Justin Calabrese is a quantitative ecologist specializing on the interface between ecological theory, statistics, and empirical data. His work combines theoretical models and field data via custom-designed statistical methods to answer pressing questions in ecology and conservation biology. Calabrese’s core focus at Smithsonian's Conservation Biology Institute (SCBI) is building a modern analytical platform for animal movement analysis, and making these techniques accessible to ecologists and conservation biologists. He has also studies how phenology affects the dynamics of both seasonal insect populations, and of emerging vector-borne diseases including Zika virus.
Justin M. Calabrese
B.S., University of Wisconsin; Ph.D., University of Maryland
Calabrese’s work in movement ecology, particularly in collaboration with postdoctoral fellow Chris Fleming, led to the development of a robust set of analytical methods for tracking data that are based on continuous-time stochastic processes. These methods have considerable advantages relative to existing techniques, and allow accurate biological insights to be extracted from limited tracking data. For example, Autocorrelated Kernel Density Estimation allows accurate home range estimates from movement data by accounting for the fact that such data are nearly always autocorrelated. This work recently culminated in the development of the continuous-time movement modeling (ctmm) R package, which makes these tools available to a broad user audience.
Calabrese has a diverse educational background that includes stints in molecular biology and tropical ecology in addition to his core training in theoretical and mathematical ecology. He holds a Bachelor of Science in biology from University of Wisconsin-Parkside where he focused on molecular methods. He then earned a doctoral degree in ecology with Bill Fagan at University of Maryland where he focused on theoretical ecology and combining theoretical models and data. After that, he held two postdoctoral positions in mathematical ecology at the German Center for Environmental Research with Ralf Seppelt (2005-2007) and Volker Grimm (2007-2010). Calabrese has been with SCBI's Conservation Ecology Center since 2010.
Calabrese’s work is driven by a belief that we need to make the most of available data. Ecologists and conservation biologists must often make urgent decisions based on whatever information is at hand. Making the most of limited datasets requires highly sophisticated quantitative tools. While these tools are available in technical fields including physics, statistics, and computer science, they are often out of reach for practicing ecologists. Calabrese ’s work focuses bridging this knowledge gap by identifying and adapting the best methods from technical disciplines for use on ecological datasets. A hallmark of his approach is close collaboration with both scientists from highly quantitative disciplines, particularly physicists and mathematicians, and empirical ecologists.
Tucker, Marlee A., Böhning-Gaese, Katrin, Fagan, William F., Fryxell, John M., Van Moorter, Bram, Alberts, Susan C., Ali, Abdullahi H., Allen, Andrew M., Attias, Nina, Avgar, Tal, Bartlam-Brooks, Hattie, Bayarbaatar, Buuveibaatar, Belant, Jerrold L., Bertassoni, Alessandra, Beyer, Dean, Bidner, Laura, van Beest, Floris M., Blake, Stephen, Blaum, Niels, Bracis, Chloe, Brown, Danielle, de Bruyn, P. J. Nico, Cagnacci, Francesca, Calabrese, Justin M., Camilo-Alves, Constan, et al. 2018. Moving in the Anthropocene: Global reductions in terrestrial mammalian movements. Science (New York, N.Y.), 466-469. http://dx.doi.org/10.1126/science.aam9712
Fleming, Christen H. and Calabrese, Justin M. 2017. A new kernel density estimator for accurate home-range and species-range area estimation. Methods in Ecology and Evolution, 571-579. http://dx.doi.org/10.1111/2041-210X.12673
Fleming, Christen H., Sheldon, Daniel, Gurarie, Eliezer, Fagan, William F., LaPoint, Scott and Calabrese, Justin M. 2017. Kálmán filters for continuous-time movement models. Ecological Informatics, 8-21. http://dx.doi.org/10.1016/j.ecoinf.2017.04.008