Development and assessment of 30-meter pine density maps for landscape-level modeling of mountain pine beetle dynamics

Abstract

Forecasting spatial patterns of mountain pine beetle (MPB) population success requires spatially explicit information on host pine distribution. We developed a means of producing spatially explicit datasets of pine density at 30-m resolution using existing geospatial datasets of vegetation composition and structure. Because our ultimate goal is to model MPB population success, three study areas in the western United States that have experienced recent MPB outbreaks were used for evaluation. Pine density estimates for each study area were compared to measures of cumulative MPB-caused pine mortality summarized from annual Aerial Detection Surveys (ADS). ADS data provide spatial and temporal representations of MPB-caused pine mortality collected by observers in fixed wing aircraft and are the most readily available estimates of landscape-scale impacts of MPB. Regression analyses using LANDFIRE ecological systems classifications (EVTs) as units of analysis showed that the best pine density estimates explained 75 to 98% of cumulative MPB-caused tree mortality. LANDFIRE EVTs, which provide an index of the plant communities growing in a particular 30-m cell, effectively delineate distinct vegetation types that are meaningful suitability indicators for MPB-caused tree mortality. Our analyses suggested that available geospatial vegetation datasets derived from field data and remotely sensed imagery are useful for producing spatially explicit measures of pine density for use in landscape-level modeling of MPB dynamics.

Differential dispersal and the Allee effect create power‐law behaviour: Distribution of spot infestations during mountain pine beetle outbreaks

“…Conifers include lodgepole and limber pines ( P. flexilis ), Engelmann spruce, subalpine fir and Douglas fir, and average pine host densities are 780 trees/ha across the area. Significant MPB impact began in the early 2000s and peaked in 2007 (Crabb et al., ).…”

“…The Chelan study area boundary was chosen to encompass pine vegetation susceptible to MPB infestation and active MPB patches based on ground surveys. MPB impact began in the late 1990s and peaked in 2008(Crabb, Powell, & Bentz, 2012).…”

The LANDFIRE Refresh Strategy: Updating the National Dataset

“…Studies have used potential vegetation and fire regime layers from LAND-FIRE to compare with local map data (Provencher et al 2009), dendrochronological analyses (Swetnam and Brown 2010), and historical fire occurrence records to evaluate current fire regimes (Reid and Fuhlendorf 2011). LANDFIRE data have also been used to quantify extent of rangelands across the coterminous US (Reeves and Mitchell 2011), simulate fire regimes in China through relationships between LANDFIRE fire regime data and climate variables (Krawchuk and Moritz 2009), infer pine densities to model mountain pine beetle dynamics (Crabb et al 2012), and model biological carbon sequestration capacity (Sundquist et al 2009 (Calkin et al 2011), the West Wide Wildfire Risk Assessment (www.westwideriskassessment.com), and the US Geological Survey (USGS) LandCarbon Program (www.usgs. gov/climate_landuse/land_carbon).…”

Analytic Approximation of Invasion Wave Amplitude Predicts Severity of Insect Outbreaks

“…[29,30] estimated R, β, γ, α, and σ from data taken during a recent outbreak of MPB in the Sawtooth National Recreation Area (SNRA), Idaho. Total host density T is estimated for the SNRA in Crabb, Powell, and Bentz [13]. Estimates for K and d were determined using reference values consistent with field observation.…”