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Passive acoustic monitoring reveals group ranging and territory use: A case study of wild chimpanzees (Pan troglodytes)

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Background Assessing the range and territories of wild mammals traditionally requires years of data collection and often involves directly following individuals or using tracking devices. Indirect and non-invasive methods of monitoring wildlife have therefore emerged as attractive alternatives due to their ability to collect data at large spatiotemporal scales using standardized remote sensing technologies. Here, we investigate the use of two novel passive acoustic monitoring (PAM) systems used to capture long-distance sounds produced by the same species, wild chimpanzees (Pan troglodytes), living in two different habitats: forest (Taï, Côte d’Ivoire) and savanna-woodland (Issa valley, Tanzania). ResultsUsing data collected independently at two field sites, we show that detections of chimpanzee sounds on autonomous recording devices were predicted by direct and indirect indices of chimpanzee presence. At Taï, the number of chimpanzee buttress drums detected on recording devices was positively influenced by the number of hours chimpanzees were seen ranging within a 1 km radius of a device. We observed a similar but weaker relationship within a 500 m radius. At Issa, the number of indirect chimpanzee observations positively predicted detections of chimpanzee loud calls on a recording device within a 500 m but not a 1 km radius. Moreover, using just seven months of PAM data, we could locate two known chimpanzee communities in Taï and observed monthly spatial variation in the center of activity for each group. Conclusions Our work shows PAM is a promising new tool for gathering information about the ranging behavior and habitat use of chimpanzees and can be easily adopted for other large territorial mammals, provided they produce long-distance acoustic signals that can be captured by autonomous recording devices (e.g., lions and wolves). With this study we hope to promote more interdisciplinary research in PAM to help overcome its challenges, particularly in data processing, to improve its wider application.
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R E S E A R C H Open Access
Passive acoustic monitoring reveals group
ranging and territory use: a case study of
wild chimpanzees (Pan troglodytes)
Ammie K. Kalan
1*
, Alex K. Piel
2,3
, Roger Mundry
1,4
, Roman M. Wittig
1,5
, Christophe Boesch
1,6
and Hjalmar S. Kühl
1,7
Abstract
Background: Assessing the range and territories of wild mammals traditionally requires years of data collection
and often involves directly following individuals or using tracking devices. Indirect and non-invasive methods of
monitoring wildlife have therefore emerged as attractive alternatives due to their ability to collect data at large
spatiotemporal scales using standardized remote sensing technologies. Here, we investigate the use of two novel
passive acoustic monitoring (PAM) systems used to capture long-distance sounds produced by the same species,
wild chimpanzees (Pan troglodytes), living in two different habitats: forest (Taï, Côte dIvoire) and savanna-woodland
(Issa valley, Tanzania).
Results: Using data collected independently at two field sites, we show that detections of chimpanzee sounds on
autonomous recording devices were predicted by direct and indirect indices of chimpanzee presence. At Taï, the
number of chimpanzee buttress drums detected on recording devices was positively influenced by the number of
hours chimpanzees were seen ranging within a 1 km radius of a device. We observed a similar but weaker relationship
within a 500 m radius. At Issa, the number of indirect chimpanzee observations positively predicted detections of
chimpanzee loud calls on a recording device within a 500 m but not a 1 km radius. Moreover, using just seven months
of PAM data, we could locate two known chimpanzee communities in Taï and observed monthly spatial variation in
the center of activity for each group.
Conclusions: Our work shows PAM is a promising new tool for gathering information about the ranging behavior and
habitat use of chimpanzees and can be easily adopted for other large territorial mammals, provided they produce
long-distance acoustic signals that can be captured by autonomous recording devices (e.g., lions and wolves).
With this study we hope to promote more interdisciplinary research in PAM to help overcome its challenges,
particularly in data processing, to improve its wider application.
Keywords: Animal communication, Autonomous recording unit, Bioacoustics, Buttress drumming, Loud calls,
Ranging pattern
Background
Any study of animal behavioral ecology requires basic
knowledge about home range and habitat use within the
natural environment to grasp the fundamental social and
ecological selection pressures acting on individual fitness.
Researchers have known for some time that determining
thehomerangeorterritoryofwildanimalsrequiresa
long-term investment of data collection [1, 2]. This is made
more difficult for large-ranging mammals, where following
individuals is physically demanding or impossible (e.g.,
cetaceans, bats), and invasive options such as tracking de-
vices or radio collars are expensive and may pose a risk to
wild animals [3]. Hence, alternative cost-effective methods
to non-invasively monitor and track wildlife are needed.
At present, indirect indices of animal presence, such
as feces, tracks, and nests, can provide us with evidence
of ranging and grouping behavior. For example, geno-
types can be extracted from dung and hair surveys that,
via sample association patterns, can provide data on
group structure and composition [4, 5]. Specific to great
* Correspondence: ammie_kalan@eva.mpg.de
1
Department of Primatology, Max Planck Institute for Evolutionary
Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany
Full list of author information is available at the end of the article
© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kalan et al. Frontiers in Zoology (2016) 13:34
DOI 10.1186/s12983-016-0167-8
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
apes, sleeping nest sites provide information on the size
of a group, whilst their spatial distribution and clustering
can be used to infer territories [6]. Nonetheless, indirect
monitoring methods still require years of longitudinal
data collection to ensure an adequate sampling effort
has been achieved to reflect the true size of a group or
territory [1]. Alternatively, direct observations, where in-
dividuals are followed by researchers to collect data on
ranging and behaviour, can help to minimize sampling
bias which is of concern when studies rely on indirect
sampling methods [3]. However, some wild animals live
in environments (e.g. underwater) or are active at times
(e.g. night) inherently difficult for researchers to visu-
ally monitor, whilst others have learned that people are
dangerous and actively avoid them.
To overcome such challenges, marine biologists have
long applied passive acoustic monitoring (PAM), a non-
invasive method of monitoring wildlife using sound re-
cording devices [7, 8]. The application of PAM is made
even more feasible in an aquatic environment because
calls can propagate much further in water than on land
[9]. Through the use of sonar and hydrophone arrays, re-
searchers have been monitoring the movements of ceta-
ceans for decades by tracking their natural acoustic
behavior [8]. Similarly, the use of remote audio recordings
to monitor birds [10], bats [11] and insects [12] has also
proven effective, facilitated by the high stereotypy of the
sounds produced by these animals.
More recently, researchers working in terrestrial envi-
ronments which limit visual observations, such as in dense
rainforests or with cryptic animals, have begun using
PAM. It has been successfully employed for the study of
tropical birds [13, 14] and land mammals such as forest el-
ephants [15, 16], Bornean orangutans [17], as well as
multi-species systems [18, 19], but one limiting factor in
all terrestrial applications continues to be effective auto-
mated approaches for mining the PAM data to extract
calls of interest. The high variability of many mammalian
vocalizations, and complex background noise present in
terrestrial ecosystems [20] have hindered the progress of
PAM for land mammals, including our closest living rela-
tives, the great apes. Generally speaking, research and
conservation of great apes has garnered great interest due
to their genetic and behavioral similarities to humans [21].
Yet, much of what we know about great apes comes from
a few groups that have been habituated by researchers to
tolerate the presence of human observers; however, this
requires an investment of years and is neither feasible nor
ethical for all wild populations [22, 23]. As such, non-
invasive monitoring methods, such as PAM, are becoming
increasingly useful to coordinate conservation efforts for
remaining healthy populations and to produce results in a
cost-effective and efficient way. Therefore, we investigated
the potential of PAM to provide information about the
ranging behavior of wild chimpanzees (Pan troglodytes), a
territorial and vocally conspicuous mammal.
Forest-dwelling chimpanzees are known to have territor-
ies ranging from 7 km
2
(Sonso, Uganda [24]) to 31 km
2
(East group, Taï, Côte dIvoire [6]). While we know less
about savanna-woodland dwelling chimpanzees, estimates
of their territories range from 72 km
2
(Semliki, Uganda
[25]) to 239 km
2
(Assirik, Senegal [26]). Individuals of a
single group, or chimpanzee community, spend the major-
ity of their time in the core of their home range, usually
representing about 7590 % of their total territory [27, 28].
Chimpanzees are also xenophobic, exhibiting sometimes
fatal aggression during inter-community encounters [29].
