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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #357553
Title: Long-Term Research at the Jornada Basin (LTER VII)

Author
item Peters, Debra
item HANAN, NIALL - New Mexico State University
item Bestelmeyer, Brandon
item OKIN, GREGORY - University Of California
item SALA, OSVALDO - Arizona State University
item SCHOOLEY, BOB - University Of Illinois
item ARCHER, STEVEN - University Of Arizona
item BESTELMEYER, STEPHANIE - Asombro Institute For Science Education
item BRUNGARD, COLBY - New Mexico State University
item GARCIA-PICHEL, F - Arizona State University
item Herrick, Jeffrey - Jeff
item MONGER, H. CURTIS - Natural Resources Conservation Service (NRCS, USDA)
item PIETRASIAK, NICOLE - New Mexico State University
item TWEEDIE, CRAIG - University Of Texas - El Paso

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 9/5/2018
Publication Date: 10/1/2018
Citation: Peters, D.C., Hanan, N., Bestelmeyer, B.T., Okin, G., Sala, O., Schooley, B., Archer, S., Bestelmeyer, S.S., Brungard, C., Garcia-Pichel, F., Herrick, J.E., Monger, H., Pietrasiak, N., Tweedie, C. 2018. Long-Term Research at the Jornada Basin (LTER VII) [abstract]. LTER All Scientists Meeting 2018, October 1-4, 2018, Pacific Grove, California.

Interpretive Summary:

Technical Abstract: Chihuahuan Desert landscapes exemplify the ecological conditions, vulnerability, and management challenges in arid and semi-arid regions around the world. The goal of the Jornada Basin Long Term Ecological Research program (JRN LTER) is to understand and quantify the key factors and processes controlling ecosystem dynamics in Chihuahuan Desert landscapes. In LTER-VII, we explore how spatial heterogeneity of dryland ecosystems evolves over time in response to disturbance triggers, positive feedbacks, and their interactions with the eco-geomorphic template. Our recent observations indicate the need to conceptually and computationally integrate data and knowledge into a Data Science Integrated System (DSIS).