Download the hunting behaviour of individual great tits in relation to spatial

Anita. Behav., 1971, 19, 695-706
THE HUNTING BEHAVIOUR OF INDIVIDUAL GREAT TITS IN RELATION
TO SPATIAL VARIATIONS IN THEIR F O O D DENSITY
BY JAMES N. M. SMITH & R I C H A R D DAWKINS
Department of Zoology, South Parks Road, Oxford
Abstract. Individual great tits responded to variations in food density by spending a large proportion
of their total searching time in the regions of highest food density. Lower densities, however, were not
treated differentially, and the birds were slow to react to spatial changes in food density. The results
are related to those of L. Tinbergen and T. Royama. The experimental birds' behaviour provides
support for Royama's hypothesis that great tits can relate their hunting effort to the profitability
of different feeding areas. Tinbergen's search image hypothesis at present lacks behavioural support
in titmice and further work is required if searching images are to be thought to play a role in the
hunting behaviour of the great tit.
pectation, but rose above expectation at moderate prey densities. Tinbergen concluded from
this that the birds were not taking prey at random. He also noted that, when a new prey
species first became available in the food complex, there was a lag before its appearance in the
tits' diet. Tinbergen offered three hypotheses
to account for the last finding:
(a) That the average size of prey, and hence
their relative acceptability was small when they
first appeared. This interpretation was not consistent with the data.
(b) That the birds were not hunting in the
area where the new prey species appeared.
There was evidence that this factor was contributing to the result in one of the prey species
studied, adults of the bordered white moth
(Bupalus pinarius) which were taken on the
ground (Mook, Mook & Heikens 1960).
Tinbergen rejected this as a general explanation,
however, on the grounds that all other prey
species occurred in the canopy of the pine wood,
where the tits might have been expected to
encounter them as soon as they appeared in the
food complex. Note that this last point would not
follow if searching in the canopy had been nonrandom.
(c) That the lag was a consequence of a learning process in the birds. Tinbergen further
considered that specific characters of the prey
were involved in this learning and that the birds
performed 'a highly selective sieving operation
on the visual stimuli reaching the retina'. He
labelled this the formation of a 'specific searching image' and generalized it to account for the
under-representation of the species at low
densities in the tits' diet. He attributed the discrepancy from expectation at high densities to
The way in which predators respond to variations in the distribution and density of their
food is important in helping to understand two
problems: 'How is the behaviour of a predator
adapted to ensure efficient feeding?' and 'What
effect does such behaviour have on populations
of the predator's prey species ?'
The relationship between the insects of a
Scots Pine (Pinus sylvestris) plantation and their
avian predators was studied in detail over eight
consecutive breeding seasons by Tinbergen
(1960) in Hulshorst (Gelderland) in the Netherlands. Tinbergen and his collaborators paid
particular attention to the density of the larvae
of pinewood insects (mainly Lepidoptera) and
their representation in the diet of their titmouse
predators (more correctly, that proportion of the
diet fed to the nestlings). The main predator
studied was the great tit (Parus major), but
other passerines were also involved in the predation. Tinbergen found that, for a number of
the main prey species, those at the lowest prey
densities tended not to be taken by the tits.
At intermediate densities, the proportion of each
species in the tits' diet rose sharply, but this
increase tended to level off at very high prey
densities. Tinbergen compared the observed
predation with sets of 'expectation curves'
relating the percentage of a particular prey
species in the tits' diet to its density in the habitat.
These were built up on the basis of the density
of alternative prey, on the relative acceptability
of the individual prey species, and on an assumption that the predators were searching at random.
None of these curves gave satisfactory fits to
the data, except for green sawfly larvae (Diprion
spp.). At low and high densities the percentage
of most prey species in the diet fell below ex695
696
ANIMAL
BEHAVIOUR,
the tits selecting for food variety to avoid a
monotonous diet.
At the time, there was little evidence for the
third hypothesis, but it has since been demonstrated that wild birds may show striking preference in feeding on rare cryptic food items (Allen
1967). The importance of the specific properties
of prey has also been shown by Croze (1970),
and M. Dawkins (1971) has proved that the
ability of domestic chickens to see cryptically
coloured food grains changes with experience.
There is, however, no direct evidence in titmice.