Accordingly, chimpanzees are observed to modify their
behavior in the periphery of their territory as a response to
an elevated degree of risk [28, 30]. Here, chimpanzees
engage in territorial boundary patrols during which mostly
adult males of the community coordinate their behavior to
remain silent and vigilant whilst inspecting the periphery
of their territory for possible excursions by stranger chim-
panzees [29, 31]. Because chimpanzees exhibit these
changes in their vocal behavior when in the periphery of
their territory, we wanted to test whether by remotely
monitoring their vocalizations throughout their territory
we could infer their ranging patterns. A previous PAM
study has already shown that primate calls can be used to
obtain reliable estimates of occurrence for chimpanzees
and sympatric forest monkeys [32] which, if monitored
overtime, could be used to assess population trends.
In this study, we investigated whether remote acoustic
monitoring of chimpanzees, living in both tropical forest
and in savanna-woodland habitat, could be used to obtain
information about a groups ranging behavior and thereby
be used as a tool to aid monitoring wild populations. We
focused on long-distance sounds produced by chimpan-
zees, namely drummingwith hands and feet on the but-
tress roots of trees and the pant hootvocalization [33, 34].
The pant hoot is a long, compound call composed pri-
marily of hoosand screams[35]. Both are long range
acoustic signals that can be heard at a distance of up to
1 km [35, 36]. These calls are particularly significant for
chimpanzees since they live in a fission-fusion society
in which community members travel in smaller sub-
groups, or parties. The long-distance pant hoots and
drums function to maintain contact with individuals
travelling in different parties and to coordinate group
movement [33, 37]. First, we tested whether detections
of chimpanzee drums or pant hoots on autonomous
acoustic recording devices were predicted by known
chimpanzee activity. Second, if these long distance acous-
tic signals proved to be reliable indicators of chimpanzee
activity, we further examined whether boundaries of chim-
panzee communities could be identified spatially using
PAM, based on the reasoning that chimpanzee activity is
Kalan et al. Frontiers in Zoology (2016) 13:34 Page 2 of 11
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highest in the core area of the territory [27]; therefore, vo-
calizations were expected to be more frequent in the core
[28]. Third, we asked whether PAM could be used to ob-
serve spatiotemporal changes in territory use given that
chimpanzees are expected to exploit different areas of
their home range based on fluctuations in resource avail-
ability [38, 39]. Finally, by testing the application of this
method for the same species but in two vastly different
habitats we aimed to determine the degree of usefulness
of PAM for other populations of chimpanzees as well as
other mammals with similar behavioral ecology.
Methods
Data collection
PAM data collection
In the Taï National Park, Côte dIvoire, we collected audio
recordings using 20 autonomous recording units (ARUs:
Songmeter SM2+ from Wildlife Acoustics) placed in a sys-
tematic grid covering 45 km
2
of primary evergreen forest
(Table 1). We mounted ARUs on small trees 12mfrom
the ground since drums and pant hoots are primarily pro-
duced by chimpanzees while traveling on the ground, and
therefore are expected to propagate close to the ground.
The ARUs were placed within the research area of the T
Chimpanzee Project, spanning two habituated, neighbor-
ing chimpanzee communities: South and East groups [6,
27]. At the time of the study the South group comprised
19 chimpanzees plus five dependent offspring, and the
East group totaled 23 individuals plus seven dependent
offspring. A previous study has estimated the South group
home range to be 27 km
2
and 31 km
2
for the East group
[6]. ARUs had a maximum detection distance of 1 km for
chimpanzee sounds [32]. ARUs recorded in stereo at
16 kHz sampling frequency and an amplitude resolution
of 16 bits/s. The devices were pre-programmed to record
for 30 min on the hour, from 6.00 am to 17.30 pm (6 h/
day). Periodically, some ARUs did not work due to tech-
nical problems, thus a total of 12,851 h of ARU recordings
were collected across 137 days from November 2011
to May 2012. These audio data are available on re-
quest from the IUCN/SSC A.P.E.S database (http://
apesportal.eva.mpg.de/).
In the Issa valley, Tanzania, ten custom built solar pow-
ered acoustic transmission units (SPATUs) were deployed
(Table 1) where the Ugalla Primate Project, a long-term
research project, has been consistently running since 2008
[40]. The vegetation at Issa is predominantly miombo-
woodland with interspersed grasslands, swamp, and river-
ine closed-canopy forest. The valleys are generally forested
while slopes and plateaus are primarily woodlands. As
such, the Issa valley represents one of the most open and
driest habitats in which chimpanzees live [36, 41], and this
population is currently undergoing habituation to re-
searcher presence. Via indirect genetic sampling, it has
been estimated that 67 chimpanzees comprise a single
community at Issa [42]. SPATUs recorded 24 h/day, with
5.5 kHz sampling rate and an amplitude resolution of 16
bits/s using a single channel [36]. The ten SPATUs used
in this study also had an effective 1 km detection distance
for chimpanzee pant hoots and covered a total area of
12 km
2
[36]. The units were installed 46 m above the
ground in trees to access enough solar energy, and re-
corded for 247 days from April 2009 to February 2010.
For comparability, we only included Issa recordings made
during the same hours of the day as the Taï dataset, for a
total of 29,640 h of SPATU data.
Detecting chimpanzee call events
A customized algorithm for the automated detection
and classification of chimpanzee sounds was used to ex-
tract chimpanzee drums from continuous ARU recordings
made in the Taï forest [19]. Only chimpanzee drums that
were subsequently verified to be true positive detections
were included in the final dataset, however; we also in-
cluded any detections that had been misclassified as chim-
panzee drum but were actually long range chimpanzee
vocalizations (screams or pant hoots). The maximum recall
for chimpanzee drum sounds with our algorithm was 11 %
and precision was 4 %, which was of low performance
compared to two monkey calls that were also targeted by
the system. However, overall we had results comparable to
other studies [19]. Please see Heinicke and colleagues [19]
for further details about the algorithm and its performance
metrics that were assessed in a separate study.
Alternatively, the Issa recordings were processed manu-
ally with the aid of the acoustic software TRITON [43],
where long-term spectral averages (LTSA) were computed
by the program on a daily basis, per SPATU, to produce a
single 24 h LTSA. Researchers then scanned LTSAs to
find areas with concentrated spectral energy within the
Table 1 Overview of data collected using autonomous recording devices at two chimpanzee field sites
# of recording months habitat chimpanzee population chimpanzee density
(inds/ km
2
)
# of devices study area acoustic signal
Issa valley, Tanzania
11 savanna-woodland unhabituated 0.25 10 12 km
2
pant hoot
Taï, Côte dIvoire
7 rainforest habituated 0.97 20 45 km
2
drum
Kalan et al. Frontiers in Zoology (2016) 13:34 Page 3 of 11
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frequency bandwidths in which chimpanzees usually call,
i.e., below 6 kHz. Such areas were then zoomed into using
10 min windows, and listened to, in order to verify
whether a chimpanzee call really was present in the re-
cording. Hence, there was no automated analysis of the
Issa data, nor any such validation study. It is important to
note that data collection and processing had been inde-
pendently done for both field sites before the present
study was conceived. This is why chimpanzee drums were
never documented in the Issa dataset while at Taï chim-
panzee drumming is well known [34] and was the primary
sound of interest.
In both PAM datasets we combined multiple chimpan-
zee detections into a single event when the time lag be-
tween the end and start of consecutive chimpanzee drums
or calls was less than one minute. ARUs in Taï were
intentionally spaced at a minimum distance of 1.2 km
from each other to reduce the probability of detecting the
same chimpanzee drum or pant hoot on multiple devices.
However, this still occurred in some cases; therefore we
removed multiple detections on neighboring ARUs of the
same drum or call in the PAM data by comparing the time
and date stamp of the call. Only the detection with the
earliest time stamp was kept in the dataset based on the
reasoning that whichever device recorded the chimpanzee
call event first, the source individual must have been clos-
est to this device. We did the same for Issa call events,
where devices were spaced much closer together (mini-
mum distance of 420 m) for the purposes of addressing
additional research questions [36]. For each combination
of recording day and device, we scored whether at least
one chimpanzee drum or call event had occurred (1) or
not (0), and this was done for both datasets to prepare
them for comparable statistical analyses.
There is a high likelihood that we underestimated the
true number of chimpanzee drums and pant hoots in both
datasets due to the different methods of data processing,
since obtaining the actual number could only be achieved if
we had listened to and annotated all recordings which is
not feasible for either dataset. For example, an individual
listening to 8 hours of recordings per day would need 4
years (Taï data) or 10 years (Issa data) to manually listen to
and annotate all the sounds. However, we are confident that
even with a low number of detections we can still obtain a
representative dataset of the spatiotemporal distribution of
drums and pant hoots at each site since we have no reason
to expect a bias for any particular device. To this end, we
ensured that all ARUs or SPATUs had identical specifica-
tions and were deployed in the same manner at their re-
spective field sites. Additionally, we found no bias for
ARUs in detecting drum events during a validation study
of the algorithm [19] developed for automated data ana-
lyses of the Taï recordings (permutation test of observed
versus expected drum detections per ARU: p=0.15).
