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Cost-intake information used in foraging
J. Insect Physiol. Vol. 31, No. II, pp. 891-897, Printed in Great Britain. All rights reserved
COST-INTAKE
1985 Copyright
INFORMATION
0022-1910/U $3.00 + 0.00 [Q 1985 Pergamon Press Ltd
USED IN FORAGING
KEITH D. WADDINGTON Department of Biology, University of Miami, Coral Gables, FL 33124, U.S.A.
Abstract-An
aspect of the round dance, called RATE, was used as quantification of 15 honey bees’ (A@
mellifera) perceptions of caloric costs and intakes experienced while foraging at artificial flowers. Caloric
intake per floral visit (CALGAIN) was manipulated by varying sucrose concentration. Both flight and handling costs per floral visit (CALCOST) were manipulated. Multiple linear regression analysis was used to quantify the relationship between the dependent variable, RATE, and the two energy variables. There was significant variation among bees in dance behaviour. However, the absolute magnitude of the CALCOST coefficients tended to be larger than CALGAIN coefficients; bees may weight perceptions of costs in relation to intakes. Honey bees were presented with a series of binary choices between yellow or blue tubular artificial flowers in order 40 assess how cost-intake information is used to make choices among flowers. In eight experiments the 2 flowers had different combinations of volume of sucrose (intake) and tube depth (handling cost). Results are consistent with an assessment of rate of net caloric intake, although they are likely consistent with other possible assessments. Key Word Index: Honey bee, foraging, perception, communication,
profitability, cost, intake, polli-
nation, choice
INTRODUCTION
An insect searching for food or any other resource (e.g. a mate or shelter) incurs costs and possible gain of the resource. Costs take the form of spent time and energy and potential increased risk of predation. Risk of predation had obvious consequences on fitness. Time and energy expended may also affect fitness negatively if it is time and energy taken away from other activities (e.g. acquisition of another resource). Recent studies of the energetics of search behaviour (as well as other activities) have been published (e.g. bumblebees, Heinrich, 1979). As an insect moves about in search of food it must “decide” which patches of food should be searched and which passed by, how long to remain in a patch before departure to another patch, which kinds of available foods should be eaten and which not eaten. The animal’s decisions regarding these questions will likely influence its gain (intakes) on time and energy spent foraging. These descisions prescribe certain behavioural patterns. This paper is concerned primarily with the perception of costs and intakes and the use of this information by the animal for making a routine but important foraging decision, choice of food. Recently, mathematical models have been constructed for understanding and predicting, from an evolutionary perspective, the behaviour of food choice as well as other aspects of foraging behaviour (Krebs et al., 1983). The crucial assumption of these optimal foraging models is that fitness is related to foraging efficiency. The animal is therefore assumed to make decisions which result in behavioural patterns that maximize the net rate of food intake. The costs and intakes associated with foraging and the ways that information on energetics is received and processed is fundamental to understanding foraging behaviour.
The theory of optimal diets (Charnov, 1976), a subset of models of optimal foraging theory, is used to predict food choice. Explicit assumptions are made on how time, energetic costs and intakes are used to make foraging decisions. Profitability, the measurement of foraging intakes and costs, is an important part of this theory. It is postulated in the optimal diet models that profitability of food is net caloric gain (caloric intake less cost) per handling time; the food items are ranked according to profitability and more profitable types are selected. The breadth of the diet depends on the densities (i.e. search time) of the more profitable, more highly ranked foods (see Fig. 2.2 in Krebs, 1978). Predictions of the optimal diet model have been tested using several taxa (Krebs et al. 1983). The match between predicted and observed foraging behaviour has sometimes been good. However, a second approach to testing predictions is to examine the inputs or assumptions of models. We have little information on perception of costs and intakes, the balance between costs and intakes and the use of this information for making complex foraging decisions. The honey bee, Apis mellifera, will be used here to address two related problems. It is usually assumed in foraging models that caloric costs and intakes are additive on the same scale; that is, an increase in caloric costs can be offset by an increase in caloric intake of the same magnitude. In fact this is an assumption of most models in behavioural ecology (e.g. Krebs and Davis, 1978). In the experiments discussed in this paper, caloric costs and intakes were manipulated using artificial flowers containing measured amounts of sucrose solution. The bees’ own communication dance was used to quantify perception of the magnitudes of costs and intakes. Secondly, bees were given a choice between two artificial flowers with different associated costs and intakes as a first 891
892
KEITH D. WADDINGTON Table
I. Stepwise multiple
Sucrose solutions used (calories per flower)
Distances (m)t or tube depth (cm)T used
0.204. 0.426, 0.666
0.15. 2.41t
0.426.
0.15.
0.666
0.3 15, 0.666
2.41.
3.u
3.56t
regression
analyses
for 15 bees*
CALGAIN
CALCOST F
b
SE
F
dJ
b,
SE
7.8 14.6 6.4 3.9 13.1 10.5
5.3 1.99 I .35 2.32 3.37 3.48
2.2 NS 53.911 22.4” 2.96 NS 15.2** 9.2**
-33.1 -4.6 -25.1 - 1.0 -21.1 3.0
23.29 5.43 22.23 6.44 25.12 35.98
2.0 NS 0.7NS 1.3NS 0.03 NS 0.7 NS 0.01 NS
2. 73 2.16 2,133 2.69 2, 50 2,86
3.8 7.8 - I.2
2.38 I .94 2.32
2.6 NS I5.9f’ 0.3NS
- I.1 -48.9 - 24.8
X.76 0.02 NS 14.69 11.1” 9.45 6.9”
2.175 2.203 2, I56
0.8 6.6 3.4 4.2 8.0 - 5.4
1.76 1.64 6.19 6.84 4.67 4.68
0.2 NS l6.4*’ 0.3NS 0.4NS 2.9NS 1.3 NS
-5.7 -0.2 - 2.8 - 127.6 - 32.2 -55.3
4.96 4.23 22.69 53.14 34.41 19.49
2.135 2, I56 2.49 2.55 2.21 2. 56
1.3 NS 0.03 NS 0.02 NS 5.x** 0.9 NS 8.l**
lb,
and b, are the regression coefficients for CALGAIN (caloric intake per floral visit) and of CALCOST (caloric cost per visit), respectively; RATE is the dependent variable. SE: standard error of the regression coefficient. Statistical significance of H,: regression coefficient = 0; NS: not significant, P > 0.05; **P < 0.01. F-values for each variable when the variable was added to the regression equation. df: degrees of freedom. CALCOST varied in these experiments between 0.028 and 0.533 calories. RATE varied between 8.3 and 42.9 reversals per minute. t.fEach day a bee experienced one sugar concentration in combination with 2 or 3 distances between the 2 flowers or flowers of 2 tube depths over separate trials. Tests at one distance (tube depth) were run in consecutive pairs of trials and were alternated with the other distances (tube depth).
step toward determining
how bees use cost-intake information to make choices among flowers. The volume containing this paper is concerned with search behaviour; my specific charge was to examine cost of search. Perhaps I violate this charge by examining both costs and intakes and I manipulate costs not usually thought of as “search” costs. For example, I sometimes manipulate handling costs (i.e. time on flower). This seems appropriate for now since we do not know that decisions based on X handling costs are any different than those made on X search costs. An understanding of search costs and their perception for making decisions will likely come through a broad view of cost. METHODS
AND MATERIALS
Perception of costs and intakes
Fifteen honey bees were tested singly indoors in a 2 x 4 x 2 m screen cage; Waddington (1982) provides complete methods. During a trial a bee repeatedly flew back and forth between 2 artificial flowers mounted on a platform and on each floral visit the bee ingested i p 1 of sucrose solution. A trial ended when the bee returned to the hive. For nine of the bees the costs of foraging were varied by changing the distance between the 2 flowers and the intake varied by manipulating the concentration of sucrose solution (Table 1): (1) For six of these nine bees, 3 sucrose concentrations were used (0.204, 0.426 and 0.666 calories per flower) and 2 inter-floral distances (0.15 and 2.41 m). For the other 3 bees, 2 sucrose solutions (0.426 and 0.666 calories per flower) were used in combination with 3 inter-floral distances (0.15, 2.41 and 3.56m). (2) For 6 bees handling costs, but not flight costs, were varied by changing the depth of tubular artificial flowers (12 mm dia) between 3 and 8 cm. The inter-floral distance was constant at 0.15 m. Two sugar solutions were used (0.315 and 0.666 calories per flower).
