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royalsocietypublishing.org/journal/rspb
Research
Cite this article: Guo X, Lin MR, Azizi A,
Saldyt LP, Kang Y, Pavlic TP, Fewell JH. 2022
Decoding alarm signal propagation of seed-
harvester ants using automated movement
tracking and supervised machine learning.
Proc. R. Soc. B 289: 20212176.
https://doi.org/10.1098/rspb.2021.2176
Received: 30 September 2021
Accepted: 23 December 2021
Subject Category:
Behaviour
Subject Areas:
behaviour
Keywords:
behaviour tracking, alarm behaviour,
supervised machine learning, social insects,
information flow networks
Author for correspondence:
Xiaohui Guo
e-mail: xguo49@asu.edu
Electronic supplementary material is available
online at https://doi.org/10.6084/m9.figshare.
c.5772172.
Decoding alarm signal propagation of
seed-harvester ants using automated
movement tracking and supervised
machine learning
Xiaohui Guo
1
, Michael R. Lin
2
, Asma Azizi
3
, Lucas P. Saldyt
4
, Yun Kang
2,5
,
Theodore P. Pavlic
1,4,6,7
and Jennifer H. Fewell
1
1
School of Life Sciences,
2
Mathematical, Computational and Modeling Sciences Center,
4
School of Computing
and Augmented Intelligence,
6
School of Sustainability, and
7
School of Complex Adaptive Systems, Arizona State
University, Tempe, AZ, USA
3
Department of Mathematics, Kennesaw State University, Marietta, GA, USA
5
Science and Mathematics, College of Integrative Sciences and Arts, Arizona State University, Mesa, AZ, USA
XG, 0000-0001-8256-2346; MRL, 0000-0002-6313-1505; AA, 0000-0002-6310-2923;
TPP, 0000-0002-7073-6932
Alarm signal propagation through ant colonies provides an empirically
tractable context for analysing information flow through a natural system,
with useful insights for network dynamics in other social animals.
Here, we develop a methodological approach to track alarm spread
within a group of harvester ants, Pogonomyrmex californicus. We initially
alarmed three ants and tracked subsequent signal transmission through
the colony. Because there was no actual standing threat, the false alarm
allowed us to assess amplification and adaptive damping of the collective
alarm response. We trained a random forest regression model to quantify
alarm behaviour of individual workers from multiple movement features.
Our approach translates subjective categorical alarm scores into a reliable,
continuous variable. We combined these assessments with automatically
tracked proximity data to construct an alarm propagation network.
This method enables analyses of spatio-temporal patterns in alarm
signal propagation in a group of ants and provides an opportunity to inte-
grate individual and collective alarm response. Using this system, alarm
propagation can be manipulated and assessed to ask and answer a wide
range of questions related to information and misinformation flow in
social networks.
1. Introduction
Coordination in biological systems often depends on complex, decentralized
processes for distributing information among system components [1]. The
decentralized mechanisms of information distribution are critical to adaptive
social function, but they also can be subject to manipulation, and under some
conditions, they can fail. Deleterious self-perpetuating ‘cytokine storms’can
lead to multisystem organ failure and death [2], and misinformation in social
networks can continue to spread even long after it has been retracted by its orig-
inal source [3]. Being able to manipulate and assess biological communication
networks in fine detail may unveil strategies converged upon by natural selec-
tion with connections to the spread of information and misinformation.
Furthermore, understanding catastrophic failures of sharing information in
one system can lead to new insights into analogous failures in others (e.g. cyto-
kine storms and misinformation spread).
Social-insect colonies are ideal model systems for studying natural com-
munication networks. Adaptive responses at the colony level emerge from
© 2022 The Author(s) Published by the Royal Society. All rights reserved.
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the collective actions of individuals, each responding to local
stimuli from their environment or from other individuals
within the colony. As a result, large numbers of individuals
can self-organize to regulate diverse aspects of task organ-
ization [4–6] including food distribution [7], social defence
[8–10], collaborative house hunting [11] and foraging
[12,13]. Two elements common to these collective phenom-
ena are an underlying individual-to-individual contact
network and a distributed system of information flow
over the network. These attributes enable colonies to flex-
ibly and adaptively respond to changing environments
and social contexts [14].