Indices of chimpanzee activity
The Taï Chimpanzee Project (TCP) has been running for
more than 35 years where neighboring chimpanzee com-
munities have been habituated to the presence of re-
searchers [30]. The ARU sampling grid overlapped two
habituated chimpanzee communities, South and East
groups, and at the time of the study the South group
had been habituated since 1997 and the East group since
2007. Field assistants of the TCP regularly conducted all
day focal follows of independent individuals (>5 years of
age), collecting standardized behavioral data. The loca-
tion of the focal individual was continuously recorded
on a map with 500 m by 500 m grid cells overlaying the
Taï research project area (~100 km
2
) to track ranging
behavior and document the identity and number of ac-
companying chimpanzees in the focals party [30]. The
map was projected onto a UTM coordinate system using
ground-truthed GPS data to determine the center point
of each grid cell. We then calculated chimpanzee activity
in hours, for each grid cell of the Taï research area corre-
sponding to the South and East groupsterritories, to
obtain a direct measure of chimpanzee ranging and
space-use. This was calculated by multiplying the size of
a particular chimpanzee party by the number of minutes
it was observed in a particular grid cell and dividing this
by 60. Chimpanzee activity hours for each grid cell were
then calculated on a daily basis for the 7 months corre-
sponding to the same 7 months the ARUs were record-
ing (November 2011 to May 2012). Here, 1,818 h of
focal follow data were available for the South (884 h of
observation) and East (934 h) group, respectively, across
a total of 120 days. If no focal follow data was available
for a particular ARU recording day due to interruption
in the data collection by field assistants (occurred for
49/137 ARU recording days) then those days were ex-
cluded from the statistical analysis. Therefore, a total of
88 days remained for the analysis, where both focal follow
data of chimpanzees was available and ARUs had re-
corded, across the 7 months study period. The chimpan-
zee activity hours within a detection radius of 500 m and
1 km of each ARU, was then totaled for each day, includ-
ing only those grid cells where the detection radius over-
lapped with the center point of the grid cell.
The chimpanzees of the Issa valley were not habitu-
ated at the time of the study, and habituation had not
yet started. However, surveys are regularly conducted in
the region to permit monitoring of chimpanzee abun-
dance [40]. Here, we used an indirect measure of chim-
panzee activity in the area by counting the number of
fresh nests (N= 101 fresh nest groups, group size range:
1- 26 nests) and encounters of other chimpanzee traces
(N= 92: fresh feces (10), fresh feeding remains (1), vocal-
izations (32), and sightings of chimpanzees (49)) col-
lected opportunistically while trying to find and locate
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chimpanzee nesting sites, or walking to and from line
transects in the surrounding region. The data were
therefore collected ad libitum throughout the Issa valley
study area. The location of every encounter and fresh
nest site found was recorded using a GPS in the field.
Again, for a detection radius of 500 m and 1 km around
each SPATU, the number of fresh nests and encounters
located within the given detection radius for each record-
ing device was summed, per day. Every unique encounter,
regardless of type, was scored as a single indirect chim-
panzee observation. This gave us the total number of in-
direct observations of chimpanzees within the respective
radii of each SPATU found on a daily basis during the
11 months study period. All SPATU recording days were
included in the analysis since it was possible for re-
searchers to collect indirect observations every day during
the study period.
At both sites, data were collected non-invasively. Re-
search at Taï was approved by the Ethical Board of the
Max Planck Society and was conducted with permissions
from the Ministère de la Recherche Scientifique in Côte
dIvoire, and adhered to the rules and regulations govern-
ing animal research in Germany and the EU. Data collec-
tion methods at Issa were approved by the Tanzania
Wildlife Research Institute (TAWIRI) and adhered to the
legal requirements of Tanzania and the American Society
of PrimatologistsPrinciples for the Ethical Treatment of
NonHuman Primates.
Statistical analyses
We conducted all statistical analyses using R version 3.1.3
[44], including the calculations of chimpanzee activity
hours and indirect observations within the two detection
radii for each recording device per day at their respective
field sites. For the Taï dataset, we tested whether our
direct measure of chimpanzee activity hours within the
vicinity of the ARU (500 m and 1 km detection radius, re-
spectively) predicted detections of chimpanzee drums on
a daily basis at that device using Generalized Linear Mixed
Models (GLMMs) [45], one for each detection radius.
Similarly, we used GLMMs for the Issa dataset to test
whether the number of indirect chimpanzee observations
(the sum of fresh nests and encounters) within a 500 m
and 1 km, respectively, detection radius of a SPATU pre-
dicted the number of chimpanzee calls detected by a
device, again on a daily basis. All mixed models had a bi-
nomial error structure and logit link function [46] and the
response was always whether or not a device had detected
a chimpanzee sound event on a particular day (0/1). The
final sample size for the Taï model was 1410 unique ARU-
recording days, and for the Issa model 2470 unique
SPATU-recording days. Models were run using the func-
tion glmerof the R package lme4[47]. GLMMs were fit-
ted separately for the two field sites (see Additional file 1
for full model descriptions). At Taï, even though devices
were set to record 6 h per day there was variation in re-
cording effort due to technical difficulties (mean = 5.9 h,
range = 0.56 h); therefore we included the log of the
number of recording hours per day as an offset term into
these models to control for this variation. Both test predic-
tors, chimpanzee activity hours (Taï data) and number of
indirect observations (Issa data), were z-transformed to a
mean of zero and standard deviation of one before run-
ning the respective models [48]. Models included the
identity of the recording device (ARU or SPATU) as a
random effect as well as the random slopes for all fixed
effects within the random effect of recording device
[49, 50]. A full versus null model comparison was con-
ducted first for every model using a likelihood ratio test
with the R function anova[46]. The null model com-
prised only the random effect, random slopes and in
the case of the Taï models, also the offset term. For all
models we verified model stability by ensuring model
estimates did not vary strongly when recording devices
were dropped one at a time. For the Taï dataset, we also
had data on daily rainfall (mm). Since we expected rain
to affect the ability of an ARU to properly record chim-
panzee sounds, we fitted the same two Taï GLMMs
(with a 500 m and 1 km detection radius, respectively)
again but included rain as a control fixed effect after z
transformation. However, rainfall data were at times
missing which reduced the overall sample size further;
therefore, we report the results for the model without
rain as a control since this includes all the data collected
and, most importantly, because rain did not affect ARU
detection probability and did not change the results for
Taï (see Additional file 2). For all GLMMs, the significance
of individual predictors was assessed with a likelihood
ratio test, again using the R function anova[46], for
which we report the Chi-square test statistic, as this is rec-
ommended to reduce the probability of making a type I
error [50].
Results
Effect of chimpanzee ranging on ARU drum detections
There was a total of 233 chimpanzee events found in the
T dataset using the algorithm (171 drums, 41 vocaliza-
tions, 21 drums with vocalizations) ranging from 0 to 47
drum events per ARU. Since the dataset was composed
primarily of chimpanzee drums given that this was the
sound targeted by the algorithm, we refer to these data
collectively as drums hereafter for ease of distinction
from the Issa data. We found that chimpanzee activity
had a positive influence on the detection of chimpanzee
drums on ARUs for the 88 days of data during the
7 months study period but only within a 1 km ARU de-
tection radius (GLMM est ± SE = 0.408 ± 0.14, X
2
= 5.90,
df = 1, N= 1410, P= 0.015; Fig. 1). The effect was weaker
Kalan et al. Frontiers in Zoology (2016) 13:34 Page 5 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
for a 500 m ARU detection radius (0.287 ± 0.13, X
2
=3.50,
df = 1, N= 1410, P= 0.061; see also Additional file 3)
meaning that chimpanzee ranging activity correlated in
space and time with ARU drum detections recorded
within a 1 km radius, but only to a limited extent within a
500 m radius of an ARU.
Additionally, mapping all the ARU data (137 days of
recordings) collected during the 7 months study period
and comparing them to the well-known territory limits
of the two habituated chimpanzee communities at Taï,
revealed a pattern in the spatial distribution of drum-
ming events detected on ARUs. Using already published
home ranges of the two chimpanzee groups from long-
term data (MCP 95 % [6]), we observed two locations
with a high number of ARU drumming events in the
west and east of the grid that corresponded roughly to
the known whereabouts of the two habituated chimpan-
zee groups, the South and East group respectively (Fig. 2
and Fig. 3). However, the spatial distribution of ARUs
was most comprehensive for the South group where the
ARU grid covered almost the entire known home range
of that habituated chimpanzee community, and here the
greatest drumming activity on ARUs was found in the
center (Fig. 2). With regards to the East group, the ARU
grid only covered half of what was known as that
chimpanzee communitys home range due to the loss of
functioning ARUs during civil unrest in the country.
