Species range shifts and the spread of diseases are both likely to be driven by extreme movements, but are difficult to statistically model due to their rarity. We propose a statistical approach for characterizing movement kernels that incorporate landscape covariates as well as the potential for heavy-tailed distributions. We used a spliced distribution for distance travelled paired with a resource selection function to model movements biased toward preferred habitats. As an example, we used data from 704 annual elk movements around the Greater Yellowstone Ecosystem from 2001 to 2015. Yearly elk movements were both heavy-tailed and biased away from high elevations during the winter months. We then used a simulation to illustrate how these habitat effects may alter the rate of disease spread using our estimated movement kernel relative to a more traditional approach that does not include landscape covariates. Supplementary materials accompanying this paper appear online.