BIO 555/755
Behavioral Ecology

Lecture Notes II - Foraging Behavior


Natural selection may favor 'efficient' foragers, and efficient may mean that

What is maximized?  Let's assume for now that an 'optimal' foragers is attempting to maximize energy intake. If so, what decisions must a forager or predator make? Any food item has both a cost (time & energy) & a benefit (net food value). The relative value of each of these determines how much 'profit' a particular item represents. In other words:

'Profit' = net food value divided by time required to obtain & handle the food item, and

Efficient foragers should select most profitable prey!

For example, let's look at two studies:


Given that foragers may want to maximize 'profit', what should they do when less than optimal prey are encountered?

What should a predator's strategy be to maximize energy intake/unit time? Should a predator take only PREY TYPE 1 & always ignore PREY TYPE 2? Should a predator always take both?

Test of the model (Krebs et al. 1977):

Extra payoff for specializing (prey/sec) on large worms rather than taking both (From Fig. 3.6b; Krebs and Davies 1993)

Exploitation of patches

Prey availability within a patch decreases as a result of the predator's foraging activity because of:

As a result, to maximize the rate of gain of a resource, predators should follow the 'marginal value theorem':

To maximize gain (e.g., energy) per unit time, a predator should leave at the point (maximum net gain) that gives the greatest gain or food intake per unit time (steepest slope of the line). The line is not as steep (which means less gain or intake per unit time) when the predator leaves too early (or too late).

If travel time increases (e.g., patches further apart), the optimal time to stay in a patch also increases:

If patches vary in quality, each patch should be exploited until the gain rate within the patch drops to the average for the environment:


Parallel lines represent equal rates of energy gain

Assumptions of the Marginal Value Theorem:

                1 - Each patch type is recognized instantaneously
                2 - Travel time between patches is known by the predator
                3 - Gain curve is smooth, continuous, & decelerating
                4 - Travel time between & searching within a patch have equal energy costs

Predictions of the Marginal Value Theorem:

                1 - If travel time & the gain curve are known, then Topt can be predicted
                2 - If there is more than one patch type in an environment, all should be reduced to the same gain rate
 

Tests of the Marginal Value Theorem:

Great Tits foraging in large aviary for pieces of mealworm hidden in sawdust-filled plastic cups on the 'branches' of artificial trees (Cowie 1977):



Each point = different aviary environment

Tests of the MVT often show qualitative but not quantitative support. Why?


Foraging for young located in a nest or den: Central Place Foraging

Assume a diminishing energy gain with increasing load size (e.g., a bird with a beakful of insects) or increasing time in patch:

Cumulative food gain within patches for an average House Martin (open circles). The straight lines show maximum overall rates of food gain within bouts for foraging near the nest (Tn), at the mean observed distance from the nest (Tm), & far from the nest (Td). The Bryant and Turner (1982) model yields a different value for optimal bolus size (Bopt) for each travel time.

House Martin (Delichon urbica)
Foraging Distance
(km)
Travel Time
(min)
Optimal Load Size
(mg)
Observed Load Size
(mg)
0.1
0.32
32
52.6
0.45
1.43
43
55.2
1.0
3.18
52
65.6


Effect on foraging behavior when a predator requires as particular nutrient?

Moose - feed in forests (deciduous leaves with lots of energy but little sodium) & small lakes (aquatic plants rich in sodium but less energy)

- What is the optimal mixture?


The diet of moose is constrained by the need for sodium & energy: the daily requirements
are shown by the lines above (and moose must eat a mixture of plants which lies in the space
above the two lines). The third constraint is the size of a moose's rumen (see line above).
Aquatic plants are bulkier than terrestrial plants, so fewer grams can be fitted into the rumen.
The moose diet was found to lie at the point inside the triangle that maximizes daily energy
intake (indicated by the star) (Belovsky 1978, as cited by Krebs and Davies 1993; Figure from
Krebs and Davies 1993, p.71)


Photo source: http://www.primatesofpanama.org/howler.htm

Howlers - feed on fruits, flowers, & leaves of trees (96 species present in study area)

Tree species
% of total 
feeding time (B)
% of total
trees present (A)
Selection ratio
B/A x 100
A
12.15
1.65
736
B
10.04
0.65
1545
C
7.92
0.94
843
D
6.19
0.41
1510
E
4.71
0.24
1963
F
3.55
0.12
2958
G
0.67
0.06
1117
H
0.60
11.60
5
I
0.07
11.83
1

Conclusions:


Stochastic foraging models

Assume that travel times & patch quality vary in an unpredictable way. Possible consequences:

Risk of doing badly or well in patches: So, Juncos switched from 'risk-averse' to 'risk-prone' as deprivation increased. EXPLANATION? Expected energy budget rule:  Be risk prone if the daily energy budget is negative & be risk averse if daily energy budget is positive.


