Statistical analysis
We used the lme4 (Bates, Maechler et al. 2015) andlmerTest (Kuznetsova, Brockhoff et al. 2017) packages in R (R Core Team 2019) to test for any difference in entrance count associated with imager. We used a mixed model with Poisson likelihood to account for the nested structure of imagers within warrens and the contrast of vegetation class between distinct warren sets. Package emmeans(Lenth 2019) was used to inspect the mean entrance count under each vegetation and imager class. Additionally, we plotted difference between estimates vs average of the estimates to check for any patterning in case agreement depended on magnitude of observation as suggested by Altman and Bland (1983). To address any disagreement in terms of presence or absence of entrances detected, the three pairings of methods (visual vs Vayu, visual vs Zenmuse and Vayu vs Zenmuse) were examined by classifying entrance counts as equal to or greater than zero and forming two-way tables (Table 2).
Table 2: two-way table used to quantify agreement (proportion of warrens where the imagers agree on presence or absence of warrens), False Nil (the proportion of warrens where imager “1” detected entrances but imager “2” detected zero entrances) and False Presence (The proportion of warrens where imager “1” detected zero entrances but imager “2” detected at least 1 entrance).