Using science management partnerships to develop landscape level indicators and assessments to measure vulnerability of Piñon-Juniper woodlands

Abstract

The process of identifying tangible actions to conserve important resources in the face of climate change is challenged because of uncertainties regarding future conditions. There is also a lack of existing methods for evaluating the effectiveness of proposed management actions for mitigating negative impacts. For piñon-juniper (PJ) ecosystems within the Southwest U.S., efforts are further hampered by a lack of information on the direction and drivers of changing population trends. PJ ecosystems, while ecologically important, have been the focus of eradication efforts due to perceptions that they are encroaching into important grasslands. However, recent impacts of drought, insect outbreaks, and fire increasingly threaten these ecosystems. It is unclear at this time where and to what extent PJ ecosystems are vulnerable to climate change and other disturbances, challenging management. To develop a method for determining the current and future status of PJ ecosystems, we engaged with local land managers to identify key indicators and drivers of PJ population change and developed a vulnerability assessment approach based on these indicators. Our goal was to use this process to create data products that would assist resource managers dealing with rapidly changing landscapes. Borrowing from other successful assessments strategies, we used Adaptation Forums to facilitate stakeholder input to vet potentially relevant indicators of PJ status and response to climate change and other stressors. Through multiple iterations, we finalized a list of indicators that could be used to quantify vulnerability of PJ ecosystems. We then developed a vulnerability assessment framework that can systematically quantify risks to PJ based on these indicators and applied this framework to two landscapes in the southwestern U.S. This process allows us to support decision making processes by generating information on future PJ status even when data are incomplete.