Effect of 'information' on foraging behavior

Lima, S.L. 1984. Downy Woodpecker foraging behavior: efficient sampling in simple stochastic environments. Ecology 65:166-174.

Day 1 ---> Downies expected only totally full patches & opened almost all holes in empty patches (although no seeds were found)
Days 2 - 8 ---> Downies learned that some patches had seeds & some did not
Days 9 - 11 ---> Downies opened 1.7 holes/empty patch
Day 12 ---> 1st day of half-full environment
Day 13 ---> Downies opened 5 holes/empty patch
Day 23 ---> 1st day of quarter-full environment
Days 29 - 31 ---> Downies opened 6.3 holes/empty patch

CONCLUSIONS:


Foraging & conflicting demands

Behavior of foraging animals may be influenced by need to watch for predators, search for mates, defend territories, defend nest sites, and so on (e.g., Martindale 1982):


Gila Woodpecker

N & F = 2 food patches identical except for distance from nest (N = near & F = far)
TN & TF = travel times
BN & BF = benefit curves (proportional to rate at which food can be delivered to the nest)
CH & CL = cost functions (proportional to probability that a successful attack on the nest occurs)
tN & tF = foraging times that optimize delivery rate
tNL & tFL= foraging times that maximize net benefit (survival of young) at low 'attack' rate ('far' patch affected more than 'near' patch)
tNH = optimal time in near patch at high attack rate (far patch no longer confers positive net benefit)

Conclusions:


Influence of a predator on the optimal foraging behaviour of sticklebacks (Milinski and Heller 1978):


From Milinksi and Heller 1978, as cited by Krebs and Davies 1993
(Figure from Krebs and Davies 1993, p. 69)


Search Paths


Age-specific foraging behavior (e.g., Wunderle 1991)

Each of these hypotheses is likely to be correct depending on the species. After young become independent of parents, recognition & selection can differ with age because: Prey capture - capture techniques commonly proceed from simple movements to more complex movements requiring coordination & skill. Generally, the greater the skill needed to capture prey, the less successful are juveniles in comparison with adults.

Food handling time & technique:


Literature cited:

Belovsky, G.E. 1978. Diet optimization in a generalist herbivore: the moose. Theor. Pop. Biol. 14:105-134.

Bryant, D.M. and A.K. Turner. 1982. Central place foraging by swallows: the question of load size. Anim. Behav. 30:845-856.

Caraco, T., S. Martindale, and T.S. Whitham. 1980. An empirical demonstration of risk-sensitive foraging preferences. Anim. Behav. 28:820-830.

Cody, M.L. 1971. Finch flocks in the Mohave Desert. Theor. Pop. Biol. 2:142-158.

Cowie, R.J. 1977. Optimal foraging in Great Tits, Parus major. Nature 268:137-139.

Davies, N. B. 1977. Prey selection and social behaviour in wagtails (Aves: Motacillidae). Journal of Animal Ecology 46:37-57.

Glander, K.E. 1981. Feeding patterns in mantled howling monkeys. Pp. 231-257 in A.C. Kamil and T.D. Sargent, ed., Foraging behavior: ecological, ethological and psychological approaches. Garland STPM Press, New York.

Krebs, J.R. and N.B. Davies. 1993. An introduction to behavioural ecology, third ed. Blackwell Scientific Publications, London.

Krebs, J. R., J. T. Erichsen, M. I. Webber & E. L. Charnov. 1977. Optimal prey selection in the great tit (Parus major). Anim. Behav. 25: 30-38.

Martindale, S. 1982. Nest defense and central place foraging: a model and experiment. Behav. Ecol. Sociobiol. 10:85-89.

Milinski, M. & R. Heller. 1978. Influence of a predator on the optimal foraging behaviour of sticklebacks. Nature 275:642-644.

Pyke, G.H. 1978. Are animals efficient harvesters? Anim. Behav. 26:241-250.

Wunderlee, J. M., Jr. 1991. Age-specific foraging proficiency in birds. Curr. Ornithol. 8: 273-324.

Zach, R. & J. B. Falls. 1978. Prey selection by captive ovenbirds (Aves: Parulidae). J. Anim. Ecol. 47: 929-943.


More lecture notes:

Living in Groups


Useful links:

Cyberbranchaea - Optimal Foraging Simulation

Food: Optimal Foraging Models

Game Theory

Introduction: Background to Optimal Foraging

Mating Walnut Flies

Optimal Foraging

Optimally Foraging Hummers

Optimal foraging experiments on captive Steller sea lions: a feasibility study

Optimality theory; Foraging Strategies

Patch use in cranes: a field test of optimal foraging predictions


Back to Behavioral Ecology syllabus