On each day of testing, a bee experienced one sugar concentration only, but the inter-floral distances (or tubular depths) were alternated between trials. Once finished foraging, the bee returned to a glass-sided observation hive (Waddington and Rothenbuhler, 1976) and performed the round dance (Frisch, 1967) which was video taped. During the round dance the LL . bee runs in a circle. , suddenly reversing direction and then turning about again to her original course. . ” (Frisch, 1967) [Fig. 11. The rate of these reversals (RATE) varies with changes in both the caloric intake per floral visit (CALGAIN) and the caloric cost per floral visit (CALCOST) [Waddington, 19821. Mean caloric cost per visit equals the mean time per visit multiplied by an estimated rate of energy expenditure, 0.0115 cal./s (Heinrich, 1975). This may be an overestimate of cost since the calculation is based on expenditure for flight. It was not possible to separate time for
Fig. 1. Stylized path of the round dance. Quick reversals occur at points X and Y; RATE is the number of these reversals per minute.
Cost-intake
information used in foraging
handling and flight. RATE is positively related to CALGAIN and is negatively related to CALCOST (Waddington, 1982). Bees report their assessments of the interaction between caloric value of the sucrose solution and the caloric expenditure during foraging in the RATE of the round dance. Stepwise multiple linear regression analysis was performed for each bee with CALGAIN and CALCOST as independent variables and RATE as the dependent variable; the variable explaining more variation in RATE entered the model first. The result is a surface of each bee’s assessments of caloric costs and intakes. Higher order than linear models did not explain significantly more variation in RATE. Choice between flowers
The “three-flower method” (Marden and Waddington, 1982) was used here. Bees foraged for sucrose solution at artificial flowers made of plastic tubes that measured 78 mm outside height and 13 mm inside diameter. A round piece of 3-mm thick Plexiglas with a drilled nectar well was glued inside of a tube at one of three depths: short flowers-l 3 mm, medium-33 mm, long53 mm. The outside of each tube was wrapped with either yellow or blue Scotch”’ plastic tape. One, two or three ~1 of 20% sucrose solution (7.4, 14.8 or 22.2 calories, respectively), lightly scented with peppermint oil, was dispensed into a flower from above using a repeating dispenser. The flowers will be designated by the combination of depth and nectar volume as follows: Ll = long and 1 p 1, M2 = medium and 2~1 and so forth. Honey bees were housed in an observation hive (Waddington and Rothenbuhler, 1976) and allowed to fly outdoors into a screen cage (3 x 2 x 2 m). The bees were occasionally fed diluted honey from a gravity feeder mounted on top of the hive. Experiments were performed inside the cage. Bees were trained singly. A bee was put under a small piece of hand-held netting with 2 touching artificial flowers, 1 yellow and 1 blue, each containing 1, 2 or 3 ~1 of peppermint scented 20% sucrose solution each (tube-depth and volume of nectar same as in test to be conducted). The solution was replaced after each visit as the bee walked between flowers. After several visits, in gradual steps the flowers were moved apart until separated by 25 cm. The bee returned to the hive ending the training period. Four flowers (2 of each type) were placed touching each other in a square on a green Plexiglas surface. The bee landed on a flower that was then moved to the starting position. A yellow and a blue flower were positioned 25 cm from the starting position (4 cm separated the 2 flowers). The bee had to choose between them on leaving the starting position. After each flight, while the bee was in the chosen flower imbibing sucrose solution, the flowers were rearranged on the board so that the original configuration was established and the empty nectar well was refilled. This process was repeated until the bee returned to the hive, thereby completing the foraging bout. Typically the bee returned within 5 min, to repeat foraging. Seven experiments were conducted. A different set of bees was used in each experiment. In each experi-
893
ment the 2 flowers had a different combination of volume of sucrose solution (1, 2 or 3 ~1) and tube depth (13, 33 or 53 mm) (Table 4). Combinations were chosen that would likely indicate, through the bee’s foraging behaviour, how information of costs and intakes is utilized; not all combinations could be tested. For each bee used in an experiment the choice of treatment (i.e. combination of sucrose solution and tube-length) for the floral colours was chosen by flip of a coin before the training period. Each bee completed 5 foraging bouts. The number of bees tested varied between experiments (Table 4). The sequence of floral visits was recorded verbally on tape and the bout duration was timed to the nearest second. The costs (time and energy) to fly to and visit the different kinds of flowers (combinations of volume of sucrose solution and tube depth) were estimated using bouts 4 and 5 where just one kind of flower was visited. The mean time per visit was calculated by dividing bout duration by the number of floral visits minus 1. Energy expended was calculated as above (O.O115cal./s x time). Net calories per time could then be calculated.
RESULTS
Perception of costs and intakes
There was significant variation among the 15 bees in dance behaviour (RATE) in relation to caloric costs (CALCOST) and caloric intakes (CALGAIN) (Table 1). The coefficients for CALCOST ranged between 3.0 and - 127.60 (x = -25.4) with 14 of 15 negative. Coefficients for CALGAIN ranged between - 5.4 and 14.6 @= 5.6) with 13 of 15 positive. There was thus agreement among bees on the direction that CALCOST and CALGAIN affect RATE. This agreement was statistically significant (H,: equal number of positive and negative regression coefficients for CALGAIN and CALCOST; binomial test, 2-tailed, P < 0.01 in both cases. The standard errors of CALCOST (range 4.23-53.14) were greater than those of CALGAIN (range 1.35-6.84). The test is of whether caloric expenditures and intakes are scaled similarly. Regression coefficients for CALGAIN and CALCOST of the same magnitude would suggest that assessments of a calorie of cost and intake are the same. In 9 of the 15 bees the absolute values of the regression coefficients of CALCOST were higher than those of CALGAIN but this was not a significant departure from one-half of the values being higher (binomial test; H,: P = 0.5, 2-tailed, P > 0.05). However, as noted above, the mean of the absolute value of CALCOST coefficients was 5 times greater than the mean of CALGAIN coefficients. The Wilcoxon Signed Ranks Test (Conover, 1971) was used to test the H, that the absolute magnitudes of the CALGAIN and CALCOST coefficients (paired comparison per bee) are equal. The H,, was rejected (2-tailed test, T = 95, P < 0.05); the values of the CALCOST coefficients tended to be larger than CALGAIN coefficients. These results suggest, at least, that bees estimate costs and intakes differently; costs are weighted in relation to intakes.
KEITHD. WADDINGTON
894
Table 2. Estimates of foraging time (per flower visit) and net caloric intake per time Tube depth. t nectar volume SI s2 s3 Ml M2 LI L2
N’ 28 20 28 13 27 20 28
Net caloric intake per time
Mean time (s)I 5.08 6.23 8.79 6.31 10.78 10.34 12.60
Table
Bout Bee El9 E20 E21 E22 E23 E24 E25 E26
1.44 2.36 2.51 I.16 I .36 0.70 1.16
+ 1.33 & 1.12 f 2.10 f I.1 I f 2.98 + 2.94 f 3.44
*N: Number of bouts (4 and 5) used. tTube depth: L = long (53 mm), M = me&urn (33 mm). S = short (13 mm). Nectar volume: I ~1 = 7.4 cdl.. 2pl = 14.8 Cal.. 3~1 = 22.2cal. $Varieties are total time per bout (flight and handling time) divided by the number of floral visits minus one during that bout.
The estimated times for bees to fly to and visit a type of flower (i.e. combination of tube depth and nectar volume) are presented in Table 2. Bouts 4 and 5, when bees were constant to one kind of flower, were used to calculate these means. The mean time increased with increased tube depth. For example, it took twice as long to visit (flight plus handling) an Ll (long flower with 1 ~1 nectar) than an Sl. Mean time per visit also increased with increasing nectar volume. One microlitre differences led to differences in mean time between 1.2s (Sl vs S2) and 4.5s (Ml vs M2). Net caloric gain per time for each kind of flower was estimated by subtracting caloric cost (estimated by multiplying 0.0115 cal./s by mean time per visit) from calories per flower and dividing this quantity by the mean time(s) per visit (Table 2). Ll yielded the lowest return per time and S3 the highest return. Bees usually specialized on one flower-type during bouts 4 and 5, although they did not always have the same preferred type within an experiment. Specialization is taken as greater than 90% but most bees visited only one flower type (100%) by bouts 4 and 5. The foraging histories of the 8 bees used in experiment 5 are typical for all results but those of experiment 8 (Table 3).
