Collaborative Research Projects

Collaborative Research Project Opportunities


Collaborative Project Form (Word doc, 35KB)

Proposed Paper Form (Word doc, 35KB)


Biological soil crust association with post-disturbance vegetation response


Mark Brunson, Hilary Whitcomb (, Jeff Burnham, Eugene Schupp

Study Dates:

Feb 2012 – Feb 2013

SageSTEP Study Plots:

Onaqui sagebrush, Owyhee, Hart Mountian - Gray Butte, Hart Mountain - Rock Creek

Study Design and Objectives:

Biological Soil Crusts (BSC) may provide certain environmental conditions conducive to successful post-treatment plant response. BSCs stabilize soils (Belnap 2001; Warren 2001), provide seed and seedling microsites (Harper and Marble 1988, Belnap et al. 2001) and contribute to nutrient cycling (Harper and Belnap 2001). All of these factors can create a site more conducive to regrowth after disturbance. Due to these factors we can predict that post-disturbance plant response will be enhanced where BSCs have survived the disturbance.

Any soil surface disturbance (e.g., mowing, drilling) can damage crusts. Lichens and mosses will die if burned, as has been noted in ongoing analyses of SageSTEP data. Fuels treatment therefore can be expected to reduce the role of BSCs in post-treatment recovery. However, some benefit may persist after disturbance in areas where BSC existed prior to disturbance. We propose to test for associations with both pre- and post-disturbance BSC cover. The objectives of this study are to better understand 1) how treatment type affects post-disturbance BSC cover, 2) what are the associations between pre- and post-treatment BSC cover and post-treatment plant response and 3) identify further research questions that may arise from the data. 

Response variables will include biological soil crust components and vegetation variables. Predictor variables and covariates would include, but not be limited to, the following: treatment type, year since treatment, year of treatment, geography, topography, interspace, understory, vegetation abundance, diversity, functional groups, litter, basal gap, and climatic variables. This data set is well suited for Principal Components Analysis and/or Classification Trees for preliminary data evaluation prior to traditional m/anova, m/ancova.