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Flocks of birds in flight represent a striking example of collective behaviour. Models of self-organization suggest that repeated interactions among individuals following simple rules can generate the complex patterns and coordinated movements exhibited by flocks. However, such models often assume that individuals are identical and interchangeable, and fail to account for individual differences and social relationships among group members. Here, we show that heterogeneity resulting from species differences and social structure can affect flock spatial dynamics. Using high-resolution photographs of mixed flocks of jackdaws, Corvus monedula, and rooks, Corvus frugilegus, we show that birds preferentially associated with conspecifics and that, like high-ranking members of single-species groups, the larger and more socially dominant rooks positioned themselves near the leading edge of flocks. Neighbouring birds showed closer directional alignment if they were of the same species, and neighbouring jackdaws in particular flew very close to one another. Moreover, birds of both species often flew especially close to a single same-species neighbour, probably reflecting the monogamous pair bonds that characterize these corvid social systems. Together, our findings demonstrate that the characteristics of individuals and their social systems are likely to result in preferential associations that critically influence flock structure.
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Heterogeneous structure in mixed-species corvid ocks in ight
Jolle W. Jolles
a
,
b
, Andrew J. King
c
, Andrea Manica
b
, Alex Thornton
a
,
d
,
*
a
Department of Psychology, University of Cambridge, Cambridge, U.K.
b
Department of Zoology, University of Cambridge, Cambridge, U.K.
c
College of Science, Swansea University, Swansea, U.K.
d
Centre for Ecology and Conservation, University of Exeter, Cornwall Campus, Penryn, U.K.
article info
Article history:
Received 5 November 2012
Initial acceptance 2 January 2013
Final acceptance 16 January 2013
Available online 1 March 2013
MS. number: 12-00829
Keywords:
collective behaviour
Corvidae
Corvus frugilegus
Corvus monedula
ocking
jackdaw
rook
Flocks of birds in ight represent a striking example of collective behaviour. Models of self-organization
suggest that repeated interactions among individuals following simple rules can generate the complex
patterns and coordinated movements exhibited by ocks. However, such models often assume that
individuals are identical and interchangeable, and fail to account for individual differences and social
relationships among group members. Here, we show that heterogeneity resulting from species differ-
ences and social structure can affect ock spatial dynamics. Using high-resolution photographs of mixed
ocks of jackdaws, Corvus monedula, and rooks, Corvus frugilegus, we show that birds preferentially
associated with conspecics and that, like high-ranking members of single-species groups, the larger and
more socially dominant rooks positioned themselves near the leading edge of ocks. Neighbouring birds
showed closer directional alignment if they were of the same species, and neighbouring jackdaws in
particular ew very close to one another. Moreover, birds of both species often ew especially close to
a single same-species neighbour, probably reecting the monogamous pair bonds that characterize these
corvid social systems. Together, our ndings demonstrate that the characteristics of individuals and their
social systems are likely to result in preferential associations that critically inuence ock structure.
Ó2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
How do large aggregations of individuals, each of which may
differ in its preferred outcome, coordinate their movements? The
spectacular displays of ocking birds led the naturalist Edmund
Selous (1931) to postulate a role for thought transference, but
recent advances have begun to unravel the mysteries of collective
movement without appealing to the supernatural (Couzin & Krause
2003;Conradt & Roper 2005;Sumpter 2006). Models of self-
organizing systems suggest that repeated interactions among in-
dividuals following simple rules can generate complex patterns and
coordinated group movements. Models of agents following simple
rules of (1) long-range attraction to group members, (2) short-
range repulsion and (3) alignment between close neighbours
have generated realistic representations of collective animal
movements (reviewed in Sumpter 2006;Petit & Bon 2010). How-
ever, empirical verication of their assumptions remains scarce and
largely conned to model systems such as starlings, Sturnus vulgaris
(e.g. Ballerini et al. 2008a,b;Hemelrijk & Hildenbrandt 2011).
Mathematical models of self-organization commonly assume
that individuals are identical, independently interacting agents
(Vicsek & Zafeiris 2012), but this is unlikely to be realistic (Sumpter
2006;Petit & Bon 2010). Group members often mix associatively
according to a variety of morphological and physiological factors
such as sex, size and energetic state (reviewed in Krause & Ruxton
2002) and speciessocial systems have been shown to inuence
the spatial distribution of individuals in a variety of contexts (Krause
1993;King et al. 2008;Jacobs et al. 2011). However, studies of col-
lective behaviour seldom consider the impact of such heterogeneity
upon the spatial dynamics of ocks, or the rules of interaction un-
derlying their coordination. Recent studies suggest that these im-
pacts may be critical. Harcourt et al. (2009), for example,
demonstrated that individual differences have substantial impacts
on coordination rules in pairs of sticklebacks, Gasterosteus aculeatus,
while Nagy et al. (2010) identied a hierarchical structure in homing
pigeon ocks, Columba livia domestica, with key individuals con-
tributing disproportionately to the groups movement decisions.
Mixed-species ocks provide excellent opportunities for
empirical investigations into the impacts of heterogeneity on ock
structure. Species differences may generate nonrandom organiza-
tions of individuals within ocks (Latta & Wunderle 1996), while
members of larger or more dominant species may play a pivotal
role in leading group movements (Goodale & Beauchamp 2010).
