estimator method based on constructing the minimum convex polygon (MCP) associated with the data. The LoCoH method has a number of advantages over parametric kernel methods. In particular: As more data are added, the estimates of the home range become more accurate than for bivariate normal kernel constructions. LoCoH handles 'sharp' features such as lakes and fences much better than parametric kernel constructions. As mentioned above, the home range is a finite region without having to resort to an ad-hoc choice, such as the 95th percentile to obtain bounded region. LoCoH has a number of implementations including a now-defunct LoCoH Web Application. LoCoH was formerly known as k-NNCH, for k-nearest neighbor convex hulls. It has recently been shown that the a-LoCoH is the best of the three LoCoH methods mentioned above (see Getz et al. in the references below). T-LoCoH T-LoCoH (time local convex hull) is an enhanced version of LoCoH which incorporates time into the home range construction. Time is incorporated into the algorithm via an alternative measure of 'distance', called time scaled distance (TSD), which combines the spatial distance and temporal distance between any two points. This presumes that each point has a time stamp associated with it, as with GPS data. T-LoCoH uses TSD rather than Euclidean distance to identify each point's nearest neighbors, resulting in hulls that are localized in both space and time. Hulls are then sorted and progressively unioned into isopleths. Like LoCoH, UDs created by T-LoCoH generally do a good job modeling sharp edges in habitat such as water bodies; in addition T-LoCoH isopleths can delineate temporal partitions of space use. T-LoCoH also offers additional sorting options for hulls, allowing it to generate isopleths that differentiate internal space by both intensity of use (the conventional UD) and a variety of behavioral proxies, including directionality and time use metrics. Time scaled distance The TSD for any two locations i and j separated in time by is given by Conceptually, TSD transforms the period of time between two observations into spatial units by estimating how far the individual could have traveled during the time period if it had been moving at its maximum observed speed. This theoretical movement distance is then mapped onto a third axis of space, and distance calculated using standard Euclidean distance equations. The TSD equation also features a scaling parameter s which controls the degree to which the temporal difference scales