Table 2.
Statistical Results for Model Selection
| Howling | Chi Square | p |
| Comparison | ||
| Final model versus null model | 25.9 | <0.001 |
| Spontaneous Howling | Chi Square | p |
| Comparison | ||
| Final model versus null model | 15.3 | <0.001 |
| Howling | AICc | AICc II |
| Full model | 224.3 | 209.9 |
| Cortisol difference | 209.9 | |
| SI | 229.7 | 214.6 |
| Rank out | 238.9 | 225.3 |
| Spontaneous Howling | AICc | AICc II |
| Full model | 115.4 | 99.8 |
| Cortisol difference | 99.8 | |
| SI | 122.1 | 103.5 |
| Rank out | 102 | 87.9 |
Likelihood ratio tests comparing final reduced and null models for response variable: number of howls and spontaneous howls and AICc values for model selection with response variable: number of howls and spontaneous howling. Akaike’s Information Criterion corrected for small samples (AICc) can be used to select the best fitting, most parsimonious model when investigating the influence of multiple fixed explanatory factors. Values represent the AICcs of the model when the specific predictor variable has been omitted. AICcs II represent the refitting of the model when excluding the predictor variable with the lowest AICc values. See also Tables S1–S3.