Mixed-species ocks are an important form of social organization
for birds worldwide, and an extensive literature suggests that
*Correspondence: A. Thornton, Centre for Ecology and Conservation, University
of Exeter, Cornwall Campus, Penryn, Cornwall TR10 9EZ, U.K.
E-mail address: alex.thornton@exeter.ac.uk (A. Thornton).
Contents lists available at SciVerse ScienceDirect
Animal Behaviour
journal homepage: www.elsevier.com/locate/anbehav
0003-3472/$38.00 Ó2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.anbehav.2013.01.015
Animal Behaviour 85 (2013) 743e750
species differences are reected in the spatial structure and
movements of foraging groups. For instance, certain species may
play a disproportionate role in ock formation and cohesion, while
species that are particularly vulnerable to predation often follow
and exploit the vigilance of heterospecics (Sridhar et al. 2009;
Goodale & Beauchamp 2010). However, as research has focused on
foraging interactions, very little is known about the structure of
mixed-species ocks in ight. Analyses of such aerial ocks can
provide important insights into the interaction rules governing
group movements.
Using high-resolution photographs of jackdaws, Corvus mon-
edula, and rooks, Corvus frugilegus, in ight, we examined the ef-
fects of species differences and social systems on mixed-species
ocks. Jackdaws and rooks spend a large portion of the year for-
aging and roosting together in large groups. During the winter,
ocks of up to 1000 or so individuals leave their foraging grounds
and y to preroost trees before aggregating in a single ock num-
bering in the thousands above the roost where they spend the night
(Coombs 1961). The social system of both species centres around
long-term monogamous pair bonds (Emery et al. 2007), but rooks
are larger and dominant in foraging interactions and access to
roosting sites (Lockie 1956;Coombs 1961). Thus, these ocks are
neither homogeneous nor composed of anonymous individuals,
and so provide an ideal system to investigate how heterogeneity
(specically species differences and social relationships) can
mediate the movement rules that individuals adopt, and hence
inuence ock structure.
We assumed that ocking rooks and jackdaws would not
interact in an identical manner to all neighbours (cf. Nagy et al.
2010), and that this would be reected in ock structure. Speci-
cally, we predicted (1) that individuals would associate preferen-
tially with conspecics and (2) that, like high-ranking members of
single-species groups (King et al. 2009;Nagy et al. 2010), the so-
cially dominant rooks would position themselves near the leading
edge of ocks. If birds preferentially interact with specic in-
dividuals, then we predicted (3) greater proximity and alignment
among conspecic than heterospecicneighbours. Alone, such
assortment and alignment could simply reect differing aero-
dynamic or morphological constraints between the two species,
rather than differential reactions depending on neighboursspe-
cies. However, such constraints would not be expected to result in
the occurrence of discrete dyads of individuals within ocks. Con-
sequently, our nal prediction (4) was that birds should show
increased proximity to a single same-species social partner, which
is likely to reect the monogamous pair-bonded societies of these
corvids (Emery et al. 2007).
METHODS
Photography
We photographed corvid ocks moving to and from pre-
roosting sites before combining in a single large ock above the
roost (sunset 45 min), between 19 October 2011 and 8 February
2012 in an area of approximately 0.3 km
2
inandaroundthe
village of Madingley, Cambridgeshire, U.K (see Appendix Fig. A1).
Photographs were taken perpendicular to the ocksight di-
rection at a distance of approximately 100e300 m, from different
locations throughout each evening so as to avoid pseudor-
eplication from repeated shots of the same ock. The number of
different ocks photographed per evening ranged from one to 11
(mean ¼3.1 0.8). We used a Canon EOS 7D digital SLR camera
with a Canon EF 100e40 0 mm f/4.5e5.6 L IS lens. We set the
camera to Auto Focus with Av exposure mode, with photos taken
in RAW and settings adjusted to maximize distinguishability
between the features of jackdaws and rooks. The drive mode was
set to high-speed continuous shooting (8 frames/s), allowing us
to capture sets of consecutive images from the front, middle and
back thirds of ocks (hereafter ock section).
Photo Editing and Species Identication
Jackdaws and rooks are visually distinctive. Jackdaws are
smaller, with a short, black bill, grey nape, blue/grey eyes and
a wide tail in ight, while rooks are larger with entirely black
plumage, a long, bald beak, dark eyes, a relatively narrow tail and
primary wing feathers typically splayed in a nger-like fashion in
ight. To maximize clarity and enable species identication of as
many birds as possible, we edited all photographs using the Adobe
Photoshop Camera Raw plugin (Adobe Systems, San Jose, CA,
U.S.A.). We then identied rooks and jackdaws from the edited
photographs based on body size, head shape, beak shape, wing
shape and tail shape. From a total of 1211 photographs, editing
allowed us to identify the species identity of >95% of birds in 144
photographs. For analysis, we excluded photographs in which the
total ock size was less than 20 (as small ocks would not permit
analyses based on seven nearest neighbours in front, middle and
back; see below) and the few images from ocks consisting entirely
of a single species. This nal data set contained a total of 115
photographs from 44 ocks (N¼44 from the front and middle and
N¼27 from the back of ocks; each ock was assigned a unique
Flock Identity). Following editing, we merged all photos of front,
middle and back sections to form one larger image of the whole
ock (ock image). We counted the total number of birds in each
ock image as a proxy for total ock size and noted the proportion
of rooks in each ock. As birds were not individually identiable in
ight, it is possible that the same ock may have been photo-
graphed on different evenings. However, ock sizes varied sub-
stantially, from 21 to 638 individuals, and there were only three
instances (from a total of 44 ocks) where we photographed ocks
of the same size over different evenings. Our collection of photo-
graphs is therefore likely to represent a large sample of different
ocks.
