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Tools and Technology
A Comparison of Automated and Traditional
Monitoring Techniques for Marbled Murrelets
Using Passive Acoustic Sensors
ABRAHAM L. BORKER,
Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Center for Ocean Health, 100
Shaffer Road, Santa Cruz, CA 95060, USA
PORTIA HALBERT, California State Park, 303 Big Trees Park Road, Felton, CA 95018, USA
MATTHEW W. McKOWN, Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Center for Ocean Health, 100
Shaffer Road, Santa Cruz, CA 95060, USA
BERNIE R. TERSHY, Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Center for Ocean Health, 100 Shaffer
Road, Santa Cruz, CA 95060, USA
DONALD A. CROLL, Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Center for Ocean Health, 100
Shaffer Road, Santa Cruz, CA 95060, USA
ABSTRACT Autonomous sensors and automated analysis have great potential to reduce cost and increase
efﬁcacy of wildlife monitoring.By increasing sampling effort, autonomous sensors are powerful at detecting rare
and elusive speciessuch as the marbled murrelet (Brachyramphusmarmoratus). Newapproaches must be tested for
comparability to existing methodologies, so we compared the results of inland audio–visual and of automated
acoustic monitoring for marbled murrelets, conducted during the 2010 breeding season, at 7 sites in the Santa
Cruz Mountains, California, USA. We found automated acoustic surveys and analysis had fewer detections per
morning compared with audio–visual surveyors, but the rate of automated acoustic detections per morning was
positively and strongly correlated with the rate of audio–visual detections per mornings (r¼0.96, P<0.01).
Furthermore, acoustic monitoring sampled 10 times more mornings per site (x¼48) than were monitored by
human surveyors (x¼4.4) at a comparable cost. We used resampling to estimate the power to detect murrelet
presence with acoustic sensors at >80% within 8 continuous days of recordings, even at low-activity sites. Our
results suggest that autonomous sensor and automated analysis approaches could greatly increase the scale and
efﬁcacy of murrelet monitoring, allowing for more cost-effective surveying of large and remote areas of potential
habitat, as well as, improved ability to measure changes in inland activity. Further study of passive acoustic
recordings would be valuable to examine for acoustic signs of breeding phenology, and site occupancy, if acoustic
surveys are to replace the utility of audio–visual surveys. Ó2015 The Wildlife Society.
KEY WORDS bioacoustics, Brachyramphus marmoratus, California, management, marbled murrelet, methods,
monitoring, murrelets, wildlife.
Marbled murrelets (Brachyramphus marmoratus), a small
seabird (188–269 g) that nests in old-growth forest from
central California to coastal Alaska, USA, are recognized as
exceptionally difﬁcult to monitor because of low detection
rates and remote habitats (Nelson 1997). In fact, the ﬁrst
record of a marbled murrelet nest was not discovered until
1974, making it one of the last bird species in North America
to have its nest site described (Binford et al. 1975). They are
sensitive to habitat disturbance (Malt and Lank 2009), and
have been U.S. Federally listed in California, Oregon, and
Washington as “threatened” since 1992 (Department of
Interior Fish and Wildlife Service 1992). In particular, their
requirement for old-growth forest for breeding has led to
signiﬁcant conservation conﬂict with commercial timber
harvest interests (Raphael 2006). Management decisions are
currently based on a combination of coastal surveys,
watershed-scale radar surveys and inland audio–visual
surveys. Audio–visual surveys by human observers measure
site occupancy, presence or probable absence, and map
breeding distribution (Mack et al. 2003).
However, human surveys are logistically challenging and
costly. Recent comparisons of monitoring data from radar
surveys and audio–visual surveys have revealed potential
sources of detection error, including observer skill (Bigger
et al. 2006b) and murrelet visibility (Rodway and Regehr
2000). Despite advances in radar monitoring (Bigger et al.
2006a), radar units are limited to forest stands with road
access and sites with open lines of sight (Mack et al. 2003).
Although radar may be an effective tool for watershed-wide
population estimates in accessible areas (Burger 2001,
Cooper and Blaha 2002), it has not been considered a
Received: 26 November 2013; Accepted: 5 September 2015
Wildlife Society Bulletin; DOI: 10.1002/wsb.608
Borker et al. Acoustic Monitoring of Murrelets 1
substitute for the inland audio–visual surveys (Mack et al.
2003), which can be used to determine critical breeding
Here, we test the use of autonomous passive acoustic
sensors and automated acoustic analysis as an alternative to
traditional inland monitoring techniques. Autonomous
acoustic sensors are increasingly applied as a tool for
monitoring elusive wildlife, and automated acoustic
analysis helps process large data streams to provide
information at large spatial and temporal scales (Van
Parijs et al. 2009, Buxton 2010, Thompson et al. 2010).
