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Behavioural
Processes
141
(2017)
141–151
Contents
lists
available
at
ScienceDirect
Behavioural
Processes
jo
ur
nal
homep
ag
e:
www.elsevier.com/locate/behavproc
Swarm
intelligence
in
fish?
The
difficulty
in
demonstrating
distributed
and
self-organised
collective
intelligence
in
(some)
animal
groups
Christos
C.
Ioannou
School
of
Biological
Sciences,
University
of
Bristol,
Bristol,
BS8
1TQ,
UK
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
17
July
2016
Received
in
revised
form
5
October
2016
Accepted
8
October
2016
Available
online
11
October
2016
Keywords:
Collective
intelligence
Swarm
intelligence
Collective
cognition
Many
wrongs
Wisdom
of
crowds
Quorum
a
b
s
t
r
a
c
t
Larger
groups
often
have
a
greater
ability
to
solve
cognitive
tasks
compared
to
smaller
ones
or
lone
individuals.
This
is
well
established
in
social
insects,
navigating
flocks
of
birds,
and
in
groups
of
prey
collectively
vigilant
for
predators.
Research
in
social
insects
has
convincingly
shown
that
improved
cog-
nitive
performance
can
arise
from
self-organised
local
interactions
between
individuals
that
integrates
their
contributions,
often
referred
to
as
swarm
intelligence.
This
emergent
collective
intelligence
has
gained
in
popularity
and
been
directly
applied
to
groups
of
other
animals,
including
fish.
Despite
being
a
likely
mechanism
at
least
partially
explaining
group
performance
in
vertebrates,
I
argue
here
that
other
possible
explanations
are
rarely
ruled
out
in
empirical
studies.
Hence,
evidence
for
self-organised
collec-
tive
(or
‘swarm’)
intelligence
in
fish
is
not
as
strong
as
it
would
first
appear.
These
other
explanations,
the
‘pool-of-competence’
and
the
greater
cognitive
ability
of
individuals
when
in
larger
groups,
are
also
reviewed.
Also
discussed
is
why
improved
group
performance
in
general
may
be
less
often
observed
in
animals
such
as
shoaling
fish
compared
to
social
insects.
This
review
intends
to
highlight
the
difficulties
in
exploring
collective
intelligence
in
animal
groups,
ideally
leading
to
further
empirical
work
to
illuminate
these
issues.
©
2016
The
Author.
Published
by
Elsevier
B.V.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/4.0/).
Contents
1.
Introduction:
the
mechanisms
for
improved
performance
in
groups
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141
2.
How
could
fish
shoals
achieve
swarm
intelligence?
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143
3.
Alternative
mechanisms
for
improved
cognitive
performance
in
fish
shoals
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144
3.1.
Individual-level
improvements
in
cognitive
performance
in
groups
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144
3.2.
Group
diversity:
the
‘pool-of-competence’
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144
4.
Distinguishing
the
mechanisms
driving
improved
cognitive
performance
in
groups
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145
4.1.
Fish
studies
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145
4.2.
Other
vertebrates
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146
5.
Ecological
and
evolutionary
factors
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146
6.
Conclusion
and
recommendations
for
future
work
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147
Acknowledgments
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148
References
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148
1.
Introduction:
the
mechanisms
for
improved
performance
in
groups
A
major
benefit
animals
derive
from
social
interactions
is
access
to
information
(Dall
et
al.,
2005;
Danchin
et
al.,
2004).
This
social
information
is
relatively
low
cost
as
it
does
not
require
direct
sam-
E-mail
address:
c.c.ioannou@bristol.ac.uk
pling
of
the
environment
(unlike
personal
or
‘private’
information,
which
can
be
costly),
and
the
formation
and
maintenance
of
groups
allows
access
to
social
information
from
more
individuals
for
longer
periods
of
time.
The
flow
of
information
can
be
uneven,
for
example
from
particular
individuals
who
have
had
relevant
experience
such
as
finding
a
rich
food
patch,
or
be
more
egalitarian
where
all
group
members
have
an
even
probability
of
detecting
an
approaching
predator
and
all
other
individuals
copy
the
anti-predatory
response
of
the
individual
who
by
chance
made
the
detection.