By tracking individual movement and social interactions,
object-tracking techniques have provided useful method-
ologies to study the dynamic structure of social networks in
diverse social groups. In social insect colonies, the technique
has been applied to European honeybees (Apis mellifera) [15],
bumblebees (Bombus ignitus) [16], carpenter ants (Camponotus
fellah) [17] and others. These tracking methodologies do not,
by themselves, provide a reliable assessment of individual
behavioural state during social interactions, nor do they
assess the reliability of information exchange during encoun-
ters between individuals. Researchers have also developed
more sophisticated statistical and mathematical modelling
tools to develop theory about how social insects spread infor-
mation across their communication networks [18,19].
Nevertheless, a gap between empirical and theoretical studies
on information movement across the network constrains
understanding of how a social-insect colony is organized as
a complex adaptive system.
In many studies of vertebrate social networks, research
has focused on several aspects of information flow, such as
the transmissibility of information, individual-characteristic
effects on information flow and the path of information
flow [20–22]. In these cases, the focus has been primarily on
dyadic relationships or association networks and how they
shape behaviour. This is more difficult for the social insects,
where individual relationships are usually ephemeral and
task based [5] because a colony has numerous informational
pathways operating simultaneously around different tasks.
These are difficult to disentangle, and the observable beha-
viours used to make inferences about the impact of
information flow are cryptic and difficult to discriminate
from baseline behaviour. Consequently, tracking and analys-
ing information dynamics using passive observations of
social-insect colonies is often prohibitively complex.
Individual-level alarm status in ants can be characterized
by observable changes in movement patterns and velocity
[23–26], and so information flow within ants’alarm networks
can be readily observed as they radiate out from an artificially
initiated alarm event. The challenge comes, however, in how
to capture the complexities of movement, contact and
response within a social group, especially at the second-to-
minute time scales relevant to the amplification and decay
of a group-level alarm response.
One way to do this is to employ machine learning (ML)
algorithms with complementary modelling techniques to
classify behavioural states at a fine-grained level [27–29].
An ML approach has the advantage of automatic characteriz-
ation of behavioural responses that normally require labour-
intensive observation that introduce both intra- and inter-rater
reliability issues [28,30]. Furthermore, the ML approach can
combine assessments of behavioural (alarm) state with
information on individual contacts, to simultaneously capture
the movement of information throughout the social group
and assess individual and group-level response to that
information.
In this study, we match individual alarm strengths with
contact networks in groups of workers from harvester ant
colonies (Pogonomyrmex californicus) to (i) characterize the
spatio-temporal dynamics of alarm spread, (ii) identify the
relative contribution of different mechanistic pathways (e.g.
chemical or physical) to alarm spread and (iii) evaluate
varied individual sensitivity to alarm stimuli. Our method-
ology offers a way to more directly assess the influence of
information spread on individual behaviour and to capture
the speed of information transmission and response across
a biological social network.
2. Materials and methods
(a) Animals and housing
Experiments were performed on three subsets, each containing
61 harvester ant workers, from three laboratory-reared colonies.
All three subsets were assessed for colony-level response to
alarm stimuli; one was additionally used to develop the ML
model for alarm state and to build the associated alarm propa-
gation network. The colonies from which the workers were
selected were housed in a circular Plexiglas nest, containing
water tubes. Colonies were fed with ad libitum Kentucky blue-
grass seeds and provided mealworms weekly. Colonies and
experimental subsets were maintained at a consistent tempera-
ture of 30°C. For test arenas, we used previously unoccupied
Plexiglas nest chambers with no other contents (15 cm diameter).
The arena was floored with plaster (thickness of 8 mm) and
placed on top of a foam pad within an enclosed glass tank
(2400 ×13
00 ×17
00) to prevent ants from being disturbed by the
internal vibration due to ants’activities and the external
vibration of the platform during experiments. A video camera
(Panasonic HC-WXF990) was securely mounted above the glass
tank to record all alarm events.
(b) Video capture of alarm events and general methods
for machine learning development
To maintain a similar density as in the ants’original nests (0.23–
0.42 ants cm
−2
), we randomly selected 61 workers from each of
the three colonies. Each selected ant was painted with unique
colour combinations on the head, thorax and gaster using Shar-
pie oil-based paint markers. The paint-marked ants were
transferred into the test arena and left to acclimate to the new
environment overnight prior to testing; no food was provided
during acclimation or testing. We video recorded 2 min of
activity in the arena as a baseline assessment of individual-
and group-level movement patterns prior to any manipulation.
