| Season/year | Bird ID | MCP (Ha) | Kernel density estimate | Autocorrelated kernel density estimates | ||
|---|---|---|---|---|---|---|
| 95% kernel density (ha) | 50% Kernel density (ha) | 95% kernel density (ha) | 50% Kernel density (ha) | |||
| Sept '22 | F02 | 115.7 | 113.9 | 29.8 | 116.5 | 32.1 |
| Sept '22 | F08 | 26.9 | 43.2 | 10.3 | 9.1 | 2.2 |
| Sept '22 | F10 | 81.9 | 185 | 51.2 | 380.4 | 95.3 |
| April '23 | F11 | 11.1 | 67.4 | 16.5 | 67.8 | 16.7 |
| April '23 | F13 | 7.1 | 21.3 | 3.4 | 8.2 | 2.4 |
| Sept '22 | F16 | 21.1 | 18.2 | 3.6 | 11.7 | 2.4 |
| Sept '22 | F18 | 10.9 | 18.6 | 5 | 14.9 | 4.1 |
| April '23 | F20 | 19.2 | 19.5 | 5 | 16.9 | 4.5 |
| Sept '23 | F54 | 2.9 | 15.8 | 4.4 | na | na |
| Sept '23 | F56 | 3.2 | 23.1 | 6.5 | 17.8 | 4.7 |
| Sept '23 | F58 | 16 | 60.1 | 16.1 | na | na |
| Sept '23 | F60 | 11.6 | 42.6 | 9.8 | 47.5 | 12.5 |
| Sept '23 | F66&M67 | 24.8 | 3 | 0.8 | na | na |
| Sept '22 | F75 | 56.5 | 59.7 | 15.2 | 18.1 | 4.6 |
| Sept '22 | F77 | 3.9 | 35.5 | 6.9 | nil | nil |
| Sept '22 | F78 | 15.1 | 9.8 | 2.3 | 8 | 1.8 |
| Sept '22 | F79 | 8.4 | 15.3 | 3.1 | na | na |
| Sept '22 | F81 | 60.9 | 52.6 | 8 | 27.8 | 4.3 |
| April '23 | M01 | 12.7 | 36.1 | 10 | na | na |
| April '23 | M02 | 6.5 | 20.8 | 6.3 | na | na |
| April '23 | M03 | 34.3 | 125.8 | 29 | na | na |
| April '23 | M07 | 34.3 | 166.7 | 36.7 | na | na |
| April '23 | M08 | 35.9 | 164.2 | 42 | na | na |
| Sept '23 | M53 | 8.9 | 19.3 | 4.5 | na | na |
| Sept '23 | M57 | 34.5 | 71.7 | 12.2 | na | na |
| Sept '23 | M59 | 5.73 | 14.4 | 3.2 | na | na |
| Sept '23 | M65 | 8.3 | 25.3 | 6.2 | na | na |
| Average | 25.1 (SD = 19.8) | 53.6 (SD = 51.5) | 12.9 (SD = 13.1) | 58.6 (SD = 74.5) | 14.4 (SD = 18.7) | |
Note: As a comparison, Continuous Time Movement Models (CTMM) were also applied to estimate autocorrelated kernel density estimates (AKDEs). Variograms were fit for each individual and used to guide model selection. The best‐fitting models were then used to compute AKDEs using the ctmm package in R (Fleming and Calabrese 2022). In cases where sufficient location data were available, AKDE values aligned closely with 95% KDE estimates, supporting the robustness of the KDE‐based approach. AKDEs could not be computed for males due to limited datasets. Given data collection was not optimised for full home‐range delineation, MCPs were retained as the primary metric for defining used habitat. However, the CTMM results did highlight the potential for broader space use beyond observed locations, which helped guide the placement of paired ‘available’ habitat surveys outside likely use areas.