Climate Change

Climate Change and Plant Community Composition in National Parks of the Southwestern US: Forecasting Regional Long-term Effects to Meet Management Needs

The National Park Service faces tremendous management challenges in the future as climates alter the abundance, distribution, and interactions of plant species. Synthesis of climate and plant community composition data from Inventory and Monitoring (I&M) networks is essential to provide resource managers with important insights to contemporary climate responses and a sound basis to forecast likely future changes at species, community, and ecosystem scales.

We are conducting a regional cross-site analysis to build an empirically-driven model that forecasts how dryland plant communities in the southwestern US respond to climate change. To do this, we integrate past local and regional patterns in climate with long-term vegetation datasets (both pre I&M, and current I&M datasets) to identify plant species and functional types that increase or decrease with climate change.

Following vegetation change detection, we compile regional-scale, gridded climate data sets including PRISM, Daymet, and WorldClim data, and possibly higher resolution rain-gauge networks. We plan to implement recently developed NPS tools, including the Climate Grid Analysis Toolset Measure to assist in data acquisition and assess regional climatological trends across the study region. Topographic aspect and slope are derived from digital elevation models (DEMs). We also characterize soils and topography of the study region using the NPS soil inventory, in addition to SSURGO/STATSGO databases.

These climate and site layers are used in a GIS environment to understand and explain the patterns of climate-induced vegetation change across the study region. A multiple regression technique is used to assess how climate has affected vegetation in the context of the topography and soils that modulate plant water availability. We expect results to highlight plant species, communities, or areas of the landscape that are most vulnerable to warmer and drier conditions forecasted by climate change models.