Preliminary experimental results indicated that this time period
is sufficiently long to capture the entirety of an alarm event
from initiation through decay to baseline activity levels.
After video recording, three ants were randomly selected to
serve as initial alarm stimuli. These were carefully removed from
the testing arena, using an aspirator to minimize disturbance, and
placed into a separate Petri dish. We provided 25 min for ants in
the testing arena to acclimate after the removal event. The three
removed ants were then pinched gently with soft forceps until
they displayed visible agitated movement and dropped into the
centre of the test arena to initiate the alarm event. The group was
video recorded for 2.5 min immediately after the disturbed ants
were added to capture the alarm response.
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This protocol was replicated for subsets of ants from each of
three colonies to validate its effectiveness of inducing a group-
level alarm response and to assess whether we could consistently
elicit a distinctive pattern of collective alarm response across
replications. The group-level behavioural responses of all three
colony subsets were assessed by measuring differences in the
mean instantaneous speeds of ants during the baseline and
alarm events.
The random forest ML (RFML) algorithm, a non-parametric
method, has been applied widely and demonstrated to have
equivalent or even better performance than parametric methods
(e.g. a logistic model) [27,31]. We applied the RFML algorithm to
the group of ants chosen from colony B, which showed the stron-
gest alarm response. The methodological protocol, as outlined in
figure 1, consisted of video recording the movement patterns of
all individuals during baseline and alarm events and then choos-
ing frame sequences in which individuals were visually
identified as being alarmed or unalarmed and with low (calm)
or intermediate (alert) movement speeds. We used a frame-by-
frame assessment of movement features (a sliding window) to
analyse movement features within the selected frame sequences
and to train and test the RFML model. Our all subsequent ana-
lyses were based on the alarm behaviour of ants chosen from
colony B and their temporal dynamics of alarm strength ð
c
ASÞ
estimated by the RFML model.
(c) Behavioural characterization of alarm states
Because we cannot observe the internal state of any ant, we
associate the alarm state of an individual ant with observable
and consistent behavioural changes linked to the alarm response
occurring in the groups. In our harvester ant groups, we defined
that two ants have contact, and therefore are each others’neigh-
bours, if their physical distance is less than or equal to 45 pixels
(electronic supplementary material, §1). Based on this definition,
an ant can have more than one contact at a time. Ants identified
as alarmed move at more rapid and erratic speeds, with more
frequent contacts with nest-mates. This aligns with the functional
expectation of alarm behaviour as reacting to a potential threat and
communicating that threat to others. To extract this behavioural
pattern, we manually applied labels (Alarmed,Unalarmed
alert
and
Unalarmed
calm
), based on our visual assessments of behaviour to
a set of video track segments in which the behaviours could be
easily differentiated (electronic supplementary material, §2). These
were used for ML training and testing.
Our raw data included tracked coordinates, instantaneous
speed and body axis orientation for each ant. The behaviour as
Alarmed presents visually as a distinct increase in movement
speed, with a generally circular trajectory and increased contacts
with other individuals. This characterization is consistent with
ethological descriptions of alarm behaviour in other ant species,
including P. badius,C. obscuripes and C. japonicus [23–25].
Outside of the context of alarm, ants vary considerably in
their speeds and associated movement patterns. Therefore, we
also subdivided ants not categorized as Alarmed into two sub-cat-
egories: Unalarmed
alert
and Unalarmed
calm
.Those categorized as
Unalarmed
alert
were observed moving at moderate speeds and
potentially covering significant area in the nest but visually pre-
sented lower speeds, less frequent speed changes and lower
levels of contact with other workers. Ants labelled as
Unalarmed
calm
were stationary or moved at a low speed; they
may have been engaged in social contacts disassociated with
alarm, such as allogrooming, such that contact rate with neigh-
bours depended more on task performance than movement.
(d) Object tracking and feature extraction
We used the multi-object tracking program ABCTracker [32] to
obtain frame-by-frame movement data for each of workers
chosen from three colonies. ABCTracker provides a sequence of
time-stamped planar coordinates and body axis orientations for
each ant tracked, which allows determination of instantaneous
speeds, turning rates and the number of neighbours.
