Assessing the Climate Change Vulnerability of Ecosystem Types of the Southwestern U.S.

Climate change is challenging scientists and decision-makers to understand the complexities of climate change and to predict the related effects at scales relevant to environmental policy and the management of ecosystem services. Extraordinary change in climate, and the ensuing impacts to ecosystem services, are widely anticipated for the southwestern United States. Predicting the vulnerability of Southwest ecosystems and their components has been a priority of natural resource organizations over the past decade. Supplementing vulnerability assessments in the region with geospatial inputs of high thematic and spatial detail has become vital for supporting local analyses, planning, and decisions. In this context has come the opportunity to build upon a framework of major ecosystem types of the Southwest and to assess vulnerability to climate change for each type. Herein are presented three studies that set the backdrop for vulnerability assessment, detail a novel correlative modeling procedure to predict the location and the magnitude of vulnerability to familiar vegetation patterns, and then explore applications of the resulting geospatial vulnerability surface: 1) considerations for evaluating or designing a vulnerability assessment; 2) an overview of the vegetation and climate of major ecosystem types, and 3) a climate change vulnerability assessment for all major ecosystem types of the Southwest. This work has resulted in a regionwide vulnerability surface of greater extent and higher spatial and thematic resolution than previous modeling efforts, giving local managers information on the location and degree of climate risk to vegetation resources

Indices of grassland biodiversity in the Chihuahuan Desert Ecoregion derived from remote sensing

We used a relatively simple and direct remote-sensing approach to determine biodiversity values in arid ecosystems and thus identify potential conservation sites. We developed indices based on regression models between grass, shrub, litter, exposed-soil groundcover components, and Landsat thematic mapper satellite imagery reflectance values over a reference site in the northern Chihuahuan Desert in New Mexico. This site supports low-disturbance desert grasslands that have been excluded from livestock grazing for 55 years and moderate-disturbance grasslands that have been under a continuous grazing regime for over 100 years. Greater richness and abundance of noninvasive and nonruderal plant species were associated with the low-disturbance grasslands that had lower shrub abundance, increased litter and grass cover, and lower exposed soil. Using the thematic mapper indices, we computed an additive grassland biodiversity index such that, as exposed soil and shrub values go down, litter and grass values go up, as does the biodiversity index. When the biodiversity index was applied to the reference-site landscape, grasslands previously identified for their high conservation value were detected. As a further test, we applied the indices to a site in Chihuahua, Mexico, that supports similar grasslands but for which there are few other data on condition and conservation values. The soil, grass, and shrub indices were moderately effective in describing the range of variation at the Mexico site, but the litter equation was not. Still, higher biodiversity value in terms of nonruderal plant diversity tended to correspond to higher grass cover and lower soil exposure and a higher overall biodiversity index. Some localized calibration with geologic substrate may be required along with an assessment of the temporal constraints, but generally the index shows promise for quickly and efficiently detecting desert grasslands of high biodiversity conservation value

Divergent responses of vegetation cover in Southwestern US ecosystems to dry and wet years at different elevations

In the semiarid Southwestern United States, prolonged drought conditions since the early 2000s have resulted in widespread declines of the vegetation productivity in this water-constrained ecosystem, as revealed by analyses of the Normalized Difference Vegetation Index (NDVI). However, the spatial pattern of the NDVI response to dry years is not uniform: a divergent response of NDVI to precipitation is observed between the low-lying desert and the high montane forests at elevations above 2,500 meter. Weanalyzed relationships between 15 years of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and gridded climate data (PRISM) along elevation gradients at scales from regional to local. Our elevation-explicit analysis captures the transition from water-limited to temperature-limited ecosystems, with a sign-reversal in the correlation coefficient between precipitation and NDVI observed at about 2,500-3,000m altitude. Wesuggest warmer temperatures and less snow cover associated with drier years as explanations for high elevation gains in vegetation productivity during dry years.NASA EOS-MODIS grant [NNX14AI74G]; NASA S-NPP-VIIRS [NNX14AP69A]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Team approach to data synthesis: the playbook for creating a centralized, dynamic, and sustainable ANPP database, A

The SGS-LTER research site was established in 1980 by researchers at Colorado State University as part of a network of long-term research sites within the US LTER Network, supported by the National Science Foundation. Scientists within the Natural Resource Ecology Lab, Department of Forest and Rangeland Stewardship, Department of Soil and Crop Sciences, and Biology Department at CSU, California State Fullerton, USDA Agricultural Research Service, University of Northern Colorado, and the University of Wyoming, among others, have contributed to our understanding of the structure and functions of the shortgrass steppe and other diverse ecosystems across the network while maintaining a common mission and sharing expertise, data and infrastructure.Includes bibliographical references.The Grasslands Data Integration (GDI) project has brought together ecologists, information managers and computer scientists to address the interdisciplinary challenges of integrating ANPP data from multiple sources. In this poster we present 1) the necessity to coordinate expertise and information to integrate ANPP data and metadata from five national and international grassland LTER sites, 2) the data model we designed to archive and serve the data, and 3) analysis planned for the future. This collaboration is an example of how professionals with inter-related work experience build a community of experts and a successful data product for the LTER (Baker and Millerand 2007).NSF Canopy Database Project (NSF Grants: DBI-0417311, DBI-0319309), JRN-LTER (NSF Grant: DEB-0080412), KNZ-LTER (NSF Grant: DEB-0218210), SEV-LTER (NSF Grant: DEB-0080529), and SGS-LTER (NSF Grant: DEB-0217631)