Autonomous acoustic sensors are a potentially cost-
effective way to assess presence and relative activity levels
across large spatial scales for marbled murrelets. In
addition, they can help reduce high sampling variability,
observer bias, and costly repetitive visits to remote ﬁeld sites
We explored correlations between murrelet activity
measured using traditional inland audio–visual surveys
with indices measured using autonomous acoustic sensor
data processed using automated acoustic analysis at 7
murrelet monitoring sites in the Santa Cruz Mountains,
California. In particular, we examined the power of
autonomous acoustic monitoring and automated analysis
(hereafter, referred to as “acoustic monitoring”) to detect
murrelets and measure levels in activity, as compared with
human audio–visual surveys.
STUDY AREA AND SPECIES
We selected 7 historical inland murrelet monitoring sites in
Big Basin and Butano State Parks, Santa Cruz County,
California (D. L. Suddjian, Command Oil Spill Trustee
Council, unpublished data; Supporting Material, Fig. S1 and
Table S1). Sites were selected to encompassa range of murrelet
activity levels based on data from historical counts in the Santa
Cruz Mountains. This population, in Zone 6 of the Northwest
Forest Plan, is the southernmost extent of the marbled
murrelet range (Raphael 2006). Murrelet detection rates
during traditional surveys in Big Basin State Park have
declined 92% from 1995to 2008, from a mean of 54.5 morning
detections/site to 4 morning detections/site (D. L. Suddjian,
Command Oil Spill Trustee Council, unpublished data).
California State Parks and a private contractor conducted 31
standard inland audio–visual surveys at 7 sites between 16
June and 5 August 2010. Surveys by 5 trained observers were
conducted according to inland forest survey protocol (IFSP;
Mack et al. 2003), for 150 min beginning 30 min before
dawn. All surveys were conducted within the IFSP
monitoring window of 15 April–5 August.
At each of the 7 monitoring sites, we also deployed
a SongMeter SM2 passive acoustic recorder (Wildlife
Acoustics, Concord, MA). We secured each SongMeter
to the trunk of a tree 3–4 m off the ground within 10 m of the
human surveyor’s location. We deployed sensors in mid-June
and collected them in September; we programmed sensors to
record for 3 hours, beginning an hour before dawn. For this
study, we only analyzed recordings during the IFSP survey
period from 15 June to 5 August. We attached an omni-
directional microphone (SMX-II; sensitivity: x¼36 4 dB,
frequency response: 20 Hz–20 kHz, signal-to-noise ratio:
>62 dB) directly to the SongMeter, and recorded on a single
channel at a samplingrate of 20 kHz. This sample rate captured
the range of marbledmurrelet vocalization as wellas other birds
present in the study area.
To detect marbled murrelet “keer” calls (Nelson 1997), we
identiﬁed sounds of interest using the spectrogram cross-
correlation detection tool in the eXtensible BioAcoustic Tool
(XBAT; Figueroa 2007)—a bioacoustics analysis package for
MATLAB (The MathWorks 2010). We modiﬁed the
software to improve performance in this complicated
soundscape. Speciﬁcally, we added 1) stationary noise
reduction to remove the noise component that is uniformly
distributed across time; 2) frequency shifting to increase
detection robustness to shifts in absolute frequency; and 3) an
approach that uses the distribution of cross-correlation scores
across templates for more ﬁne-grained detection accuracy.
For search templates, we selected 5 murrelet keer calls of high
signal-to-noise ratio from the ﬁeld recordings collected for
this study. We carried out processing with spectrograms
calculated at a fast Fourier transform size of 512, Hann
window, and frame advance setting of 0.336.
After automated processing, we manually reviewed all
events identiﬁed as potential murrelet calls by the detector.
Thus, a human reviewer conﬁrmed all murrelet vocalizations
and removed all events that were incorrectly classiﬁed by the
software by viewing the spectrogram and listening to
the recording. Most misclassiﬁcations were generated by
the songs of American robins (Turdus migratorius) and songs
of Swainson’s thrush (Catharus ustulatus), with features
similar to murrelet keer calls. Finally, we grouped all murrelet
calls separated by <5 s into calling bouts to meet IFSP
guidelines (Mack et al. 2003); each calling bout is henceforth
referred to as a murrelet detection.