Alignment and Proximity of Neighbours
To examine the alignment and proximity of neighbours, we
randomly selected four focal birds from each ock section (front,
middle and back), noting their species and that of their nearest
neighbours. We chose four focal birds because (1) this allowed us
to have several representatives from each ock section but (2)
the number of focal birds per section was sufciently low that we
could ensure focal birds would never be nearest neighbours to
each other, which would result in pseudoreplication. If two
randomly selected birds were both nearest to one other, they
were only considered in the analysis once and a new bird was
randomly selected. We determined the distance between the
midpoints of neighbouring birds in jackdaw lengths (based on
the average body length of seven randomly selected jackdaws in
the ock). To determine the directional alignment between
neighbours, we used the ruler toolin Photoshop CS5, by drag-
ging the tool from the midpoint of the tail and beyond the
midpoint of the head of each bird, thus providing the angle of the
line through the body, relative to horizontal in the photograph.
The difference between the angles of neighbouring birds was
used as a measure of alignment. Our estimates of distances and
alignment between neighbours necessarily involve some error as
they rely on two-dimensional representations of the true three-
dimensional structure of ocks. However, while these errors
introduce some noise into the data, they generate no directional
J. W. Jolles et al. / Animal Behaviour 85 (2013) 743e750744
biases. Our estimates are therefore likely to provide robust yet
conservative measures of the true degree of structure in ocks.
Statistical Analyses
Data were analysed in Genstat 14.1 (VSN International, Hemel
Hempstead, U.K.) using linear mixed models (LMM) or generalized
linear mixed models (GLMM) for normal and non-normal data,
respectively, with ock identity nested in date as a random term to
control for repeated measures in all cases. Initially, all probable
explanatory variables were entered into the model. All possible
interactions between them were investigated and terms were
sequentially dropped until the minimal model contained only
terms whose elimination would signicantly reduce the explan-
atory power of the model. Wald statistics and probability values for
signicant terms were derived from the minimal model containing
only signicant terms, while values for nonsignicant terms were
obtained by adding each term individually to the minimal model
(Crawley 2002). The residuals for all models were visually inspec-
ted to ensure homogeneity of variance, normality of error and
linearity. All results with P<0.05 are reported as signicant. Means
are quoted SE throughout. Post hoc analyses of differences be-
tween levels within categorical variables (e.g. front, middle, back)
were conducted by sequentially excluding each level from (G)LMM
analyses to enable comparisons of the remaining category levels.
Tables of results for all multifactorial analyses including all effect
sizes and SEs are in the Appendix.
Preferential associations by species
To test whether the birds showed preferential associations by
species (prediction 1) we randomly selected four focal birds per
ock section and ran a GLMM with binary response term (1,0)
testing the probability that a focal birds neighbour was a jackdaw.
Explanatory terms were focal bird species and the proportion of
rooks in the ock.
Positional differences by species
To compare positional differences between the species in ocks,
we randomly selected one focal bird in each ock section, noting its
species and that of its seven nearest neighbours. We used seven
neighbours because previous research indicates that individuals in
starling ocks interact with a xed number of six or seven neigh-
bours (Ballerini et al. 2008a). Unlike the analyses of associations,
distances and alignments between neighbours, there was no need to
restrict analyses to four birds per ock section to avoid pseudor-
eplication. To test whether rooks ew disproportionately near the
leading edge of ocks (prediction 2) we used a GLMM with a bino-
mial response term (number of rooks out of the total of eight birds)
and ock section (front, middle or back) as an explanatory variable.
Flock size, the proportion of rooks, month (to control for possible
seasonal variation) and time relative to sunset (because individuals
motivation to reach preferred sites within the roost may increase as
night approaches) were tted as additional variables.
Proximity and alignment between neighbours
To test whether distance and alignment differed between con-
specicand heterospecic neighbours (prediction 3) we noted the
distance (in jackdaw lengths) and directional alignment between
focal birds (four per ock section) and their nearest neighbours (see
Appendix). We then ran two LMMs with neighbour distance and
neighbour alignment as response terms and dyad type (jackdaws,
rooks or mixed) as our variable of interest, along with ock section
(front, middle, back) and ock size. Distances were square-root
transformed and alignments were normalized for analysis using
aBoxeCox power transformation.
Identication of discrete dyads within ocks
Field observations and visual inspection of photographs indi-
cated that jackdaws and rooks commonly y in discrete dyads
within ocks (Coombs 1961 reported similar observations). To
conrm this, we used a custom-made script written in R (www.R-
project.org) to measure the distance between all individuals (in
jackdaw or rook lengths, from the midpoint of each bird) and their
seven nearest same-species neighbours in a selection of nine ock
section photographs. In very dense ocks, even discrete dyads
would tend to y near other dyads. As an illustrative sample, we
therefore chose photographs of ock sections in which the density
was sufciently low to allow us to identify dyads clearly. The pho-
tographs used to examine jackdaw and rook dyads were not always
the same, as some images contained insufcient rooks. Using the
neighbour distance measurements, we conducted the following
analyses.
