Species diversity indices by survey method
All analyses were completed R (R Core Team, 2019). For diversity indices, we restricted our datasets to sites that were surveyed by both ARUs and PC methods (BC: n=52 sites, Chile: n=30 sites). We then produced species accumulation curves for each method, using species incidence frequencies and the program iNEXT (Hsieh et al. 2016). For ARUs, within-day hourly measures (dawn - 5 hrs after) were sampled independently (BC: n=44-47 site-surveys/habitat/hour; Chile: n=30 site-surveys/habitat/hour) and were also pooled (BC: n=220-236 site-surveys/habitat; Chile: n=150 site-surveys/habitat) for direct comparison with point count survey data (BC: n=48-54 site-surveys/ habitat; Chile: n=30 site-surveys/habitat). Sample sizes are larger for BC because we had access to more ARUs (see above). In both BC and Chile, diversity indices were calculated for each accumulation curve at 97% sample completeness throughinterpolation/extrapolation. This allowed for a fair comparison of the performance of each method (and each time period within ARU counts) regardless of sample size/effort. We report two diversity metrics (Hill numbers): richness (q=0) and the effective number of species calculated by the exponential of the Shannon-Wiener Index (q=1), plus their 84% CI (MacGregor-Fors & Payton, 2013) (Fig. 1). Richness is presented as the count of species captured by either method. The exponential Shannon-Wiener value weights species by their frequency of occurrence and therefore the importance of species detected only once or twice by either method.
We used the ChaoRichness function in iNext to predict the asymptote of the species richness accumulation curves of each method (Chao, 1984). This value is the predicted final species richness detected by each method if effort was increased. We compare these values to our minimum species diversity in each habitat.