We compared human surveys and automated acoustic
monitoring at 2 temporal scales. At the seasonal scale, we
calculated mean rate of morning detections for all sites (n¼7)
with each method (inland audio–visual and acoustic) and
compared them using Pearson’s product-moment correlation
coefﬁcient. We calculated a 95% conﬁdence interval around
the correlation coefﬁcient by resampling 7 points with
replacement bootstrapped 1,000 times to examine the
inﬂuence of outliers. At the individual morning scale, we
compared automated and human IFSP detections during
simultaneous surveys (n¼29) using the same approach
(Supporting Material, Fig. S2). Two sample comparisons of
detection rates weredone with paired Wilcoxon signed-ranked
tests to address non-normality. All statistical comparisons and
analysis were conducted in the R programming environment
(R Development Core Team 2011). We used a P-value
threshold of 0.05 to assess signiﬁcance and a P-value of 0.10 for
trends (i.e., marginal support) for all tests.
We used a resampling exercise to estimate the power of
acoustic monitoring to detect the presence of murrelets if not
2 Wildlife Society Bulletin 9999
deploying sensorsfor the entire breeding season. We calculated
the cumulative likelihood of detecting 1 murrelet call given
successive mornings of acoustic monitoring from resampled
continuous setsof mornings with random start datesduring the
IFSP monitoring window.
We estimated costs of a hypothetical 10-year acoustic
monitoring program based on initial equipment investment
and annual staff time needed to collect and analyze
recordings, and produce a ﬁnal summary report (Supporting
Material, Table S2). We compared our estimates of cost
per site per season with the cost of previous marbled
murrelet monitoring activities conducted at Big Basin State
Park (P. Halbert, personal communication).
Between 15 June and 5 August, autonomous acoustic sensors
recorded for 338 mornings at 7 sites, tallying 2,463 murrelet
detections. Simultaneously, we conducted 29 inland audio–
visual surveys, with a minimum of 3 surveys/site, and tallied
724 detections. Both acoustic monitoring and audio–visual
surveys detected marbled murrelets at all 7 monitoring sites
(Table 1; Fig. 1).
Automated Recording and Analysis
Autonomous sensors performed well, with only one sensor
malfunctioning after 34 mornings. Other sensors recorded
from 45 to 52 continuous mornings spanning peak murrelet
activity. These sensors functioned for as long as 82 mornings,
but we removed these recordings outside the IFSP window
from our analysis. Spectrogram cross-correlation ﬂagged
19,216 sounds as potential murrelet vocalizations all of which
were reviewed by 2 human observers (34 hr of review).
Thirty-eight percent of those sounds were positively
identiﬁed as murrelets (7,218 calls). Of those, 2,463 murrelet
calls occurred >5 s after other murrelet calls and were
recorded as independent murrelet detections.
Seasonal Comparisons of Acoustic Activity and Human
At all monitoring sites the mean rate of automated acoustic
detections was less than half of the mean rate of human inland
audio–visual detections (x¼6.8 vs. 19.3 detections/morning;
1-tailed paired Wilcoxon signed-rank test, W¼2, P¼0.02;
Table 1). Because, on average, autonomous acoustic sensors
sampled 10 times more mornings than human surveyors, the
mean total number of automated acoustic detections across
sites throughout the season was >3 times higher than the total
number of human detections per site; however, nonparametric
approaches found only marginal support for a greater central
tendency (x¼351.9 vs. 103.4 detections/season, 1-tailed
paired Wilcoxon signed-rank test, W¼23.5, P¼0.06). The
mean rate (acoustic detections per morning) of acoustic activity
at each site was positively correlated with the mean rate of
human audio–visual detections per morning (Bootstrapped
Pearson’s correlation coeff. ¼0.96, 95% CI ¼0.857–0.999;
Table 1. Marbled murrelet activity detected during the 2010 breeding season by acoustic and traditional audio–visual surveys at 7 sites in the Santa Cruz
Mountains, California, USA.
Automated acoustic detections/morning Human detections/morning
Site xSD Mornings sampled xSD Mornings sampled
GMCA 11.9 10.5 51 48.6 32.4 9
GSCR 1.0 2.5 45 6.9 4.2 7
HUCK 1.3 5.6 52 0.3 0.6 3
HUND 0.1 0.3 52 2.0 2.6 3
LBUT 29.4 22.1 52 61.3 53.7 3
RDWD 3.9 6.6 52 15.3 18.9 3
SEMP 0.1 0.5 34 0.7 1.2 3
GMCA, Gazos Camp; GSCR, Girl Scout Creek; HUCK, Huckleberry; HUND, 100 Acre Woods; LBUT, Little Butano Creek; RDWD, Redwood
Meadow; SEMP, Sempevirens.
