A suite of statistical methods are used to study animal movement. Most of these methods treat animal trajectory data in one of three ways: as discrete pro- cesses, as continuous processes, or as point processes. We brie y review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statis- tically treat animal trajectory data. However, most resource selection analyses provide inference based on approximations of point process models. The forms of these models have been limited to a few types of specications that provide infer- ence about relative resource use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis often proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specications related to resource selection. We then provide an example of a case where a spatio-temporal point process model coincides with that implied by a mechanistic model for movement expressed as a partial dier- ential equation derived from rst principles of movement. We demonstrate that inference from this form of point process model is intuitive (and could be useful for management and conservation) by analyzing a set of telemetry data from a mountain lion in Colorado, USA, to understand the eects of spatially explicit environmental conditions on movement behavior of this species.