is Part Of: Ecosphere. Volume 5:Issue 3 (2014)
• Page Start: 1
• Page End: 28
• Publication Date: 2014-03-24
• Subjects: dendroecology -- ecological modeling -- fire behavior -- forest reconstruction -- fuels -- historical ecology -- Lake Tahoe -- predictive vegetation mapping; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1890/ES13-00294.1
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract : Forest conditions prior to extensive land clearing are often used as a point‐of‐reference by ecologists and resource managers for characterizing the historical range of variability in forest conditions shaped by intact disturbance regimes. Quantitative data on forest reference conditions can be developed from forest surveys and reconstructions using dendroecology; however, these methods lack the spatial resolution needed for landscape management. In this paper, we combine predictive vegetation mapping methods with reference forest conditions inferred from early forest surveys, dendroecology, and fire simulation models to develop landscape‐scale reference conditions for forest structure, forest fuels, fire frequency, and fire behavior using the Lake Tahoe Basin, California as an example. The dendroecological reconstruction method used for the Lake Tahoe Basin forests was not sensitive to variation in decomposition rates suggesting that our method provided robust estimates of reference period forest characteristics. The cluster analysis procedure identified five forest structure types (white fir, Jeffrey pine, red fir, lodgepole pine, and subalpine) and 15 subtypes. Each forest type had a characteristic composition, density, and basal area. Our random forests approach to classifying and mapping the spatial distribution of the five dominant reference forest structure types resulted in 51.5% classification accuracy using 14 physiographic and climatic variables. The random forests model to identify subtypes within each forest group had an average percent correct classification of 47.8%. The random forests model for fire intervals explained 67% of the variance in the point fire return interval estimates from fire‐scarred trees. Estimates of reference period fuels modeled from stand structure suggested moderate fuel loads for reference forests. The predicted potential fire type for forest subtypes under extreme weather was surface fire except for red fir and the lodgepole pine subtypes with potential for crown fire. By characterizing the reference forest composition, structure, and disturbance frequency with a range of variability, managers can develop a forested landscape more resilient to changes in disturbance regime and climate. Although our approach was developed for the Lake Tahoe Basin, California, it could be applied to a wide range of forest landscapes to identify forest reference conditions.