Experiment 1 2 3 4 5 6 7 8
lColour
behaviour
Flowers* 2 I L2 M2 s2 s3 SI Ml Sl Ml
LI Ml Sl SI Ml LI M2 L2
.- ; 0.98
I .oo 0.08 I .oo 0.57 0.91 0.84 1.00
*Proportion bout.
Choice between ,flowers
Table 4. Preference
3. Foraging historIes* of bees used m experiment 5
2
3
0.98 1.00 0.00
1.00 1.oo 0.00 1.oo 0.89 I .oo 0.88 1.oo
1.oo 0.70 1.00 0.98 I .oo
-4. I 00 1.00 0.00 1.00 0.96 1.oo 0.94 1.oo
5 I .oo ~ 0.00 1.00 0.98 1.00 0.98 I .oo
of visits to Sl flowers during each
The bees’ choices among flowers used in the eight experiments are summarized in Table 4. In experiments, 1, 2 and 4 where the 2 flowers had the same depths, most bees specialized on the type with the higher volume of nectar. Just one bee in experiment 2 and 2 bees in experiment 4 specialized on the less rewarding flowers. The same nectar volumes but different tube depths were offered in experiments 5 and 6. Nearly all bees specialized on the shorter, more profitable flowers. These 5 experiments show that bees can choose flowers with the lower cost or with the higher intake. Even though the result is choice of the flower with the higher net intake rate, the experiments do not show that the bees use both costs and intakes simultaneously for making decisions. In experiment 3 the tube depths were both short and the nectar volumes differed (flowers Sl and S2), but the bees’ behaviour was different; about half the bees specialized on each flower type. Either the bees perceived differently which flower was more profitable or they could not discriminate between the flowers and settled at random on a specialization. Marden and Waddington (1981) showed that bees specialize on one flower when both flowers have the same depth and nectar volume but that half the bees specialize on each flower. This suggests that in the present experiment they could not discriminate between flowers. This means that 1 ~1 vs 2~1 nectar is a different problem for bees depending on tube-depth (cost), suggesting that choice is made based on costs
of bees during
the eight experiments
N’
Individual preference?
Group’s preferencef
I5 15 17 15 8 5 IO II
l5L2 l4M2. lM1 9S2,7Sl, 1 mixed 13S3,2SI 8 Sl, I Ml 5SI,5M2 5SI.5M2 see Fig. 2
L2 M2 None s3 SI Ml None ?
(blue or yellow) of Flower 1 and Flower 2 chosen by flip of coin. Colou.~ remained the same throughout bouts of each bee. S = short tube (depth, 13 mm), M = medium (33 mm), L = long (53 mm); 1, 2 or 3 ~1. Flower I has the higher net caloric gain per time except for in experiment 8 where the flowers are equal in value. N: number of bees tested. tThe number in front of a flower-type indicates the number of bees showing high fidelity to that Rower during bouts 4 and 5 (criteria, > 90%). SSummary statement based on bees sampled. “None” indicates no significant difference from 1 :I (binomial test), P > 0.05). ? indicates a variety of behaviours exhibited by different bees.
Cost-intake information used in foraging and intakes (rather than, for example, on the amount of nectar in flowers only). Perhaps when costs are low (short-depth tubes) and, therefore, intakes from both flowers are high in relation to costs the differences in intakes between the flowers must be higher for discrimination than when costs are high. This notion is supported by the results of experiment 4. Here both tubes were short but the bees overwhelmingly specialized on the 3 ~1 (S3) flowers; just 2 of the 15 bees specialized on the Sl flowers. This result is not surprising given what is known about responses to other stimuli. It is well known that physical measurements and subjective measurements along a given stimulus dimension do not always coincide. When stimulus intensity is high, a change in intensity of some magnitude results in a smaller subjective change than when the starting intensity is low. It is easy for a person to detect a difference between objects weighing 7 and 14 kg but difficult to discern the difference between 100 and 107 kg. The important point is that the results suggest that bees make decisions based on integrating information on costs and intakes. The 2 flowers used in experiment 7 and 8 differed in both tube depths and nectar volume. In experiment 7, half the bees specialized on each flower type (Sl and M2). The bees as a group selected neither the higher nectar volume nor the lower cost. Their behaviour in experiment 8 (L2 vs Ml) was unique compared with the other experiments (Fig. 2). The behaviour of some individuals changed markedly between foraging bouts. For example 1 bee visited L2 over five bouts in the following proportions: 0.20, 0.94, 0.24, 0.80 and 0.63. Most bees had no clear specialization; just 3 bees specialized on L2 and one on M 1.
Including bouts 4 and 5 for all 11 bees tested the mean proportion of visits to L2 was 0.66 ( +_0.307) indicating, perhaps, a group preference for L2. Again, however, there is no clear specialization as to higher nectar volume of shorter tube depth. Again the indication is that decisions may be based on both cost and intake information. Within each experiment the 2 flowers have similar values of rate of net caloric intake. Estimates are 1.16 cal./s for M 1 and L2
v)
0.8
$ 2 F c,
0.6
0.0 1
2
Foraging
3
4
895
I
’ ‘W
1.0 Net
1.5
calories
per second
Fig. 3. The subjective value is a ranking of flower types based on the bees’ choice behaviour. Lines connect tested pairs of flowers. Solid dots (0) indicate the preferred flower. Dashed lines indicate pairs preferred equally (about 50% of bees preferred each type). These pairs are taken as having the same subjective value. L2 and Ml (experiment 8) also have similar subjective value (Fig. 2). Subjective value is rational and consistent with the notion that bees assess *‘net calories per second” and choose the higher valued flower.
(experiment 8) and in experiment 7 144cal./s for Sl and 1.36/s for M2. The results of these 8 experiments are reasonably consistent with the notion that bees assess flowers on the basis of rate of net caloric intake; associations are made between flower colour and the respective subjective evaluations, and the more highly rewarding flower is chosen. Figure 3 shows the bees’ subjective evaluation of the flowers used in this experiment (based on their choice behaviour) in relation to the “physical” values of flowers in net caloric intake per time. However, the results are likely consistent with other possible assessments and “rules” of action. Experiments of similar design and other experiments may suggest other possibilities and help discriminate between these possibilities. In the bee dance study, presented above, caloric intakes and costs were quantified in a non-traditional way, “per flower visited”, rather than “per time”, because calories expended per time could not be directly measured. Yet, in order to compare the bees’ perception of intakes and costs it was necessary to give them the same terms, i.e. the terms intake/visit and cost/visit. Strictly for explorative purposes those data can be used to examine the relationship between RATE and the rate of net caloric intake ([CALGAIN - CALCOSTJ/ mean time per floral visit). The results of simple linear regression analyses are presented in Table 5. Eleven of the fifteen regression coefficients are significantly different from zero (P < 0.10) and for all bees the coefficients are positive. These results further suggest that bees are sensitive to gains per time or some closely related assessment.
5
bout
Fig. 2. Results from experiment 8 showing the proportion of visits to L2 by 11 bees on a per bout basis. Proportion of visits to Ml is 1 minus the graphed proportions.