With
this
shar-
http://dx.doi.org/10.1016/j.beproc.2016.10.005
0376-6357/©
2016
The
Author.
Published
by
Elsevier
B.V.
This
is
an
open
access
article
under
the
CC
BY
license
(http://creativecommons.org/licenses/by/4.0/).
142
C.C.
Ioannou
/
Behavioural
Processes
141
(2017)
141–151
Fig.
1.
Ways
in
which
individuals
in
larger
groups
can
make
better
decisions
than
individuals
or
smaller
groups.
Individuals
are
represented
by
circles,
and
the
flow
of
information
by
arrows
from
the
source
to
the
recipient.
(a)
When
there
is
individual-
level
improvement
of
cognitive
ability,
there
is
no
flow
of
information
between
individuals.
Instead,
being
in
a
group
reduces
the
perception
of
predation
risk,
allow-
ing
individuals
to
allocate
more
cognitive
resources
into
other
tasks,
or
vigilance
for
predators
where
risk
is
not
(or
less)
affected
by
prey
group
size.
(b)
Information
can
be
centralised
from
all
or
some
group
members,
where
it
is
processed
and
either
an
overall
group
decision
is
made,
or
the
information
is
made
available
to
group
mem-
bers
to
use.
(c)
In
the
case
of
leadership,
information
flows
from
a
single
(or
a
few)
individual(s)
with
pertinent
knowledge
to
other
group
members.
(d)
Only
in
swarm
intelligence
is
there
information
flow
and
no
obvious
key
individual
that
centralises
or
leads.
Note
that
for
illustration,
(a–d)
show
extreme
cases.
In
the
case
of
house-
hunting
ants
for
example,
swarm
intelligence
occurs
via
self-organised
interactions
between
scout
ants
(as
in
(d),
but
which
are
only
a
subset
of
the
whole
colony),
and
once
a
decision
is
made,
the
rest
of
the
colony
is
led
(c)
by
these
individuals
to
the
new
nest
site
(Franks
et
al.,
2003).
Similarly,
these
processes
are
not
mutually
exclusive
even
at
the
same
time.
In
a
foraging
group
of
birds
(Morand-Ferron
and
Quinn,
2011),
for
example,
individuals
may
have
improved
foraging
due
to
a
reduced
perception
of
risk
(a)
and
also
benefit
from
the
vigilant
individual
who
detected
the
threat
(c).
In
fish
shoals,
interactions
occur
directly
between
individuals
(Ioannou
et
al.,
2011),
while
in
social
insects
they
can
be
direct,
for
example
resulting
in
lane
formation
(Fourcassié
et
al.,
2010;
Perna
et
al.,
2012),
or
indirect,
as
occurs
in
trail
formation
via
the
deposition
of
pheromones
(Moussaid
et
al.,
2009).
ing
of
information,
cognitive
performance
in
ecologically
relevant
tasks
such
as
predator
avoidance
and
foraging
can
be
improved
for
individuals
and/or
the
group
as
a
whole.
There
are
diverse
ways
by
which
improved
intelligence
via
group
behaviour
can
be
achieved:
there
can
be
statistical
aggre-
gation
of
multiple
opinions
by
a
centralised
agent
(Galton,
1907),
a
reliance
on
a
knowledgeable
or
motivated
minority
of
the
group
leading
others
(Ioannou
et
al.,
2015),
or
the
decentralised
decision
making
most
commonly
seen
in
social
insect
colonies
(Bonabeau
et
al.,
1999;
Camazine
et
al.,
2001)
and
slime
moulds
(Boisseau
et
al.,
2016;
Reid
et
al.,
2016).
Although
the
terms
‘swarm’
and
‘collec-
tive’
intelligence
have
been
sometimes
used
to
describe
all
of
these
mechanisms
(e.g.
Krause
et
al.,
2009),
here
it
is
useful
to
explicitly
define
swarm
intelligence
as
only
improved
cognitive
performance
in
groups
that
arises
from
distributed,
self-organised
decision
mak-
ing
(Bonabeau
et
al.,
1999;
Garnier
et
al.,
2007;
Kennedy
et
al.,
2001;
Reid
and
Latty,
2016).
