Table 2.
Proximity analysis results using binary generalized linear mixed-effects models (GLMM; each model contains a random effect [intercept] for each fisher). Models within 2 AICc units of the top model are shown. 
represents the conditional R2 for general linear mixed-effects models developed by Nakagawa and Schielzeth (2013)
| Model | K | ΔAICc | AICcWt | Cum.Wt | |
|---|---|---|---|---|---|
| Year 1 – 250 m data | |||||
| Sex × Season + Locs250 | 6 | 0 | 0.39 | 0.39 | 0.24 |
| Sex + Season + Locs250 | 5 | 0.83 | 0.26 | 0.65 | 0.20 |
| Year 1 – 500 m data | |||||
| Sex × Season × Locs500 | 9 | 0 | 0.82 | 0.82 | 0.42 |
| Year 2 – 250 m data | |||||
| Season + Locs250 | 4 | 0 | 0.48 | 0.48 | 0.13 |
| Season × Locs250 | 5 | 1.53 | 0.22 | 0.7 | 0.13 |
| Sex + Season + Locs250 | 5 | 1.79 | 0.19 | 0.9 | 0.14 |
| Year 2 – 500 m data | |||||
| Season + Locs500 | 4 | 0 | 0.4 | 0.4 | 0.18 |
| Season × Locs500 | 5 | 0.15 | 0.37 | 0.77 | 0.17 |
AIC, Akaike Information Criterion; K, number of parameters; AICcWt, AICc weight; Cum.Wt, cumulative AICc weight.