DISCUSSION
I have advocated elsewhere (Waddington, 1983) the testing of assumptions on behavioural and phys-
KEITH D. WADDINGTON
896 Table 5. Regression
Regression equationt
Bee I
2 3 4 5 6 7 8 9 IO II I2 13 14 ii
analyses using net caloric intake per time for 15 honey bees
1’ = .v = :v = ?‘ = y = 1’ = 1,s= ?’ = 1’ = .r = ;, = :v = r’ = I’ = ;I =
16.4 = 71.46.~ 15.5 + 70.90x 17.1 +38.19.x 22.2 + 37.45-Y 19.4 + 52.87; 15.7 + 50.07x 20.1 f33.9O.Y 18.1 +70.39-v 25.1 + 13.31~ 14. I + 29.68s 19.4 + 28.37x 24.1 + 24.54.~ 15.3 + 112.9x I5 7 + 59.49.x ii.6 + 50.69.x
F’ 4.7. 36.4’ 23.8’ 3.5 NS 13.4’ 5.7’ 6.1; 24.2* 3.5 NS 4.0’ 6.0’ 0.27 NS 6.9’ 4.3* 2.1 NS
tba: RATE reversals tm minute during second: I = correlation coefficient: iStatistical significance NS not significant (P > 0.05) ‘P < 0.05
iological mechanisms before those assumptions are included in foraging models (whether they be “optimal foraging models” or other models which attempt to understand or predict foraging behaviour). The present paper is an attempt to examine how honey bees assess and compare costs and intakes for making foraging decisions. Energy balance was considered because sufficient experimental and observational evidence indicates that bees and other animals are sensitive to cost-intake information and this information plays a role in foraging decisions (Frisch, 1967). Frisch (1967) investigated the honey bee’s perception of “profitability” that he defined operationally by the “liveliness” and duration of the waggle dance (Figure-of-eight dances are performed when the resource is distant from the hive) and by the probability of dancing. In general he was investigating the factors that regulate the relationship between supply and demand. He found that sugar concentration, ease of obtaining a solution, viscosity, fragrance, uniformity of flow, time of day and other factors influenced a bee’s perception of profitability. For example, as sugar concentration or viscosity of a sugar solution increases the liveliness and duration of the dance increases. Both (1956) was able to detect, using the waggle dance, apparent “trade-offs” between costs and intakes. He found that the greater the distance between a feeding station and the hive the more highly concentrated must a sugar solution be in order to release dancing. However, no specific attempts were made to quantify these trade-offs. Dethier’s (1976) work is also instructive; through behavioural and physiological studies he (and colleagues) characterized many aspects of blowfly feeding biology. Tbe work on food choice is complete with respect to preferences based on concentration of sugar solutions and types of sugars and other foods (and non-foods). However, little is known about the cost-side of the blowfly’s energy balance sheet. My studies were designed to examine assessments of costs and intakes and integration of such information for making decisions.
SE of regression coefficient
df
r
33.02 I I .75 7.82 20.11 14.45 21.04 13.68 14.30 7.15 14.83 II.58 47.09 42.92 28.68 34.96
I, 74 I. 77 I. 134 1,70 I. 51 I. 87 I. 176 1.204 1,157 I, 136 I. 157 1. 50 I, 56 1.22 1;57
0.24 0.57 0.39 0.22 0.46 0.25 0.18 0.33 0.15 0.17 0.19 0.07 0.33 0.40 0.19
round dance, x = net caloric intake per
Perception of costs and intakes
Bees danced differently in relation to foraging costs and intakes. Absolute magnitudes of nine of fifteen regression coefficients of CALCOST (with RATE, an index of a bee’s perception) were higher than CALGAIN coefficients; six CALGAIN coefficients were higher. There are three possible interpretations: each bee’s perception of the separate energy variables differs, or the way the variables are combined differs, or the bee’s report (RATE) of the combination differs. The results cannot be used to discriminate between these possiblities. Despite the disparity among bees, the magnitudes of the CALCOST coefficients tended to be larger than coefficients of CALGAIN. I interpret this to mean that bees may weight costs in relation to intakes. Weighting could result because caloric costs for flight and feeding movements are just one part of a forager’s actual costs. The bee’s report may reflect additional costs including, for example, risk of predation and the cumulative wear on the structural machinery. A small increase in caloric costs, since it may reflect an overall larger “cost”, must therefore be offset only by a larger increase in caloric intake. Foraging theory assumes that both energetic costs and time are important. I chose to quantify costs in terms of energy (calories) alone. The conclusion, drawn from several experiments, is that bees estimate distance based on energy expended for flight rather than on true distance or duration of flight. Although
Input
Fig. 4. Costs and intakes are manipulated and a bee’s behaviour (dance or floral choice) observed. This paradigm is used to understand the animal perception and use of information on the input parameters.
Cost-intake
information used in foraging
the results are not definitive, they do suggest a reasonable path for an initial study such as mine. Secondly, without information to suggest how time might be used in the assessment of cost, it was most parsimonious to quantify costs and intakes in the same units, calories. Despite such justifications, it is important that future studies attempt to determine whether assessments of cost are indeed based on energy expenditure, or time, or both; and if both are used, how is the information integrated. This will require manipulation and direct measurement of the rate of energy expenditure. The equal weighting of intakes and costs used in optimal foraging theory is also in contrast to some recent theoretical considerations of decision-making in humans. In prospect theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1981) an alternative model to utility theory for examining decision-making in humans under risk, outcomes are assigned “value” in terms of gains and losses with respect to a neutral reference point. The value function among individuals likely varies, but a number of studies indicate that is is commonly S-shaped and has two properties. First, the rate of change in perceived value for both losses and gains decreases as the magnitude of actual losses and gains is increased. Second, the change in value associated with losses is greater than the change in value associated with gains. This second attribute of the value function in human beings is qualitatively comparable to possible weighting of caloric costs by bees; that is, it takes more caloric intakes than cost to offset any level of cost and maintain the same profitability. Choice between flowers
Results of the 3-flower experiments suggest that bees choose among flowers by integrating information of costs and intakes. I suggest bees may assess net caloric intake per time and choose the flower with the higher value although the limited array of experiments completed thus far do not rule out other possibilities. However, the results of the 3-flower experiment and the regression analyses (Table 5) of the dance data are consistent with this conclusion. Further considerations
There are potential problems to consider when trying to understand an animal’s means of processing information and making decisions (i.e. understanding perception); it is the problem long grappled with by economists and psychologists studying decisionmaking in human beings. The basic paradigm used in the 3-flower experiment is illustrated in Fig. 4. Behavioural output (in this case choice between flowers) is compared to manipulation of inputs (costs or intakes) to make inferences about internal processing (i.e. perception, decision processes). Use of this paradigm to understand and predict the bees’ choice using the energyrelated variables assumes that relationships between
897
costs/intakes and choice behaviour are consistent and coherent. We know that people, at least, violate this assumption (Tversky and Kahneman, 1981). This may be the case for animals as well. For example, foraging juncos are risk prone with respect to food choice when they are operating at a negative energy balance and risk averse when energy balance is positive (Caraco et al., 1980). The results of a study can be easily misinterpreted if the animal’s decision “rules” or processes change with respect to a manipulated variable. Acknowledgements-This research was supported in part by NSF grants BNS 8004537 and DEB 8119280. R. Breitwisch, J. Lewis and K. Hooker assisted with the experiments. Thanks to W. Bell, R. Breitwisch, B. Roitberg and S. Green for commenting on a draft of the manuscript. This is contribution number 157 from the Program in Ecology, Behavior and Tropical Biology, The University of Miami. REFERENCES Caraco T., Martindale S. and Whittam T. S. (1980) An empirical demonstration of risk-sensitive foraging preferences. Anim. Behav. 28, 820-830. Charnov E. L. (1976) Optimal foraging: attack strategy of a mantid. Am. Nat. 110, 141-151. Conover W. J. (1971) Practical Nonparametric Statistics. John Wiley, New York. Dethier V. G. (1976) The Hungry Fly. Harvard, Cambridge. Frisch K.v. (1967) The Dance Language and Orientation of Bees. Harvard, Cambridge. Heinrich B. (1975) Energetics of pollination. A. Rev. Ecol. Sysr. 6, 1399170.
