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Title: Deriving empirical benchmarks from existing monitoring datasets for rangeland adaptive management

item STAUFFER, NELSON - New Mexico State University
item Karl, Jason
item MCCORD, SARAH - New Mexico State University
item MILLER, SCOTT - Bureau Of Land Management

Submitted to: Society for Range Management Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 12/1/2015
Publication Date: 1/30/2016
Citation: Stauffer, N., Karl, J.W., Mccord, S., Miller, S. 2016. Deriving empirical benchmarks from existing monitoring datasets for rangeland adaptive management [abstract]. 69th Annual Meeting of the Society for Range Management. January 31-February 4, 2016, Corpus Christi, TX.

Interpretive Summary:

Technical Abstract: Under adaptive management, goals and decisions for managing rangeland resources are shaped by requirements like the Bureau of Land Management’s (BLM’s) Land Health Standards, which specify desired conditions. Without formalized, quantitative benchmarks for triggering management actions, adaptive management is challenging and subjective. Traditionally, monitoring and management benchmarks have been derived from key sites or expert opinion. However, these techniques are limited by time, scale, subjectivity, and may inform only on conditions that are no longer achievable. Empirical approaches for developing benchmarks for different indicators based on a growing corpus of rangeland monitoring data may provide a useful and defensible alternative. We describe use of BLM Assessment, Inventory, and Monitoring program data from northern California to establish quantitative monitoring benchmarks for BLM’s Land Health Standards. Bare ground, canopy gap, and perennial grass cover were explored as testbed indicators. Benchmarks are necessarily tied to land potential units (e.g., ecological sites) and can be used to assign categories (e.g. “meets objectives” and “does not meet objectives”). When an indicator’s value range is broadly distributed and encompasses potential reference conditions, as with bare ground and canopy gap, simple quantiles may be sufficient for setting criteria. For indicators with narrow and skewed distributions due to disturbance or management legacies, (e.g. perennial grass cover), this technique has limited application. Comparing conditions across land potential units is simpler with categories than with numerical values. Additionally, summarizing areas of interest by the proportion of sample units meeting success criteria makes clear that, although an area may overall meet management objectives, portions may not. This approach has potential for broad application, particularly with existing and ever-growing quantitative monitoring datasets available, and complements historic approaches which remain viable options in some cases.