Tinbergen's conclusions have been criticized
strongly by Royama (1970). Royama correctly
points out that Tinbergen's hypothesis has since
come to be treated as an established fact, and
that the evidence scarcely warrants such a
conclusion. Royama made extensive studies of
the food fed to nestlings by great tits in both
broad-leaved and larch woodlands. He also
found that there were no proportional or linear
relations between the density of prey species
and their occurrence in the nestlings' diet,
but he disagreed with Tinbergen's conclusions
on both factual and theoretical grounds and
constructed an alternative model to account for
his own and Tinbergen's data. The fundamental
assumption of this model is that the predator
tries to maximize its hunting efficiency by
sampling the food in different parts of the
habitat ('niches') and spending most time where
its success rate is high. Prey occurring at low
densities are under-represented in the predator's
diet, not because the predator does not learn
to find them because they are rare, but because
the predator rejects them as being 'unprofitable',
i.e. providing a low return in biomass or energy
per unit hunting time. This is consistent with
Royama's finding that some large lepidopterous
larvae, though relatively rare, are extensively
taken by great tits, and that very few encounters
with a prey species seem to be necessary to lead
to a sequence of them being brought to the nest.
Though it is not clear that use of searching images need be contrary to efficiency in hunting,
Royama's suggested mechanism of sampling
'niches' and distributing hunting effort, according to their 'profitability' makes clearer adaptive
sense.
Royama's model generates predictions which
accord with both his own and Tinbergen's data,
and it makes a prediction about th~ behaviour
of the tits (sampling a number of niches and
distributing search effort non-randomly between
them) which is readily testable. Some support
19. 4
for this suggestion is provided by the work of
Gibb (1958, 1962), who found that blue tit
(Parus eaeruleus) and coal tit (Parus ater)
predation on a single species of moth larvae
living in pine cones could be assessed by the
traces of attacks left by the fits. Although the
predation was low at low densities, it was clear
from the traces of attack that this was not
because the tits had failed to find the prey at
low density. Whether this would be true for
different prey species in different areas is another
question.
The possibility of an experimental approach
into the distribution of hunting effort in relation
to food density was suggested by the work of
Hassell (1971), who found that individual parasites (Nemeritis eaneseens, Hymenoptera) searching over a range of host densities in a laboratory
population, spent a disproportionately large
percentage of their time at the highest host
densities. Other evidence which suggests that a
laboratory approach to this problem might be
fruitful comes from studies on 'probability
learning' in birds, which suggest that titmice
could behave in a similar way to Hassell's
parasites. In such studies (e.g. Mackintosh 1969)
a pigeon or a chicken in a problem box is presented with two stimuli, one of which provides
it with a reward on a random, say 75 per cent,
of all responses (key-pecking, etc.), while the
other stimulus is rewarded on only 25 per cent
of responses. The subject learns to make more
than 75 per cent of its responses to the 75 per
cent rewarded stimulus. (The most efficient
strategy, if the situation is stable, is to direct
all the responses to the 75 per cent rewarded
stimulus.) This is effectively the problem encountered by a predator whose food occurs at
differing densities in different spatial locations
in its habitat, except that the predator is faced
with more than two choices and that the situation is inherently less stable, and hence less
'predictable'.
An experiment was therefore designed, on the
probability learning principle, but using a
number of different food densities, as did Hassell,
to test whether a small group of great tits would
indeed learn to distribute their hunting effort in
relation to food density. It is likely that great
tits in the wild may have secondary cues to the
density of their prey species, e.g. leaf damage, or
webs spun by some species of prey such as
Aeantholyda nemoralis, one of the most important prey species of Tinbergen's great tits.
However, this factor was eliminated by making
SMITH & D A W K I N S : H U N T I N G B E H A V I O U R OF G R E A T TITS
the tits perform an operant response (removing
the cap from a small, cylindrical pot) before
they were able to see the prey or any manifestation of them. The motor patterns and context
of the behaviour are close to those shown by
wild great tits searching for beech mast and
ground-dwelling invertebrates among leaf litter.
Methods
The Birds
Two male ('white' and 'blue-white') and three
female ('red-white', 'mauve' and 'blue') great tits
were used. The birds were named after the combination of colour rings they carried. They had
been hand-raised from the nestling stage (about
12 days post-hatching) and were 9 to 10 months
old when tested. They had previously been used
in an investigation of the relationship between
flocking behaviour and feeding and were accustomed to working for food rewards by searching
in a number of types of food container. Their
staple diet was a mash composed of commercial
chick crumbs, hemp seed, grit, bran, dried meat,
puppy meal, boiled egg and vitamin additives.
The mash was removed from the test area during
experiments. The birds were also given 'mealworms' (larvae of the flour beetle, Tenebrio
mollitor) which served as prey in the experiment;
these were the birds' preferred food.
The Aviary
The indoor aviary used was divided into two
portions each of which had access to an outside compartment. The layout is illustrated
in Fig. 1. Part of the indoor area was used as a
Out side
A
I
B
I Trapdoor
I
Slack
cage
i
1/
OI
Search area:
12f1" (3-7m)
Fig. 1. Plan view of the experimental area. Full lines
around the perimeter indicate solid walIs. Lines marked
with crosses are wire mesh partitions. 01 and 02 are
positions of observers.
