Table 3. Comparison of the areas in km2 estimated for the four buffalo GPS collar sets of data (n points) by the 95% and 100% isopleths for nonparametric (LoCoH) and parametric kernela methods.
Collar | n | HR | k-LoCoH | r-LoCoH | a-LoCoH | Kernel | |
parameter | (k 1) | (r 1) | (a 1) | (â) | |||
T07 | 27 | 95% | 190 | 268 | 166 | 173 | 244 |
89 | 100% | 289 (53) | 321 (3475) | 236 (32383) | 253 (44850) | ||
T13 | 25 | 95% | 96 | 114 | 89 | 95 | 142 |
72 | 100% | 238 (51) | 144 (1470) | 211 (28156) | 224 (35000) | ||
T15 | 28 | 95% | 126 | 146 | 128 | 153 | 121 |
46 | 100% | 276 (53) | 156 (1555) | 205 (35684) | 257 (72000) | ||
T16 | 16 | 95% | 56 | 46 | 55 | 55 | 84 |
75 | 100% | 118 (41) | 49 (678) | 90 (23401) | 90 (23401) |
For the k, r, and a-LoCoH methods the parameters used, as described in the text, are the heuristic values k 1, r 1, and a 1 and the MSHC value â (given in parentheses).