Predicting postfire mortality of seven western conifers
Kevin C. Ryan, Elizabeth D. Reinhardt
Abstract
Kevin C. Ryan, Elizabeth D. Reinhardt
Abstract
We used data on 2356 trees from 43 prescribed fires in Idaho, Montana, Oregon, and Washington states to model postfire tree mortality. Data were combined for seven species of conifers to develop binary logistic regression models for predicting the probability of mortality. Probability of mortality increased with percentage of the crown killed, and decreased as bark thickness increased. Models are presented with and without species as a categorical variable. The models predicted well for trees burned in both slash fires and fires in natural fuels. The models are applicable for assessing fire-caused mortality both of individual trees and in mixed conifer stands of the Pacific Northwest.
OpenAlex reports 379 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
We used data on 2356 trees from 43 prescribed fires in Idaho, Montana, Oregon, and Washington states to model postfire tree mortality. Data were combined for seven species of conifers to develop binary logistic regression models for predicting the probability of mortality. Probability of mortality increased with percentage of the crown killed, and decreased as bark thickness increased. Models are presented with and without species as a categorical variable. The models predicted well for trees burned in both slash fires and fires in natural fuels. The models are applicable for assessing fire-caused mortality both of individual trees and in mixed conifer stands of the Pacific Northwest.
Key concepts: Forestry, Geography, Crown (dentistry), Dead tree, Logistic regression, Environmental science, Slash (logging), Bark (sound)