Megan Curtis, Utah Valley University
Academic Affairs
Capitol Reef Field Station (CRFS) is located in south-central Utah within Capitol Reef National Park (CARE). Due to its arid climate and diverse geology, many plant species found within CARE have unique adaptations that are sensitive to disturbance. Cryptobiotic crusts, which play a vital role to the health of vegetation by stabilizing soils, cycling nutrients, and reducing erosion, are extremely sensitive to disturbance and can take many years to recover after being damaged by footprints. In addition, the spread of invasive species can harm native vegetation by competition for resources. Since CRFS’s founding in 2008, it has been frequented by visitors who come to learn about CARE’s natural and cultural history. Consequently, various trails and dirt roads surrounding CRFS may be a source of human disturbance and spread invasive species. Another source of disturbance is cattle that graze in this area on their route through CARE twice each summer. The objective of this study is to characterize the vegetation surrounding CRFS and determine the present level of human and domestic impact on this vegetation. Our specific questions were; (1) Is the level of human disturbance associated with plant community structure and proximity to CRFS, (2) Which communities have the highest levels of cattle/human disturbance?, (3) How does community structure vary by vegetation type? To address these questions, two 100-m transects with differing proximities to CRFS were established in each of four vegetative communities’ Pinyon-Juniper, Big Sagebrush, Riparian, and Grassland. Species frequency, cover, and disturbance (density of tracks within a quadrat) were recorded within each transect using a nested plot frequency design -four nested subplots ranging from 0.25 4m2. To account for seasonal variance, data were collected in four sampling periods throughout growing season. To characterize community abiotic factors, slope, aspect, and soil attributes were measured for each transect. To analyze the data, we ran multivariate analysis, including Non-metric Multidimensional Scaling (NMS), and Multi-response Permutation Procedure (MRPP). The two-dimensional NMS solution explained 80.1% of variability in community structure.