The postdoctoral fellow will work closely with Dr. Bradley Cosentino (Hobart & William Smith Colleges; HWS) and collaborators (Dr. James Gibbs, SUNY-ESF; Dr. Adalgisa Caccone, Yale University) on an NSF-funded project to better understand how urbanization causes phenotypic evolution. The study takes advantage of the pigmentation model system in eastern gray squirrels (Sciurus carolinensis), a species with two genetically-based color morphs: gray and melanic. We seek to clarify patterns of squirrel melanism at multiple spatial scales and understand the evolutionary processes driving those patterns.
The postdoctoral fellow will lead statistical analysis and write manuscripts to better understand spatial variation in squirrel melanism and abundance along urban-rural and latitudinal gradients in North America. We have large datasets in hand from multiple survey methodologies (e.g., point counts, trail cameras, distance sampling, incidental encounters), so experience with hierarchical models (e.g., occupancy models, N-mixture models, integrated models) and an interest in applying those models to questions about phenotypic variation and evolution is desired. Development of additional lines of inquiry by the postdoctoral fellow is anticipated. In addition to the research priorities, the postdoc will teach a January-Term or Maymester course in biology or data science at HWS. Opportunities exist to contribute to mentoring undergraduate and graduate students on the project and assist with outreach efforts via community science and engagement with K12 schools.
This is a 21-month position with benefits. The position is based at Hobart and William Smith Colleges (Geneva, NY), but applicants desiring fully remote work will be considered. This position is ideal for anyone seeking professional development for a career focused on quantitative ecology, evolutionary ecology, or conservation biology with an interest in understanding wildlife responses to global change.
*PhD in ecology, evolution, wildlife biology, or a related field.
*Demonstrated proficiency in data organization, statistical analysis, and the R programming language.
*Experience with hierarchical models with explicit models for abundance/distribution and observation error due to imperfect detection.
*Strong communication and writing skills and ability to work in a collaborative environment.
*A proven record of articulating scientific questions and publishing research in scientific journals.
Preference will be given to applicants with a strong publication record, experience in hierarchical modeling, and an interest in applying hierarchical models of abundance/distribution to questions about phenotypic variation and evolution.