A research opportunity is currently available with the U.S. Geological Survey (USGS) at the Northwest Climate Adaptation Science Center (NW CASC) located in Corvallis, OR.

Research Project:  In partnership with NatureServe, this project will provide technical assistance and research on climate data analysis, statistical model development and testing, and human-computer interaction applications in support of developing a next-generation version of the Climate Change Vulnerability Index (CCVI). The CCVI is extensively used by state and other wildlife management partners for conducting climate change vulnerability assessments (CCVAs) for species. CCVAs provide important information for prioritizing and planning conservation management under a changing climate. However, advances in science dictate that the tool be updated and improved. This project will contribute to the development, testing, and release of a modern version of CCVI that will be web hosted, use the latest CMIP climate data, and support collaboration and data sharing. Additionally, the updated version will be made more robust with improved accounting for uncertainty and incorporation of new science on climate change vulnerability (e.g., adaptive capacity) and vulnerability assessments, along with a better understanding and presentation of the sensitivity of the CCVI algorithm to its various inputs. The project will involve:

1. Collation and critical evaluation of updated climate exposure variables and climate models (and climate model ensembles), including sources of uncertainty and appropriate scales or contexts for application.
2. Quantitative sensitivity analysis of the independent and combined effects of climate exposure variables on the CCVI algorithm and resulting vulnerability score. Of particular interest is the relationship between—and relative importance of—exposure (abiotic conditions) and adaptive capacity (i.e., biological traits) variables in their influence on the resulting score.
3. Evaluating the need for variable weighting (or down-weighting) the importance of variables in the CCVI algorithm.
4. Pilot testing the use of climate scenarios to guide selection of exposure metrics.

Project will involve close interaction with ecologists involved in refining the biological components of CCVI and engagement with natural-resource managers at federal and state agencies.

Learning Objectives: 

1. Develop or strengthen understanding of how climate exposure variables influence predictions of species vulnerability and response to climate change.
2. Develop or strengthen understanding of how human-computer interaction applications are created and pilot tested.
3. Develop understanding of technical and scientific needs of natural-resource managers to address climate-related challenges in conservation planning.

Mentor: The mentor for this opportunity is Lindsey Thurman (lthurman@usgs.gov). If you have questions about the nature of the research please contact the mentor.

Anticipated Appointment Start Date: September 2023.  Start date is flexible and will depend on a variety of factors.

Appointment Length: The appointment will initially be for one year but may be extended upon recommendation of USGS and is contingent on the availability of funds.

Level of Participation: The appointment is full-time.

Participant Stipend: The participant will receive a monthly stipend based on education and experience. The current stipend for this opportunity is $84,923 per year plus an insurance supplement estimated at $18,936.  

Citizenship Requirements: This opportunity is available to U.S. citizens only.

The qualified candidate should have received a doctoral degree in one of the relevant fields listed in the eligibility requirements section or be currently pursuing the degree with completion before December 31, 2023. Degree must have been received within the last five years.

Preferred Skills:

• Experience with principles, theory, and concepts of the climate system and climate models, including routine access, use, and interpretation of climate model output.
• Experience in advanced statistical methods, analysis of climate models, spatial analysis using R, Matlab, Python, or similar programming.
• Understanding of climate model downscaling approaches, climate projections, and sea level rise projections.
• Ability to manage, manipulate, analyze, and distribute very large climate datasets.
• Experience studying climate impacts to natural resources and communication/translation of climate concepts to non-experts.