Heinrich B. (1979) Bumblebee Economics. Harvard, Cambridge. Kahneman D. and Tversky A. (1979) Prospect theory: an analysis of decision under risk. Econometrica 47,263-291. Krebs J. R. (1978) Optimal foraging: decision rules for predators. In Behavioural Ecology: An Evolutionary Approach. (Ed. by Krebs J. R and Davies N. B.), pp. 23-63. Blackwell Scientific Publications, Oxford. Krebs J. R. and Davies N. B. (1978) Behavioural Ecology: An Evolutionary Approach. Blackwell Scientific Publications. Oxford. Krebs J. R. and McCleery R. H. (1983) Optimization in behavioural ecology. In Behavioural Ecofogy: An Evolutionary Approach, 2nd ed (Ed. by Krebs J. R. and Davies N. B.), pp. 91-121. Blackwell Scientific Publications, Oxford. Marden J. H. and Waddington K. D. (1981) Floral choices by honeybees in relation to the relative distances to flowers. Physiol. Ent. 6, 431-435. Pyke G. H. (1984) Optimal foraging theory: a critical -review. A Rev. Ecol. Syst. 15, 523-575. Tverskv A. and Kahneman D. (1981) The framing of decision and choice. Science 211; 453%. Waddington K. D. (1982) Honey bee foraging profitability and round dance correlates. J. camp. Physiol. 148, 297-30 I. Waddington K. D. (1983) Foraging behavior of pollinators. In Pollinafion Biology. (Ed. by Real L. A.). Academic Press, New York. Waddington K. D. and Rothenbuhler W. C. (1976) Behaviour associate with hairless-black syndrome of adult honeybees. J. apic. Res. 15, 35-41.
COST-INTAKE
1985 Copyright
INFORMATION
0022-1910/U $3.00 + 0.00 [Q 1985 Pergamon Press Ltd
USED IN FORAGING
KEITH D. WADDINGTON Department of Biology, University of Miami, Coral Gables, FL 33124, U.S.A.
Abstract-An
aspect of the round dance, called RATE, was used as quantification of 15 honey bees’ (A@
mellifera) perceptions of caloric costs and intakes experienced while foraging at artificial flowers. Caloric
intake per floral visit (CALGAIN) was manipulated by varying sucrose concentration. Both flight and handling costs per floral visit (CALCOST) were manipulated. Multiple linear regression analysis was used to quantify the relationship between the dependent variable, RATE, and the two energy variables. There was significant variation among bees in dance behaviour. However, the absolute magnitude of the CALCOST coefficients tended to be larger than CALGAIN coefficients; bees may weight perceptions of costs in relation to intakes. Honey bees were presented with a series of binary choices between yellow or blue tubular artificial flowers in order 40 assess how cost-intake information is used to make choices among flowers. In eight experiments the 2 flowers had different combinations of volume of sucrose (intake) and tube depth (handling cost). Results are consistent with an assessment of rate of net caloric intake, although they are likely consistent with other possible assessments. Key Word Index: Honey bee, foraging, perception, communication,
profitability, cost, intake, polli-
nation, choice
INTRODUCTION
An insect searching for food or any other resource (e.g. a mate or shelter) incurs costs and possible gain of the resource. Costs take the form of spent time and energy and potential increased risk of predation. Risk of predation had obvious consequences on fitness. Time and energy expended may also affect fitness negatively if it is time and energy taken away from other activities (e.g. acquisition of another resource). Recent studies of the energetics of search behaviour (as well as other activities) have been published (e.g. bumblebees, Heinrich, 1979). As an insect moves about in search of food it must “decide” which patches of food should be searched and which passed by, how long to remain in a patch before departure to another patch, which kinds of available foods should be eaten and which not eaten. The animal’s decisions regarding these questions will likely influence its gain (intakes) on time and energy spent foraging. These descisions prescribe certain behavioural patterns. This paper is concerned primarily with the perception of costs and intakes and the use of this information by the animal for making a routine but important foraging decision, choice of food. Recently, mathematical models have been constructed for understanding and predicting, from an evolutionary perspective, the behaviour of food choice as well as other aspects of foraging behaviour (Krebs et al., 1983). The crucial assumption of these optimal foraging models is that fitness is related to foraging efficiency. The animal is therefore assumed to make decisions which result in behavioural patterns that maximize the net rate of food intake. The costs and intakes associated with foraging and the ways that information on energetics is received and processed is fundamental to understanding foraging behaviour.
The theory of optimal diets (Charnov, 1976), a subset of models of optimal foraging theory, is used to predict food choice. Explicit assumptions are made on how time, energetic costs and intakes are used to make foraging decisions. Profitability, the measurement of foraging intakes and costs, is an important part of this theory. It is postulated in the optimal diet models that profitability of food is net caloric gain (caloric intake less cost) per handling time; the food items are ranked according to profitability and more profitable types are selected. The breadth of the diet depends on the densities (i.e. search time) of the more profitable, more highly ranked foods (see Fig. 2.2 in Krebs, 1978). Predictions of the optimal diet model have been tested using several taxa (Krebs et al. 1983). The match between predicted and observed foraging behaviour has sometimes been good. However, a second approach to testing predictions is to examine the inputs or assumptions of models. We have little information on perception of costs and intakes, the balance between costs and intakes and the use of this information for making complex foraging decisions. The honey bee, Apis mellifera, will be used here to address two related problems. It is usually assumed in foraging models that caloric costs and intakes are additive on the same scale; that is, an increase in caloric costs can be offset by an increase in caloric intake of the same magnitude. In fact this is an assumption of most models in behavioural ecology (e.g. Krebs and Davis, 1978). In the experiments discussed in this paper, caloric costs and intakes were manipulated using artificial flowers containing measured amounts of sucrose solution. The bees’ own communication dance was used to quantify perception of the magnitudes of costs and intakes. Secondly, bees were given a choice between two artificial flowers with different associated costs and intakes as a first 891
892
KEITH D. WADDINGTON Table
I. Stepwise multiple
Sucrose solutions used (calories per flower)
Distances (m)t or tube depth (cm)T used
0.204. 0.426, 0.666
0.15. 2.41t
0.426.
0.15.
0.666
0.3 15, 0.666
2.41.
3.u
3.56t
regression
analyses
for 15 bees*
CALGAIN
CALCOST F
b
SE
F
dJ
b,
SE
7.8 14.6 6.4 3.9 13.1 10.5
5.3 1.99 I .35 2.32 3.37 3.48
2.2 NS 53.911 22.4” 2.96 NS 15.2** 9.2**
-33.1 -4.6 -25.1 - 1.0 -21.1 3.0
23.29 5.43 22.23 6.44 25.12 35.98
2.0 NS 0.7NS 1.3NS 0.03 NS 0.7 NS 0.01 NS
2. 73 2.16 2,133 2.69 2, 50 2,86
3.8 7.8 - I.2
2.38 I .94 2.32
2.6 NS I5.9f’ 0.3NS
- I.1 -48.9 - 24.8
X.76 0.02 NS 14.69 11.1” 9.45 6.9”
2.175 2.203 2, I56
0.8 6.6 3.4 4.2 8.0 - 5.4
1.76 1.64 6.19 6.84 4.67 4.68
0.2 NS l6.4*’ 0.3NS 0.4NS 2.9NS 1.3 NS
-5.7 -0.2 - 2.8 - 127.6 - 32.2 -55.3
4.96 4.23 22.69 53.14 34.41 19.49
2.135 2, I56 2.49 2.55 2.21 2. 56
1.3 NS 0.03 NS 0.02 NS 5.x** 0.9 NS 8.l**
lb,
and b, are the regression coefficients for CALGAIN (caloric intake per floral visit) and of CALCOST (caloric cost per visit), respectively; RATE is the dependent variable. SE: standard error of the regression coefficient. Statistical significance of H,: regression coefficient = 0; NS: not significant, P > 0.05; **P < 0.01. F-values for each variable when the variable was added to the regression equation. df: degrees of freedom. CALCOST varied in these experiments between 0.028 and 0.533 calories. RATE varied between 8.3 and 42.9 reversals per minute. t.fEach day a bee experienced one sugar concentration in combination with 2 or 3 distances between the 2 flowers or flowers of 2 tube depths over separate trials. Tests at one distance (tube depth) were run in consecutive pairs of trials and were alternated with the other distances (tube depth).
step toward determining
how bees use cost-intake information to make choices among flowers. The volume containing this paper is concerned with search behaviour; my specific charge was to examine cost of search. Perhaps I violate this charge by examining both costs and intakes and I manipulate costs not usually thought of as “search” costs. For example, I sometimes manipulate handling costs (i.e. time on flower). This seems appropriate for now since we do not know that decisions based on X handling costs are any different than those made on X search costs. An understanding of search costs and their perception for making decisions will likely come through a broad view of cost. METHODS
AND MATERIALS
Perception of costs and intakes
Fifteen honey bees were tested singly indoors in a 2 x 4 x 2 m screen cage; Waddington (1982) provides complete methods. During a trial a bee repeatedly flew back and forth between 2 artificial flowers mounted on a platform and on each floral visit the bee ingested i p 1 of sucrose solution. A trial ended when the bee returned to the hive. For nine of the bees the costs of foraging were varied by changing the distance between the 2 flowers and the intake varied by manipulating the concentration of sucrose solution (Table 1): (1) For six of these nine bees, 3 sucrose concentrations were used (0.204, 0.426 and 0.666 calories per flower) and 2 inter-floral distances (0.15 and 2.41 m). For the other 3 bees, 2 sucrose solutions (0.426 and 0.666 calories per flower) were used in combination with 3 inter-floral distances (0.15, 2.41 and 3.56m). (2) For 6 bees handling costs, but not flight costs, were varied by changing the depth of tubular artificial flowers (12 mm dia) between 3 and 8 cm. The inter-floral distance was constant at 0.15 m. Two sugar solutions were used (0.315 and 0.666 calories per flower).