697
stock cage where all the birds, except the one
being tested, were held. The dimensions of the
experimental area were 4.6 • 3.7 • 2.0 m
(minimum height). Each of the four feeding areas
(stippled in Fig. 1) consisted of a hardboard
base to which were glued 256 pots in a 16 • 16
square array. The pots were made from cylindrical sections of plastic pipe 38 mm in diameter
and 30 mm high. Each pot wa~ covered by a
cap of aluminium alloy foil 0-024 mm thick,
as shown in Figs 1 and 2.
OTHERS
Fig. 2. Methods used by great tits to remove caps from
the food pots.
Recording Methods
The main record of the behaviour of each
bird was recorded by an observer at position 01
(Fig. 1) using an automatic keyboard recorder
which represents behavioural events as notes
on a small electronic organ. The sequence of
coded events is tape recorded and subsequently
decoded by a small digital computer which
recognizes the frequency of each note and prints
out a record of the event and time at which it
occurred. The system is described in detail by
R. Dawkins (1971). Four behaviours were
recorded: lands in one of four areas; leaves area;
searches (i.e. removes foil cap); and finds a mealworm. The vast majority of the time spent on
each board was spent in actually moving across
the board removing food caps rhythmically. As
an illustration of this, bird 'blue-white' removed
698
ANIMAL
BEHAVIOUR,
a total of 100 caps in a single bout of searching
on a board with density 1, the mean interval
between cap removals being 1.18 s, with a
standard error of 0.05 s. In addition to the timed
measures, the bird left a visual record of its
searching activity by the trail of removed caps
and this was recorded at the end of each experiment.
Training and Testing Procedure
Single birds were trained to remove caps to
obtain a mealworm by first presenting food at a
density of one mealworm to four pots with
some of the caps removed so that the tit could
see the mealworms from a perch. The five birds
used learned to remove caps to obtained mealworms within 30 min of the first presentation;
a sixth bird that used an inefficient technique
to remove the caps (pecking through the cap it
was perched on), was not tested in the experiment. The techniques used by the birds to remove the caps are illustrated in Fig. 2.
Once the birds had learned to remove the
caps, the ratio of rewarded to unrewarded pots
was reduced to 1 : 1 5 and three to six more
trials were carried out to determine the birds'
preferences for different feeding areas. In these,
four half-areas with 128 pots were used. In all
cases the working rate of the birds increased
during this training period.
The birds were then tested, using the four
complete areas (as shown in Fig. 1) for the first
time. When a test was due to occur, the test
bird was driven gently into outside compartment
B and the trapdoor (Fig. 1) was closed. The
bird was then deprived of food for approximately
one hour while the experiment was laid out.
The stock birds were kept in outside area A
during the actual test period to prevent any
possibility of observational learning. On all
occasions, the trapdoor was opened at the start
of the test period and the test bird entered immediately. Each trial was timed to last for 5 min
from the time that the test bird first landed in
any feeding area. At the end of the 5-rain period,
the bird was driven gently back into the outside
compartment.
The birds were provided with different densities of mealworms in each area so that the highest
density area contained 16, the next highest 8,
then 4 and finally one mealworm per 256 pots.
The location of each density was kept constant
between trials. The highest density was sited in
the location that the bird had visited least during
training and the lowest density in the most
19,
4
visited location. The actual location of the mealworms within the 256 pots in a feeding area
was determined by random number tables with
the restriction that no pot contained more than
one mealworm. Each bird was given a series of
5-min trials until the criterion that more than
half of its searching time was spent on one board
on seven out of eight successive trials was
reached. This t o o k between twelve and fifteen
trials for four of the birds, but required twentytwo for the fifth, 'red-white'. Trials were repeated at approximately hourly intervals during
daylight. U p to seven trials were run per day,
six being the commonest number. When the
consistency criterion was reached, the positions
of densities 1 and 16 were reversed so that the
birds were now rewarded at a low rate in the
location where they had previously been rewarded at a high rate and vice versa. Intermediate density boards were not changed. This new
condition was then held stable for a further
ten to thirteen trials.
All statistical tests were taken from Seigel
(1956).
Results
The overall results are presented in two different
ways in Figs 3 and 4. In Fig. 3 examples are
given of cumulative records of the searching
time (ordinate, see figure legend for definition)
plotted against the number of trials for two individual birds. I n Fig. 4 the overall response of
all the birds to the highest density (16) is shown.
Performance of Birds Up to the Reversal Point
Figure 3 shows that the individual birds
learned to discriminate the highest density areas
from the lower densities, but that the speed of
this learning differed within individuals. 'Mauve'
spent the greater part of its time searching in the
area of highest density almost from the start
while 'red-white' learned much more slowly.
These two birds were extremes, the other three
birds being more like 'mauve' than 'red-white'.
I f the distribution of searching times in the less
dense areas is considered, there was no clear
tendency for the birds to visit these in proportion
to the density of food present. This was also true
for the three individuals not shown in the cumulative plots. There were in fact striking differences in the way that individual birds treated
the lower density areas.
Bearing this heterogeneity in mind, the pooled
data for the responses of all five birds to food
density is shown in Fig. 5. The black bars of
SMITH & DAWKINS: HUNTING BEHAVIOUR OF GREAT TITS
3000l
......
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point
...~
,~176
o..,.~
.......4
....
Reversal
Reversalpoinf
T
Densffies,
.....,,,
..~
.(~1/1
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r ,.~176
8
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l.-
,ooo
........... "'"'" I" ._/-;-.:-"
,-"7;"
0
5
I0
15
20
25
50
55
No of 5 raintrials
Fig. 3. The cumulative distribution of searching time over the four densities
for two individual great tits, (a) 'red-white' and (b) 'mauve'. Searching time
is the total time spent on a feeding area, not including the time taken to
handle prey. After the reversal point, the locations of densities 1 and 16 are
reversed while those of 4 and 8 remain unchanged.
1001
/,. . . . Reversal
.,.
9
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DISTRIBUTIONOF
SEARCH EFFORT
(on last 8 trials
before reversat)
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100
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TRIALS
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20
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9
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Fig. 4. Response of all five birds to the highest prey
density. The ordinate is the time spent by each bird at
the highest density expressed as a percentage of the total
searching time on that trial. The switch in location of
the richest prey area is indicated by the symbol 'R' and
the break in each record.
0
~
1
.
4
8
1 ~
.
16
0
~
PREY DENSITY
Fig. 5. Pooled data from all birds on the distribution of
hunting effort in relation to food density over the last
eight trials before the locations of densities 1 and 16
were reversed. Black bars indicate the observed distribution of searching; unfilled bars indicate the proportional distribution of food over the four areas.
the histogram represent the percentage of time
the birds spent actually searching ( n o t including
the time required to eat mealworms) at each
density of food, over the last eight trials before
the reversal point. The total time spent searching
b y each bird at each density is given in Table I.
The r a n k sum ( F r i e d m a n Analysis of Variance)
for the density 16 has the m a x i m u m possible
score a n d all the other r a n k sums do n o t differ
from each other. A simple interpretation of this
result is that the birds did learn the location
of the highest food density, b u t t h a t they failed
ANIMAL
700
BEHAVIOUR,
Table I. Total Times (seconds) Spent Searching at Each
Density Over the Last Eight Trials before Reversal
Bird
Density 16 Density 8 Density 4 Density 1
'White'
1256
64
197
201
'Blue-white'
932
16
238
290
'Red-wbite'
1224
284
126
0
'Mauve'
1509
58
0
124
'Blue'
1453
37
0
0
10"0
8"5
11.5
Rank sums
20.0
The rank sums differ significantly (P<0-05) on the
Friedman two-way analysis of variance.
4
It is clear that, although all birds did visit the
new location, only one ('blue-white') reversed
its previous behaviour by the time the experiment was terminated and spent the majority o f
the available time searching in the new density
o f 16. This was not simply a consequence o f the
birds failing to find food by chance when they
did visit the new high density. This is clearly
shown by ' m a u v e ' in Fig. 6, where data from Fig.
5 are plotted along with a measure o f the actual
n u m b e r o f mealworms f o u n d on visits to density
16. This bird actually f o u n d mealworms in the
first three pots it searched, on first visiting the
new location o f density 16. Even this dramatically
high reward rate did n o t overrule the bird's
previous lack o f success in that location, and it
to discriminate between the lower densities.
A possible contributory factor to the latter
failure could be that, for each bird, the preferred area during training was subsequently
assigned the lowest density during actual testing,
but this does not account for the failure to
distinguish densities 4 and 8.
w
'Y
,<[
EFFECT OF REWARDS
AFTER REVERSAL
P,n
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m
>:_
5
.
? \"1 1
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--:1,jv.v,:;,
:,
MAUVE
I--
Performance After the Reversal of the Locations
of Densities 1 and 16
All the five birds continued to search intensively in the location o f the former high density
area for at least four trials after the reversal
(Table II and examples in Fig. 3). By the tenth
trial after reversal, however, the birds spent, on
average, only 19 per cent of their searching time
in the old high density area. The responses to the
new location o f density 16 are shown in Fig. 4.
19,
i ""!i'~i
i i~!iii:...
o'~
uJ
0
5
10
i
15
20
25
]'RIALS
Fig. 6. The upper part of the figure is taken from Fig. 4.
The lower part shows the number of mealworms eaten
on each trial. One dot represents the capture of one
mealworm.
Table II. Persistence of Response to High Density Location Despite Change in Density to 1 Mealworm Per Board
Percentage of total search time in area of density 16 before
reversal and density 1 after reversal
No. of trials
Before reversal
4
3
2
1
Average
White
Blue-white
Red-white
Mauve
Blue
Average
38
87
66
90
66
76
71
81
94
95
78
100
81
100
52
100
100
100
77
76
76
92
69
89
70
73
92
83
88
82
86
79
15
44
68
52
46
45
84
68
47
91
77
98
68
55
42
79
98
92
71
75
55
65
58
53
72
74
78
67
After reversal
1
2
3
4
Average
SMITH & D A W K I N S : H U N T I N G BEHAVIOUR. OF GREAT TITS
only returned to it on four of twelve subsequent
trials.
An effect of previous experience is also seen
by comparing the responses to the new location
of density 16 on the first five visits made to it
(i.e. ignoring whether they came on the same
trial or not), with their responses on the first
five visits to density 16 at the beginning of testing.
Figure 7 shows the distributions of intervals
between successive visits to this density. (If the
DENSITY = 16
(a) Treatment w h e n
first e n c o u n t e r e d
1
Z
O
cF
t/'} 0 t
I,I
I-I
,
,1-1
o
(b) Treatment a f t e r
locations reversed
h
o
701
did visit it after reversal. This suggests that the
birds may have reacted to the recent reward rate,
but that the effect decayed rapidly with time if it
conflicted with longer term experience.
This last conclusion is reinforced if the
measure of the interval between successive visits
to density 16 is correlated with the rank order
of the visit to that density (first, second, etc.).
There is a marginally significant decrease in
interval size ( r = - - 0 . 3 4 3 , P < 0 . 0 5 , Spearman
Rank Correlation) as the number of visits to
density 16 increases during the first five visits
when the test series begins, i.e. the birds became
increasingly ready to visit the board, but there
is no such decrease (r=-}-0.036, P > 0 . 5 ) in
the same measure during the first five visits
after reversal.
The birds' response to densities 4 and 8
after reversal contrasts with their apparent
failure to discriminate these two densities before
reversal. I f the overall numbers of visits paid
to the two densities after reversal are compared
(Table III), over twice as many visits are made to
Table HI. Comparison of the Numbers of Visits Made to
Densities 4 and 8 by Individual Birds
0 I
11
0
5
'P' value for
Before
After
comparison of
l(reversal reversal before and after
ast eight (all trials)
reversal
trials)
(Fisher exact
probability test)
r-q
10
15
20
INTERVAL
Fig. 7. The upper histogram (a) shows the frequency
distribur
of intervals between successive visits to the
density 16, when it was first encountered by all birds at
the beginning of testing. The lower histogram (b) shows
the same measures when the new location of density 16
was first encountered after the locations of densities
1 and 16 had been interchanged. Only the first five
4
8
4
8
'Mauve'
1
1
14
7
0 89
'Red-white'
3
8
10
29
0.70
'Blue'
0
2
0
25
1.00
10
6
11
26
0"03
9
1
9
3
0.37
23
18
44
90
0.007
intervals for each bird are plotted in each histogram.
'White'
first visit is followed by an immediate return,
the interval is zero; if fifteen visits are made to
other densities, then the interval is fifteen.)
The upper histogram shows that the birds returned after less than three visits in twenty-three
of the twenty-five cases when they first encountered density 16. After the location reversal, however, only eleven of the twenty-two
intervals are less than three and the whole distribution is shifted to the right. The two distributions differ significantly (P<0.01, Kolmogorov-Smirnov two-sample test, one-tailed) and
t h e effect is consistent between birds. The lower
histogram also shows that birds were still likely
to return immediately to the density 16, if they
'Blue-white'
Totals
density 8 than to density 4 (P=0.007, Fisher exact
probability test). Although at first it might seem
that the birds had either learned at last that the
two densities differed, or that they had already
possessed the basis for making the discrimination
bat had no expressed it, the data show (Table III)
that the contributions of individuals are heterogeneous. Only one bird ('white') shows a change
in behaviour under the two different conditions,
while 'blue' and 'red-white' behave in a more or
less consistent way but make more visits overall
702
ANIMAL
BEHAVIOUR,
and hence inflate the total visits to density 8
because they already showed an excess of visits
to density 8 compared with density 4. To sum
up, it seems that the low frequencies of visits
to densities 4 and 8 in the last eight trials before
reversal may have been obscuring the fact that
strong individual differences existed between the
birds in their treatment of these areas, and that
the overall excess of visits to density 8 after
reversal is partly an expression o f these differences.
The Effect of the Asymmetry of the Test Area
It can be seen from Fig. 1 that birds moving
between the four searching areas do not always
have to travel the same distance. The area near
the centre of the room is nearer all the other
areas than they are to each other, and this is
reflected in the birds' behaviour. If the total
number of transitions between areas is accumulated and classified according to whether the
transitions are 'long' or 'short' (the latter class
involving the central area and the former not),
the birds make 130 short transitions and only
sixty long ones. This differs significantly ( P =
0.0003, Fisher exact test) from the expected
numbers of long and short transitions calculated on the basis of the total number of visits
to each location. That the effect of the asymmetry did not over-rule the response to density
is emphasized by the fact that, despite the
topographical bias, the central area is not the
area to which the largest number of visits were
directed. Only rarely did a bird make a long
transition without flying up into a tree or other
vantage point. It should, however, be pointed
out that the measure of the number of visits
is perhaps a poor one, as this was also affected
by the fact that two of the birds ('red-white'
and 'blue') left the searching area to eat captured
mealworms in a high percentage of cases
(87 per cent and 94 per cent respectively). The
other three birds all ate more than 60 per cent
of their total captures on the search areas. The
differential treatment of densities 4 and 8
discussed above was not apparently a simple
consequence of the topography, as the central
board had a density of 4 in two cases, but a
density of 8 in only one case.
The Effect of the Concentration of the Searching
Effort on Density 16
Although the birds' searching did not seriously deplete the average density of mealworms
per pot (since at most 155 pots were searched
19,
4
and eleven mealworms captured in any one trial),
the intensive searching on density 16 did influence the results. The birds searching strategy was
generally forward-biased, so that they cut a
swathe of searched pots through the area they
were visiting. When the number o f searches
exceeded about 70, these paths began to cross
and the searching bird was forced to cross areas
which had already been searched out to reach
'new ground'. Until the recrossing of paths
happened, the birds' search speed was much as
in other areas, but at such times, the birds also
tended to pick up and toss away foil caps which
had previously been removed and this did not
always seem to be related to the mere removal of
an obstruction to further searching. The net
effect of this factor caused a lower average
searching speed (i.e. number of caps removed
per unit time) on density 16 areas which were
the only places where enough depletion occurred.
This lowered searching speed was not evident
on visits to density 16 before serious depletion
had occurred. This factor was also responsible
for an apparent difference in the treatment
accorded to density 16 by the birds. The number
of searches the birds made on each area before
leaving unrewarded were smaller on density 16
compared with density 4 ( P = 0 . 0 3 ; M a n n Whitney 'U' test), but, if the 'giving-up' times
were compared, this difference was no longer
significant (P=0.27, 'U' test). This agrees with
the finding of Croze (1970, p. 19). The depletion
factor could also have been at least partly
responsible for an increase in the average time
taken to handle each mealworm with increasing
number eaten on any one trial (Fig. 8). This last
effect may, however, be a real one, as observation indicated that the birds tended to swallow
mealworms whole at the beginning of a trial.
Later in a trial they often prepared the mealworm by first removing the head end, then removing the gut and finally, swallowing the
abdomen. Handling times involving the latter
process were not measured, as the bird usually
flew off to a perch before decapitating the mealworm and the behaviour off the feeding area
was not recorded.
Discussion
The Potential Effects of the Density Response on
Prey Populations
It is clear that, if wild great tits were faced
with a similar range of densities and behaved
like the experimental birds, they would exert a
disproportionately high predation on those prey
SMITH & DAWKINS: HUNTING BEHAVIOUR OF GREAT TITS
14'
12C-(.9
z
r'~
z
,r
-1-
z
w
~r
10"
8
6"
I
2Oi
.
1st
.
.
.
3rd
ORDER
.
.
5th
.
.
.
7th
.
9th
OF C A P T U R E
Fig. 8. The relation of the mean time taken to handle a
single mealworm by all birds to the order of capture
within a 5-rain trial. 95 per cent confidence limits are
given for the means.
occurring at highest density. Since the time spent
searching at lower densities was roughly equal,
it would be expected that the effect of individual
tits hunting at these densities would be proportional to food density, but of relatively low
intensity. Tinbergen's data showed that the
transition of predation from below to above
the expectations on a random searching assumption occurred at low to moderate relative prey
densities, but his measures of density were
average figures for each territory rather than
micro-habitat densities comparable to the experimental situation. Ideally the behaviour of
the tits should be investigated under field conditions to test whether the laboratory findings
apply to wild birds, but this is of course very
hard to achieve.
Some evidence, which suggests that the
situation could potentially occur in the wild
is provided by the work of Gibb (1958). Gibb
demonstrated significant variations in the 'intensity' of the larvae of the eucosmid moth
Enarmonia conieolana, which inhabits pine
cones and is heavily preyed on during winter by
coal and blue tits. The 'intensity' of the larvae
(the number of larvae per five pine cones)
showed a variation of up to sixteen-fold. This
was not necessarily a measure of absolute
density as the number of cones per tree was,
m one year, inversely related to the intensity.
703
However, it would provide a density estimate
in terms of the search effort required by the
hunting bird. The relative frequencies of different larval intensities found by Gibb are shown in
Table IV). Gibb's data show that the higher
intensities are relatively less frequent than the
lower intensities and this would potentially
operate against a predator attempting to concentrate its hunting effort at the highest intensities. Gibb indeed found that the concentration of his tit predators on the high intensities
was much weaker than that shown by our experimental birds, but it should be remembered
that these were predominantly non-territorial
birds searching over fairly large areas. Our
experiments were envisaged as a simple model
of a stable situation where a bird is familiar
with a small area, such as its territory. A more
appropriate model of a winter situation would
be provided by a situation where the location
of the high densities was not fixed and the tit
had to sample new feeding areas and assess
their profitability without the benefit of recent
previous experience.
Table IV. Distribution of 'Intensities' (See Text) of
Enarmonia conicolana Larvae in Pine Cones
Larval intensity
(no. of larvae per
5 pine cones)
No. of plots
<2
93
2-3
131
3--4
43
>4
15
(15 x 15 m)
Data from Gibb (1958, Fig. 4, p. 387).
A further example of variations in density of
an important prey species of the great tit is provided by the work of G. R. Gradwell (personal
communication) on the winter moth, Operophtera brumata. Data collected over a 20-year
study by Gradwell and Professor G. C. Varley
on the numbers of winter moth larvae that fall
to pupate from each of five dispersed oak
(Quercus robur) trees, show that five to tenfold
variations in numbers between the least and
most infested trees were common.
It is also likely that, contrary to Tinbergen's
assumption that the prey of his great tits was
randomly distributed, that the prey of great
fits will show aggregated distributions. A survey
by Taylor (1961) has shown that a wide variety
704
ANIMAL
BEHAVIOUR,
of both cryptic and conspicuous animals tend
to show such aggregation, admittedly over
differing sizes of sample units. An aggregated
prey distribution would favour non-random
searching by the predator, as one of us has
shown (J.N.M.S. unpublished results) in wild
blackbirds, Turdus merula, and has been shown
for flocks of the same individual great tits by
J. R. Krebs and M. H. MacRoberts and J. M.
Cullen (in preparation).
The strong persistence of the experimental
birds' searching in areas which had previously
contained a high density of food, and their
tendency not to return, even if they did visit
an area where a high density of food had recently appeared, are relevant to the finding of
Tinbergen that the appearance of a new prey
species in the tits' diet lagged behind its increase
in density in the food complex. Both types of
behaviour could contribute to the occurrence
of such a lag, but other explanations, such as
changes in the relative profitabilities of different
species (Royama 1970), are also possible. It
is also likely that finding a new prey type, particularly a larger, more profitable species, might
direct the tits' hunting behaviour to a new area.
The delay in reacting to a change in the spatial
distribution of food is obviously contrary to the
short term hunting efficiency of the birds, and
may surprise ecologists. Psychologists, on the
other hand, will recognize that the experiment
provided a partial reinforcement situation,
which is well known (e.g. Hilgard & Marquis
1961) to produce operant behaviour that is
resistant to extinction. It remains to be seen
whether the degree of persistence shown is an
artefact of the simplified experimental environment, but it is interesting to note that Allen
(1967) found a striking parallel to this spatial
conservatism in the responses of wild birds to
new varieties of coloured food items.
The Adaptiveness of the Tits' Behaviour
The concentrated searching at the highest
prey density is clearly a more efficient strategy
than random searching. The tits' behaviour is
very similar to the behaviour of single Nemeritis
searching over a range of host densities (Hassell
1971). The Nemeritis, however, probably used
tactile, visual or olfactory clues about prey
density to orient their searching, rather than a
learned assessment of the profitability of different areas. The failure of the birds to show a
discrimination of the lower densities does not
accord so simply with Royama's hypothesis.
19,
4
It is possible that further experiments might
demonstrate that great tits do discriminate the
lower densities in this type of situation, but it
could also be the case that the result stems from a
fundamental property of the birds' choice
behaviour.
With this latter possibility in mind, it is
interesting that the indiscriminate behaviour
shown between the lower densities is predicted
by a version of the threshold model of choice
behaviour developed by Dawkins (1969a, b).
In the model, stimuli to which responses may be
directed (e.g. feeding areas) possess threshold
values for each stimulus dimension (e.g. different relative profitabilities or distance from the
nearest perch). The animal is then supposed to
respond according to the size of a fluctuating
hypothetical variable, so that, if the magnitude
of this variable rises above the threshold of the
strongest stimulus, then all responses are
directed to that stimulus. If the variable rises
above the threshold of any weaker stimulus, the
animal will respond indiscriminately, because of
a switch of attention to a different stimulus
dimension (e.g. distance from the nearest perch,
etc.). This would produce qualitative effects
just like those shown by the tits, but there are too
few birds to make any quantitative assessment
of the predictive value of the model.
An important factor in these experiments is
that there is a pressure on the tits to maximize
their hunting efficiency, since each trial is preceded and followed by periods of food deprivation. This will, no doubt, often be true for wild
great tits, particularly during the feeding of the
young. However, at other times there will not be
continuous pressure on wild tits to maintain their
hunting efficiency at a maximum. These periods
will be used for other maintenance activities, but
they may also be used in exploratory foraging,
which may be very important in allowing individuals to monitor changes in the spatial
pattern and species composition of the food
complex. Tame great tits explore and manipulate
strange objects intensely and wild titmice are
also noted for their exploratory behaviour,
which has allowed them to acquire such feeding
habits as breaking through the foil caps of milk
bottles to obtain cream (Fisher & Hinde 1949).
One of the few field studies on the distribution
of hunting behaviour in relation to food density
has been the work of Goss-Custard (1970) on
the redshank, Tringa totanus. He studied flocks
of redshank feeding in winter on the burrowing
amphipod, Corophium volutator, in an estuarine
SMITH & DAWKINS: HUNTING BEHAVIOUR OF GREAT TITS
habitat. He found that redshank also were
choosing to feed in more profitable areas and,
in one transect, there was a suggestion that the
redshank were spending a disproportionately
large amount of time feeding at the highest
Corophium densities. In a second transect, the
situation was complicated by the redshank
taking a large alternative prey (the polychaete,
Nereis diversicolor) and there was no correlation between redshank density and Corophium density or biomass. A factor which could
have been inhibiting the redshank from concentrating their hunting strongly in the most
profitable areas was mutual interference in
feeding efficiency between flock members.
Some comparable experimental work on the
response of vertebrate predators to food density
is that of Holling (1959, 1965), who found that
deermice (Peromyscus leucopus) showed a relatively weak tendency to concentrate on the
higher densities of sawfly coccoon prey. Hol!ing
presented the different prey densities successively, in combination with an ad libitum supply
of less palatable food, It is less easy to see this
design as a plausible model o f a natural situation and it would be interesting to repeat
Holling's experiments with a range of prey
densities simultaneously available to the rodents.
Another factor which might influence the
density level at which it would be profitable for
great tits to search intensively for a particular
prey species, would be an explanation like the
search image hypothesis favoured by Tinbergen.
The best evidence that birds have to 'learn to
see' prey objects, comes from laboratory experiments on chickens (M. Dawkins 1971). When
similar careful work has been carried out on
great tits in either laboratory or field situations,
the status of Tinbergen's hypothesis as a contributory factor to their responses to food
density will become dear. Until then, R o y a m a ' s
interpretations provide a simpler explanation
which accords better with both the field data
and these preliminary experiments.
Acknowledgments
Dr J. M. Cullen, J. G. Frazier, D r M. H. MacRoberts, Professor N. Tinbergen, D r J. D. GossCustard and Dr M. Dawkins criticized the
manuscript and offered many helpful comments.
Miss R. L. de Boer provided valuable assistance
in carrying out the experiments. We are especially grateful to D r J. R. Krebs and D r M. H.
MacRoberts for allowing us to use their handreared great tits. D r M. Dawkins, D r M. P.
705
Hassell, D r G. R. Gradwell, D r J. R. Krebs,
Dr M. H. MacRoberts and Dr J. M. Cullen
kindly allowed us to quote from their unpublished work. We are grateful to Dr S. Neill for
the drawing in Fig. 2, to L. C. Shaffer for photographic advice and assistance and to Mrs P. M.
Searle for typing the manuscript. Financial
support was received from the Science Research
Council. The work was done in the Department
of Zoology, Oxford, by kind permission of
Professor J. W. S. Pringle.
The work forms part of the thesis work of
one of us (J.N.M.S.), who is entirely responsible
for its conception, design and presentation.
R.D.'s role was confined to developing the
recording apparatus, and performing some of
the experimental observations.
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(Received 10 February 1971 ; revised 30 April 1971 ;
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