Interestingly, when the PAM data were plotted on a
monthly basis, drum detections were distributed across
all ARUs and were not always highest in the core of the
home range as predicted (Fig. 3).
Effect of indirect chimpanzee activity on SPATU call
detections
A total of 690 chimpanzee calls were in the Issa dataset,
ranging from 0 to 159 call events per SPATU. In the
Issa valley, where unhabituated chimpanzees roam
across a savanna-woodland mosaic, we found a positive
effect of the number of indirect chimpanzee observa-
tions within a radius of 500 m of a SPATU on its detec-
tion of chimpanzee calls (GLMM est ± SE: 0.13 ± 0.047,
X
2
= 4.06, df = 1, N= 2470, P= 0.044; Fig. 4 and Fig. 5)
but not within a radius of 1 km (est ± SE: 0.184 ± 0.14,
X
2
= 2.15, df = 1, N= 2470, P= 0.15).
Discussion
This study demonstrates that detections on autonomous
recording devices, for two types of long-distance acous-
tic signals produced by chimpanzees, reflected direct
and indirect indices of chimpanzee presence collected by
field workers. Specifically, we found that the number of
hours T chimpanzees were observed ranging in a given
grid cell strongly predicted the probability of detecting a
chimpanzee drum on an ARU located up to 1 km away
(Fig. 1). At a second field site in the Issa valley, we also
found that ad libitum, indirect observations of chimpan-
zees predicted detections of pant hoots and screams on
Fig. 1 Chimpanzee drum detection probability at ARUs versus
chimpanzee ranging activity within a 1 km radius of the device.
Data were collected across 7 months in Taï forest where the probability
that an ARU detected a chimpanzee drum was influenced by nearby
chimpanzee ranging activity (est ± SE: 0.408 ± 0.14, X
2
=5.90,df=1,
N= 1410, P= 0.015). The dashed line shows the results of the fitted
model per six hours of ARU recording effort. For plotting purposes only,
data points were binned per 10 h of chimpanzee activity to obtain a
mean detection probability per bin (blue points).Therelativeareaofthe
circles corresponds to the log of the number of data points (range =2
to 1111) per bin
Fig. 2 Chimpanzee ranging activity per 500 m by 500 m grid cell
plotted alongside ARU drum detections. Ranging activity was the
sum of the number of hours individual chimpanzees were observed
per grid cell during focal follows across the 7 months study period.
The darker the squares the higher the chimpanzee activity in that
grid cell and the darker the red the greater the number of drum
detections within a 500 m detection radius of the respective ARU
(black points)
Kalan et al. Frontiers in Zoology (2016) 13:34 Page 6 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
SPATUs already within a 500 m radius (Fig. 4). These re-
sults are all the more promising since both datasets suf-
fered from technical problems during data collection
(typical for innovative solutions to field challenges) that
resulted in discontinuous recording effort in their re-
spective study periods. Additionally, the latter dataset
came from unhabituated chimpanzees thereby further
validating this method for monitoring poorly known and
difficult to study populations.
The finding that the relationship was weaker within a
smaller detection radius of 500 m for ARUs at Taï may be
a consequence of many factors, particularly the propaga-
tion properties of a low frequency drum sound in a trop-
ical rainforest. Chimpanzee drums are known to propagate
well in a closed canopy habitat, up to 1 km, suggesting
this sound is adapted for long distance communication
[34, 35]. Also drum sounds were often confused with
other background noise, namely thunder, rain, tree falls,
Fig. 3 Changes in monthly centers of chimpanzee drumming activity illustrated by plotting PAM data. Drums detected on ARUs (black points)
in Taï forest overlapped with two habituated chimpanzee communities. These 4 months (panels a-d) show the extent of the variation observed
during the 7 months study period. The solid black lines outline the known home ranges for the two research communities (MCP 95 %: Kouakou
et al. [6]). The detection distance of the ARU is shown at its maximum value of 1 km and the darker the color within the circle the greater the
number of drums recorded on the respective device
Kalan et al. Frontiers in Zoology (2016) 13:34 Page 7 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
and airplanes when using our algorithm [19] and as
such many drums may have gone undetected. In fact,
the algorithm used to process the Taï data had low
overall performance, as was demonstrated in a previous
study[19].Hence,therearelikelymanymoredrums
present in the data that were not extracted by our algo-
rithm. For example, during all day focal follows of the
South group chimpanzees we calculated an average rate
of 2.47 pant hoots per hour for adult males (N=5) and
0.64 pant hoots per hour for adult females (N= 4). The
frequency of buttress drums was much lower overall,
withonly0.9drumsperhouronaverageforadult
males and 0.07 drums per hour, on average, for adult
females. Therefore, we are undoubtedly missing many
chimpanzee drum events in our Taï dataset due to the
automated method of drum detections using our cus-
tomized algorithm [19] and are likely to have missed
some pant hoots in the Issa data as well due to human
error [36]. These data processing methods, coupled
with the fact that devices are fixed at a location, meant
that chimpanzees had to pass within the detection ra-
dius to have even the possibility of their calls being re-
corded. Hence, a low sample size of sound events,
particularly drums, may also contribute to the generally
weak effects observed (Fig. 1 and Fig. 4). However, des-
pite the low numbers of sound events in both datasets
the PAM data still correlated with indirect and direct
indices of chimpanzee ranging activity and were there-
fore still informative.
Alternative to chimpanzee drums, high frequency sounds,
such as screams, which are also an integral component of
the pant hoot [34], generally attenuate much quicker than
low frequency sounds [51]. Therefore, chimpanzee calls at
Issa may simply not propagate as far as 1 km which may
be why indirect indices had no effect on call detections
within a 1 km radius of a SPATU. However the Issa valley
is characterized by a lot of open woodland-savanna which
reduces the reverberation of sounds but the effects of
shifting elevation and steep valleys would also be expected
to disrupt call propagation [36, 51]. Due to these various
conflicting effects more work needs to be done to investi-
gate call transmission in such a habitat.
Interestingly, some of the highest rates of chimpanzee
activity, direct and indirect, appeared to be associated
with a lowered detection probability on ARUs and SPA-
TUs at T and Issa, respectively (Figs. 1 and 4) although
not consistently. This seems counterintuitive at first, but
may in fact be a consequence of the long distance sig-
nals, pant hoots and drums, that were targeted in this
study. The principal function of these long range sounds
is for chimpanzees to maintain contact with other par-
ties of their group [33, 37]. Therefore, although detec-
tion probability of these sounds is expected to increase
when there are more chimpanzees, it may be that high
Fig. 4 Chimpanzee call detection probability on SPATUs versus
number of indirect observations (fresh nests and chimpanzees signs)
nearby. At Issa, the probability of detecting a chimpanzee call on a
SPATU was predicted by the total number of chimpanzee signs found
within a 500 m radius (est ± SE: 0.126 ± 0.047, X
2
=4.06, df=1,
P= 0.044, N= 2470). The fitted model results are depicted with a
dashed line. Again, for plotting purposes only, data points were
binned for every two observations of indirect chimpanzee activity to
obtain a mean detection probability per bin (blue points). The relative
area of the circles corresponds to the log of the number of data points
(range: 1 to 2427) per bin
Fig. 5 Spatial distribution of the 10 SPATUs located in Issa valley,
Tanzania. The proportion of chimpanzee vocalizations detected on
each SPATU (black points) for the 11 months study period, with respect
to the locations of fresh chimpanzee nests (flake: area covered depicts
the number of nests (126) in that location) and all other indirect
chimpanzee observations (diamonds with crosses) are plotted. The
darker the gray within the 500 m detection radius of the SPATU the
greater the number of chimpanzee pant hoots recorded on the
respective device
Kalan et al. Frontiers in Zoology (2016) 13:34 Page 8 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
values of chimpanzee activity hours reflect larger, cohe-
sive parties, where individuals are in visual contact with
multiple members of their community, reducing the
need for frequent use of long distance calls or drums.