(1) Categorization of discrete dyads and triads. We dened
discrete dyads as same-species neighbours whose interindividual
distance was less than half the distance to the second closest
neighbour. This conservative measure is likely to underestimate the
true frequency of discrete dyads in ocks as discrete dyads could
nevertheless y close to other discrete dyads. We also investigated
the occurrence of same-species triads of birds, dened as cases in
which the nearest-neighbour distances between three birds were
all less than half the distance to the fourth neighbour. Triads may
occur among corvids when unpaired individuals (either adult birds
that had lost their partner or offspring from the previous breeding
season) associated with reproductive adult pairs, as described by
Lorenz (1952). The results are summarized in Table 1.
(2) Histograms of neighbour distances. For each of the photo-
graphs used in Table 1, we plotted, for each species, histograms
showing the frequency distribution of neighbour distances. If birds
often y in discrete dyads one would expect frequency distribu-
tions to exhibit a bimodal character, with the distribution of rst
neighbour distances being considerably lower than that of the next
six neighbours. As there is no generally accepted formal test of
bimodality, we present the histograms in Appendix Fig. A2 as
qualitative support for the presence of discrete dyads within ocks.
RESULTS
Preferential Association by Species
After controlling for the proportion of rooks within ocks, we
found that a focal birds nearest neighbour was signicantly more
likely to be of the same species (Appendix Table A1).
Table 1
Occurrence of discrete dyads and triads of jackdaws and rooks in ocks
Flock
ID
Total
jackdaws
Jackdaw
dyads
Jackdaw
triads
Flock
ID
Total
rooks
Rook
dyads
Rook
triads
A 75 10 (27) 6 (24) A 12 8 (67) 0
B 48 12 (50) 2 (13) B 11 4 (36) 0
C 108 12 (22) 5 (14) C 15 4 (27) 3 (20)
D 54 15 (56) 4 (22) E 7 4 (57) 3 (43)
E 19 6 (63) 1 (16) J 43 16 (37) 3 (7)
F 82 17 (41) 7 (26) K 33 14 (42) 0
G 76 12 (32) 2 (8) L 22 12 (55) 0
H 43 10 (47) 1 (7) M 17 10 (59) 6 (35)
I 90 13 (29) 5 (17) N 16 6 (38) 3 (19)
Mean percentages SE 41 5172464146
Numbers in parentheses indicate the percentage of birds of each species ying in
discrete dyads or triads.
J. W. Jolles et al. / Animal Behaviour 85 (2013) 743e750 745
Positional Differences by Species
Rooks made up only 21.8 0.03% of ocks on average, but were
disproportionately likely to be positioned at the front of ocks
(Fig. 1a, Appendix Table A2). The rst bird at the leading edge was
a rook in 19 out of 44 ocks (¼43.2%), more than twice as often as
expected by chance (binomial test: P¼0.001). Species distributions
within ocks were not signicantly affected by ock size, month or
time to sunset (Appendix Table A2).
Proximity and Alignment between Neighbours
Neighbours ew more closely together in larger ocks (Fig. 1b,
Appendix Table A3) and in the middle of ocks relative to the front
and back (Fig. 1c, Appendix Table A3). Jackdaw dyads ew sig-
nicantly closer together than rook dyads or mixed dyads (Fig. 2a,
Appendix Table A3), and the directional alignment of same-species
dyads was greater than that of mixed dyads (Fig. 2b, Appendix
Table A4).
Do Birds Fly in Discrete Dyads?
An average of 41 5% of jackdaws (range 22e63%) and 46 4%
(range 37e67%) of rooks in the illustrative selection of photographs
ew in clearly identiable, discrete dyads (Fig. 2c, Table 1). Histo-
grams of neighbour distances commonly showed a bimodal char-
acter with a peak before the average nearest-neighbourdistance for
each species (Figure A2), suggestive of discrete dyads of birds ying
in close proximity.
DISCUSSION
Contrary to the assumptions of many mathematical models of
single-species aggregations, which treat individuals as equivalent
and interchangeable, our results suggest that the structure of
mixed-species ocks may be critically inuenced by species dif-
ferences and social systems. The larger and socially dominant
rooks were disproportionately likely to be located in the front of
ocks. This effect is unlikely to result from the inuence of par-
ticular individual rooks, as our data set contained photographs of
numerous ocks of differing size, but rather seems to represent
a general property of mixed rookejackdaw ocks. Nor is the
pattern readily explicable by species differences in ight velocity
as rooks tend to be found towards the front of ocks despite
observational evidence suggesting that jackdaws can y faster
(Coombs 1961). Previous work on sh schools (Krause et al.
