AIM is a tool that can answer multiple questions at multiple scales, from national reporting all the way down to monitoring the effectiveness of vegetation treatments. After the 2012 Rush Fire, the BLM Eagle Lake Field Office in California wanted to look at the success of their post-fire vegetation treatments and worked in conjunction with the Applegate field office (formerly the Alturas and Surprise field offices) in Northern California to create a multi-scale monitoring effort. The Rush Fire intensification is an example of a more intensive monitoring effort for a specific question. They collected information in Emergency Stabilization and Rehabilitation (ES&R) treatment areas within the Rush Fire boundaries, as well as untreated burned areas and unburned areas, using the AIM terrestrial core indicators as well as a supplemental indicator (density).  Initial results showed that aerial seeding and drill seeding treatments did not perform significantly differently from the untreated portion of the Rush Fire (Figure 2 – see “Results and Management Implications” section). This indicates that these treatments had a neutral or slightly positive impact in restoring plant communities and decreasing site susceptibility to erosion one year post-treatment.

For a more detailed description of the project and how results were generated, please read more below. Such details are developed during the monitoring design process.

Management Objectives:

Management objectives included the following from the Rush Fire Emergency Stabilization and Rehabilitation: Environmental Assessment in 2012:

  • “To reduce soil erosion, provide watershed stability, rehabilitate wildlife habitat, improve water quality, prevent off-highway vehicle incursions in the burn area, monitor and protect newly exposed cultural sites, monitor and prevent invasive plant infestations, facilitate regeneration of endemic plant species burned in the fire, and prevent human safety hazards.”

Indicators used to assess attainment of management objectives:

The terrestrial core indicators addressed many management objectives.  

In addition, the project chose to monitor plant density. Plant density is an indicator of seedling establishment and helps determine how successful a seeding treatment is. The method for plant density is documented in Volume 2: Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems.

Application of indicators to management objectives:

Management objectives were defined in the Rush fire emergency stabilization and rehabilitation: Environmental Assessment in 2012.  Treatment effectiveness is generally evaluated after 3 years or more.

One year post-treatment, there was interest in an initial comparison of burned areas within treatments to burned areas outside of treatments as well as comparing those areas to sites outside the Rush fire boundary.  This analysis would address the question: are treatments resulting in improvements in indicators after year 1, relative to burned untreated areas?  Even though it was too soon to evaluate the monitoring objective, the comparison would also provide information about how close treated areas were to unburned conditions.

What components are needed to do the analysis?

  • Study area 
    • all BLM lands within the Rush fire boundary and intensification done within the emergency stabilization and rehabilitation (ES&R) treatments.  The highest intensity monitoring was conducted for post-fire treatment effectiveness, while lower intensity monitoring provided information about the broader Rush Fire burn area and the field office Land Use Plan areas.
  • Reporting units 
    • BLM lands within the Rush fire boundary that had undergone vegetation treatments, by treatment type
    • BLM lands within the Rush fire boundary that burned but had not undergone vegetation treatments
    • BLM lands in grazing allotments overlapping the Rush Fire boundary that did not burn
    • BLM lands within the Eagle Lake and Applegate Field Offices, by field office
  • Stratification:
    • The plots within the ES&R treatments were stratified by ES&R seeding method and seed mix which resulted in six strata.
    • No stratification was done within the Rush Fire burned areas that did not receive ES&R treatment.
    • This study did not assess the effects of treatment by ecological site due to the large number of samples required in order to successfully monitor the effectiveness of the vegetation treatments.
  • Sample point locations:
    • Monitoring locations were selected using the GRTS method which produces random, spatially balanced points across the landscape of interest.
    • Due to the multi-scale approach in this study, plots were used at multiple levels for analysis. Sampling locations were generated at the land use plan (LUP) scale and also intensified at smaller scales.
    • 67 plots from the extensive AIM network (see above bullet) fell within the Rush fire boundary that did not receive ES&R treatments.  To provide more information within the grazing allotments that intersected the Rush Fire, an additional 50 points were generated (using an inclusion of distance to existing points) using the GRTS method.  
    • 3-5 plots were selected per treatment type within the ES&R treatments.
  • Monitoring Objectives include:
    • Three years post-treatment, greater than 70% foliar cover is present for the rangeland ecological site when compared to a control area that is in similar unburned ecological condition. See “Next Steps”.

Results and Management Implications:

After one year, the efficacy of the fire restoration and rehabilitation efforts was variable. Bare ground cover is significantly greater in drill seeding 1, as compared to the other treatments. Sample plots in drill seeding 1 also had lower annual plant foliar cover compared to all other treatments, while sample plots in drill seeding 2 had lower perennial plant foliar cover than other restoration treatments. Both drill seeding treatments had the lowest soil stability values; consequently, drill seedings may have increased the susceptibility of these sites to erosion.

The unburned portions of the allotments had similar conditions to the untreated, burned portions of the Rush Fire. This may be due to other disturbances on the landscape which produces responses similar to wildfire (e.g., drought, improper livestock grazing, presence of nonnative invasive plant species). In addition, the location of restoration and rehabilitation treatments was selected based on burn intensity, absence of a native community seed bank, and ecological site potential to respond to treatments. As a result, untreated portions of the burned area may be exhibiting natural recovery from wildfire although these results are only based off of one year of data

In order to successfully address the monitoring objective above, analysis must be done after three growing seasons. Please see “Next Steps” for further information.

Next Steps:

This analysis was conducted using only one year of data to compare untreated, treated, and unburned areas after the first growing season. As the 2016 field season comes to a close, this study has three growing season’s worth of data. A second analysis should be conducted to see if there have been significant differences between burned areas outside of treatments and burned areas within treatments as well as differences between burned and unburned areas (see Monitoring Objectives). Some of this study’s long term objectives were to look at vegetation response after the third growing season which is now possible with the amount of monitoring data available. Also, the initial results showed that untreated portions of the burned area may have been exhibiting natural recovery from wildfire, so it would be interesting to see if this is still the case or if the burned areas within treatments have made more progress.

Lessons Learned:

  • These comparisons are not the result of a paired design and consequently, the influence of ecological site potential is not included in this analysis. 
  • Clearly define your monitoring objectives/management questions before you begin monitoring. This will significantly help with interpreting your data. 

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