Importantly, the relationship between long range sounds
and chimpanzee presence might then become blurred at
high rates of chimpanzee activity (e.g., see large values of
chimpanzee activity in Figs. 1 and 4), and as such hamper
monitoring efforts. This suggests that it may be fruitful to
also incorporate short range calls, for example chimpan-
zee grunts, not just long distance sounds into any PAM
scheme. Such a system would provide a more complete
overall window into any given speciesvocal behaviour
and thus, ranging patterns.
Identifying patterns in territory use is considered essen-
tial information for understanding ranging behaviour of
many large mammals [5254]. By visually tracking PAM
data for just 7 months we were able to detect Taï chim-
panzee drums produced by two neighboring chimpanzee
communities, the habituated South and East groups. In
addition, it appeared that both groups used different parts
of their territories on a monthly basis (Fig. 3). However,
while the South community had the greatest number of
ARU drum detections in the core of their territory (Fig. 2),
this was not true for the East group where most drum-
ming events were detected on ARUs very close to the
South group border (Fig. 2 and 3). This may have resulted
from a high frequency of intergroup interactions, where
chimpanzees often engage in vocal exchanges near com-
munity boundaries including buttress drumming, which
can be used for territorial defense [29, 30]. However, the
ARUs did not cover the entire East group territory so we
cannot be sure, but it would suggest a pattern that is alter-
native to our original prediction, and should therefore be
investigated further.
It is important to note that without individual identifica-
tion of callers the determination of non-overlapping terri-
tories is only possible using PAM when there are several
ARUs recording data per group. Devices should also be
placed in a systematic grid design, as at Taï (Fig. 2), to
avoid overlapping detection areas, as at Issa (Fig. 5). Still,
drums along the border between chimpanzee commu-
nities would be difficult to assign to a single group
when working with an unhabituated population, al-
though PAM activity prior to arriving at the border
could help to distinguish this if recordings are made at
a high spatiotemporal resolution. It is recommended
that the total detection area covered by the ARUs
should cover at least the minimum home range size
known for that species. For example, at Issa we cannot
infer about the territory size or number of groups be-
cause a study area of only 12 km
2
is much smaller than
the smallest reported home range size for savanna-
woodland chimpanzees (72 km
2
[25]).
With increasingly sophisticated data processing tech-
niques becoming available, automated individual identifica-
tion of primate vocalizations should also become feasible
in the future, permitting acoustic localization of individual
callers, allowing us to map territories, individual movement
patterns, and associations [14]. Generally, ARUs expand
the spatial and temporal scale of data collection, also facili-
tating longitudinal data on multiple groups simultaneously.
The application of PAM could therefore be extended to
address questions regarding intergroup dynamics be-
tween neighboring groups which can rarely be done
using traditional observational methods of single indi-
viduals from a single group, as well as how individuals
living within fission-fusion species coordinate move-
ment among sub-groups [5557]. With the aid of radio
transmission, PAM data can also be centralized and
processed in near real-time if data mining methods
continue to improve for the automated detection of
sounds from continuous recordings [18, 19, 36, 58, 59].
Conclusions
The present study has demonstrated the usefulness of re-
mote acoustic sensing for accurately reflecting ranging
patterns in three communities of wild apes. Our results
have shown that PAM is a useful method to employ for
the study of other, especially unhabituated, wild chimpan-
zee populations, to learn about group territories and their
spatiotemporal patterns of habitat use. Additionally, PAM
is non-invasive and causes negligible disturbance to wild-
life. There is a strong caveat, however, accompanying our
recommendation, which is that PAM relies on animals vo-
calizing; therefore, silent individuals will go undetected.
Since wild primates may adapt their vocal behavior, espe-
cially by becoming quieter in areas with high poaching
pressure [60], care should be taken when applying this
method for the first time in such regions. That not-
withstanding, PAM can be used to monitor presence, de-
tection rates, and occupancy probabilities [32], as well as
territory use and group ranging patterns (this study).
Whereas we focused on a single species here, our findings
can easily be adapted to other territorial mammals that
use long-distance acoustic signals to communicate such as
lions [61], spotted hyenas [62], and wolves [63].
With the advent of wireless technologies, we expect
that PAM will become easier to implement, particularly
over larger geographical scales; however, there are still
many improvements needed before this method can be
widely employed. In particular, PAM would benefit from
further interdisciplinary research into automated sound
recognition and classification methods since data ana-
lysis is still a limiting factor for continuous and large
scale acoustic monitoring schemes. As automated ap-
proaches to data processing become available in real-
time [18], wildlife managers will then be able to rapidly
Kalan et al. Frontiers in Zoology (2016) 13:34 Page 9 of 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
and efficiently collect, process, and incorporate empirical
data into conservation policy. Tracking spatiotemporal
shifts in animal activity using remote acoustic data could
then be used to inform conservation priorities, identify
key resources, as well as threats (e.g., gunshots and log-
ging). To date, we have only begun to explore the power
of PAM for addressing research questions in conserva-
tion science and behavioral ecology and we encourage
researchers of other taxa to add this promising new
method to their field work toolbox.
Additional files
Additional file 1: Description of the GLMMs fitted for PAM data
collected from both field sites. (DOCX 15 kb)
Additional file 2: GLMM results for the effect of chimpanzee activity
on ARU drum detections of chimpanzees at Taï, with daily rainfall (mm)
added as a control fixed effect. We checked for collinearity using variance
inflation factors derived using the function vifof the package car[1]
applied to a standard linear model lacking the random effects. The effect
of chimpanzee ranging activity remained significant at a one kilometer
detection radius, and rain had no effect on ARU detection probability
(N= 1391). Similarly, results remained a trend (P= 0.065) with a 500 m
detection radius (see Additional file 3 and main manuscript). (DOCX 15 kb)
Additional file 3: Chimpanzee drum detection probability at ARUs and
chimpanzee ranging activity within a 500 m detection radius of the
device in Taï (est ± SE: 0.287 ± 0.12, X
2
= 3.50, df = 1, P= 0.061, N= 1410).
The dashed line shows the results of the fitted model per 6 hours of ARU
recording effort. Data points were binned per 10 hours of chimpanzee
activity to obtain a mean detection probability per bin (blue circles).
The relative area of the circles corresponds to the log of the number
of data points (range: 1 to 1291) per bin. (DOCX 662 kb)
Abbreviations
ARU, autonomous recording unit; PAM, passive acoustic monitoring; SPATU,
solar powered acoustic transmission unit; TCP, Taï Chimpanzee Project
Acknowledgements
Research at Taï was conducted with permissions from the Ministère de la
Recherche Scientifique, the Ministère de lEnvironnement et des Eaux et
Forêts, and the Office Ivoirien des Parcs et Réserves of Côte dIvoire. The
work at Taï was funded by the SAISBECO project, a collaboration funded by
the Max Planck Society and the Fraunhofer-Gesellschaft under the Pact for
Research and Innovation for research, and was additionally supported by the
Robert Bosch Foundation. We thank WCF, Centre Suisse de Recherche
Scientifique, and field assistants of the TCP for logistical support in Taï.
Research at Issa was approved by the Tanzania Wildlife Research Institute
(TAWIRI) and was funded by The National Science Foundation, Wenner
Gren Foundation, Royal Anthropological Institute, and UCSD. AP thanks
Kimmy Kissinger for help with data mining of the Issa audio recordings
and Fiona Stewart for support in system deployment. Long term funding
for ongoing research at Issa is supported by the UCSD/Salk Center for Academic
Research and Training in Anthropogeny (CARTA). We would also like to thank
two anonymous reviewers for greatly improving the manuscript with their
feedback.
Authorscontributions
AK, HK, CB, AP conceived and designed the study; AK and AP collected the
data, RW and CB helped with data acquisition, AK and RM analyzed the data,
AK wrote the manuscript with input from all co-authors. All authors read and
approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Primatology, Max Planck Institute for Evolutionary
Anthropology, Deutscher Platz 6, 04103 Leipzig, Germany.
2
School of Natural
Sciences and Psychology, Liverpool John Moores University, James Parsons
Building, Rm 653, Byrom Street, Liverpool L3 3AF, UK.
3
Ugalla Primate Project,
Kigoma, Tanzania.
4
Department of Developmental and Comparative
Psychology, Max Planck Institute for Evolutionary Anthropology, Deutscher
Platz 6, 04103 Leipzig, Germany.
5
Taï Chimpanzee Project, Centre Suisse de
Recherches Scientifiques, BP 1301, Abidjan 1, Côte dIvoire.
6
Wild
Chimpanzee Foundation, Deutscher Platz 6, 04103 Leipzig, Germany.
7
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig,
Deutscher Platz 5e, 04103 Leipzig, Germany.
