|MCCORD, SARAH - New Mexico State University|
|KACHERGIS, EMILY - Bureau Of Land Management|
|WILSON, DERECK - Bureau Of Land Management|
Submitted to: Ecological Society of America Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 4/17/2014
Publication Date: N/A
Technical Abstract: Background/Question/Methods Standardized monitoring data collection efforts using a probabilistic sample design, such as in the Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring (AIM) Strategy, provide a core suite of ecological indicators, maximize data collection efficiency, and promote reuse of monitoring datasets to address multiple ecosystem concerns at multiple scales. While AIM data have been collected in many locations, the process and potential insights of upscaling and/or downscaling monitoring data has not yet been demonstrated with these data. Our objective was to apply multi-scale monitoring data to answer questions related to overall landscape health, post-fire grazing closures, and emergency stabilization and rehabilitation (ES&R) treatment effectiveness in a 2.7 million acre study area in northern California and northwestern Nevada. To meet this multi-scale objective, we deployed the AIM strategy at three spatial scales and intensities: low-intensity landscape sampling across the study area, medium-intensity sampling of the nine livestock grazing allotments which intersected the Rush Fire of 2012 (610,345 acres), and high-intensity sampling of ES&R treatments within the fire boundary (2,558 acres). Terrestrial plots were established within a probabilistic sampling framework, and the BLM core indicators were measured at each sample point in order to meet our objectives. Sample weights were adjusted for each scale of analysis. Results/Conclusions We collected data at a total of 206 sample plots in 2013. At the landscape scale, indicator estimates varied with ecological potential, disturbance history, and land use. The sample points were then subset to answer management questions at local scales. ES&R treatment effectiveness plots (n=28) were found to have high bare ground, low foliar cover, and low perennial plant density. When we applied the data (n=106) to answer post-fire livestock grazing monitoring questions, low perennial grass cover and high bare ground indicate insufficient recovery 1 year after the fire to support extensive livestock grazing. However, the sample plot distribution was insufficient to answer management questions for all allotments studied at this scale. As we moved from fine-scale to broad-scale analysis, uncertainty increased. We anticipate additional years of sampling at additional locations will increase the power associated with the indicator estimates and decrease variance. Measuring indicators of ecosystem health describes ecosystem responses to pressures such as fire and grazing which can in turn guide land management decisions. This multi-scale monitoring approach maximizes efficiency in data collection, analysis, and reporting by acquiring the same ecological information at each scale and reusing data at each scale.