@conference {JRN54448,
	title = {Multiscale ecosystem monitoring: an application of scaling data to answer multiple ecological questions},
	booktitle = {68th Annual Society for Range Management Meeting},
	year = {2015},
	month = {02/2015},
	publisher = {Sociiety for Range Management},
	organization = {Sociiety for Range Management},
	address = {Sacramento, CA},
	abstract = {<p>Standardized monitoring data collection efforts using a probabilistic sample design, such as in the Bureau of Land Management{\textquoteright}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. Our objective is to apply multi-scale monitoring data to answer questions related to overall landscape health, post-fire grazing closures, and emergency stabilization and rehabilitation (ES\&amp;R) treatment effectiveness 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\&amp;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 plot (n=205) 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\&amp;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. We also found that uncertainty increased with increased scale and decreased sampling density. 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. This will enable land managers to efficiently and effectively collect monitoring data to inform decision making.</p>
},
	author = {Sarah McCord and D. K. James and Wilson, Dereck and Jason W. Karl and Emily Kachergis}
}