On each day of testing, a bee experienced one sugar concentration only, but the inter-floral distances (or tubular depths) were alternated between trials. Once finished foraging, the bee returned to a glass-sided observation hive (Waddington and Rothenbuhler, 1976) and performed the round dance (Frisch, 1967) which was video taped. During the round dance the LL . bee runs in a circle. , suddenly reversing direction and then turning about again to her original course. . ” (Frisch, 1967) [Fig. 11. The rate of these reversals (RATE) varies with changes in both the caloric intake per floral visit (CALGAIN) and the caloric cost per floral visit (CALCOST) [Waddington, 19821. Mean caloric cost per visit equals the mean time per visit multiplied by an estimated rate of energy expenditure, 0.0115 cal./s (Heinrich, 1975). This may be an overestimate of cost since the calculation is based on expenditure for flight. It was not possible to separate time for
Fig. 1. Stylized path of the round dance. Quick reversals occur at points X and Y; RATE is the number of these reversals per minute.
Cost-intake
information used in foraging
handling and flight. RATE is positively related to CALGAIN and is negatively related to CALCOST (Waddington, 1982). Bees report their assessments of the interaction between caloric value of the sucrose solution and the caloric expenditure during foraging in the RATE of the round dance. Stepwise multiple linear regression analysis was performed for each bee with CALGAIN and CALCOST as independent variables and RATE as the dependent variable; the variable explaining more variation in RATE entered the model first. The result is a surface of each bee’s assessments of caloric costs and intakes. Higher order than linear models did not explain significantly more variation in RATE. Choice between flowers
The “three-flower method” (Marden and Waddington, 1982) was used here. Bees foraged for sucrose solution at artificial flowers made of plastic tubes that measured 78 mm outside height and 13 mm inside diameter. A round piece of 3-mm thick Plexiglas with a drilled nectar well was glued inside of a tube at one of three depths: short flowers-l 3 mm, medium-33 mm, long53 mm. The outside of each tube was wrapped with either yellow or blue Scotch”’ plastic tape. One, two or three ~1 of 20% sucrose solution (7.4, 14.8 or 22.2 calories, respectively), lightly scented with peppermint oil, was dispensed into a flower from above using a repeating dispenser. The flowers will be designated by the combination of depth and nectar volume as follows: Ll = long and 1 p 1, M2 = medium and 2~1 and so forth. Honey bees were housed in an observation hive (Waddington and Rothenbuhler, 1976) and allowed to fly outdoors into a screen cage (3 x 2 x 2 m). The bees were occasionally fed diluted honey from a gravity feeder mounted on top of the hive. Experiments were performed inside the cage. Bees were trained singly. A bee was put under a small piece of hand-held netting with 2 touching artificial flowers, 1 yellow and 1 blue, each containing 1, 2 or 3 ~1 of peppermint scented 20% sucrose solution each (tube-depth and volume of nectar same as in test to be conducted). The solution was replaced after each visit as the bee walked between flowers. After several visits, in gradual steps the flowers were moved apart until separated by 25 cm. The bee returned to the hive ending the training period. Four flowers (2 of each type) were placed touching each other in a square on a green Plexiglas surface. The bee landed on a flower that was then moved to the starting position. A yellow and a blue flower were positioned 25 cm from the starting position (4 cm separated the 2 flowers). The bee had to choose between them on leaving the starting position. After each flight, while the bee was in the chosen flower imbibing sucrose solution, the flowers were rearranged on the board so that the original configuration was established and the empty nectar well was refilled. This process was repeated until the bee returned to the hive, thereby completing the foraging bout. Typically the bee returned within 5 min, to repeat foraging. Seven experiments were conducted. A different set of bees was used in each experiment. In each experi-
893
ment the 2 flowers had a different combination of volume of sucrose solution (1, 2 or 3 ~1) and tube depth (13, 33 or 53 mm) (Table 4). Combinations were chosen that would likely indicate, through the bee’s foraging behaviour, how information of costs and intakes is utilized; not all combinations could be tested. For each bee used in an experiment the choice of treatment (i.e. combination of sucrose solution and tube-length) for the floral colours was chosen by flip of a coin before the training period. Each bee completed 5 foraging bouts. The number of bees tested varied between experiments (Table 4). The sequence of floral visits was recorded verbally on tape and the bout duration was timed to the nearest second. The costs (time and energy) to fly to and visit the different kinds of flowers (combinations of volume of sucrose solution and tube depth) were estimated using bouts 4 and 5 where just one kind of flower was visited. The mean time per visit was calculated by dividing bout duration by the number of floral visits minus 1. Energy expended was calculated as above (O.O115cal./s x time). Net calories per time could then be calculated.
RESULTS
Perception of costs and intakes
There was significant variation among the 15 bees in dance behaviour (RATE) in relation to caloric costs (CALCOST) and caloric intakes (CALGAIN) (Table 1). The coefficients for CALCOST ranged between 3.0 and - 127.60 (x = -25.4) with 14 of 15 negative. Coefficients for CALGAIN ranged between - 5.4 and 14.6 @= 5.6) with 13 of 15 positive. There was thus agreement among bees on the direction that CALCOST and CALGAIN affect RATE. This agreement was statistically significant (H,: equal number of positive and negative regression coefficients for CALGAIN and CALCOST; binomial test, 2-tailed, P < 0.01 in both cases. The standard errors of CALCOST (range 4.23-53.14) were greater than those of CALGAIN (range 1.35-6.84). The test is of whether caloric expenditures and intakes are scaled similarly. Regression coefficients for CALGAIN and CALCOST of the same magnitude would suggest that assessments of a calorie of cost and intake are the same. In 9 of the 15 bees the absolute values of the regression coefficients of CALCOST were higher than those of CALGAIN but this was not a significant departure from one-half of the values being higher (binomial test; H,: P = 0.5, 2-tailed, P > 0.05). However, as noted above, the mean of the absolute value of CALCOST coefficients was 5 times greater than the mean of CALGAIN coefficients. The Wilcoxon Signed Ranks Test (Conover, 1971) was used to test the H, that the absolute magnitudes of the CALGAIN and CALCOST coefficients (paired comparison per bee) are equal. The H,, was rejected (2-tailed test, T = 95, P < 0.05); the values of the CALCOST coefficients tended to be larger than CALGAIN coefficients. These results suggest, at least, that bees estimate costs and intakes differently; costs are weighted in relation to intakes.
KEITHD. WADDINGTON
894
Table 2. Estimates of foraging time (per flower visit) and net caloric intake per time Tube depth. t nectar volume SI s2 s3 Ml M2 LI L2
N’ 28 20 28 13 27 20 28
Net caloric intake per time
Mean time (s)I 5.08 6.23 8.79 6.31 10.78 10.34 12.60
Table
Bout Bee El9 E20 E21 E22 E23 E24 E25 E26
1.44 2.36 2.51 I.16 I .36 0.70 1.16
+ 1.33 & 1.12 f 2.10 f I.1 I f 2.98 + 2.94 f 3.44
*N: Number of bouts (4 and 5) used. tTube depth: L = long (53 mm), M = me&urn (33 mm). S = short (13 mm). Nectar volume: I ~1 = 7.4 cdl.. 2pl = 14.8 Cal.. 3~1 = 22.2cal. $Varieties are total time per bout (flight and handling time) divided by the number of floral visits minus one during that bout.
