BCI EDGE (Barro Colorado Island – Eco-Evolutionary Dynamics and Genomic Ecology) is an international research initiative aimed at understanding how ecological and evolutionary processes interact to shape the structure, dynamics, and resilience of tropical forest ecosystems. Centered on the 50-ha forest dynamics plot on Barro Colorado Island, Panama, the project integrates population genomics, long-term ecological monitoring, and environmental data to understand the ecological and evolutionary dynamics of tropical forests across scales ranging from genes to ecosystems. By combining genomic data from dozens of tropical tree species with over four decades of demographic observations, BCI EDGE seeks to uncover the mechanisms governing biodiversity maintenance, species coexistence, adaptation, and ecosystem change in one of the world’s most intensively studied forests. The project brings together a multidisciplinary team of ecologists, evolutionary biologists, and genomicists from institutions across North America and Latin America providing exceptional opportunities for postdoctoral researchers to contribute to cutting-edge science at the interface of ecology, evolution, and genomics
Postdoctoral Scholar – Community Genomics and Selection in Space and Time, Oregon State University/Smithsonian Tropical Research Institute
The postdoctoral scholar, based in Panama, will lead efforts to quantify how density-dependent ecological interactions shape genomic variation within the BCI forest community across different size classes/stages. This individual will coordinate spatially explicit genomic sampling within the 50-ha plot, integrating whole-genome resequencing data with long-term demographic records of recruitment, growth, and mortality. The postdoc will collaborate with field technicians collecting tissue and phenotypic data and will lead analyses testing for genetic signatures of selection across life stages, spatial neighborhoods, and temporal cohorts. A major focus will be linking genomic variation at candidate defense loci to patterns of survival, growth, and metabolomic traits, thereby providing a direct test of how conspecific negative density dependence acts as a selective force and shapes genetic variation within natural populations.
The successful applicant should have the following skills:
- Statistical genomics and selection inference (e.g., detecting selection across life stages, spatial structure, temporal cohorts)
- Integration of genomic data with long-term demographic datasets (e.g., ForestGEO data)
- Spatial analysis and ecological modeling (neighborhood models, density dependence, spatial statistics)
- Experience with whole-genome or population resequencing datasets
- Field coordination in complex ecological systems (linking sampling design to hypothesis testing)
To apply for this position, please send a cover letter, no more than 2 page research interest statement, a current CV, and the names and contact information of 3 profession references to andrew.jones@oregonstate.edu.