Submitted by Ian Wang on
At high latitudes, seasonal cycles are driven primarily by the orbital geometry of the earth and sun, whereas at lower latitudes the seasonality at a location can be more heavily influenced by the dynamics of airflow over complex topography. This difference means that seasonal cycles tend to be synchronized across broad areas at high latitudes, but could be spatially decoupled at lower latitudes. If this is true, it could have major evolutionary implications. This is because even in the tropics many organisms exhibit annual phenologies, so chance migrants between sites would more often be phenologically out of sync with recipient populations, thus less likely to successfully interbreed. This could lead to reduced gene flow, increased genetic isolation, and therefore higher speciation rates. This hypothesis, known as the asynchrony of seasons hypothesis (ASH), is an intriguing but still largely untested theory about the origin of the latitudinal diversity gradient. This study will 1.) use space-based remote sensing data on sun-induced chlorophyll fluorescence (SIF), a powerful proxy for photosynthesis, to test the hypothesis that phenological seasonality is more asynchronous at lower latitudes and in more topographically complex regions; then, data permitting, 2.) test whether seasonal asynchrony correlates with genetic distance within species and/or with speciation rates across regions, using publicly available genetic and/or phylogeographic datasets.
Each SPUR student would have the potential to be involved in all parts of the research, to varying degrees, dependent on experience and time available. Responsibilities would include some combination of: 1.) identifying, downloading, and preparing datasets to be used in pre-processing of the main data to be analyzed (e.g. global water bodies data, global land use data); 2.) helping to script pre-processing and analysis (will probably be written mostly in Julia and Python, and may be run on some combination of Google Earth Engine and Google Cloud); and 3.) identifying, filtering, downloading, and collating publicly available datasets and/or published phylogeographic datasets, for use in the second step of the above-explained analysis. Students would meet with me weekly, either in person or remotely, to review recent progress, discuss and plan next steps, and brainstorm when necessary. Students would also be required to read a curated set of scientific papers, to develop necessary background knowledge when the semester starts.
Minimally, students should be highly interested in the subject matter, motivated, organized, and capable of searching, reviewing, downloading, and collating data sets and information from published studies. Ideally, students would have solid background in statistics and quantitative analysis (or at least some background and a strong passion), experience with scientific programming (in Julia, Python, Bash, and/or R), and some working knowledge of high-performance and cloud computing.