Figure 1. Seasonal activity of marbled murrelets detected by automated
acoustic sensors and analysis (black lines) and human audio–visual surveys
(gray triangles connected by lines) during the 2010 breeding season at 7
sites in the Santa Cruz Mountains, California, USA. GMCA, Gazos
Camp; GSCR, Girl Scout Creek; HUCK, Huckleberry; HUND, 100 Acre
Woods; LBUT, Little Butano Creek; RDWD, Redwood Meadow;
Borker et al. Acoustic Monitoring of Murrelets 3
Simultaneous Comparisons of Acoustic Activity and
Human Audio–Visual Surveys
audio–visual surveyors (x¼4.8 vs. 24.9 detections/
morning [1-tailed paired Wilcoxon signed-rank test,
W¼9, P<0.01]), but these indices of activity were
positively correlated (Bootstrapped Pearson’s correlation
coeff. ¼0.82, 95% CI ¼0.588–0.937).
Power Analysis for Presence or Absence
Automated acoustic monitoring exceeded a 90% mean
likelihood of detecting murrelets after 10 mornings of
acoustic monitoring at all 7 sites. In general, sites with lower
activity levels required a greater number of surveys in order to
achieve a 90% likelihood of detecting presence (Fig. 3).
The cost of monitoring 7 sites with acoustic sensors,
including staff time for analysis, and long-term data storage,
was estimated at US$7,780 (US$1,111/site) with purchasing
acoustic sensors. Assuming a 10-year monitoring program
(and a 10-year life of equipment), with equipment
investment spread across years, automated acoustic moni-
toring costs US$427/site/year. Previous contracts in
the Santa Cruz Mountains have cost approximately US
$432/survey to conduct human audio–visual surveys (P.
Halbert, personal communication). Given the norm of 3
surveys/year, the cost of site per year is approximately US
$1,296, or US$9,072 for 7 sites.
This study provides more evidence that automated sensors
are a powerful tool for wildlife monitoring, by increasing the
temporal and spatial scale of sampling and reducing biases
(Gauthreaux and Belser 2003, Porter et al. 2005, Rovero and
Figure 2. The relationship between relative activity levels of marbled
murrelets measured with 2 methods, automated acoustic monitoring, and
human audio–visual surveys during the 2010 breeding season, at 7 sites the
Santa Cruz Mountains, California, USA.
Figure 3. Likelihood of detecting marbled murrelets after successive days of automated acoustic monitoring during the 2010 breeding season, at 7 sites in the
Santa Cruz Mountains, California, USA. Shading is ranked from lowest levels of activity (light gray) to highest levels of activity (black) as measured by human
surveyors. Symbols denote different sites. GMCA, Gazos Camp; GSCR, Girl Scout Creek; HUCK, Huckleberry; HUND, 100 Acre Woods; LBUT, Little
Butano Creek; RDWD, Redwood Meadow; SEMP, Sempevirens.
4 Wildlife Society Bulletin 9999
Marshall 2009). Passive acoustic monitoring of vocal wildlife
is a scalable solution for achieving monitoring goals (Grava
et al. 2008, Blumstein et al. 2011, Borker et al. 2014), and has
proven effective with rare and elusive species (Wade et al.
2006, Thompson et al. 2010). Removal of a human observer
comes with some statistical and cost advantages, but
no microphone will match the ecological insights to be
gained from a human observer. Our results indicate that
compared with traditional surveys, automated acoustic
sensors detected fewer murrelets during each morning, but
greatly expand the number of mornings that can be sampled
and the total number of murrelet detections at the seasonal
scale for a comparable cost. At a broad scale, detecting the
presence of marbled murrelets is important to prioritize areas
for occupancy surveys and potential management actions.
Both traditional and acoustic methods succeeded in
detecting murrelets at all sites in the study. Given the
relatively high cost of human surveys, and the ineffectiveness
of a single survey to suggest probable absence, automated
acoustic recording seems a promising technique to survey
large remote areas for murrelets.
Acoustic monitoring had an 80% likelihood of detecting
murrelet presence by 8 mornings, even at sites with low levels
of murrelet activity. These results suggest that sensors could
be moved across sites throughout a season to survey multiple
sites for detecting presence, providing potential cost savings.
The inland forest survey protocol has set a standard of 5
audio–visual surveys to determine murrelet presence,
costing an estimated US$1,400–2,160. Comparatively, near-
continuous acoustic sampling of a site throughout the entire
breeding season could cost US$1,111, or less if part of a
larger or longer term monitoring program. Bigger et al.