In
this
case,
the
critical
factor
is
that
the
improved
performance
is
primarily
due
to
information
being
exchanged
via
repeated
local
interactions
between
individuals
without
any
supervision
of
the
process
or
centralisation
of
infor-
mation
(Fig.
1).
The
resulting
network
of
interactions
that
arises
during
swarm
intelligence
is
thus
much
more
complex
than
in
cases
of
leadership
and
centralising
of
information
(Fig.
1).
It
is
this
rela-
tive
complexity
compared
to
other
mechanisms
(Fig.
1),
resembling
a
seemingly
disorganised
but
cohesive
swarm
of
insects,
that
puts
the
‘swarm’
in
swarm
intelligence
(Kennedy
et
al.,
2001).
Under
these
definitions,
statistical
averaging
of
many
guesses
can
give
accurate
decisions
in
some
tasks
due
to
the
cancelling
out
of
noisy
individual
estimates
(often
referred
to
as
the
“wisdom
of
crowds”;
Krause
et
al.,
2009)
and
hence
can
be
considered
a
mech-
anism
that
improves
cognitive
performance
in
groups
through
(albeit
simple)
interactions
between
individuals
(e.g.
Galton,
1907;
Krause
et
al.,
2009).
However,
its
reliance
on
centralising
informa-
tion
where
it
is
processed
(Fig.
1b)
would
exclude
it
from
swarm
intelligence.
In
contrast,
in
the
‘many-wrongs’
principle
the
noisy
and
inaccurate
estimates
of
individuals
in
which
direction
to
travel
are
cancelled
out
through
a
process
of
self-organisation
(Codling
et
al.,
2007;
Simons,
2004).
Here,
individuals’
direction
of
travel
are
influenced
by
both
their
private
information
(their
opinion
or
pref-
erence
to
move
in
a
desired
direction)
and
the
direction
of
travel
of
neighbours
(Codling
and
Bode,
2016).
While
this
can
still
be
con-
sidered
a
form
of
averaging,
it
occurs
at
a
local
scale
and
there
is
a
decentralised
exchange
of
information
as
individuals
are
both
influ-
enced
by,
and
influence,
their
neighbours
(Fig.
1d).
Importantly,
improved
performance
in
decision
making
from
swarm
intelligence
arises
as
an
emergent
property
of
individual
contributions
and
the
interactions
between
individuals
(i.e.
the
whole
is
greater
than
the
sum
of
its
parts).
This
makes
performance
through
swarm
intelli-
gence
much
less
predictable
than
via
other
mechanisms,
and
often
requires
computer
simulations,
as
well
as
empirical
work,
to
under-
stand
fully
(Bonabeau
et
al.,
1999).
To
further
illustrate
the
distinction
between
swarm
intelligence
and
other
mechanisms
that
result
in
better
cognitive
performance
of
groups,
consider
collective
vigilance
for
predators.
Here,
groups
have
a
greater
ability
to
detect
predators
due
to
pooled
vigilance
(Elgar,
1989;
Godin
et
al.,
1988;
Magurran
et
al.,
1985;
Taraborelli
et
al.,
2012),
often
referred
to
as
the
‘many-eyes’
effect.
This
is
an
example
of
group
intelligence
familiar
to
anyone
who
has
taken
an
undergraduate
course
in
animal
behaviour,
and
is
probably
the
earliest
example
of
groups
providing
a
cognitive
benefit
in
non-
human
vertebrates
to
be
documented
(e.g.
Miller,
1922).
With
more
individuals
in
a
group,
there
is
a
greater
chance
a
predator
will
be
detected,
with
this
information
being
transferred
to
others
in
the
group
via
intentional,
active
signals
(Seyfarth
et
al.,
1980)
or
passively
from
individuals
copying
fright
or
fleeing
responses
(Treherne
and
Foster,
1981).
Although
the
transmission
of
infor-
mation
can
occur
at
a
local
scale,
and
with
individuals
responding
to
the
potential
threat
only
via
others
that
also
did
not
directly
respond
(Herbert-Read
et
al.,
2015),
the
flow
of
information
can
radiate
out
directly
from
a
single
responding
individual
(who
acts
as
a
temporary
leader:
Fig.
1c).