To detect behavioural transitions at a fine temporal resol-
ution, we developed a sliding time-window method that
creates a five-dimensional representation of each individual’s
movement characteristics and social context at each video
frame. For a given ant at frame t, we take a local track window
within frames [t, t + 29] (1 s) and construct a feature vector con-
sisting of five metrics computed over this window. These
include the frame-wise mean speed (MS), s.d. of frame-wise
speeds (SS), s.d. of body axis orientations (SO), convex hull
area (AR) and mean frame-wise number of contacts with
I: object tracking
frames
tXYq
tXYq
tXYq
tXYq
tXYq
ID
II: feature extraction
using sliding window
III: segment selection
and labelling
unalarmed calm
unalarmed alert
IV: random forest training
AS = 0.5
AS = 1
AS
AS = 0
random forest
samples
Figure 1. Stages of the algorithm development from data collection and encoding to estimation of alarm strength ð
b
ASÞ.(a) Object tracking is applied to raw ant
videos, producing a set of track matrices, each with size (no of frames × 4), representing the (x,y) position, speed and orientation data for each frame. (b) A sliding
window of length 30 frames is moved across each track. At each frame, we extract a multi-dimensional feature vector containing five metrics computed over the
window. (c) We select track segments that are visual exemplars of ‘Unalarmed
calm
’(AS ¼0), ‘Unalarmed
alert
’(AS = 0.5) or ‘Alarmed’(AS = 1) behaviour and add
them to the training and testing datasets with their respective AS numerical labels. (d) We train a random forest model to estimate these regression values from the
presented feature vectors. (Online version is in colour.)
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neighbours (MC) over the window. Sliding the window over a
track of length nproduces (n−30) feature vectors.
(e) Supervised machine learning
To create training and testing datasets, we selectively identified
16 track segments, each visually assessed to contain only one
of the three visually identified behavioural states. All feature vec-
tors extracted from a given sub-track were also assigned the same
human-rated value as a measure of alarm strength for regression:
AS
calm
=0,AS
alert
= 0.5 or AS
alarmed
= 1.0. Here, the ordinal value
(alarm strength (AS)) represents a unitless measure of the prob-
ability that ants are labelled as alarmed [31].
Using our data labelled with categorical and ordinal values,
we trained a RFML regression model to estimate continuous
‘alarm strength’ð
c
ASÞbecause of the versatility and simplicity
of this model in categorical/ordinal data (RandomForest pack-
age in Rv. 3.5.0). We then applied the trained model to
estimate
c
AS for unlabelled ant tracks over the entire video.
3. Results
(a) Validation of experimental procedure
The movement feature that is most often associated with
alarm state in ants is velocity [23,24]. Therefore, we first
employed the mean instantaneous speed of ants on each
frame to estimate collective alarm responses (figure 2).
Then, we employed the randomization test to examine the
difference of average ant speed between the alarm treatment
condition and the baseline condition (ms
t–c
=ms
t
–ms
c
) in the
groups of ants from colony A, B and C. The frequency distri-
bution of 5000 randomly calculated ms
t–c
gave the 95% CI of
(0.044–0.037) for colony A, (−0.073 to 0.069) for colony B and
(−0.0329 to 0.0361) for colony C. Furthermore, the ms
t–c
assessed in the raw data was 0.657 for colony A, 1.58 for
colony B and 0.752 for colony C, all of which were larger
than the ms
t–c
intervals from the randomly generated data,
yielding p= 0. Those results demonstrate that ants’speed sig-
nificantly increased, and their alarm responses were
successfully induced via re-introducing three alarmed ants
into the nest.
(b) Features extraction of alarm behaviour
By applying the sliding window technique on the raw data
of time-stamped planar coordinates and body axis orien-
tations for ants during the focal alarm event, we extracted
6462 vectors from track segments of the 16 focal ants. Each
vector pairing with manual annotations of categorical
alarm status and ordinal alarm strength value includes
five feature variables: MS, SS, SO, AR and MC. Pairwise
comparison within each feature variable after logarithmic
500 100 150
time (s)
0
mean instantaneous speed of all ants on each frame (pixels/frame)
1
2
3
4
5
colony A colony B colony C
baseline
alarm event
500 100 150
time (s)
500 100 150
time (s)
Figure 2. Comparing the mean speed of ants in subsets from three colonies at the absence (black dots) and the presence (grey dots) of the alarm event. Each frame
of the plot refers to a sample of 61 ants from a distinct colony. There is a significant difference between the baseline and alarm event for all three subsets
(randomization test, p≤0.00001) (1 pixel/frame = 4.1 mm s
−1
).