The estimated times for bees to fly to and visit a type of flower (i.e. combination of tube depth and nectar volume) are presented in Table 2. Bouts 4 and 5, when bees were constant to one kind of flower, were used to calculate these means. The mean time increased with increased tube depth. For example, it took twice as long to visit (flight plus handling) an Ll (long flower with 1 ~1 nectar) than an Sl. Mean time per visit also increased with increasing nectar volume. One microlitre differences led to differences in mean time between 1.2s (Sl vs S2) and 4.5s (Ml vs M2). Net caloric gain per time for each kind of flower was estimated by subtracting caloric cost (estimated by multiplying 0.0115 cal./s by mean time per visit) from calories per flower and dividing this quantity by the mean time(s) per visit (Table 2). Ll yielded the lowest return per time and S3 the highest return. Bees usually specialized on one flower-type during bouts 4 and 5, although they did not always have the same preferred type within an experiment. Specialization is taken as greater than 90% but most bees visited only one flower type (100%) by bouts 4 and 5. The foraging histories of the 8 bees used in experiment 5 are typical for all results but those of experiment 8 (Table 3).
Experiment 1 2 3 4 5 6 7 8
lColour
behaviour
Flowers* 2 I L2 M2 s2 s3 SI Ml Sl Ml
LI Ml Sl SI Ml LI M2 L2
.- ; 0.98
I .oo 0.08 I .oo 0.57 0.91 0.84 1.00
*Proportion bout.
Choice between ,flowers
Table 4. Preference
3. Foraging historIes* of bees used m experiment 5
2
3
0.98 1.00 0.00
1.00 1.oo 0.00 1.oo 0.89 I .oo 0.88 1.oo
1.oo 0.70 1.00 0.98 I .oo
-4. I 00 1.00 0.00 1.00 0.96 1.oo 0.94 1.oo
5 I .oo ~ 0.00 1.00 0.98 1.00 0.98 I .oo
of visits to Sl flowers during each
The bees’ choices among flowers used in the eight experiments are summarized in Table 4. In experiments, 1, 2 and 4 where the 2 flowers had the same depths, most bees specialized on the type with the higher volume of nectar. Just one bee in experiment 2 and 2 bees in experiment 4 specialized on the less rewarding flowers. The same nectar volumes but different tube depths were offered in experiments 5 and 6. Nearly all bees specialized on the shorter, more profitable flowers. These 5 experiments show that bees can choose flowers with the lower cost or with the higher intake. Even though the result is choice of the flower with the higher net intake rate, the experiments do not show that the bees use both costs and intakes simultaneously for making decisions. In experiment 3 the tube depths were both short and the nectar volumes differed (flowers Sl and S2), but the bees’ behaviour was different; about half the bees specialized on each flower type. Either the bees perceived differently which flower was more profitable or they could not discriminate between the flowers and settled at random on a specialization. Marden and Waddington (1981) showed that bees specialize on one flower when both flowers have the same depth and nectar volume but that half the bees specialize on each flower. This suggests that in the present experiment they could not discriminate between flowers. This means that 1 ~1 vs 2~1 nectar is a different problem for bees depending on tube-depth (cost), suggesting that choice is made based on costs
of bees during
the eight experiments
N’
Individual preference?
Group’s preferencef
I5 15 17 15 8 5 IO II
l5L2 l4M2. lM1 9S2,7Sl, 1 mixed 13S3,2SI 8 Sl, I Ml 5SI,5M2 5SI.5M2 see Fig. 2
L2 M2 None s3 SI Ml None ?
(blue or yellow) of Flower 1 and Flower 2 chosen by flip of coin. Colou.~ remained the same throughout bouts of each bee. S = short tube (depth, 13 mm), M = medium (33 mm), L = long (53 mm); 1, 2 or 3 ~1. Flower I has the higher net caloric gain per time except for in experiment 8 where the flowers are equal in value. N: number of bees tested. tThe number in front of a flower-type indicates the number of bees showing high fidelity to that Rower during bouts 4 and 5 (criteria, > 90%). SSummary statement based on bees sampled. “None” indicates no significant difference from 1 :I (binomial test), P > 0.05). ? indicates a variety of behaviours exhibited by different bees.
Cost-intake information used in foraging and intakes (rather than, for example, on the amount of nectar in flowers only). Perhaps when costs are low (short-depth tubes) and, therefore, intakes from both flowers are high in relation to costs the differences in intakes between the flowers must be higher for discrimination than when costs are high. This notion is supported by the results of experiment 4. Here both tubes were short but the bees overwhelmingly specialized on the 3 ~1 (S3) flowers; just 2 of the 15 bees specialized on the Sl flowers. This result is not surprising given what is known about responses to other stimuli. It is well known that physical measurements and subjective measurements along a given stimulus dimension do not always coincide. When stimulus intensity is high, a change in intensity of some magnitude results in a smaller subjective change than when the starting intensity is low. It is easy for a person to detect a difference between objects weighing 7 and 14 kg but difficult to discern the difference between 100 and 107 kg. The important point is that the results suggest that bees make decisions based on integrating information on costs and intakes. The 2 flowers used in experiment 7 and 8 differed in both tube depths and nectar volume. In experiment 7, half the bees specialized on each flower type (Sl and M2). The bees as a group selected neither the higher nectar volume nor the lower cost. Their behaviour in experiment 8 (L2 vs Ml) was unique compared with the other experiments (Fig. 2). The behaviour of some individuals changed markedly between foraging bouts. For example 1 bee visited L2 over five bouts in the following proportions: 0.20, 0.94, 0.24, 0.80 and 0.63. Most bees had no clear specialization; just 3 bees specialized on L2 and one on M 1.
Including bouts 4 and 5 for all 11 bees tested the mean proportion of visits to L2 was 0.66 ( +_0.307) indicating, perhaps, a group preference for L2. Again, however, there is no clear specialization as to higher nectar volume of shorter tube depth. Again the indication is that decisions may be based on both cost and intake information. Within each experiment the 2 flowers have similar values of rate of net caloric intake. Estimates are 1.16 cal./s for M 1 and L2
v)
0.8
$ 2 F c,
0.6
0.0 1
2
Foraging
3
4
895
I
’ ‘W
1.0 Net
1.5
calories
per second
Fig. 3. The subjective value is a ranking of flower types based on the bees’ choice behaviour. Lines connect tested pairs of flowers. Solid dots (0) indicate the preferred flower. Dashed lines indicate pairs preferred equally (about 50% of bees preferred each type). These pairs are taken as having the same subjective value. L2 and Ml (experiment 8) also have similar subjective value (Fig. 2). Subjective value is rational and consistent with the notion that bees assess *‘net calories per second” and choose the higher valued flower.
(experiment 8) and in experiment 7 144cal./s for Sl and 1.36/s for M2. The results of these 8 experiments are reasonably consistent with the notion that bees assess flowers on the basis of rate of net caloric intake; associations are made between flower colour and the respective subjective evaluations, and the more highly rewarding flower is chosen. Figure 3 shows the bees’ subjective evaluation of the flowers used in this experiment (based on their choice behaviour) in relation to the “physical” values of flowers in net caloric intake per time. However, the results are likely consistent with other possible assessments and “rules” of action. Experiments of similar design and other experiments may suggest other possibilities and help discriminate between these possibilities. In the bee dance study, presented above, caloric intakes and costs were quantified in a non-traditional way, “per flower visited”, rather than “per time”, because calories expended per time could not be directly measured. Yet, in order to compare the bees’ perception of intakes and costs it was necessary to give them the same terms, i.e. the terms intake/visit and cost/visit. Strictly for explorative purposes those data can be used to examine the relationship between RATE and the rate of net caloric intake ([CALGAIN - CALCOSTJ/ mean time per floral visit). The results of simple linear regression analyses are presented in Table 5. Eleven of the fifteen regression coefficients are significantly different from zero (P < 0.10) and for all bees the coefficients are positive. These results further suggest that bees are sensitive to gains per time or some closely related assessment.
5
bout
Fig. 2. Results from experiment 8 showing the proportion of visits to L2 by 11 bees on a per bout basis. Proportion of visits to Ml is 1 minus the graphed proportions.
