Date of Award:


Document Type:


Degree Name:

Master of Science (MS)


Wildland Resources

Committee Chair(s)

Eric Thacker


Eric Thacker


David Dahlgren


R Douglas Ramsey


The greater sage-grouse (Centrocercus urophasianus) is being used as an umbrella species to manage for 350 plant and animal species that also depend on rangeland communities. Sage-grouse habitat assessments have been carried out using multiple methods. Standard sage-grouse methods described by Connelly et al 2003, include line intercept (LI) and Daubenmire frames (DF) measuring canopy cover. These methods were adopted broadly among sage-grouse biologist and used to develop habitat objectives for greater sage-grouse. Federal land management agencies now use the Habitat Assessment Framework (HAF). Specifically, HAF employs line-point intercept (LPI), to assess foliar cover in sage-grouse habitat. While there is evidence that the different methods are not entirely compatible in their specifics plant cover estimates, researchers who helped develop the methods used by land management agencies suggest that when determining the suitability of habitat, outcomes would be similar. To date there has been no effort to reconcile the outcomes of standard sage-grouse methods and HAF methods in the context of the sage-grouse habitat objectives framework. Of the 74 sites sampled 19 fell within the range of implication and demanded the outcomes of standard sage grouse biologist and HAF methods be reconciled. Over all 19 sites secondarily sampled 67% showed agreement in outcomes. More specifically the sites produced the same outcome 83% of sites sampled for shrub species and 60% of sites sampled for herbaceous species.

The primary commercial use of rangelands in the U.S. is livestock grazing. An economical and consistent means of predicting and visualizing cattle distributions in rangelands could help inform managers to make grazing decisions. Open Range Consulting has developed the Piosphere tool that uses abiotic GIS data to quantify and predict cattle distributions. The intent of this study is to evaluate the Piosphere tool using observed global positioning system (GPS) cow collar data. The GPS collar data was combined with the same set of abiotic GIS data that informs the Piosphere tool and was used to build a resource selection function (RSF) independently of the Piosphere tool. This RSF controls for the telemetry bias associated with collar data and produces a landscape scale analysis that was used to evaluate the Piosphere tool’s predicted distribution. Validation was performed in two ways. Firstly, calculating the proportion of cow collar locations captured within the predicted distribution of the Piosphere tool and secondly a comparison of pixel values for each landscape scale analysis across the whole study area. 96% of collar location fell within the predicted distribution of the Piosphere tool. Regressing each of the landscape analyses produced and R2 of 0.64.



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