2000), zebra herds (Fischhoff et al. 2007) and small pigeon
ocks (Nagy et al. 2010) suggests that individuals located at the
0.5 8
7
6
5
4
3
2
1
00 100 200 300
Flock size
400 500 600
(a)
(c)
(b)
**
NS
0.4
0.3
0.2
0.1
Front
Front
Middle
Middle
Flock section
Flock section
*
***
Neighbour distance (jackdaw lengths)
Neighbour distance (jackdaw lengths) Proportion of rooks
Back
Back
0
6
5
4
3
2
1
0
Figure 1. (a) Proportion of rooks in the front, middle and back of ocks. The horizontal line indicates the average proportion of rooks across all ocks. (b) Relationship between ock
size and neighbour distances. (c) Distance between neighbours in the front, middle and back of ocks. Bars show means SE. Asterisks indicate signicance levels between
categories in post hoc analyses: *P<0.05; **P<0.001.
J. W. Jolles et al. / Animal Behaviour 85 (2013) 743e750746
front of groups tend to assume leadership roles, initiating
changes in direction or pace of movement that are followed by
group members. Similarly, rooks may play a dominant role in
inuencing collective movements of mixed-species corvid ocks.
It is possible that rookspreference for the front of ocks may
simply reect their motivation to reach the roost rst and obtain
favoured positions (Coombs 1961). If this was the case, one might
expect rooks to move to the front as sunset approaches, but we
found no such effect. Moreover, roosting ocks form spectacular,
swirling displays similar to starling murmurations (King &
Sumpter 2012) before settling, so individuals at the front of
preroosting ocks may not necessarily land rst at the roost.
Thus, it remains unclear whether rooks derive benets from
positioning themselves towards the front of ocks, whether jack-
daws preferentially follow rooks or whether speciesrelative posi-
tions reect aerodynamic considerations Future workincorporating
GPS technology to track ock members (Nagy et al. 2010) could
assist in discriminating between these possibilities.
The general rules of attraction, short-range repulsion and
alignment among neighbours proposed by models of self-
organization provide a valuable framework for understanding
ocking (Bajec & Heppner 2009;Petit & Bon 2010), but our results
indicate that their specic manifestations may be inuenced by the
characteristics of social systems. Our measurements of neighbour
distances and alignments are somewhat crude and, given the noise
in the data, they are likely to underestimate the true extent of
spatial structure within ocks. Nevertheless, a number of impor-
tant patterns were apparent. First, the extent of attraction and
repulsion may vary depending on the position within a ock, the
size of the ock (see Beauchamp 2012 for similar results in semi-
palmated sandpipers, Calidris pusilla) and the relationships be-
tween group members. Critically, corvids were not evenly
distributed across the ock but typically ew near conspecics,
with jackdaws being particularly closely attracted to same-species
neighbours, and birds of both species often appeared to y in dis-
crete dyads. The occurrence of discrete dyads of birds would not be
610
8
6
4
2
0
5
4
3
2
Jackdaws Rooks
NS
NS
(a)
(c)
(b)
** **
**
Neighbour category
Neighbour distance (jackdaw lengths)
Neighbour alignment (degrees)
Mixed Jackdaws Rooks
Neighbour category
Mixed
1
0
Figure 2. (a) Distance and (b) alignment between neighbours in jackdaw, rook and mixed dyads. Bars show means SE. Asterisks indicate signicance levels between categories in
post hoc analyses: **P<0.001. (c) Jackdaws ying in clearly identiable, discrete dyads.
J. W. Jolles et al. / Animal Behaviour 85 (2013) 743e750 747
expected to emerge from morphological or aerodynamic con-
straints alone and is likely to result from social partners ying
together, although further studies with identiable individuals
would be needed to conrm this. Second, the alignment of neigh-
bours was signicantly higher if they were of the same species,
with jackdaw dyads showing near perfect parallel alignment
(a mean difference of only 3.8
). Both species form lifelong,
monogamous pair bonds characterized by high levels of afliative
behaviour and close proximity (Emery et al. 2007), and our results
suggest the possibility that these relationships are reected in ock
structure.
Together, our results suggest that the theoretical convenience of
treating group members as identical and interchangeable does not
adequately reect biological reality in mixed-species ocks. Indeed,
we would argue that this assumption is similarly unlikely to hold in
single-species ocks in which individuals vary and have social
relationships. Differences between individuals can give rise to
leadership roles, which may be particularly pronounced in mixed-
species aggregations in which larger and more dominant species
may commonly take the lead (King et al. 2009). Moreover, studies
of both single-species and mixed-species ocks must consider how
the relationships between individuals may modulate the degree of
attraction, separation and alignment between group members.
Thus, ock structure cannot be fully understood without taking
speciescharacteristics, their social systems and individualsre-
lationships into account. Future work incorporating information on
the movements of known individuals will provide further empirical
data that can be integrated into mathematical models to better
understand the inuences of within-group heterogeneity on col-
lective movements.
Acknowledgments
We thank Neeltje Boogert for her friendship, help and valuable
comments. This work was funded by a British Ecological Society
grant and a BBSRC David Phillips Fellowship to A.T. (BB/H021817/1).
A.J.K was supported by a NERC Fellowship.
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Appendix
Coefcient estimates in all tables represent the change in the
dependent variable relative to the baseline category and can thus
be interpreted as measures of effect size.
Figure A1. Map of Madingley and surroundings. Photographs were taken within the
large shaded area. To avoid pseudoreplication, photographs taken within a given
evening were shot from different locations within this area. The hatched area shows
the roost, where ocks would combine into a single large ock and spend the night.
