Understanding how phenotypic variation is generated and maintained in natural populations is a fundamental goal in biology. We are studying the evolution of color and pattern traits in Aegean wall lizards (Podarcis erhardii), an island-dwelling lizard native to the Greek Cycladic islands, to understand how local ecological and environmental conditions influence color and pattern evolution within this species across its geographic distribution. The Aegean wall lizard is an excellent system for the study of intraspecific trait variation because we have a good approximation of the time islands were formed by rising sea-levels and because there is little gene flow between island populations. Thus, we can compare phenotypic traits among distinct yet closely-related populations, test for correlations between traits and island characteristics, and use our knowledge of sea-level rise and the evolutionary relatedness of populations to estimate the pace at which these phenotypic changes occurred.
The specific goal of this project is to quantify colors and patterns on different regions of the body (the back, sides, and belly) of lizards from 46 different island populations from full-spectrum photographs of lizards using image analysis techniques. Once lizard color and pattern data are obtained from photographs, we will compare the color and pattern of island populations and use existing environmental (e.g., light intensity, vegetation greenness, canopy cover) and ecological data (e.g., predator diversity) to identify associations between color and pattern traits and the local environment.
We are recruiting students to help with several aspects of this long-term project, including: (1) Extracting color values from photographs of lizards, (2) Quantifying the size of color patches using ImageJ or other computer image software, and (3) Data analysis of color and pattern data in R. Students will have the opportunity to gain training in evolutionary biology research, image analysis, and data analysis in R. Highly motivated students will be encouraged to use the data they help generate from image analysis for an independent project or undergraduate thesis under our mentorship. This training will help to develop research and analytical skills that are transferable to a variety of careers such as data management and analysis.
We are interested in recruiting students who are in their sophomore or junior year (or seniors who plan to return to campus for at least the Fall 2022 semester), with the hope that they will remain involved in the project throughout the 2021-22 academic year and potentially join our research team for three weeks of field work in the summer of 2022. Strong organizational skills and thorough documentation of tasks are required for this position. Some background in biology, ecology, and evolution is desirable. Previous coursework in organismal biology is encouraged. Experience with ImageJ software and R programming language are a plus but not required.