Alternatively,
there
can
be
a
more
complex
exchange
of
information
between
individuals,
for
example
there
may
be
feedback
of
information
between
individuals
before
they
decide
whether
to
respond
(Fig.
1d).
In
both
cases,
there
is
an
improvement
in
predator
avoidance
in
larger
groups
as
vigilance
is
distributed
between
many
eyes,
but
whether
it
can
be
referred
to
as
swarm
intelligence
according
to
the
above
definition
will
depend
on
how
information
subsequently
flows
between
individuals.
This
is
likely
to
vary
between
species,
contexts,
and
with
group
size.
Another
issue
is
that
there
is
a
spectrum
of
interaction
networks
ranging
from
the
simple
(Fig.
1b,
c)
to
the
complex
(Fig.
1d),
and
it
is
not
clear
at
what
level
of
complexity
an
improved
decision
by
a
group
should
be
attributed
to
swarm
intelligence.
For
exam-
ple,
if
individuals
require
a
cue
from
a
threshold
number
of
other
individuals
before
responding
themselves,
as
in
quorum
decision
making
(Sumpter
and
Pratt,
2009),
but
there
is
no
bidirectional
exchange
of
information
between
individuals,
should
it
count
as
swarm
intelligence?
Improved
cognitive
ability
in
larger
fish
shoals
has
been
known
for
some
time.
Evidence
suggests
it
is
a
taxonomically
widespread
phenomenon
and
occurs
in
a
range
of
ecologically
relevant
contexts,
including
vigilance
for
predators
(Godin
et
al.,
1988;
Magurran
et
al.,
1985;
Ward
et
al.,
2011),
foraging
(Day
et
al.,
2001;
Pitcher
et
al.,
1982;
Smith
and
Warburton,
1992)
and
avoidance
of
pollutants
(Hall
Jr
et
al.,
1982;
McNicol
et
al.,
1996).
When
observing
a
large,
densely
packed
school
of
fish
respond-
ing
dynamically
to
avoid
attacks
from
predators
(e.g.
Handegard
et
al.,
2012;
Magurran
and
Pitcher,
1987),
it
is
hard
to
believe
that
C.C.
Ioannou
/
Behavioural
Processes
141
(2017)
141–151
143
such
responses
are
not
a
result
of
self-organisation
and
hence
an
example
of
swarm
intelligence.
However,
the
case
for
swarm
intel-
ligence
in
fish,
defined
in
a
strict
sense
where
the
improvement
in
cognitive
performance
comes
from
the
integration
of
information
from
multiple
individuals
via
self-organised
local
interactions,
is
maybe
not
as
well
established
experimentally
as
it
may
first
appear.
While
self-organisation
in
fish
shoals
is
not
in
doubt
(Hemelrijk
and
Hildenbrandt,
2012;
Ioannou
et
al.,
2011;
Parrish
et
al.,
2002),
there
are
few
studies
that
show
better
performance
in
a
cognitive
task
in
larger
shoals
and
also
demonstrate
that
the
mechanism
is
based
on
self-organisation.
In
this
review,
I
will
argue
it
is
actually
quite
difficult
to
rule
out
other
explanations
for
improved
performance
in
larger
groups,
and
suggest
possible
avenues
for
future
work
to
determine
more
clearly
the
mechanisms
underlying
improved
cog-
nitive
performance
in
fish
shoals.
Part
of
the
motivation
for
the
review
is
a
tendency
for
studies
to
usually
favour
one
mechanism
over
others,
while
below
I
suggest
instead
that
multiple
mecha-
nisms
are
likely
to
be
operating
simultaneously
in
many
real
animal
groups
(Morand-Ferron
and
Quinn,
2011).
The
review
aims
to
bring
together
what
is
known
about
improved
cognitive
performance
in
groups
of
fish
with
other
relevant
aspects
of
fish
behaviour
to
high-
light
outstanding
issues
and
encourage
further
work
to
solve
these
issues
in
the
future.
Where
relevant,
I
will
draw
on
the
literature
in
non-fish
species,
particularly
social
insects
and
birds,
where
a
lot
of
previous
work
has
been
published
on
group
performance
in
cog-
nitive
tasks
(although
often
not
referred
to
as
collective
or
swarm
intelligence).