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conversion over the three alarm states supports the validity
of using these features to differentiate the ants’alarm status
(Wilcoxon test with large-sample approximation [33]: p<
0.005) (figure 3a). Principal component analysis of features
indicates that those five features variables are effective pre-
dictors for classifying alarm status (figure 3b). Among the
five principle components, the first two components
explained over 80% of variance; PC1 represented variance
in properties of locomotion patterns, with PC2 representing
variance in the number of contacts with nest-mates
(figure 3c,d). Therefore, we applied those feature vectors to
train the random forest regression model (electronic sup-
plementary material, §2).
(c) Alarm strength regression and classification
A random forest regression model was trained to estimate
continuous alarm strengths of individual ants ð
c
ASÞ, and its
accuracy was estimated using the root mean square error
(r.m.s.e. = 0.0276). The MS was ranked as the most significant
feature variable in predictions followed by SS, AR, SO and
finally MC. To identify alarmed ants and estimate their tran-
sition from an unalarmed to alarmed state, we applied the
multiclass receiver operator characteristic (ROC) pairwise
analysis to find the best threshold value for differentiating
alarmed ants from unalarmed ants (ants in states of
Unalarmed
calm
and Unalarmed
alert
) [34]. On an ROC curve
for the comparison between alarmed and unalarmed states,
a threshold of classification (0.749) was estimated correspond-
ing to the Youden index, J, a metric identifying the maximum
potential effectiveness of the classification [35]. The area
under the ROC curve for this comparison was 0.8906 (elec-
tronic supplementary material, §3). From this, we
categorized ants with
c
AS ≥0.749 as alarmed, and those
with lower
c
AS as unalarmed.
20
(a)(b)
(c)
(d)
*** *** ***
*** *** ***
***
*** ***
*** *** ***
*** *** ***
Unalarmedcalm
Unalarmedalert
Alarmed
Unalarmedcalm
Unalarmedalert
Alarmed
15
10
5
0
–5
0
–2
–4
0
0.8
0.6
0.4
weight2
0.2
0.8
PC2
PC1
0.6
0.4
weight2
0.2
SO
SS
MS
MC
AR
SO
SS
MS
MC
AR
2.5 5.0 7.5
1st component
20
15
10
5
0
–5
log (AR)
2nd component
log (MS)
20
15
10
5
0
–5
20
15
10
5
0
–5
log (SO)log (SS)
20
15
10
5
0
–5
alarm state
log (MC)
Figure 3. Pairwise comparison with a Wilcoxon test and principal component analysis on five feature variables after logarithmic conversion. (a) Pairwise comparison
of each feature variable with a Wilcoxon test over three different alarm status, which were visually assessed as Unalarmed
calm
(grey), Unalarmed
alert
(light blue) and
Alarmed (red). Three alarm states of ants differ from each other significantly within each feature variable (***p< 0.0005). (b) The five feature variables in the
training data were plotted in the 2D subspace of first two principal components. Grey represents frames when ants were identified as Unalarmed
calm
, light blue as
Unalarmed
alert
and red as Alarmed.(c,d) PCA weights for the training data. PC1 is a measure of locomotion pattern, and PC2 is a measure of social context. SO, s.d.
of body axis orientations; SS, s.d. of frame-wise speeds; MS, mean frame-wise speed; MC, mean frame-wise number of contacts with neighbours; AR, convex hull
area over the sliding window. (Online version in colour.)
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We also used the Granger causality test to examine the
relation between the group average speed v
group
(t) and the
group average alarm strength
c
ASgroupðtÞ[36] (figure 4).
The highly significant causal connection between them
(F
1,117
= 46.596, p< 0.00001) validates the significance of
velocity in random forest regression model. Meanwhile,
the average alarm response, as expected, Granger-causes
average velocity, but average velocity does not Granger-
cause average alarm response because alarm strength of
ants estimated in the RFML model carries the information
about movement and social context of ants beyond that of
the information contained in velocity alone.
