Lecture 4: Optimal Foraging Flashcards Preview

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Flashcards in Lecture 4: Optimal Foraging Deck (25):

optimal foraging is a balance between the

costs and benefits in question
--benefits of calorific intake and cost of time and costs it has getting it


optimality logic =

selection will favour animals that forage most efficiently


optimality modelling is used to

determines the best course of action for an animal e.g. maximising food intake or offspring provisioning rate per unit time


john maynard smith linked to

optimality modelling, brought economic idea of costs and benefits


Whelk Choice by Northwestern Crows OBSERVATIONS by Zach 1979

-Crows always chose large whelks (3.5-4.4cm)
-drop them from about 5cm onto rocks to break them open
-they keep dropping a whelk until it breaks


Whelk Choice by Northwestern Crows PREDICTIONS by Zach 1979

-large whelks should break more easily at 5m than small
-whelks dropped at <5m should be less likely to drop, dropped at >5m should not be more likely
-chance of whelk breaking should be independent of the number of times its dropped


Whelk Choice by Northwestern Crows EXPERIMENT by Zach 1979

Drop whelks from tower on beach


Whelk Choice by Northwestern Crows EXP RESULTS by Zach 1979

-large whelks broke more easily at 5m
-took around 4/5 drops, way less than smaller
-5m optimum drop height
-found drops and breaking to be independent


When a hypothesis based on cost benefit logic is found to be incorrect this can lead to further insights.

1) The animal may not have been well ‘designed’ by selection
2) The observations may have been inappropriate
3) An important factor may have been omitted from the model
4) The assumptions may not have been valid


Oyster catchers mistaken study, found that oyster catchers were choosing mussels smaller than predicted BECAUSE

large mussels were impossible to open (model A and model B)


Moose Belovsky 1978: foraging is strongly affected by

-->Sodium requirements
-moose feed in two habitats, deciduous forest (high energy, low sodium), lakes (high sodium, low energy)
-graph plotting both areas to determine optimal model (energy constraint, rumen constraint, sodium constraint) --> trying to gain as much energy with just eating enough sodium


nutrient quality of food is often more important for __ than ___

herbivores rather than carnivores (as must ensure they're getting enough nutrients, e.g. Moose)



Marginal Value Theorem
-animals feeding in patchy environments (when to move from one patch to another)
-tangent to loading curve = optimal time to leave


foraging environments tend to be



when animal arrives at patch of food

-has high food quality
-loading curve (arc ^ ending upwards)
--when do they give up on this patch and move onto next?


consequences of animal leaving patch too early/late

-too early: waisting time travelling (miss out on food)
-too late: waisting time at patch


Charnel's Marginal value theorem: if travel time between patches varies then we'd expect

different optimal time spent at patch (due to different tangent)


loading curve =

line of diminishing returns


optimal foraging: Starlings
-Kacelnik 1984

-prey leather jackets to feed chicks
-starlings get diminishing returns as they forage because it is harder to find food (probe) when carrying prey


Kacelnik 1984 starlings experiment

-trained starlings to feed from artificial patches with diminishing returns placed at 8-600m from nest
-RESULTS: load size increased with distance from nest


Assumptions of Marvel value theorem

- travel time between patches is known
-travel costs = patch costs
-patch profitability is known
-no predation


MVT assumption: to travel costs = patch costs COWIE 1977

Great tits, experimental trees
--> expend more energy during travelling time than during patch time.


MVT assumption: Is patch profitability known? LIMA 1984

Downy Woodpecker: trained to forage from logs each with 24 holes -empty or with seeds
--pretty close findings


optimality models and behaviour entail:

-they provide testable quantitive predictions
-they involve explicit assumptions
-they illustrate the generality of decision making


What to do when the optimality model fails to predict observations?

-Ignore it (count as acceptable error)
-Accept animal is sub-optimal
-Re-build model