I am a senior landscape ecologist with The Nature Conservancy’s, Central Science Development by Design team. I attempt to bring vigor from diverse fields such as landscape ecology, spatial statistics, remote sensing and applied mathematics to answer practical conservation questions. I hold Affiliate Assistant Professor status at University of Wyoming and have over 65 publications in peer-reviewed journals. My research is focused on spatial statistics in ecological applications, species distribution modeling, climate change, landscape genetics, Bayesian statistics, Lidar and spectral remote sensing and gradient modeling.

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With a background in quantitative ecology and spatial statistics. One of my focuses is on developing and applying new analytical methods in conservation planning. Areas of particular interest are spatial and nonparametric statistics. Needless to say, these methods tend to be a bit “out of the box” and often require specialized software and programming. The software I frequently turn to when implementing new analytical methods is R (http://cran.r-project.org/). Besides being a very powerful language in itself, R provides the flexibility to call C++ and FORTRAN code natively. The R software is an open-source, object-oriented statistical language (built on the S+ language) that has its roots in the academic community. As such, R has a large number of libraries that are submitted and vetted through; consistency checking protocols, informal academic review and formal peer-review. These libraries implement a large variety of well-known and newly-developed statistical methodologies. Specifically, there are libraries that implement spatial object handling (vector and raster), spatial and geostatistical methods and non-parametric statistics. Since R is an object oriented language, this provides the user high-level access to data. Because of this it is possible to write code to manipulate/summarize data and leverage statistical capacity not available in GIS software.

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