J. W. Jolles et al. / Animal Behaviour 85 (2013) 743e750748
70 20 50
40
30
20
10
0
35
30
25
20
15
10
5
0
17.5
15
12.5
10
7.5
5
2.5
0
60
50
40
30
20
Frequency Frequency
10
0
70 25
20
15
10
5
0
60
50
40
30
20
10
0
30
35 50
40
30
20
10
0
30
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0
25
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5
0
0 5 10 15 20 25 0 5 10 15 20 0 5 10 15
817.5 20
15
10
5
0
15
12.5
10
7.5
5
2.5
0
7
6
5
4
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2
1
0
10 30 50
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0
10
8
6
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2
0
25
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0
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8
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2
0
510152025
Distance in rook len
g
ths
Distance in jackdaw lengths
30 35 40 5 10 15 20 25 30 35
8
6
4
2
0
6
5
4
3
5 101520253035404550
2
1
0
0 5 10 15 20 25 30 0 5 10 15 0 5 10 15 20 25
0 5 10 0 5 10 15 0 51015
0 5 10 15 20 0 5 10 15 20 25 0 5 10 15
05
(a)
(b)
10 0 5 10 0 5 10 15
Figure A2. Histograms of neighbour distances for (a) jackdaws and (b) rooks. Panels show the frequency distribution for the ocks in Table 1. There was considerable variation in
neighbour distances within and between ocks, resulting in part from variation in ock shape and density. Nevertheless, a number of ocks exhibit a binomial character, with the
frequency distribution of rst neighbours (dark bars) showing a distinct peak. Critically, these peaks are lower than the mean nearest-neighbour distances of 2.4 jackdaw lengths or
3.4 rook lengths, indicating the presence of discrete same-species dyads of birds ying in close proximity to one another.
Table A1
GLMM on the probability that the nearest neighbour of the focal bird was a jackdaw
Wald statistic (
c
2
)df P
Full model
Proportion of rooks in ock 50.27 1 <0.001
Focal species (jackdaw, rook) 27.78 1 <0.001
Minimal model Effect size SE
Constant 1.46 0.17
Proportion of rooks in ock 4.95 0.70
Focal species
Jackdaw 0 0
Rook 1.37 0.26
This analysis used data from 454 neighbour dyads in 44 ocks. The binary response
term (1,0) indicated whether the neighbouring bird was a jackdaw. Flock identity
nested in date was tted as a random term (estimated variance component SE:
0.00 0.000).
Table A2
GLMM on factors affecting the proportion of rooks among focal birds and their seven
nearest neighbours
Wald statistic (
c
2
)df P
Full model
Proportion of rooks in ock 41.11 1 <0.001
Flock section (front, middle, back) 26.61 2 <0.001
Month (Oct, Nov, Dec, Jan, Feb) 8.84 4 0.065
Flock size 1.18 1 0.277
Time relative to sunset (min) 1.19 1 0.275
Minimal model Effect size SE
Constant 0.29 0.20
Proportion of rooks in ock 4.72 0.74
Location
Front 0 0
Middle 0.93 0.24
Back 0.78 0.19
This analysis used data from 115 photographs of 44 ocks, with ock identity nested
in date tted as a random term (estimated variance component SE:
0.201 0.138). Post hoc analyses by exclusion showed that there were signicantly
more rooks in the front than in the rest of the ock (front >middle:
c
2
¼23.67,
P<0.001; front >back:
c
2
¼11.07, P<0.001; middle ¼back:
c
2
¼0.61, P¼0.436).
Table A3
LMM on factors affecting the distance between neighbours
Wald statistic (
c
2
)df P
Full model
Neighbour category
(jackdaws, rooks, mixed)
48.95 2 <0.001
Flock section
(front, middle, back)
17.35 2 <0.001
Flock size 6.09 1 0.019
Minimal model Effect size SE
Constant 1.67 0.07
Neighbour category
Jackdaws 0 0
Rooks 0.51 0.09
Mixed 0.31 0.07
Location
Front 0 0
Middle 0.13 0.06
Back 0.16 0.07
Flock size 0.001 0.0004
This analysis used data from 454 neighbour dyads in 44 ocks. The response term
was the distance between each of four focal birds per ock section and its nearest
neighbour, measured in jackdaw lengths, and square-root transformed for analysis.
Flock identity nested in date was tted as a random term (estimated variance
component SE: 0.065 0.024). Post hoc tests by exclusion showed that jackdaw
dyads ew closer together than rook dyads or mixed dyads (jackdaws <rooks:
c
2
¼40.65, P<0.001; jackdaws <mixed dyads:
c
2
¼27.16, P<0.001;
rooks ¼mixed:
c
2
¼1.64, P¼0.203) and dyads in the middle of the ock were
closer than those in the front or back (middle <front:
c
2
¼5.83, P¼0.016; mid-
dle <back:
c
2
¼22.94, P<0.001; front <back:
c
2
¼5.19, P¼0.023; Fig. 1c).
