DISCUSSION
Avian surveys using ARUs can overcome major limitations experienced by
point count methods. In our study system, these include limitations
associated with remote, difficult terrain and late snowmelt as well as
the disruption of surveys due to inclement weather. Such advantages
potentially make ARUs a powerful substitute for point counts (e.g.
Darras et al., 2019). Our results here, however, indicate that ARUs
should be augmented by point counts: dual methods allowed us to identify
detection differences between methods where they were not anticipated.
In this specific case, performance differences are likely attributable
to differences in community composition between regions (as we discuss
below). More generally however, our results show how dual methods enable
monitoring programs to flag detection issues associated with survey
method and thus enhance comparisons across habitat types and ecosystems.
High mountain habitats in BC and Chile are structurally similar, yet ARU
performance was markedly better in BC than in Chile. This illustrates
that avian community composition can matter as much as habitat
composition in influencing method performance. As in Klingbeil and
Willig (2015), we believe differences in detection probability that
favour point counts in Chile are largely due to visual identification of
species rather than audio detection. Raptor diversity is higher in Chile
than BC and this largely silent group is best monitored by point counts.
ARUs missed 6 raptor species that were picked up by point counts (Table
S1). Similarly, Tyrannidae) rarely vocalize: the Xeno-canto Foundation
notes that, of all neotropical genera, ground-tyrants and shrike-tyrants
are the difficult to record. 5/9 tyrant species recorded in this study
were missed by ARUs. Changes in vocalization frequency may also drive
the seasonal variation in ARU detectability observed for 5/11 families
in Chile. Song activity likely wanes when females are incubating or when
pairs are feeding young (Moussus et al., 2009); yet, these individuals
may remain visible during point counts when foraging. Interestingly,
seasonal variation in detection probability was not supported for any
family in BC.
ARUs provide the ability to re-play audio in order to capture all calls
and confirm species identity. In contrast, point counts are more
vulnerable to observer effects: individuals at point counts may miss
species because they subconsciously screen out certain calls (“window
species”; Kepler & Scott, 1981), are overwhelmed with the number of
calling species (Celis-Murillo et al., 2009; Hutto & Stutzman, 2009),
or because they mis-identify difficult calls (Bart, 1985; Celis-Murillo
et al., 2009). This may explain why ARUs perform well in the
species-rich upper montane (Fig. 1), and why a single ARU count/site in
BC detected more species than a single point count/site, despite
observation effort being equivalent (6 min/site; Fig. 2A and Fig. 3A).
Two alternative explanations - that ARUs capture species’ peak activity
because they sample a broader period of the morning, or that ARUs fail
to screen out songs originating outside of their focal habitat and
therefore overstate species diversity - were not well supported by our
data. First, richness by hour showed no evidence of a peak in BC (Fig.
1A). Neither was there an ARU detection peak over the morning
within-families (Fig. S2). Warblers, thrushes and kinglets were all,
however, less likely to be detected by point counts later in the
morning, pointing toward observer bias in point counts (Fig. S2).
Secondly, as vocalizations tend to carry upslope, we would expect ARUs
near habitat transition zones to mis-assign species to higher elevation
habitats. Instead, ARUs in BC detected greater species diversity than
point counts in upper montane habitat, not in the subalpine or alpine
(Fig. 1A).
The ability to collect large amounts of data from ARUs is one of their
advantages and, because the collection process itself is cheap, there is
a temptation to obtain as much data as possible. However, the added time
cost per sample associated with processing ARU data, when compared to
point count surveys, needs to be carefully considered when planning
monitoring protocols. Advances in automated processing may change this
calculation (e.g. Knight et al., 2020), but additional time costs
associated with training algorithms and proofing output still need to be
considered (Joshi et al., 2017; Knight et al., 2017). Where ARUs perform
poorly, as in the mountains of southern Chile, repeated sampling does
not improve survey coverage (Fig. 2B). In other words, ARUs, like point
counts, may miss large portions of communities regardless of effort.
Programs should ascertain if this is the case before investing in
increased ARU sampling. In this study, increased effort involved
increased sampling within-day: it is possible that sampling more days,
with lower effort within-day, would yield better returns. Detections of
four nocturnal species in dawn ARU recordings highlight the benefit of
synchronous sampling across survey sites.
Our work aligns with smaller studies that conclude dual methods are
advantageous across a range of habitats (Celis-Murillo et al., 2009;
2012 (in specific cases); Tegeler et al., 2012; Alquezar & Machado,
2015; Vold et al., 2017), as well as two larger studies within temperate
and boreal forest (Holmes et al., 2014; Van Wilgenburg et al., 2017).
Our comparison across structurally similar habitats in different
geographic regions highlights the importance of the avian community, in
addition to habitat, in impacting method performance. We additionally
show that the benefit-to-time-cost ratio of dual methods that employ 1-2
point counts/site is comparable or better than single-method approaches.
Because our study system has relatively low species richness, our time
costs for ARU transcription is relatively short. Where ARU processing is
more time consuming, the benefits of employing dual methods should be
more pronounced.