Similarly,
although
the
focus
is
on
fish,
many
of
the
issues
I
highlight
will
apply
to
other
animals.
2.
How
could
fish
shoals
achieve
swarm
intelligence?
As
discussed
above,
the
key
to
determining
the
underlying
mechanism
for
improved
cognition
in
larger
groups
is
to
under-
stand
the
network
of
information
transfer
between
individuals.
In
a
diverse
range
of
collectives,
such
as
neural
systems
(Couzin,
2009),
slime
moulds
(Reid
and
Latty,
2016)
and
social
insects
(Wilson
and
Hölldobler,
2009),
the
networks
of
how
information
is
trans-
ferred
and
decisions
ultimately
made
are
relatively
clear
and
easily
mapped,
allowing
researchers
to
demonstrate
distributed
and
self-
organised
decision
making.
In
fish
shoals
and
other
types
of
groups
like
bird
flocks,
the
cues
that
transfer
information
within
groups
are
less
clearly
observed.
Although
group
behaviour
in
animals
has
been
studied
for
many
decades
(Krause
and
Ruxton,
2002;
Ward
and
Webster,
2016),
only
recently
have
advances
in
com-
puter
vision
tracking
of
animals
from
video
and
lightweight
GPS
units
allowed
high
resolution
data
to
be
obtained
from
multiple
individuals
in
a
group
simultaneously
(Attanasi
et
al.,
2014;
Perez-
Escudero
et
al.,
2014;
Pettit
et
al.,
2015).
These
advances
are
now
allowing
models
for
how
fish
shoals
and
bird
flocks
form,
move
and
make
decisions
(Aoki,
1982;
Gautrais
et
al.,
2008;
Hemelrijk
and
Hildenbrandt,
2012)
to
be
tested
with
real
animals,
allowing
the
networks
of
information
transfer
to
be
determined
(e.g.
Attanasi
et
al.,
2014;
Strandburg-Peshkin
et
al.,
2013).
There
are
a
diverse
range
of
ways
in
which
fish
communicate
actively
with
signals
including
sound
(Ladich,
2000),
body
gesture
signals
(Godin,
1995),
and
colour
change
(Nilsson
Sköld
et
al.,
2013),
particularly
in
the
context
of
reproductive
behaviour.
However,
our
current
understanding
is
that
in
the
vast
majority
of
shoal-
ing
fish,
group
formation,
maintenance
and
information
transfer
occur
through
passive
cues
where
each
individual
responds
to
the
position
and
movement
of
near
neighbours,
the
basic
principle
underlying
models
of
collective
motion.
It
is
generally
believed
that
fish
primarily
use
two
sensory
modalities
to
achieve
this
(Ioannou
et
al.,
2011):
vision
mediates
attraction
and
alignment
between
individuals
(Kowalko
et
al.,
2013),
while
the
mechanosensory
lat-
eral
line,
a
shorter-range
modality
that
allows
fish
to
detect
water
movements,
is
believed
to
regulate
repulsion
so
that
individuals
can
avoid
getting
too
close
(Burgess
and
Shaw,
1981;
Faucher
et
al.,
2010),
and
allows
fish
to
respond
to
rapid
changes
in
the
move-
ment
of
neighbours
as
occurs
during
a
startle
response
(Partridge
and
Pitcher,
1980).
Importantly,
cues
from
motion
are
relatively
short
range,
limiting
the
extent
of
global
cues
(especially
in
larger
shoals)
that
could
transfer
information
from
a
single
individual
to
all
others
directly.
Thus,
information
transfer
occurs
locally,
and
increases
the
chance
that
information
flows
between
individuals
in
complex
ways
(Fig.
1d),
potentially
allowing
for
swarm
intelligence
to
emerge.
From
an
empirical
perspective,
this
relatively
simple
form
of
information
transfer
has
advantages.
Using
automated
computer
tracking
from
video
(e.g.
Delcourt
et
al.,
2013;
Perez-Escudero
et
al.,
2014),
the
changes
in
movement
of
individuals
in
response
to
environmental
and
social
cues
can
be
quantified
in
detail,
allow-
ing
researchers
to
determine
how
fish
balance
these
sources
of
information
in
deciding
where
to
move
next
(Berdahl
et
al.,
2013;
Strandburg-Peshkin
et
al.,
2013).
Further
technology
to
record
sound
production
or
colour
change
of
individuals
(for
example)
does
not
seem
to
be
necessary.
It
also
allows
manipulation
through
the
use
of
robotic
fish
(e.g.
Faria
et
al.,
2010),
which
can
be
used
to
experimentally
manipulate
individual
fish
or
shoals.
For
exam-
ple,
individual
robot
fish
can
be
used
to
initiate
fright
responses
to
examine
whether
and
how
information
spreads
through
shoals,
or
as
a
conspecific
for
a
focal
individual
that
will
maintain
shoal
cohe-
sion
but
not
contribute
to
a
cognitive
task,
such
as
detecting
food
or
a
predator.
A
disadvantage
however
to
having
to
track
and
quantify
individ-
ual
movements
is
that
this
is
difficult
in
the
field,
which
is
especially
true
when
filming
underwater
where
visibility
is
restricted
com-
pared
to
in
air.
Thus
a
lot
of
work
on
group
performance
in
fish
has
been
limited
to
the
laboratory
by
testing
fish
on
a
high-contrast
background
to
facilitate
observation
and
tracking,
and
the
eco-
logical
validity
of
such
studies
not
conducted
in
the
wild
remains
unknown
(Morand-Ferron
and
Quinn,
2011).
While
tracking
of
fish
movements
is
possible
from
sonar,
it
has
yet
to
be
used
to
demon-
strate
collective
intelligence
under
field
conditions,
although
the
responses
of
prey
shoals
to
predatory
attacks
are
likely
to
be
a
fruitful
area
of
future
research.
For
example,
Handegard
et
al.
(2012)
used
high
resolution
‘acoustic
video’
sonar
to
track
move-
ment
in
schools
of
juvenile
Gulf
menhaden
(Brevoortia
patronus)
while
being
attacked
by
spotted
sea
trout
(Cynoscion
nebulosus).
In
this
study,
we
showed
that
the
distance
over
which
informa-
tion
transferred
in
a
group
increased
with
group
size,
suggesting
that
more
individuals
have
access
to
socially
derived
information
in
larger
groups,
which
would
help
facilitate
predator
avoid-
ance.
The
relative
ease
with
which
aspects
of
fish
sensory
systems
can
be
measured
(e.g.
Kowalko
et
al.,
2013;
Pita
et
al.,
2015)
gives
great
potential
for
testing
how
the
information
transfer
driving
swarm
intelligence
is
in
turn
determined
by
sensory
systems.
This
is
especially
true
with
new
models
of
collective
movement
and
col-
lective
detection
of
predators
that
make
more
realistic
assumptions
about
the
sensory
properties
of
animals
(Lemasson
et
al.,
2013;
Rountree
and
Sedberry,
2009),
an
approach
which
is
supported
by
older
experimental
work
in
fish
(Hunter,
1969).
The
study
of
Pita
et
al.
(2015),
for
example,
measured
the
field-of-view
and
visual
acuity
of
two
species
commonly
used
to
study
collective
behaviour,
zebrafish
(Danio
rerio)
and
the
golden
shiner
(Notemigonus
crysoleu-
cas).
This
was
then
used
to
make
quantitative
predictions
for
the
improvement
of
collective
detection
of
predators
as
a
function
of
nearest
neighbour
distance,
which
showed
that
the
potential
for
improved
detection
was
greater
in
zebrafish
compared
to
golden
shiners.
144
C.C.
Ioannou
/
Behavioural
Processes
141
(2017)
141–151
Table
1
Summary
of
studies
using
fish
that
have
demonstrated
an
improved
performance
in
a
cognitive
task
in
larger
shoals
and
proposed
a
mechanism.
Studies
are
given
in
the
order
they
are
discussed
in
the
main
text.
Refs.
Species
Proposed
mechanism
Smith
and
Warburton
(1992) Blue-green
chromis
(Chromis
viridis) Individual-level
improvement
Sumpter
et
al.
(2008)
Three-spined
sticklebacks
(Gasterosteus
aculeatus)
Swarm
intelligence
(quorum
decision
making)
Ward
et
al.
(2011)
Mosquitofish
(Gambusia
holbrooki)
Swarm
intelligence
Pitcher
et
al.
(1982)
Goldfish
(Carassius
auratus)
and
minnows
(Phoxinus
phoxinus)
Not
pool-of-competence
Berdahl
et
al.
(2013)
Golden
shiners
(Notemigonus
crysoleucas)
Swarm
intelligence
Bisazza
et
al.
(2014)
Guppies
(Poecilia
reticulata)
Pool-of-competence
Wang
et
al.
(2015)
Zebrafish
(Danio
rerio)
Pool-of-competence
3.
Alternative
mechanisms
for
improved
cognitive
performance
in
fish
shoals
3.1.
Individual-level
improvements
in
cognitive
performance
in
groups
Although
numerous
studies
have
demonstrated
improved
per-
formance
in
larger
fish
shoals,
there
is
evidence
from
studies
of
fish
and
other
animals
that
other
mechanisms
can
provide
(albeit
non-
mutually
exclusive)
alternatives
to
swarm
intelligence
(Table
1).
Firstly,
a
major
driver
for
the
evolution
of
group
living
is
a
reduc-
tion
in
predation
risk
(Krause
and
Ruxton,
2002;
Ward
and
Webster,
2016).
This
can
occur
because
of
attack
abatement
(risk
is
diluted
between
individuals
when
predators
can
only
consume
a
limited
number
of
prey
and
the
rate
of
attack
is
less
than
proportional
to
group
size,
e.g.
Santos
et
al.,
2016),
the
confusion
of
predators
when
multiple
prey
are
within
the
visual
field
(Ioannou
et
al.,
2009;
Lemasson
et
al.,
2016),
and
collective
vigilance
for
predators
(Elgar,
1989;
Godin
et
al.,
1988).
These
effects
have
been
shown
to
reduce
the
perception
of
risk
in
groups
(e.g.
Magurran
and
Pitcher,
1983),
allowing
individuals
to
be
less
vigilant
for
predators
and
spend
more
of
their
cognitive
resources
on
other
activities
such
as
forag-
ing
(Goldenberg
et
al.,
2014;
Griffin
and
Guez,
2015;
Morgan,
1988).
Thus,
individuals
themselves
can
afford
to
devote
more
of
their
limited
cognitive
resources
(Dukas,
2002)
to
other
tasks
and
may
show
greater
cognitive
performance
in
groups,
without
any
infor-
mation
exchange
taking
place
between
individuals.
If
the
cognitive
task
is
related
to
foraging,
this
effect
can
also
occur
as
a
result
of
perceived
competition
for
food,
which
generally
increases
in
larger
groups
(Grand
and
Dill,
1999;
Johnsson,
2003).
This
individual-level
explanation
for
improved
performance
in
groups
has
tended
to
be
the
mechanism
favoured
in
older
studies
showing
improved
cogni-
tive
ability
in
fish
shoals.
For
example,
Smith
and
Warburton
(1992)
found
blue-green
chromis
(Chromis
viridis)
in
larger
groups
fed
more
quickly
on
concentrated
swarms
of
prey,
which
they
argued
was
due
to
a
reduced
confusion
effect
as
the
fish
could
reduce
their
anti-predatory
vigilance
in
larger
groups.
This
was
supported
by
further
behavioural
observations:
as
the
fish
fed,
feeding
became
less
efficient
and
shoal
cohesion
increased,
suggesting
an
increase
in
perceived
risk
relative
to
the
need
to
feed
as
the
fish
became
satiated.
An
interlinked
potential
problem
in
experiments
is
whether
the
preferences
of
individuals
change
as
group
size
increases,
which
results
in
changes
to
motivation
at
the
individual
level
rather
than
information
being
the
key
factor.
Sumpter
et
al.
(2008),
using
three-spined
sticklebacks
(Gasterosteus
aculeatus),
attributed
the
improved
ability
of
groups
to
discriminate
between
which
of
two
different
replica
fish
to
follow
to
a
quorum
decision
mak-
ing
mechanism,
a
well
documented
way
in
which
bees
and
ants
achieve
swarm
intelligence
(Sumpter
and
Pratt,
2009).