(2006a) reported a less costly US$280/survey in northern
California, but did not include costs of data management and
Although acoustic monitoring was able to detect murrelets
at all sites, it barely managed to do so at 2 sites with <5
detections. At both those sites audio–visual surveyors only
detected murrelets on one of the 3 surveys. Given limited
budget and unlimited time, acoustic sensors may provide a
more effective tool for detecting presence. However,
ignoring expense, repeated audio–visual surveys would
provide the fastest way to assess murrelet presence at very
low activity sites, because they achieve higher rates of
detection given an equal number of mornings sampled.
Activity levels measured by human surveyors and acoustic
sensors were highly correlated. At a given sampling effort
(mornings), human observers consistently detected more
The potential utility of autonomous sensors and analysis
for detecting threatened species is promising, because staff
and ﬁnancial resources are a major limitation in all
monitoring programs. Furthermore, costly monitoring can
divert resources from important conservation actions. For
murrelets, acoustic sensors could be widely applied over
large areas of suspected breeding habitat to survey areas for
presence and probable absence. Acoustic monitoring
results could guide intensive surveys for determining
occupancy, which is currently delineated mainly by visual
cues and very rare acoustic events (Mack et al. 2003).
Future directions should include comparing acoustic
activity at occupied and unoccupied breeding sites, and
trying to detect rare acoustic signs of breeding occupancy.
Ongoing studies have used acoustic tools to measure
seabird abundance, phenology, and breeding occupancy
(Buxton and Jones 2012, Borker et al. 2014). Seasonal
patterns of acoustic activity at occupied sites should be
investigated as an indicator of breeding effort where
breeding phenology is independently measured. Finally, a
watershed-scale network of sensors might be used to
identify areas of high calling activity and gradients,
identifying potential nesting areas. Acoustic activity across
a watershed could be paired with radar monitoring to
evaluate acoustic indices of abundance.
Unlike traditional human audio–visual surveys that
produce no permanent record, acoustic sensors created
>1,000 hr of acoustic recordings that can be reanalyzed and
interpreted as new questions arrive. By allowing reanalysis of
archived data sets, these data streams eliminate inter- and
intra-observer biases. Compared with human surveys, an
entire season of acoustic recordings can be collected at
minimal cost, and sensors can be deployed in remote areas
not accessible by trained observers for multiple human or
Further study is needed to examine the relationship
between acoustic activity and breeding status, but sites with
acoustic detections could be prioritized for human surveys
that can be used to establish occupancy. This could greatly
expand the inland surveying effort for murrelets on a ﬁxed
budget. Monitoring at inland sites is important because
murrelets have sensitive breeding requirements and are
threatened by habitat loss and habitat management (Peery
et al. 2004, Raphael et al. 2013). Actions that affect breeding
habitat (including timber harvest or predator control) should
be evaluated at those sites and compared with inland controls
to identify the most effective actions for conservation and for
mitigation of impacts.
The application of passive acoustic monitoring to marbled
murrelets is promising. Despite lower detection rates during
simultaneous audio–visual surveys and acoustic recordings
(likely to be improved with new analysis tools), automated
sensors consistently detected more murrelets by sampling
>10 times more mornings than human surveyors, at less than
a ﬁfth of the cost.
We acknowledge the assistance of S. Singer, T. Kastner, D.
Suddjian, California State Parks, Klamath Wildlife Resour-
ces, C. Sullivan, R. W. Henry, and an uncertain abundance
of marbled murrelets. Additional thanks to anonymous
reviewers, C. Ribic, L. Webb, and T. Mabee for their
comments on the manuscript. Funding was provided by the
Packard Foundation Marine Birds Program. D. Croll, B.
Tershy own shares in, and M. McKown owns shares in and
works for Conservation Metrics Inc., a company that
provides acoustic wildlife monitoring services.
Borker et al. Acoustic Monitoring of Murrelets 5
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Additional supporting information (survey site information,
cost estimates, and graphical comparison of methods during
simultaneous surveys) may be found in the online version of
this article at the publisher’s web-site.
Figure S1. Map of marbled murrelet monitoring stations
(both automated acoustic and human surveys) used in this
study within the Santa Cruz Mountains, California, USA.
Figure S2. Relationship of automated acoustic and human
detections of marbledmurrelets on the same 29 mornings across
7 sites the Santa Cruz Mountains, USA. Bootstrapped Pearson’s
correlation coefﬁcient ¼0.82, 95% CI ¼0.588–0.937.
Table S1. Locations and full names of marbled murrelet
monitoring sites used in this study within the Santa Cruz
Mountains, California, USA.
Table S2. Cost estimates for acoustic marbled murrelet
monitoring at 7 sites in the Santa Cruz Mountains, USA, for
6 Wildlife Society Bulletin 9999