Dr. Isaac Park
Dr. Isaac Park is an Assistant Project Scientist and Co-PI on an NSF grant (Phenological sensitivity to climate across space and time: harnessing the diversity of digital herbarium data to generate and to test novel predictions) who joined the Mazer lab in February 2017. Our collaborative research uses digital records of historical plant specimens to evaluate the factors influencing historical and contemporary flowering times within and among an unprecedented diversity of angiosperms across the continental U.S. (2,468 distinct taxa, each represented by >100 specimens). We have filtered >894,392 digital herbarium records available from 72 universities, museums, and other institutions throughout the U.S. to identify 563,501 specimens for which the phenological status and precise GPS coordinates were recorded.
Using this dataset, we have designed a series of novel analyses that capitalize on the unparalleled taxonomic diversity, spatial breadth, and temporal depth of this dataset to evaluate the ecological impacts of climate (and climate change) on these taxa, as well on the collective ecological properties of the floras they constitute. First, we created species-specific phenoclimate models capable of predicting annual variations in mean flowering dates among all 2,468 taxa included in this study, and we have compared the predictive power of models derived from such specimen-based data to models constructed from high-quality in situ observations of flowering time among a subset of those taxa. This work is currently under review for publication in Global Change Biology. We are also currently developing a self-contained application through which investigators, students, and members of the public can easily use the output of these models to predict shifts in the timing of a given species under projected climate conditions at their locale. Access to this software will be available through the "Software" tab of the Mazer lab website.
Isaac's broader research includes identifying the factors that influence vegetative and reproductive phenology within and among species and higher taxa across space and time, and building quantitative predictive models that can be used to forecast the effects of future climate change on the phenological activity of populations, species, and communities.