(d) Spatio-temporal pattern of individual alarm
response
Alarm behaviour in a social group functions in part to trans-
mit information to others about potential danger. Here, we
examined the impact of the presence of alarmed ants that
entered the neighbouring space of unalarmed ants (electronic
supplementary material, §§S1 and S4). We identified 20
approaching events between unalarmed ants and their
alarmed neighbours. We then measured the s.d. of
c
AS for
the unalarmed ant sAS(Au
j) during the time of approach
and used the nonlinear least-squares fit of model to data. In
the model, unalarmed ants have an increased tendency to
advance in alarm strength as they approach an alarmed
ant, with this effect falling off roughly exponentially with dis-
tance from the alarmed ant (d
min
) (figure 5; equation (3.1); β=
0.94878, F
1,19
= 13.8137, p= 0.00146). This result demonstrates
the importance of proximity in the transmission of alarm
between ants.
sASðAu
jÞ¼lnð
b
Þ
b
ðdminÞ
:ð3:1Þ
It has been found that alarm-sensitive neurons exhibit
spike activity of 0–4 s in response to alarm stimulation in
the ant brain [37]. Therefore, to study the tendency of ants
to relay alarm signals received from others, we focus on
ants that become alarmed within 4 s of a prior ant’s own tran-
sition into the alarmed state. We use the resulting temporal
associations between alarmed ants to estimate alarm response
latency. Our results showed 46 ants eventually became
alarmed after the introduction of three alarmed seed ants.
Among those 46 ants, 39 unalarmed ants transitioned to the
alarmed state within 1.51 ± 0.1 s after contact-mediated
alarm stimulations, and seven unalarmed ants were induced
to be alarmed with no contact-mediated alarm stimulations
or exhibited alarm behaviour at timescales beyond the 4 s
response window.
(e) Network of alarm signal propagation
To assess how alarm propagates through the group, we con-
structed a time-ordered propagation network with weighted
and directed pathways (electronic supplementary material,
§§S5 and S6). Ants were considered to have transmitted an
alarm signal if the behavioural state of contacted unalarmed
ants changed within 4 s, considered from our rule above. In
the time-ordered propagation network, successful alarm
state transitions occurred primarily via contact-mediated
interactions (83%), with approximately 17% occurring as
independent excitement events. These independent exci-
tations may have been caused by alarm pheromone
diffusion from the alarmed ant. Tracing the path of alarm
excitation from three initially alarmed ants in the time-
ordered propagation network indicated that a burst of mul-
tiple alarm transitions and rapid spreading dynamics
happened initially, and the intensity of propagation declines
precipitously after the initial events (figure 6a; electronic
supplementary material, figure S7 and movie).
The 61 ants were highly interconnected with each other in
the social contact network (average degree = 48.98, cluster
coefficient = 0.862) (figure 6b). The alarm propagation net-
work was built as a subset of the larger social contact
network by assessing the primary pathway of alarm propa-
gation via contact-mediated interactions (figure 6b,c)
(electronic supplementary material, §§S5 and S6). During
the progression of alarm signal spread, the longest path
length from initial signal senders to receivers was four
edges (figure 6c). The number of signal receivers on each
0 25 50 75 100 125
0.3
0.4
avera
g
e alarm response level estimated
3.0
2.5
2.0
3.5
average velocity of ants (pixels/frame)
time (s)
Figure 4. The average
b
AS per second estimated by RFML model (grey line)
and the average velocity per second obtained by ABCTracker from experiments
(black line) (1 pixel/frame = 4.1 mm s
−1
).
50
0.02
0.01
0
data
fitted curve
100
distance (
p
x)
change in alarm strength of unalarmed ants
Figure 5. Spatial characteristics of alarm recruitment. Variation in unalarmed
ants
b
AS during the time of approaching their alarmed neighbours. Each point
indicates one unalarmed ant. The y-axis shows the variation in
b
AS during the
time the two ants were near each other, and the x-axis shows the minimum
distance between the two ants during that time. Unalarmed ants which came
closer to alarmed neighbours varied more in their
b
AS (7.3 pixel = 1 mm).
Circles represent observations; curve represents expectations in exponential
decay model equation (3.1).
