New feature on our Software & Data repository page: Hot off the press! Featuring code from Hannah Hoff's 2025 PNAS paper, The Apportionment of Dietary Diversity in Wildlife.

This paper presented a potentially paradigm-shifting strategy to quantify and characterize the number of unique 'diet types' that exist within a population or community. The strategy is based on a simple machine-learning algorithm and described in the Hoff et al. 2025 PNAS paper, which used the community of migratory large mammalian herbivores -- such as bison and elk -- as a prime example.

By Hannah Hoff

The most recent map-making tutorial focuses on unlocking the potential of rasters. We start by adding hillshade, which provides a clearer picture of the topography of the area by mimicking the sun's effects (illumination, shadows) on hills and mountains, which is particularly useful when mapping a place like Yellowstone National Park. However, when adding multiple layers onto a map, we suddenly end up with a number of different legends which may vary in utility and make the map look "busy." Simple functions allow us to suppress legends and guides that aren't useful, while retaining those that are. As usual, using well-documented and flexible packages like ggplot2 allow us to easily incorporate those functions, which may make downstream figure formatting more efficient. Thanks to the Kartzinel Lab for sharing ideas about useful map elements in a recent lab meeting :) 

A link to this PDF tutorial is available here. A link is also be posted on the Lab Wiki, where we are making an effort at compiling useful computing resources. Hannah has also set up a GitHub repository for these tutorials (https://github.com/hkhoff/Map-making-in-R-tutorials-), which includes the R code used in each tutorial, and the ('dummy') data that was created as an example of what sorts of data might be fed into the code; both files can be downloaded for a more interactive tutorial experience :)