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Research Interests

  • Movement ecology
  • Statistical methods development
  • Theoretical ecology


  • 2 Postdoctorates at Helmholtz Centre for Environmental Research, Leipzig, Germany
  • PhD, Behavior, Ecology, Evolution, and Systematics, University of Maryland
  • BS, Biology, University of Wisconsin


Dr. 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 BS in biology from University of Wisconsin-Parkside where he focused on molecular methods. He 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. Dr. Calabrese held two postdoctoral positions in mathematical ecology at the German Center for Environmental Research (UFZ) with Ralf Seppelt (2005-2007) and Volker Grimm (2007-2010). Since 2010 Calabrese has headed the quantitative ecology lab at SCBI.

Calabrese’s work is driven by a belief that we must make the most of available data. He achieves this by combining theoretical models and field data via custom-designed statistical methods to answer pressing questions in ecology and conservation biology. Calabrese’s core focus at SCBI has been building a modern analytical platform for animal movement analysis, and making these techniques accessible to ecologists and conservation biologists. His work in movement ecology, in collaboration with postdocs Christen Fleming, Michael Noonan, and GuillaumePéron, has led to the development of a robust set of analytical methods for tracking data that are based on continuous-time stochastic processes. These methods are made accessible to a broad user audience via the ctmm R package, and the graphical web app cttmweb.

Selected Publications:

Noonan MJ, Tucker M, Fleming CH, …(53 other authors)…, Mueller T, and Calabrese JM. Accepted. A comprehensive analysis of autocorrelation and bias in home range estimation. Ecological Monographs.

Calabrese JM, Fleming CH, Fagan WF, Rimmler M, Kaczensky P, Bewick S, Leimgruber P, and Mueller T. 2018. Disentangling social interactions and environmental drivers in multi-individual wildlife tracking data. Philosophical Transactions of the Royal Society of London B, 373 (1746): 20170007.

Calabrese JM, Fleming CH, and Gurarie E. 2016. ctmm: An Rpackage for analyzing animal relocation data as a continuous-time stochastic process. Methods in Ecology and Evolution, 1124-1132.

Fleming CH, Fagan WF, Mueller T, Olson KA, Leimgruber P, and Calabrese JM. 2015. Rigorous home range estimation with movement data: a new autocorrelated kernel density estimator. Ecology, 1182-1188.

Fleming* CH, Calabrese* JM, Mueller T, Olson KA, Leimgruber P, and Fagan WF. 2014. From fine-scale foraging to home ranges: A semivariance approach to identifying movement modes across spatiotemporal scales. American Naturalist, E154-E167.  *Co-first authors

Courses Taught:

  • CONS 625/MCCS 0501: Statistics for Ecology and Conservation Biology
  • MCCS 0517: AniMove: Animal Movement Analysis for Conservation

See his SCBI profile

Justin Calabrese