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initial alarmed ants
alarmed via contact
alarmed independently
initial alarmed ants
alarmed via contact
alarmed independently
unalarmed ants
(a)
contact independent excitement
no. alarm transitions
10
8
6
4
2
010203040506070 160
time (s)
(b)
(c)
Figure 6. Dynamics of alarm signal propagation. (a) The temporal dynamics of alarm propagation via physical contact or independent excitement. Green bars
indicate the alarm transition via contact-mediated interactions. Blue bars indicate the independent excitations. (b) The background social contact network of
61 ants (nodes) with 4140 social contacts (grey edges) within 2.5 min, and alarm propagation network residing above the social contact networks after edge
pruning. (c) The alarm propagation network extracted from (b). Yellow nodes represent initially alarmed ants placed into the test arena. Green nodes represent
ants which become alarmed independently of alarm contact. Red nodes represent ants transited to alarmed via contacting with an alarmed neighbour. Grey nodes
indicate unalarmed ants. Edges highlight the propagation pathways via contact-mediated interactions. (Online version in colour.)
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network distance showed a significant pattern of linear
decline (slope = 5.1, intercept = 22.5, p<0.025, R
2
=0.95).
The time-ordered propagation network allows for quanti-
fication of varied individual alarm responsiveness,
potentially indicating variation in alarm sensitivity, which
can be measured by the quantity of key sensory cues before
a response is elicited [38]. Before each alarm state transition,
the number of contacts an unalarmed ant made with alarmed
neighbours was used as a measure of alarm responsiveness.
In other words, an unalarmed ant that underwent fewer
alarm stimulations prior to becoming alarmed would have
a higher responsiveness. We estimated 46 ants transitioned
to the alarmed state after a mean of 5.17 ± 1.04 contact-
mediated alarm stimulations. In figure 7, a geometric distri-
bution was regressed on the data by using the Lilliefors-
corrected K-S test, and the expected and observed frequencies
were not significantly different (D=0.33, d.f. = 46, p=0.12).
This right-skewed geometric distribution shows many ants
demand less than five alarm contacts, while a few ants
require more, to transition to the alarm status.
4. Discussion
Alarm behaviour in social-insect colonies functions to rapidly
communicate information about emergent potential threats
among individuals both proximal and distal to the threat. It
triggers further explorative and scattering behaviours, such
as ‘panic alarm’[39], or defensive behaviours, such as mobi-
lizing a collective aggression [40], or leads to a qualitative re-
configuration of the defence system [41]. In laboratory-reared
P. californicus colonies, alarm behaviour can be experimen-
tally induced and observed by introducing alarmed
individuals into a quiescent group or colony. The three
colony subsets tested within this study consistently showed
an immediate increase in alarm behaviour after the introduc-
tion of alarmed ants, which gradually decayed back to a
resting state within 2.5 min. This behavioural profile is con-
sistent with other descriptions of alarm behaviour [23,24]
and is illustrative of the ability of colonies to respond
immediately to potential threats and also to quickly damp
alarm response to spurious threat stimuli.
As with many complex social phenomena, it is not trivial
to study the rapid sequence of interactions during alarm
events using visual assessments or even with motion-tracking
analyses alone. Using the RFML model, we were able to con-
sistently quantify changes in individual-level alarm behaviour
across timeframes of seconds or less from tracking data and
categorical/ordinal annotations [31], which allowed us to
identify the significance of individual motion and interaction
most critical to alarm spread and decay. We trained our
RFML model using large sets of high-resolution motion data
from ants that were each subjectively scored by human raters
into different levels of alarm. A PCA uncovered relationships
between machine-identifiable features of motion and the
human ratings of alarm, which we then leveraged to train
our RFML model so as to be a reliable, highly repeatable auto-
matic labeller for alarm state in ants (as an example that could
possibly be replicated for other social systems).
Our findings demonstrate that, unlike the chemical diffu-
sion of volatile alarm pheromone, as the classic mechanism
for broadcasting alarm signals in social insects [23,42,43],
the transmission of signals in the context of potential
within-colony threats for P. californicus ants also requires
contact-mediated interactions. By contrast to large-scale
defensive signalling via alarm pheromones, these individ-
ual-to-individual contacts may function to locally scrutinize
cryptic potential intruders without generating an immediate
and costly full-scale response. Contact-based propagation
may thus allow a moderate collective alarm response and
damp quickly when no threat is confirmed. Thus, our results
confirm the utility of using proximity-based social networks
as proxies for potential information transfer in in-nest con-
texts where other communication modalities might also be
used (e.g. acoustic or chemical).