Table A4
LMM on factors affecting the difference in alignment between neighbours
Wald statistic (
c
2
)df P
Full model
Neighbour category
(jackdaws, rooks, mixed)
26.93 2 <0.001
Flock section
(front, middle, back)
1.05 2 0.592
Flock size 0.05 1 0.821
Neighbour distance
(jackdaw lengths)
0.01 1 0.919
Minimal model Effect size SE
Constant 1.09 0.01
Neighbour category
Jackdaws 0 0
Rooks 0.01 0.01
Mixed 0.06 0.01
The analysis used data from 454 neighbour dyads in 44 ocks, with ock identity
nested in date tted as a random term (estimated variance component SE:
0.001 0.000). The response term was normalized for analysis using a BoxeCox
power transformation. Post hoc tests by exclusion showed that same-species
dyads were more closely aligned than mixed dyads (jackdaws <mixed:
c
2
¼25.24, P<0.001; rooks <mixed:
c
2
¼15.64, P<0.001; jackdaws ¼rooks:
c
2
¼0.19, P¼0.663).
J. W. Jolles et al. / Animal Behaviour 85 (2013) 743e750750
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Thesis
La grégarité compte parmi les phénomènes les plus communs du vivant et produit à son tour des phénoménologies parmi les plus impressionnantes observables dans le monde. Chez plusieurs espèces animales, des structures complexes émergent, alors qu'elles semblent à priori inaccessibles à l'échelle individuelle comme les formes spectaculaires prises par les bancs de sardines ou les étourneaux. De nombreux travaux sont dédiés à l'étude des déplacements collectifs et cherchent à comprendre comment des interactions locales permettent l'émergence de fonctionnements et structures complexes des groupements. La simple mise en mouvement d'un groupe nécessite coordination et transfert d'information rapide pour répondre aux contraintes environnementales et ainsi conserver l'intégrité du groupe. De nombreux mécanismes sont proposés dans la littérature pour rendre compte des règles d'interactions qui permettent de tels phénomènes. Bien souvent, ce sont des forces sociales qui sont utilisées et les modèles qui les utilisent ont prouvé leur robustesse dans la reproduction de ces phénomènes. Il me semble cependant qu'au niveau conceptuel, assimiler des interactions sociales à des forces présente de nombreuses limites notamment dans la prise en compte de comportements intermittents. J'ai dans ce travail de thèse investigué l'apport d'une hypothèse alternative basée sur des transitions probabilistes entre des états comportementaux. Nous avons dans un premier temps approfondi une étude expérimentale réalisée dans l'équipe permettant de mettre en lumière la constitution du voisinage influent, prérequis nécessaire à la formation d'interactions. Nous avons notamment pu rendre compte de la possibilité que les interactions dépendent de la distance. Dans un deuxième temps, nous avons construit un modèle individu-centré, modélisant la dynamique de transition entre stationnarité et départ collectif, en une puis deux dimensions. Nous avons quantifié la propagation d'une information, le départ d'un individu, dont la nature très particulière lui permet de rétroagir sur sa propre propagation. Nous avons ainsi révélé une phénoménologie très diverse, dépendante de la vitesse de déplacement des individus qui est un paramètre du système. Nous avons également pu mettre en lumière des propriétés de criticalité très affectées par les fluctuations liées à la stochasticité des transitions. Par ailleurs, nous avons construit un système d'équations aux dérivées partielles en support des simulations réalisées. Ce système a permis d'une part, de démontrer mathématiquement des propriétés et phénoménologies produites par les simulations, mais aussi de faire un parallèle conceptuel avec des équations de réaction-diffusion de type Fisher-Kolmogorov-Petrovskii-Piskunov. Enfin nous avons démontré dans une dernière étude que notre modèle était capable de conserver l'intégrité du groupe en dépit de l'absence d'une force explicite d'attraction. Cela nous a permis à la fois de définir ce qu'était la cohésion d'un groupe, et de quantifier une partie de ces caractéristiques. Nous avons finalement réussi à donner un modèle minimal qui permet la conservation de la cohésion du groupe.
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Feeding rates of mixed shoals of juvenile roach and chub were observed in a shallow stream near Cambridge (UK). Roach at the front of the shoal had significantly higher feeding rates than roach at the back and than chub in either front or back positions. Position in the shoal also had a significant effect on the kind of food consumed, with front roach feeding more on plankton and back roach more on bottom food. Altogether 36 fish from the stream were caught and marked. Half of these were deprived of food and the other half well-fed for 3 days in captivity. After release 36% of them joined their old shoal again. Individuals from the starved group occupied front positions significantly more often than well-fed fish, but after 2 days this difference disappeared.
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How animals in groups coordinate their movements to produce cohesive collective patterns has been the subject of many models but remains poorly documented empirically in the field. I investigated waves of escape in flocks of a shorebird roosting on beaches to pinpoint factors that influence coordination in the initiation of group movements. In roosting flocks of semipalmated sandpipers, Calidris pusilla, the escape of a single alarmed bird triggered similar responses in nearby companions, eventually propagating through the whole flock as a wave. Among flocks, wave speed through the whole flock increased with size but decreased with density over nearly three orders of magnitude in size and density. Large flocks are attacked more often by predators, and individuals in such groups may therefore respond more quickly to the escape of companions. The negative effect of density suggests that birds escaped more slowly in denser flocks to avoid collision. This interpretation is bolstered by the fact that wave speed increased with density at the edges of the flock, where the risk of collision is reduced. Waves propagated faster at the edges of the flock than through the whole flock, suggesting a selfish herd mechanism whereby edge individuals try to avoid being left behind or overtaken by others. Findings suggest that birds can integrate a host of factors to adjust escapes in a coherent fashion very rapidly.