Received: 3 March 2016 Accepted: 3 August 2016
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... Additionally, the main advantage is precisely the detection of animals within areas of limited visibility (Marques et al., 2013). Overall, the best results were obtained for species that routinely use long-distance communication (Kalan et al., 2016). ...
... There is now a growing number of studies using PAM to determine primate distribution and behavior: spider monkeys (Wahlberg et al., 2002), chimpanzees Kalan et al., 2016), colobus, diana monkeys , orangutans (Spillmann et al., 2015), Japanese macaques (Enari et al., 2019), gibbons (Vu & Tran, 2019), and recently, the Neotropical black and gold howler monkey (Pérez-Granados & Schuchmann, 2021). ...
... One of the difficulties currently associated with PAM is the distance from the emitter of the interest sound, and the background noise (Kalan et al., 2016 ;Waser & Waser, 1977). Unwanted sounds present in an area-different biophony, anthropophony, and geophony-are bound to be captured by the recorders and may mask sounds produced by the target species, consequently confusing the detection program, potentially generating a higher false positive rate (sounds mistakenly identified as of the target species), and false negative rate (sound of the target species missed because other sounds masked it) (Priyadarshani et al., 2018). ...
Article
Information about species distribution is important for conservation but the monitoring of populations can demand a high sampling effort with traditional methods (e.g., line transects, sound playback) that are poorly efficient for cryptic primates, such as the black lion tamarin (Leontopithecus chrysopygus). Here we investigated the effectiveness of passive acoustic monitoring (PAM) as an alternative method to identify the presence of vocalizing lion tamarins in the wild. We aimed to: (1) determine the maximum distance at which autonomous recorders (Song Meter 3) and Raven Pro acoustic software can respectively detect and identify lion tamarin long calls emitted by two captive subjects (ex situ study); and (2) determine the sampling effort required to confirm the presence of the species in the wild (in situ study). In captive settings, we recorded lion tamarin long calls with one to two autonomous recorders operating at increasing distances from the animals' enclosure (8-202 m). In a 515 ha forest fragment, we deployed 12 recorders in a grid, 300 m apart from each other, within the estimated 100 ha home range of one group, and let them record for 10 consecutive days, totaling 985 h. In the ex situ study, hand-browsing of spectrograms yielded 298 long calls emitted from 8 to 194 m, and Raven's Template Detector identified 54.6% of them, also emitted from 8 to 194 m. In the in situ study, we manually counted 1115 long calls, and the Raven's Template Detector identified 44.75% of them. Furthermore, the presence of lion tamarins was confirmed within 1 day using four randomly sorted recorders, whereas 5 days on average were necessary with only one device. While specific protocols still need to be developed to determine primate population size using this technology, we concluded that PAM is a promising tool when considering long term costs and benefits.
... Chimpanzees have a prolonged development where maternal effects on social (Kalcher-Sommersguter et al., 2015;Markham et al., 2015;van Leeuwen et al., 2014), but not vocal behavior have been identified. The pant hoot is a long-distance contact call that carries distances of at least 500 m (Ghiglieri, 1984;Kalan et al., 2016) and encodes information about the caller identity (Crockford et al., 2004;Desai et al., 2021;Marler and Hobbett, 1975;Mitani et al., 1996). Pant hoots likely function to maintain contact between subgroups of community members, particularly with allies (Goodall, 1986;Marler and Hobbett, 1975), and may function to recruit allies (Kalan and Boesch, 2015) in this fission-fusion species, in which group composition and size change frequently over time (Ramos-Ferná ndez and Morales, 2014 iScience Article development in long-lived mammals are rare but may provide an additional useful social metric for assessing the impact of maternal effects on social development. ...
... A pant hoot was defined in our study as the focal individual having to produce a series of alternating pants and hoots with increasing volume. We considered both pant hoots with and without a climax phase as both call structures function as contact calls well beyond the range of visibility in a forest habitat (see: Girard-Buttoz et al., 2022a;Kalan et al., 2016). Each change in party composition was also documented. ...
Article
Full-text available
Early-life experiences, such as maternal care received, influence adult social integration and survival. We examine what changes to social behaviour through ontogeny lead to these lifelong effects, particularly whether early-life maternal environment impacts the development of social communication. Chimpanzees experience prolonged social communication development. Focusing on a central communicative trait, the ‘pant-hoot’ contact call used to solicit social engagement, we collected cross-sectional data on wild chimpanzees (52 immatures and 36 mothers). We assessed early-life socioecological impacts on pant-hoot rates across development, specifically: mothers’ gregariousness, age, pant-hoot rates and dominance rank, maternal loss and food availability, controlling for current maternal effects. We found that early-life maternal gregariousness correlated positively with offspring pant-hoot rates, whilst maternal loss led to reduced pant-hoot rates across development. Males had steeper developmental trajectories in pant-hoot rates than females. We demonstrate the impact of maternal effects on developmental trajectories of a rarely investigated social trait, vocal production.
... However, it is hard to conceive how this mechanism would select individuals producing PH or PG first within GH sequences, given that both populations of chimpanzees are forest dwelling, living in closed habitats. Furthermore, PH is loud calls that can travel up to 500 m (Kalan et al., 2016) and it is unlikely that PH lose their far reaching properties if they are preceded or succeeded by a PG. Call order may rather be impacted by a combination of the social environment and visibility and in particular, by group cohesion. ...
Article
Full-text available
Primates rarely learn new vocalisations, but they can learn to use their vocalizations in different contexts. Such ‘vocal usage learning’, particularly in vocal sequences, is a hallmark of human language, but remains understudied in non-human primates. We assess usage learning in four wild chimpanzee communities of Taï and Budongo Forests by investigating population differences in call ordering of a greeting vocal sequence. Whilst in all groups, these sequences consisted of pant-hoots (long-distance contact call) and pant-grunts (short-distance submissive call), the order of the two calls differed across populations. Taï chimpanzees consistently commenced greetings with pant-hoots whereas Budongo chimpanzees started with pant-grunts. We discuss different hypotheses to explain this pattern and conclude that higher intra-group aggression in Budongo may have led to a local pattern of individuals signalling submission first. This highlights how within-species variation in social dynamics may lead to flexibility in call order production, possibly acquired via usage learning.
... As gibbons often produce loud vocalizations when no obvious recipient is present (Raemaekers et al., 1984), we hypothesized that gibbon song may not only function in close-range communication, but also may broadcast to distant animals, including those who live beyond the territories of immediate, bordering neighbors. We used Passive acoustic monitoring (PAM), which has proven to be an effective means of studying vocal behavior in many animal taxa (Pijanowski et al., 2011;Sugai et al., 2019), including primates (Clink et al., 2019;Crunchant et al., 2020;Enari et al., 2019;Heinicke et al., 2015, Kalan et al., 2016Perez-Granados and Schuchmann, 2021). We expected PAM to be especially well-suited for use with gibbons, because these apes live in small groups with stable territorial boundaries and produce loud vocalizations often and at all times of the year (Reichard, 2009;Reichard et al., 2016). ...
Article
Full-text available
Few primates are characterized by strict territoriality, pair-bonding, and loud, complex vocalizations. Gibbons (Hylobatidae) are amongst them, with mated pairs of most species performing duet songs that may strengthen pair-bonds and/or defend a territory. Gibbon duets often are audible well beyond a pair’s territory boundaries, suggesting that they function in intergroup signaling not just with close neighbors, but also with distant neighbors, yet no clear evidence for communication between distant neighbors has been found and direct tests of long-distance signaling are lacking. We studied vocal interactions in wild, white-handed gibbons (Hylobates lar) over two 10-day collection periods, from 340 recording hours on passive acoustic monitors at Khao Yai National Park, Thailand. Singing animals exhibited rapid control over vocal output (call inhibition) in response to short climax-coda components of distant pairs’ songs, supporting the intergroup long-distance signaling hypothesis. These climax-coda sequences are the only occasions in the species’ repertoire when phrases from both members of a pair regularly and unambiguously occur in rapid succession, suggesting a signaling of pair-bonded status to other groups, because only pair-bonded individuals sing in this way together. The call inhibition may allow distant receivers to estimate the location of a singing group’s territory, to identify potential mates or rivals, and may provide information that facilitates natal dispersal of mature offspring. We also provide evidence to support the hypothesis that the great call may have evolved a lengthy onset to allow distant receivers to detect the great call sequence before its climax-coda components occur.
