Optimal foraging and the currency of a behavioural decision
Optimal-foraging models compare net energy gained with search, travel and handling time. Prey acceptance and patch leaving depend on the alternatives available. