Fire Rehabilitation Effectiveness: A Chronosequence Approach for the Great Basin
Dr. David A. Pyke - email@example.com, USGS FRESC – Corvallis, OR
Dr. David S. Pilliod - firstname.lastname@example.org, USGS FRESC – Boise, ID
Dr. Jeanne C. Chambers - email@example.com, USFS RMRS – Reno, NV
Dr. Matthew L. Brooks - firstname.lastname@example.org, USGS WERC – El Portal, CA
Dr. James Grace - email@example.com, USGS NWRC – Lafayette, LA
Funding provided by the Joint Fire Science Program (project no. 09-S-02-1)
Federal land management agencies, particularly the Bureau of Land Management (BLM), have invested heavily in seeding vegetation for emergency stabilization and rehabilitation (ES&R) of non-forested lands over the past 10 years (GAO 2003).
ES&R projects are implemented to reduce post-fire dominance of non-native annual grasses, such as cheatgrass (Bromus tectorum) and red brome (Bromus rubens), minimize probability of recurrent fire, and ultimately result in plant communities with desirable characteristics including resistance to invasive species and resilience or ability to recover following disturbance.
Although monitoring efforts and research investigations of ES&R effectiveness have recently been initiated, land managers currently lack scientific evidence to verify whether seeding non-forested lands achieves their desired long-term ES&R objectives. These efforts will need to be continued for many years before conclusions can be reached. In order to expedite the gathering of information on these questions, we traded space for time in a chronosequence approach that involved revisiting and evaluating post-fire seeding projects implemented from 1990 to 2003. This will enable us to provide managers with more immediate information about seeding effectiveness.
This approach provides data on both short- and long-term vegetation recovery of treated sites and allows an assessment of treatment responses over a range of ages, climate patterns (e.g., effective precipitation at time of seeding or long-term decadal climate variation), and plant associations.
Chronosequences have been used commonly in forested communities to understand successional changes following treatments or disturbances (e.g., Pyke and Zamora 1982), but to our knowledge has rarely been attempted in rangeland ecosystems. The recently completed feasibility study (JFSP 08-S-08; Knutson et al. 2009) concluded that an adequate number of well-documented post-fire seeding treatments exist to conduct a robust chronosequence analysis of ES&R effectiveness for the Intermountain West.
The Land Treatment Digital Library (LTDL) is a spatially explicit database of land treatments that has been developed for BLM Field Offices that includes information on ES&R project locations, fire characteristics, treatment methods and dates, and post-treatment monitoring. Using the
LTDL database we selected seeding treatments to examine the characteristics of burned and seeded, burned and unseeded, and unburned locations at ES&R projects for the major ecological site types (vegetation associations) and climatic regimes in the Great Basin.
Progress in 2010 and 2011
In 2010 and 2011, field crews collected vegetation and fuels data at 101 seeding projects (826 individual plot locations) throughout Oregon, Idaho, Nevada, and Utah. Data collection efforts required a total of 2 field crews of 3 people in 2010, and 5 field crews of three people in 2011, along with three supervisors. Data was entered into tablet PCs in the field or entered into databases after the field season, quality checked, and merged into one large database.
Collecting belt transect data for shrub density at a chronosequence site in Nevada, June 2011
Data analysis and manuscript preparation is currently ongoing. A poster presentation was delivered on August 6, 2012 at the 97th annual meeting of the Ecological Society of America in Portland, OR. The abstract for this presentation can be found at
We are currently working on a manuscript that will address the four main hypotheses addressed in the objectives. Additional papers will evaluate environmental features that influence seeding success at projects where data was collected. We will also develop a structural equation model of major factors to explain the observed results.