Dr. Robert S. Harbert, Ph.D.


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Dr. Robert S. Harbert, Ph.D. | public-page
Contact Info:
Rob Harbert, rharbert@stonehill.edu
Stonehill College
Easton, MA
(540)354-8104 (cell)
ORCID iD iconorcid.org/0000-0002-1714-5033

Biosketch: Spatial Biodiversity Informatics – Data mining for the past, present, and future of ecological change on earth.

I am a new Assistant Professor at Stonehill College in Easton, MA. I teach Bioinformatics, Botany, and Intro. Biology courses. My current projects include using fossil plant community composition to generate paleoclimate estimates of Late Quaternary climate in Western North America. I am also developing a project to attempt to reconstruct plant communities from ancient DNA extracted from packrat paleomiddens.

I am broadly interested in trying to illuminate the processes determining global patterns of biodiversity. Everything from individual species distributions to the great latitudinal species diversity gradients and the distribution of functional traits are the fascinating result of biological processes and the environment.

Classes and Educational Material

BIO316 – R Programming for Biologists ( A fork of Data-Carpentry for biologists )

BIO331 – Introduction to Bioinformatics

BIO332 – – Applied Bioinformatics



Reconstruction paleoclimate from biological community data using CRACLE modeling. Recent publication details a paleoclimate reconstruction from Late Quaternary packrat midden macrofossils from western North America here . Preprint and new R package ‘cRacle’ are coming soon.

aDNA – Plant community metagenomics

Ancient DNA from plants can be preserved for thousands of years in sediments that remain cold or low-oxygen (e.g., permafrost and lake sediments). I am currently exploring the analysis of ancient DNA from very dry sources (e.g., packrat paleomiddens). More coming soon…

Species Distribution Modeling: Methods for spatial bioinformatics

Modeling of past, present, and future species distributions is essential for understanding the ecological impact of Anthropogenic climate change. The lab is working on methods to streamline and automate the distribution modeling process. Projects include: Estimation of sampling bias and density with parallel computing rasterExtras , and rapid spatial thinning and bias detection.