As in the physical interactions used in foraging recruit-
ment by the desert ant Cataglyphis niger [44] and alarm
recruitment by Atta insularis ants [26], the alarm signal trans-
mission in our seed-harvester ants primarily involved
contact-mediated interactions. Over 80% of alarm transition
events occurred maximally 4 s after physical-mediated con-
tact. However, harvester ants also use a high-volatile
contact pheromone (4-methyl-3-heptanone) as part of their
signalling process [45], and our results showed that a small
subset of individuals became alarmed independently of
physical contact likely via contact with alarm pheromone.
Thus, it may be that these ants mix some degree of local
and non-local spread, or it may be that the local signalling
mechanism is modulated by interactions with non-local
signalling.
Generating synchronous traces of liquid food and trophal-
laxis events helps to characterize the process of food
dissemination in laboratory-reared carpenter ant colonies
(C. sanctus) [46]. Similarly, the co-occurrence of alarm tran-
sition and contact-mediated interactions allows us to
characterize information spread dynamics. In our alarm
signal propagation network, the number of nodes was
observed to linearly decay with network distance from the
source of alarm signal, which suggests that sustained alarm
spread requires updating, which could theoretically serve to
differentiate initial perception of threat from sustained infor-
mation that a threat is real.
The temporal network analysis also offered opportunities
to evaluate the sensitivity of the alarm response. The geo-
metric distribution of alarm responses in the 46 ants we
characterized is right-skewed, which indicates that most indi-
viduals will transition to an alarmed state after only a few
contacts with other alarmed individuals, while a few
0
0.4
0.3
0.2
0.1
0
density
5 101520253035
alarm responsiveness
(no. alarm contact)
Figure 7. A right-skewed distribution of individual alarm responsiveness. The
Lilliefors-corrected K-S test on the alarm responsiveness, indicates observed
frequencies are not significantly different from expectations in a geometric
distribution (D=0.33, d.f. = 46, p=0.12). Bars represent the proportion
of observations; curve represents expectations in a geometric distribution.
royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 289: 20212176
8
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individuals are far less sensitive and require many more con-
tacts to become alarmed. A higher individual sensitivity may
increase the speed of alarm propagation, as more ants will be
triggered into alarm with fewer contacts during any alarm
event. However, such increased sensitivity may also lead to
longer propagation of false alarms. The significant variation
in alarm sensitivity opens the question of how inter-individ-
ual heterogeneity may contribute to adaptive modulation of
alarm propagation [47,48]. Methodologies developed here
allow us to bridge from varied individual responses and
adaptive collective responses to unveil potential modulatory
effects of individual variation on group-level propagation of
important signals (and damping of false signals).
Data accessibility. Data and electronic supplementary material are avail-
able at ASU Library Research Data Repository: https://doi.org/10.
48349/ASU/OYZWEK.
Authors’contributions. X.G.: conceptualization, data curation, formal
analysis, investigation, methodology, validation, visualization, writ-
ing—original draft; M.R.L.: formal analysis, investigation,
methodology, visualization, writing—review and editing; A.A.:
formal analysis, visualization, writing—review and editing; L.P.S.:
software, visualization, writing—review and editing; Y.K.: writ-
ing—review and editing; T.P.P.: writing—review and editing; J.H.F.:
funding acquisition, supervision, writing—review and editing.
All authors gave final approval for publication and agreed to be
held accountable for the work performed therein.
Competing interests. We declare we have no competing interests.
Funding. Funding for this research was provided by contract number
FA8651-17-F-1013 from the United States Air Force/Eglin AFB/FL
and in part by NSF (1716802 and 1558127) and The James
S. McDonnell Foundation (UHC Scholar Award no. 220020472).
Acknowledgements. We thank Min Shin, Lance Rice and David Farynyk
for technical supports on tracking; Kaitlin Baudier, Ioulia Bespalova,
Nathan Smith, Madeleine Ostwald and Colin Lynch for their com-
ments on the manuscript.
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