... Most studies using PAM have been conducted on marine mammals (Mellinger et al., 2007), especially cetaceans (Marques et al., 2013). However, several recent studies have demonstrated the applicability of PAM for biodiversity monitoring in terrestrial taxa, including bats (Froidevaux et al., 2014), birds (Darras et al., 2018a), frogs (MacLaren et al., 2018), insects (Aide et al., 2013), elephants (Thompson et al., 2009(Thompson et al., , 2010, and primates Kalan et al., 2015Kalan et al., , 2016Clink et al., 2018;Vu & Tran, 2019). Furthermore, PAM can assess biodiversity at the soundscape level and provide bioacoustic time capsules for the future (Sugai & Llusia, 2019). ...
Article
Full-text available
Developing new cost-effective methods for monitoring the distribution and abundance of species is essential for conservation biology. Passive acoustic monitoring (PAM) has long been used in marine mammals and has recently been postulated to be a promising method to improve monitoring of terrestrial wildlife as well. Because Madagascar’s lemurs are among the globally most threatened taxa, this study was designed to assess the applicability of an affordable and open-source PAM device to estimate the density of pale fork-marked lemurs (Phaner pallescens). Using 12 playback experiments and one fixed transect of four automated acoustic recorders during one night of the dry season in Kirindy Forest, we experimentally estimated the detection space for Phaner and other lemur vocalizations. Furthermore, we manually annotated more than 10,000 vocalizations of Phaner from a single location and used bout rates from previous studies to estimate density within the detection space. To truncate detections beyond 150 m, we applied a sound pressure level (SPL) threshold filtering out vocalizations below SPL 50 (dB re 20 μPa). During the dry season, vocalizations of Phaner can be detected with confidence beyond 150 m by a human listener. Within our fixed truncated detection area corresponding to an area of 0.07 km2 (detection radius of 150 m), we estimated 10.5 bouts per hour corresponding to a density of Phaner of 38.6 individuals/km2. Our density estimates are in line with previous estimates based on individually marked animals conducted in the same area. Our findings suggest that PAM also could be combined with distance sampling methods to estimate densities. We conclude that PAM is a promising method to improve the monitoring and conservation of Phaner and many other vocally active primates.
... Their method was implemented in the MATLAB programming environment using the Triton software package (Wiggins, 2007) which also facilitates navigation to specific segments of the raw audio for manual analysis. Published examples have applied visualization using the Triton package in marine environments, for which it was designed, to detect whale calls (Soldevilla et al., 2014) and describe marine soundscapes (Rice et al., 2017), but it has also been used in freshwater environments to detect chorusing of an underwater-calling frog (Nelson et al., 2017), and in terrestrial environments to detect chimpanzee vocalizations (Kalan et al., 2016). ...
Article
Full-text available
Continuous recording of environmental sounds could allow long-term monitoring of vocal wildlife, and scaling of ecological studies to large temporal and spatial scales. However, such opportunities are currently limited by constraints in the analysis of large acoustic data sets. Computational methods and automation of call detection require specialist expertise and are time consuming to develop, therefore most biological researchers continue to use manual listening and inspection of spectrograms to analyze their sound recordings. False-color spectrograms were recently developed as a tool to allow visualization of long-duration sound recordings, intending to aid ecologists in navigating their audio data and detecting species of interest. This paper explores the efficacy of using this visualization method to identify multiple frog species in a large set of continuous sound recordings and gather data on the chorusing activity of the frog community. We found that, after a phase of training of the observer, frog choruses could be visually identified to species with high accuracy. We present a method to analyze such data, including a simple R routine to interactively select short segments on the false-color spectrogram for rapid manual checking of visually identified sounds. We propose these methods could fruitfully be applied to large acoustic data sets to analyze calling patterns in other chorusing species.
... Autonomous recording units (ARUs) can be deployed in areas that are difficult to access and can detect vocal activity that is difficult or even impossible to hear (e.g., ultrasonic echolocation) by human observers (Brownlie et al., 2020;Desjonquères et al., 2020;Tuneu-Corral et al., 2020;Wallis and Elmeros, 2020). In addition, PAM causes less disturbance to wildlife than human surveys (Kalan et al., 2016) and represents a higher detectability of some taxa than other automated monitoring approaches, such as camera trapping (Crunchant et al., 2020;Enari et al., 2019). One of the most significant advantages of PAM is its high cost-effectiveness when being applied in studies spanning large spatial and temporal scales because the ARUs can be deployed for an extended period and collect a massive amount of high temporal resolution audio data without skilled experts (Darras et al., 2019;Duchac et al., 2020;Ducrettet et al., 2020;Shamon et al., 2021;Szymański et al., 2021;Zwart et al., 2014). ...
Article
Full-text available
1.Passive acoustic monitoring (PAM) offers many advantages comparing with other survey methods and gains an increasing use in terrestrial ecology, but the massive effort needed to extract species information from a large number of recordings limits its application. The convolutional neural network (CNN) has been demonstrated with its high performance and effectiveness in identifying sound sources automatically. However, requiring a large amount of training data still constitutes a challenge. 2.Object detection is used to detect multiple objects in photos or videos and is effective at detecting small objects in a complex context, such as animal sounds in a spectrogram and shows the opportunity to build a good performance model with a small training dataset. Therefore, we developed the Sound Identification and Labeling Intelligence for Creatures (SILIC), which integrates online animal sound databases, PAM databases and an object detection-based model, for extracting information on the sounds of multiple species from complex soundscape recordings. 3.We used the sounds of six owl species in Taiwan to demonstrate the effectiveness, efficiency and application potential of the SILIC framework. Using only 786 sound labels in 133 recordings, our model successfully identified the species' sounds from the recordings collected at five PAM stations, with a macro-average AUC of 0.89 and a mAP of 0.83. The model also provided the time and frequency information, such as the duration and bandwidth, of the sounds. 4.To our best knowledge, this is the first time that the object detection algorithm has been used to identify sounds of multiple wildlife species. With an online sound-labeling platform embedded and a novel data preprocessing approach (i.e., rainbow mapping) applied, the SILIC shows its good performance and high efficiency in identifying wildlife sounds and extracting robust species, time and frequency information from a massive amount of soundscape recordings based on a tiny training dataset acquired from existing animal sound databases. The SILIC can help expand the application of PAM as a tool to evaluate the state of and detect the change in biodiversity by, for example, providing high temporal resolution and continuous information on species presence across a monitoring network.
... That said, within the last decade technical advancements have meant that there are now a number of commercially available ARUs (Browning et al. 2017). The application of PAM to study wild primates is relatively new, but thus far it has provided information on the occurrence of species , localizing individuals (Spillmann et al. 2015), changes in communicative behaviors (Duarte et al. 2018), territoriality and ranging patterns (Kalan et al. 2016) as well as detecting threats to wild populations (Astaras et al. 2017; Table 2). Similar to camera traps, PAM is a cost-effective method of collecting data on wild primates at large spatial and temporal scales, it requires minimal maintenance after the initial installation of devices and is an effective means by which primatologists can garner information on wild populations without having to habituate individuals to human observers. ...
Article
Full-text available
Many studies have revealed that animal vocalizations, including those from mammals, are individually distinctive. Therefore, acoustic identification of individuals (AIID) has been repeatedly suggested as a non-invasive and labor efficient alternative to mark-recapture identification methods. We present a pipeline of steps for successful AIID in a given species. By conducting such work, we will also improve our understanding of identity signals in general. Strong and stable acoustic signatures are necessary for successful AIID. We reviewed studies of individual variation in mammalian vocalizations as well as pilot studies using acoustic identification to census mammals and birds. We found the greatest potential for AIID (characterized by strong and stable acoustic signatures) was in Cetacea and Primates (including humans). In species with weaker acoustic signatures, AIID could still be a valuable tool once its limitations are fully acknowledged. A major obstacle for widespread utilization of AIID is the absence of tools integrating all AIID subtasks within a single package. Automation of AIID could be achieved with the use of advanced machine learning techniques inspired by those used in human speaker recognition or tailored to specific challenges of animal AIID. Unfortunately, further progress in this area is currently hindered by the lack of appropriate publicly available datasets. However, we believe that after overcoming the issues outlined above, AIID can quickly become a widespread and valuable tool in field research and conservation of mammals and other animals.
