Lecture 4: Optimal Foraging Flashcards
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)
Charnov:
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
patchy
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