|YAO, JIN - New Mexico State University|
|DUNIWAY, MICHAEL - Us Geological Survey (USGS)|
|HUANG, HAITAO - New Mexico State University|
|PETERS, STACEY - New Mexico State University|
Submitted to: American Geophysical Union
Publication Type: Abstract Only
Publication Acceptance Date: 10/15/2012
Publication Date: 12/3/2012
Citation: Peters, D.C., Yao, J., Browning, D.M., Duniway, M., Huang, H., Rango, A., Peters, S. 2012. Deciphering landscape complexity to predict (non)linear responses to extreme climatic events [abstract]. 2012 Fall Meeting, American Geophysical Union. December 3-7, 2012, San Francisco, CA. Paper No. H54D-04.
Technical Abstract: Extreme events are increasing in frequency and magnitude for many landscapes globally. Ecologically, most of the focus on extreme climatic events has been on effects of either short-term pulses (floods, freezes) or long-term drought. Multi-year increases in precipitation are also occurring with little known consequences, in particular for landscapes with high spatial variation in soils, geomorphology, and vegetation. We used long-term data from the Chihuahuan Desert in North America to compare ecosystem responses to precipitation in dry, average, and wet periods for a landscape consisting of five major ecosystem types. These types are dominated by one of two life forms (grasses or shrubs) that are located on different soils and topographic positions. We then used a process-based simulation model integrated with additional long-term data to identify the processes driving dynamics in each climatic period for different landscape locations. For some ecosystem types, ecological responses in average and dry periods could be linearly extrapolated to predict responses in an extended wet period, and the same set of processes occurred regardless of climatic period. However, for much of the landscape, a different set of biotic-abiotic processes and their feedbacks became operative in a wet period. This sequence of processes led to nonlinear dynamics through time. Our results show that dynamics of complex landscapes can be deciphered given information and long-term data on linked biotic-abiotic processes across spatial and temporal scales. Understanding the role of sequential processes and landscape complexity in ecosystem responses to multi-year climatic patterns is needed to improve predictions of landscape scale responses to climate.