Chapter
Animals share acoustic space to communicate vocally. The employment of passive acoustic monitoring to establish a better understanding of acoustic communities has emerged as an important tool in assessing overall diversity and habitat integrity as well as informing species conservation strategies. This chapter aims to review how traditional and more emerging bioacoustic techniques can address conservation issues. Acoustic data can be used to estimate species occupancy, population abundance, and animal density. More broadly, biodiversity can be assessed via acoustic diversity indices, using the number of acoustically conspicuous species. Finally, changes to the local soundscape provide an early warning of habitat disturbance, including habitat loss and fragmentation. Like other emerging technologies, passive acoustic monitoring (PAM) benefits from an interdisciplinary collaboration between biologists, engineers, and bioinformaticians to develop detection algorithms for specific species that reduce time-consuming manual data mining. The chapter also describes different methods to process, visualize, and analyse acoustic data, from open source to commercial software. The technological advances in bioacoustics turning heavy, non-portable, and expensive hardware and labour and time-intensive methods for analysis into new small, movable, affordable, and automated systems, make acoustic sensors increasingly popular among conservation biologists for all taxa.
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As habitat degradation and fragmentation continue to impact wildlife populations around the world, it is critical to understand the behavioral flexibility of species in these environments. In Uganda, the mostly unprotected forest fragment landscape between the Budongo and Bugoma Forests is a potential corridor for chimpanzees, yet little is known about the status of chimpanzee populations in these fragments. From 2011 through 2013, we noninvasively collected 865 chimpanzee fecal samples across 633 km(2) and successfully genotyped 662 (77%) at up to 14 microsatellite loci. These genotypes corresponded to 182 chimpanzees, with a mean of 3.5 captures per individual. We obtained population size estimates of 256 (95% confidence interval 246-321) and 319 (288-357) chimpanzees using capture-with-replacement and spatially explicit capture-recapture models, respectively. The spatial clustering of associated genotypes suggests the presence of at least nine communities containing a minimum of 8-33 individuals each. Putative community distributions defined by the locations of associated genotypes correspond well with the distribution of 14 Y-chromosome haplotypes. These census figures are more than three times greater than a previous estimate based on an extrapolation from small-scale nest count surveys that tend to underestimate population size. The distribution of genotype clusters and Y-chromosome haplotypes together indicate the presence of numerous male philopatric chimpanzee communities throughout the corridor habitat. Our findings demonstrate that, despite extensive habitat loss and fragmentation, chimpanzees remain widely distributed and exhibit distinct community home ranges. Our results further imply that elusive and rare species may adapt to degraded habitats more successfully than previously believed. Their long-term persistence is unlikely, however, if protection is not afforded to them and habitat loss continues unabated.
Article
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One proposed benefit for the seemingly costly behaviour of food calling is the recruitment of social allies and mates by the signaller. In chimpanzees, food calls are only produced for approximately half of all feeding events. Therefore, we investigated the influence of social and ecological context on the probability of making a food call upon arriving to a food patch in a group of wild chimpanzees. First, we tested whether feeding events where food calls had been uttered did in fact attract more individuals to join the caller. Secondly, we examined the influence of two sources of audience effects: those who were physically present with the caller and those who were presumed nearby but out of sight, and thirdly the effect of various ecological factors. We found that when feeding on fruit species, events where food calls had been produced had a higher probability of group mates arriving, even whilst controlling for the effect of pant hoots. Furthermore, the probability of uttering a food call was motivated by social more than ecological context. Specifically, high-ranking males were more likely to make food calls when estrous females were nearby, while low-ranking males and females generally called more when more females were nearby, irrespective of their reproductive state. These effects were independent of the increase in food call probability when male callers were accompanied by more males. Our findings support the recruitment function of food calls and suggest that high-ranking males call to attract estrous females to food patches to obtain mating opportunities.
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Full-text available
Recent advancements in technology have made possible the use of novel, cost-efficient biomonitoring techniques which facilitate monitoring animal populations at larger spatial and temporal scales. Here, we investigated using passive acoustic monitoring (PAM) for wild primate populations living in the forest of Taï National Park, Côte d’Ivoire. We assessed the potential of using a customized algorithm for the automated detection of multiple primate species to obtain reliable estimates of species occurrence from acoustic data. First, we applied the algorithm on continuous rainforest recordings collected using autonomous recording units (ARUs) to detect and classify three sound signals: chimpanzee buttress drumming, and the loud calls of the diana and king colobus monkey. Using an occupancy modelling approach we then investigated to what extent the automated, probabilistic output needs to be listened to, and thus manually cleaned, by a human expert, to approach occupancy probabilities derived from ARU data fully verified by a human. To do this we explored the robustness of occupancy probability estimates by simulating ARU datasets with various degrees of cleaning for false positives and false negative detections. We further validated the approach by comparing it to data collected by human observers on point transects located within the same study area. Our study demonstrates that occurrence estimates from ARU data, combined with automated processing methods such as our algorithm, can provide results comparable to data collected by humans and require less effort. We show that occupancy probabilities are quite robust to cleaning effort, particularly when occurrence is high, and suggest that for some species even naïve occupancy, as derived from ARU data without any cleaning, could provide a quick and reliable indicator to guide monitoring efforts. We found detection probabilities to be most influenced by time of day for chimpanzee drums while temperature and, likely, poaching pressure, affected detection of diana monkey loud calls. None of the covariates investigated appeared to have strongly affected king colobus loud call detection. Finally, we conclude that the semi-automated approach presented here could be used as an early-warning system for poaching activity and suggest additional techniques for improving its performance.
Book
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the theoretical background of generalized linear models (GLMs) before focusing on methods for analyzing particular kinds of data. It covers normal, Poisson, and binomial distributions; linear regression models; classical estimation and model fitting methods; and frequentist methods of statistical inference. After forming this foundation, the authors explore multiple linear regression, analysis of variance (ANOVA), logistic regression, log-linear models, survival analysis, multilevel modeling, Bayesian models, and Markov chain Monte Carlo (MCMC) methods. Using popular statistical software programs, this concise and accessible text illustrates practical approaches to estimation, model fitting, and model comparisons. It includes examples and exercises with complete data sets for nearly all the models covered.
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Linear mixed-effects models (LMEMs) have become increasingly prominent in psycholin-guistics and related areas. However, many researchers do not seem to appreciate how random effects structures affect the generalizability of an analysis. Here, we argue that researchers using LMEMs for confirmatory hypothesis testing should minimally adhere to the standards that have been in place for many decades. Through theoretical arguments and Monte Carlo simulation, we show that LMEMs generalize best when they include the maximal random effects structure justified by the design. The generalization performance of LMEMs including data-driven random effects structures strongly depends upon modeling criteria and sample size, yielding reasonable results on moderately-sized samples when conservative criteria are used, but with little or no power advantage over maximal models. Finally, random-intercepts-only LMEMs used on within-subjects and/or within-items data from populations where subjects and/or items vary in their sensitivity to experimental manipulations always generalize worse than separate F 1 and F 2 tests, and in many cases, even worse than F 1 alone. Maximal LMEMs should be the 'gold standard' for confirmatory hypothesis testing in psycholinguistics and beyond.
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More than 75 percent of Tanzania's chimpanzees live at low densities on land outside national parks. Chimpanzees are one of the key conservation targets in the region and long-term monitoring of these populations is essential for assessing the overall status of ecosystem health and the success of implemented conservation strategies. We aimed to assess change in chimpanzee density within the Masito-Ugalla Ecosystem (MUE) by comparing results of re-walking the same line transects in 2007 and 2014. We further used published remote sensing data derived from Landsat satellites to assess forest cover change within a 5 km buffer of these transects over that same period. We detected no statistically significant decline in chimpanzee density across the surveyed areas of MUE between 2007 and 2014, although the overall mean density of chimpanzees declined from 0.09 individuals/km(2) in 2007 to 0.05 individuals/km(2) in 2014. Whether this change is biologically meaningful cannot be determined due to small sample sizes and large, entirely overlapping error margins. It is therefore possible that the MUE chimpanzee population has been stable over this period and indeed in some areas (Issa Valley, Mkanga, Kamkulu) even showed an increase in chimpanzee density. Variation in chimpanzee habitat preference for ranging or nesting could explain variation in density at some of the survey sites between 2007 and 2014. We also found a relationship between increasing habitat loss and lower mean chimpanzee density. Future surveys will need to ensure a larger sample size, broader geographic effort, and random survey design, to more precisely determine trends in MUE chimpanzee density and population size over time. Am. J. Primatol. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.
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Passive acoustic monitoring is frequently used for marine mammals, and more recently it has also become popular for terrestrial species. Key advantages are the monitoring of (1) elusive species, (2) different taxa simultaneously, (3) large temporal and spatial scales, (4) with reduced human presence and (5) with considerable time saving for data processing. However, terrestrial sound environments can be highly complex; they are very challenging when trying to automatically detect and classify vocalizations because of low signal-to-noise ratios. Therefore, most studies have used manual preselection of high-quality sounds to achieve better classification rates. Consequently, most systems have never been validated under realistic field conditions.