Detection probability by method
For species that were detected by one method only, we assessed the
probability that this was due to a detection difference between methods
versus chance using the Fisher’s exact test on the frequency of
detection by method across all site-surveys (Fisher, 1992).
Because we were also interested in generalizable patterns of detection,
we pooled species into family groups and assessed each family’s
detection probability, by method, using the R package, ‘unmarked (Fiske
& Chandler, 2011; Table S2(A) and S2(B)). For point counts, detections
at all sites were used for modelling detection probability, including
sites that did not have ARUs installed (BC: n=129 sites; Chile: n=150
sites). The number of repeated surveys at each site ranged from 3 (point
count only sites) to 23 (sites with both ARU and point count data) (BC:
n=1065 site-surveys; Chile: 900 site-surveys). We restricted the
families modelled to those that occupied at least 15% of sites in any
of our three habitat types. Detection modeling was restricted to those
habitats where 90% of occupied sites occurred.
Because ARUs were repeatedly sampled within-day with spacing of
~ 1 hour (58 ±13 min), we expected temporal
autocorrelation between surveys within-site and incorporated this into
our models using a first-order Markov covariate (Wright et al., 2016).
Our base detection probability model was:
detection ~ wind score + hours after sunrise + hours
after sunrise2 + date + date2 +
canopy cover + canopy cover2 + temporal
autocorrelation term
And site occupancy probability was modeled as:
occupancy ~ site elevation + residuals of canopy cover
by elevation.
Canopy cover residuals were used in the occupancy model to account for
co-linearity between elevation and canopy (i.e. trees become more sparse
at higher elevations). In Chile, canopy cover values at the time of
sampling were used for modeling detection to account for leafing-out,
while maximum canopy cover at each site (reflective of habitat type) was
used for modeling occupancy.
To our base detection model, we added an effect of method (ARU vs. PC)
on detection plus interactions between method and 3 survey parameters
where effects on detection were predicted to differ between ARU and
point counts. These were: canopy cover, hours after sunrise, and date.
We tested the performance of the basic model, the basic + method model,
and the seven possible models that included combinations of ‘method x
survey condition’. In total, nine detection models were tested for each
bird family.
We selected the best model for each family based on QAIC, incorporating
ĉ for the most complex model (detection ~ basic model +
method + all three method interactions) (Burnham & Anderson, 2002,
MacKenzie et al., 2017; Mazerolle, 2017). Goodness-of-fit tests were run
for these best models and, where ĉ > 1, we inflate the CIs
accordingly. We do not present output for any family where ĉ
> 4 (suggesting lack of fit; Mazerolle, 2017) or where ĉ
< 0.3 (indicating insufficient data). We report the 84% and
95% CIs: no overlap at the 84% CI is consistent with a significant
difference (P<0.05) between methods (Payton et al., 2003)
while the 95% CI represents the 95% CI of the actual detection
probability. Further detail on detection probability modelling is
available in the Supplement.
e assessed the efficiency of single-method and mixed-method sampling
protocols as the percent of the total community detected as a function
of hours of effort. For ARUs, site visitation and sample processing cost
was assessed at 40 min/site and 9 min/sample. For point counts, these
values were 20 min/site and 7 min/sample. When protocols were mixed, we
assumed that the visitation cost was shared for ARUs and PCs, i.e. that
point counts were conducted when ARUs were deployed and/or retrieved. In
protocols that involved 3 point counts per site, the additional point
count incurred an additional visitation cost (20 min/site). We randomly
sampled ARU and point count surveys with replacement (10,000X) at each
survey site to produce a bootstrapped mean species richness detected
(±SE) across all sites for different sampling intensities of: ARUs alone
(1-15 counts/site), point counts alone (1-3 counts/site), and point
count plus ARU surveys (1 point count plus 1-15 ARU counts/site, 2 point
counts plus 1-15 ARU counts/site, etc.). We identify the “best”
protocols as those that detected the greatest percentage of the total
community for the least effort.