Evaluation of a post-fire tree mortality model for western USA conifers

Abstract

Abstract. Accurately predicting fire-caused mortality is essential to developing prescribed fire burn plans and post-fire salvage marking guidelines. The mortality model included in the commonly used USA fire behaviour and effects models, the First Order Fire Effects Model (FOFEM), BehavePlus, and the Fire and Fuels Extension to the Forest Vegetation Simulator (FFE-FVS), has not been tested with independently collected post-fire tree mortality data. The model predicts mortality for a wide range of conifer species based on crown scorch and species-specific bark thickness. We evaluated the mortality model on 13 western USA conifers: subalpine fir, red fir, white fir, Douglas-fir, incense cedar, western larch, western hemlock, Engelmann spruce, whitebark pine, lodgepole pine, ponderosa pine, Jeffrey pine, and sugar pine. Predicted stand-level mortality was within ±20% of observed mortality for all species except incense cedar, western larch, red fir, and western hemlock. Individual tree mortality prediction was most accurate for subalpine fir, incense cedar, ponderosa pine, and Jeffrey pine. Evaluation of the model provides managers with an accuracy assessment for estimating the probability of mortality for the majority of western USA conifers when using the mortality model to make land management decisions. A

Fire intensity impacts on post-fire temperate coniferous forest net primary productivity

“…NPP loss at two years post-fire (~19-152 g C m -2 yr -1 ) in forests dominated by fire-resistant species is comparable to two-year post-fire aboveground NPP differences between unburned and burned temperate Pinus ponderosa forest stands (~83-148 g C m -2 yr -1 ), estimated using field measurements (Irvine et al, 2007 There was considerable variability in the dose-response relationships within each fire resistance grouping, which could potentially be attributed to differences in stand structure and age as well as differing proportions of burned and unburned area within each NPP pixel (mixed pixels). Previous studies have indicated that smaller trees are more susceptible to fire-induced mortality than larger trees (Hood et al, 2007). Additionally, there is evidence that similar FRP doses can lead to widely different growth responses depending on tree age Sparks et al, 2017).…”

Building Fuel Models and Simulating Their Surface Fire Behavior in the “Serra De Itabaiana” National Park, Sergipe, Brazil

“…Vollmer (2005) and Mohr et al (2004), for example, use to verify the efficiency of fuel reduction treatments, such as prescribed burning, mechanical and chemical treatment; Hood et al (2007) used the module "Mortality" to determine the after-fire mortality of 13 species of coniferous trees in the states of Arizona, California, Idaho, Montana and Wyoming; Curt and Delcros (2007) used the module "Ignite" and "Surface" to simulate the fire ignition and initial propagation in road-forest interfaces, focusing on byways and state ways around Aix-en-Provence (southern France); and Dimitrakopoulos (2002) used to delineate the potential fire behavior from Mediterranean fuel models in Greece. With the inputs properly inserted into the BehavePlus all these applications can be done for any vegetation type anywhere in the world, however, the use of this software or even efficient strategies in order to extinguish a forest fire in progress inside Brazilian conservation lands is not common.…”

Unexpected redwood mortality from synergies between wildfire and an emerging infectious disease

“…We compared the fit and predictive power of the models using AIC and the area under the receiver-operating curve (ROC) for all trees and separately for infested and uninfested stands. These models all predicted one-year mortality although the traditional equations (Hood et al 2007) are intended to predict three-year mortality. Importantly, the observed one-year mortality met or exceeded the three-year mortality predictions, providing a conservative assessment of potential synergies (i.e., three-year mortality would be even greater than one-year mortality).…”

“…We only modeled the survival of the largest dbh live stem per tree because burn severity markers had been assessed at the tree, not stem, level. We first calculated mortality predictions using standard equations based on bark thickness and percentage of crown volume scorched (Hood et al 2007). We then fit to our data a logistic regression model of stem mortality similar to standard equations by using those same predictor variables and four other models for comparison of other factors known to affect tree mortality following fire: (1) additional fire injury indicators (soil scorch rating and height of char on tree bole [Metz et al 2011]); (2) binary pathogen absence/ presence; (3) disease impacts (categorical stage of disease invasion); and (4) topographic environment (plot slope and aspect) and stand structure (live basal area, dead basal area, coarse woody debris volume).…”