Abstract
Western juniper (Juniperus occidentalis Hook.) is a tree species occurring on 3.6 million ha in the northern Great Basin. This native species can be quite invasive, encroaching into sagebrush-grassland vegetation, forming woodlands, and dominating extensive landscapes. Control of encroaching juniper is often necessary and important. Efficacy of prescribed fire for western juniper control depends on many factors for which our understanding is still quite incomplete. This knowledge gap makes fire management planning for western juniper control more difficult and imprecise. Natural resource managers require a fire efficacy model that accurately predicts juniper mortality rates and is based entirely on predictors that are measurable prefire. We evaluated efficacy models using data from a fall prescribed fire conducted during 2002 in southwestern Idaho on mountain big sagebrush (Artemisia tridentata Nutt. ssp. vaseyana [Rydb.] Beetle) rangelands with early to midsuccessional juniper encroachment. A logistic regression model, which included vegetation cover type, tree height, fire type, and bare ground as predictors, accurately predicted (area under the receiver operating characteristic [ROC] curve [AUC] = 0.881 ± 0.128 standard deviation [SD]) the mortality rate for a random sample of western juniper trees marked and assessed prefire and 5 yr post fire. Trees occurring in an antelope bitterbrush (Purshia tridentata [Pursh] DC.) type, which had a heavy fuel load, were 8 times more likely to be killed by fire than trees in a mountain big sagebrush type, where loading was typically lighter. Probability of mortality decreased by 28.8% for each 1-meter increase in tree height. Trees exposed to head fire were 3 times as likely to be killed as those exposed to backing fire. Findings from this case study suggest that with just four factors which are readily quantifiable prefire, managers can accurately predict juniper mortality rate and thus make better informed decisions when planning prescribed fire treatments.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.