Table 5. A priori binomial regression models exploring the effect of time as a proxy for population density and prey biomass density on the success of dispersal in known-fate subadult leopards in Phinda Private Game Reserve, South Africa, 2002–2012.
| Model | Model parameters | AICc | ΔAICc | w |
|---|---|---|---|---|
| Female success | null | 17.68 | 0 | 0.62 |
| Prey biomass | 20.13 | 2.45 | 0.18 | |
| Density | 20.52 | 2.84 | 0.15 | |
| Density + Prey biomass | 22.78 | 5.10 | 0.05 | |
| Male success | null | 18.70 | 0 | 0.49 |
| Prey biomass a | 20.87 | 2.17 | 0.17 | |
| Dispersal strategy b | 21.29 | 2.59 | 0.13 | |
| Density | 21.33 | 2.62 | 0.13 | |
| Prey biomass + Dispersal strategy | 24.50 | 5.80 | 0.03 | |
| Density + Prey biomass | 24.52 | 5.82 | 0.03 | |
| Density + Dispersal strategy | 24.75 | 6.05 | 0.02 | |
| Density + Prey biomass + Dispersal strategy | 29.20 | 10.50 | 0.00 |
For males, the effect of dispersal strategy was also tested. AICc = Akaike Information Criteria adjusted for small sample sizes; ΔAICc = (AICc)–(AICc)min; w = Akaike weight. Candidate models with ΔAICc < 2 (bold face) were selected as final models.
a Combined nyala, impala and warthog biomass density estimates derived from aerial count data in Phinda GR, 2002–2012.
b Philopatry or emigration.