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We have looked at different taxonomic groups to reveal where self-organization theory can make an important contribution to explaining collective behavioral patterns. Because this is a newer area of research, and because vertebrate groups may be difficult to study, developing theories of self-organization for these groups (which can then be tested empirically) is particularly challenging. Consequently we focused on how modeling approaches (particularly those that are individual based) have been, and are being, used to help reveal the organizational principles in human crowds (Sections II.B.1 and II.C), ungulate herds (Section II.A), fish schools, bird flocks (Section II.D), and primate groups (Section III.C). The collective behavior of such systems is largely characterized by the interactions among individual components, and thus is well suited to an approach that seeks to elucidate generative behavioral rules. We also discussed the evolution of collective behaviors (Section II.D.3). Here, theory has been important in demonstrating that different collective behaviors can exist for identical individual behaviors, suggesting that the evolution of collective (extended) phenotypes may be more complex than it may, at first, appear. Behavioral differences among individuals within a group may have an important internal structuring influence, and by using simulation models we showed how individuals can modify their positions relative to other group members (e.g., to move relative to the front or center of a group) without necessitating information about their current position within the group (Section III.B). This is important because it is unlikely that individuals within large groups (e.g., pelagic fish schools) can determine their absolute position relative to all other group members; thus we argue that natural selection is likely to act on the kind of local rules we discussed. In Section IV we discussed how local self-organized interactions result in the distribution of animals at a larger spatial and temporal scale, showing how mathematical studies of group size distributions are being used to make testable predictions about how individual behavior translates to that at the level of a population (Sections IV.A and IV.B) and how differences among individuals within a population may lead to phenotypically assorted groups within a population (Section IV.C). We also addressed the "optimal group size" concept (Section IV.D). As an alternative to the view in which individuals explicitly assess the size of groups and then make a decision to leave or join, we showed how local rules of thumb could be used by individuals to modify their probability of being within a group of a given size. We demonstrated that in real organisms (schooling fish) group size distributions (and hence the probability of an individual being within a group of certain size) is context dependent, and that this behavior is entirely consistent with a self-organized mechanism whereby individuals change local interactions as conditions change. In considering self-organization within vertebrate groups it is evident that the organization at one level (e.g., that of the group) relates to that at higher levels (e.g., that of the population). For example, self-sorting processes that lead to internal structuring within groups also result in population-level patterns when such groups fragment (e.g., phenotypic assortment), thus affecting the probability that an individual will be in a group of a given size and composition at any moment in time. These population properties then feed back to the individual interactions by changing the probability of encounters among different members of a population. Thus, to understand collective behaviors fully these properties cannot necessarily be considered in isolation.
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1. Observations were made on three Rook parishes in South-west Cornwall from 1943 to 1953. 2. The Rook roosts in the area were relatively small, the largest being less than 15,000 birds of which between 75 and 83 per cent were Jackdaws. 3. Regular use of the winter roost started when the rookeries are revisited in the autumn, i.e. when the moult was nearly over, the young were well grown, and post-nuptial testis regeneration has started. 4. The first assembly points before the Rooks left for the roost were at rookeries; second assembly points for the flocks from several rookeries were near to the roost. 5. Rooks from some rookeries go to two roosts. 6. Times of going to roost, and of leaving the roost are related to sunset and sunrise, but were very variable. 7. The feeding flocks were composed of the members of the rookery rather than of the roost. 8. The communal roost was used irregularly in the summer, possibly by young, non-breeding birds, and by adults whose attempts to breed have failed.
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Among the various species that form mixed-species bird flocks, “nuclear species” are thought to be important in flock formation and maintaining flock cohesion. Such nuclear species have been noted to occur in large groups on their own and to lead flocks, but the relationship between leadership and intraspecific group size has not been quantitatively tested at a large scale. Using a dataset of descriptive studies of terrestrial flock systems collected over 75 y, we found that intraspecific group size was significantly larger in flock leaders than in species that attend the same flocks but do not lead. The relationship held in a reduced dataset with phylogenetically-independent flock systems. We discuss a framework for explaining the connection between leadership and intraspecific group size, contrasting between those hypotheses that emphasize that gregariousness serves to attract the attention of other species, and those hypotheses that suggest that gregariousness leads to kin-selected behavior from which other species can also benefit.
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Bird flocking is a striking example of collective animal behaviour. A vivid illustration of this phenomenon is provided by the aerial display of vast flocks of starlings gathering at dusk over the roost and swirling with extraordinary spatial coherence. Both the evolutionary justification and the mechanistic laws of flocking are poorly understood, arguably because of a lack of data on large flocks. Here, we report a quantitative study of aerial display. We measured the individual three-dimensional positions in compact flocks of up to 2700 birds. We investigated the main features of the flock as a whole (shape, movement, density and structure) and we discuss these as emergent attributes of the grouping phenomenon. Flocks were relatively thin, of various sizes, but constant proportions. They tended to slide parallel to the ground and, during turns, their orientation changed with respect to the direction of motion. Individual birds kept a minimum distance from each other that was comparable to their wing span. The density within the aggregations was nonhomogeneous, as birds were packed more tightly at the border than the centre of the flock. These results constitute the first set of large-scale data on three-dimensional animal aggregations. Current models and theories of collective animal behaviour can now be tested against these data.