However,
this
assumes
that
individuals
alone
or
in
small
groups
are
as
dis-
criminatory
regarding
group
members’
phenotypes
as
those
in
larger
groups.
In
moderately
sized
shoals,
the
presence
of
a
phe-
notypically
odd
individual
can
increase
predation
on
both
the
odd
and
non-odd
group
members
(the
“oddity”
effect;
Landeau
and
Terborgh,
1986).
This
suggests
that
members
of
larger
groups
should
be
more
discerning
of
the
phenotypic
traits
of
who
they
shoal
with,
while
single
or
pairs
of
fish
may
benefit
more
from
following
any
individual
(and
hence
increasing
their
group
size)
compared
to
the
cost
of
shoaling
with
a
phenotypically
different
individual.
Conversely,
if
smaller
groups
are
expected
to
be
more
discerning,
then
this
would
be
expected
to
dampen
the
apparent
improvement
of
cognitive
ability
in
larger
groups.
In
two
studies
discussed
in
more
detail
below,
single
fish
rather
than
pairs
would
be
expected
to
prefer
the
larger
of
two
groups
(Bisazza
et
al.,
2014),
and
individuals
in
smaller
groups
would
be
likely
to
be
more
risk
averse,
and
be
more
likely
to
avoid
a
model
predator
(Ward
et
al.,
2011).
In
fact,
there
is
also
recent
evidence
from
ants
that
individ-
uals
in
smaller
colonies
compensate
for
their
smaller
numbers
by
working
harder,
an
effect
which
should
decrease
the
effect
of
group
size
on
cognitive
ability
(Cronin
and
Stumpe,
2014).
3.2.
Group
diversity:
the
‘pool-of-competence’
Inter-individual
variation
has
been
shown
to
be
important
in
collective
animal
behaviour,
from
variation
in
overt
traits
such
as
sex,
body
size
and
age,
to
less
conspicuous
variation
in
traits
such
as
hunger
(Nakayama
et
al.,
2012)
and
risk-taking
tendency
(Ioannou
and
Dall,
2016).
These
traits
will
often
contribute
to
vari-
ation
between
individuals
in
knowledge
(McComb
et
al.,
2001),
motivation
(McDonald
et
al.,
2016)
and
cognitive
ability
(Trompf
and
Brown,
2014).
Social
interactions
themselves
can
even
estab-
lish
or
magnify
differences
between
individuals
(Bergmüller
and
Taborsky,
2010;
Rands
et
al.,
2003).
More
knowledgeable,
moti-
vated
or
cognitively
able
individuals
are
more
likely
to
influence
other
group
members,
and
are
less
likely
to
be
influenced
by
them
(Calovi
et
al.,
2015;
Couzin
et
al.,
2005).
The
greater
the
variation
between
individuals
in
these
traits,
the
more
likely
group
decisions
are
made
by
a
minority
and
are
followed
by
the
rest
of
the
group
(Griffin
and
Guez,
2015).
Larger
groups
are
statistically
more
likely
to
contain
knowledgeable,
motivated
and
able
individuals,
which
can
thus
explain
improved
cognitive
performance
in
larger
groups.
This
is
known
as
the
‘pool-of-competence’
effect
(Morand-Ferron
and
Quinn,
2011)
and
is
a
leading
explanation
for
why
rates
of
problem-solving
improve
with
group
size
in
flocks
of
birds
(Liker
and
Bókony,
2009;
Morand-Ferron
and
Quinn,
2011).
It
is
also
a
form
of
leadership
(King,
2010),
which
is
taxonomically
widespread
including
in
fish,
and
has
been
shown
to
correlate
with
holding
pertinent
information
(Ioannou
et
al.,
2015;
Reebs,
2000)
and
moti-
vation
(Harcourt
et
al.,
2009).
This
effect
may
explain
why
Godin
and
Morgan
(1985),
using
banded
killifish
(Fundulus
diaphanus),
found
a
strong
negative
relationship
between
group
size
and
the
variability
in
the
distance
groups
responded
to
a
model
predator.
Larger
groups
are
more
likely
to
contain
a
representative
sample
of
the
population
and
hence
be
less
variable
than
smaller
groups,
which
is
a
sample
size
effect.