DISCUSSION
I have advocated elsewhere (Waddington, 1983) the testing of assumptions on behavioural and phys-
KEITH D. WADDINGTON
896 Table 5. Regression
Regression equationt
Bee I
2 3 4 5 6 7 8 9 IO II I2 13 14 ii
analyses using net caloric intake per time for 15 honey bees
1’ = .v = :v = ?‘ = y = 1’ = 1,s= ?’ = 1’ = .r = ;, = :v = r’ = I’ = ;I =
16.4 = 71.46.~ 15.5 + 70.90x 17.1 +38.19.x 22.2 + 37.45-Y 19.4 + 52.87; 15.7 + 50.07x 20.1 f33.9O.Y 18.1 +70.39-v 25.1 + 13.31~ 14. I + 29.68s 19.4 + 28.37x 24.1 + 24.54.~ 15.3 + 112.9x I5 7 + 59.49.x ii.6 + 50.69.x
F’ 4.7. 36.4’ 23.8’ 3.5 NS 13.4’ 5.7’ 6.1; 24.2* 3.5 NS 4.0’ 6.0’ 0.27 NS 6.9’ 4.3* 2.1 NS
tba: RATE reversals tm minute during second: I = correlation coefficient: iStatistical significance NS not significant (P > 0.05) ‘P < 0.05
iological mechanisms before those assumptions are included in foraging models (whether they be “optimal foraging models” or other models which attempt to understand or predict foraging behaviour). The present paper is an attempt to examine how honey bees assess and compare costs and intakes for making foraging decisions. Energy balance was considered because sufficient experimental and observational evidence indicates that bees and other animals are sensitive to cost-intake information and this information plays a role in foraging decisions (Frisch, 1967). Frisch (1967) investigated the honey bee’s perception of “profitability” that he defined operationally by the “liveliness” and duration of the waggle dance (Figure-of-eight dances are performed when the resource is distant from the hive) and by the probability of dancing. In general he was investigating the factors that regulate the relationship between supply and demand. He found that sugar concentration, ease of obtaining a solution, viscosity, fragrance, uniformity of flow, time of day and other factors influenced a bee’s perception of profitability. For example, as sugar concentration or viscosity of a sugar solution increases the liveliness and duration of the dance increases. Both (1956) was able to detect, using the waggle dance, apparent “trade-offs” between costs and intakes. He found that the greater the distance between a feeding station and the hive the more highly concentrated must a sugar solution be in order to release dancing. However, no specific attempts were made to quantify these trade-offs. Dethier’s (1976) work is also instructive; through behavioural and physiological studies he (and colleagues) characterized many aspects of blowfly feeding biology. Tbe work on food choice is complete with respect to preferences based on concentration of sugar solutions and types of sugars and other foods (and non-foods). However, little is known about the cost-side of the blowfly’s energy balance sheet. My studies were designed to examine assessments of costs and intakes and integration of such information for making decisions.
SE of regression coefficient
df
r
33.02 I I .75 7.82 20.11 14.45 21.04 13.68 14.30 7.15 14.83 II.58 47.09 42.92 28.68 34.96
I, 74 I. 77 I. 134 1,70 I. 51 I. 87 I. 176 1.204 1,157 I, 136 I. 157 1. 50 I, 56 1.22 1;57
0.24 0.57 0.39 0.22 0.46 0.25 0.18 0.33 0.15 0.17 0.19 0.07 0.33 0.40 0.19
round dance, x = net caloric intake per
Perception of costs and intakes
Bees danced differently in relation to foraging costs and intakes. Absolute magnitudes of nine of fifteen regression coefficients of CALCOST (with RATE, an index of a bee’s perception) were higher than CALGAIN coefficients; six CALGAIN coefficients were higher. There are three possible interpretations: each bee’s perception of the separate energy variables differs, or the way the variables are combined differs, or the bee’s report (RATE) of the combination differs. The results cannot be used to discriminate between these possiblities. Despite the disparity among bees, the magnitudes of the CALCOST coefficients tended to be larger than coefficients of CALGAIN. I interpret this to mean that bees may weight costs in relation to intakes. Weighting could result because caloric costs for flight and feeding movements are just one part of a forager’s actual costs. The bee’s report may reflect additional costs including, for example, risk of predation and the cumulative wear on the structural machinery. A small increase in caloric costs, since it may reflect an overall larger “cost”, must therefore be offset only by a larger increase in caloric intake. Foraging theory assumes that both energetic costs and time are important. I chose to quantify costs in terms of energy (calories) alone. The conclusion, drawn from several experiments, is that bees estimate distance based on energy expended for flight rather than on true distance or duration of flight. Although
Input
Fig. 4. Costs and intakes are manipulated and a bee’s behaviour (dance or floral choice) observed. This paradigm is used to understand the animal perception and use of information on the input parameters.
Cost-intake
information used in foraging
the results are not definitive, they do suggest a reasonable path for an initial study such as mine. Secondly, without information to suggest how time might be used in the assessment of cost, it was most parsimonious to quantify costs and intakes in the same units, calories. Despite such justifications, it is important that future studies attempt to determine whether assessments of cost are indeed based on energy expenditure, or time, or both; and if both are used, how is the information integrated. This will require manipulation and direct measurement of the rate of energy expenditure. The equal weighting of intakes and costs used in optimal foraging theory is also in contrast to some recent theoretical considerations of decision-making in humans. In prospect theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1981) an alternative model to utility theory for examining decision-making in humans under risk, outcomes are assigned “value” in terms of gains and losses with respect to a neutral reference point. The value function among individuals likely varies, but a number of studies indicate that is is commonly S-shaped and has two properties. First, the rate of change in perceived value for both losses and gains decreases as the magnitude of actual losses and gains is increased. Second, the change in value associated with losses is greater than the change in value associated with gains. This second attribute of the value function in human beings is qualitatively comparable to possible weighting of caloric costs by bees; that is, it takes more caloric intakes than cost to offset any level of cost and maintain the same profitability. Choice between flowers
Results of the 3-flower experiments suggest that bees choose among flowers by integrating information of costs and intakes. I suggest bees may assess net caloric intake per time and choose the flower with the higher value although the limited array of experiments completed thus far do not rule out other possibilities. However, the results of the 3-flower experiment and the regression analyses (Table 5) of the dance data are consistent with this conclusion. Further considerations
There are potential problems to consider when trying to understand an animal’s means of processing information and making decisions (i.e. understanding perception); it is the problem long grappled with by economists and psychologists studying decisionmaking in human beings. The basic paradigm used in the 3-flower experiment is illustrated in Fig. 4. Behavioural output (in this case choice between flowers) is compared to manipulation of inputs (costs or intakes) to make inferences about internal processing (i.e. perception, decision processes). Use of this paradigm to understand and predict the bees’ choice using the energyrelated variables assumes that relationships between
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costs/intakes and choice behaviour are consistent and coherent. We know that people, at least, violate this assumption (Tversky and Kahneman, 1981). This may be the case for animals as well. For example, foraging juncos are risk prone with respect to food choice when they are operating at a negative energy balance and risk averse when energy balance is positive (Caraco et al., 1980). The results of a study can be easily misinterpreted if the animal’s decision “rules” or processes change with respect to a manipulated variable. Acknowledgements-This research was supported in part by NSF grants BNS 8004537 and DEB 8119280. R. Breitwisch, J. Lewis and K. Hooker assisted with the experiments. Thanks to W. Bell, R. Breitwisch, B. Roitberg and S. Green for commenting on a draft of the manuscript. This is contribution number 157 from the Program in Ecology, Behavior and Tropical Biology, The University of Miami. REFERENCES Caraco T., Martindale S. and Whittam T. S. (1980) An empirical demonstration of risk-sensitive foraging preferences. Anim. Behav. 28, 820-830. Charnov E. L. (1976) Optimal foraging: attack strategy of a mantid. Am. Nat. 110, 141-151. Conover W. J. (1971) Practical Nonparametric Statistics. John Wiley, New York. Dethier V. G. (1976) The Hungry Fly. Harvard, Cambridge. Frisch K.v. (1967) The Dance Language and Orientation of Bees. Harvard, Cambridge. Heinrich B. (1975) Energetics of pollination. A. Rev. Ecol. Sysr. 6, 1399170.
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