PARENT SESSION
Poster Session # 7: Restoration, Resource Management, and Conservation.
Monday, August 4 Presentation from 5:00 PM to 6:30 PM. SITCC Exhibit Hall B.
Understanding the landscape ecology of cryptobiotic crust cover of Grand Staircase-Escalante National Monument, Utah: A map and spatial models.
Kalkhan, Mohammed*,1, Stohlgren, Thomas2, Guenther, Debbie1, Evangelista, Paul1, 1 Natural Resource Ecology Laboratory, Fort Collins, CO, USA2 The National Institute of Invasive Species Science, Fort Collins, CO, USA
ABSTRACT- Grand Staircase-Escalante National Monument (GSENM), Utah, USA, represents a complex landscape of plant diversity and covers an area of about 2 million acres. Key biological parameters can be estimated using multi-scale sampling with multi-phase design to provide unbiased estimates of vegetation and soil characteristics. We evaluated the vulnerability of various habitats to invasion by exotic plants over the entire Monument. This paper will provide examples only on cryptobiotic crust cover. A total of 367 Modified-Whittaker nested plots (0.1 ha) were established, and 19 vegetation cover types were found. For modeling large-scale and small-scale variability to predict distribution, presence, and pattern of cryptobiotic and soil characteristics, we integrated remotely sensed data, GIS, field data, and spatial statistics. These models are based on trend surface analysis and stepwise regression. We present results of trend surface models that describe the large-scale spatial variability. Models with small variance were selected. In addition, the residuals from the trend surface model were then modeled using regression classification trees (RTC). The final surfaces were obtained by combining the trend surface model with RTC. Our research program is using these new tools for forecasting the landscape-scale levels, especially the ability to predict and map the cryptobiotic crusts with an R2 value of 67%. These models and spatial maps can be used for better resource management for such a large area like the GSENM.
Key words: landscape, cryptobiotic crust cover, trend surface analysis, regression trees classifications, multi-phase, multi-scale nested designs, spatial information,mapping,modeling