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.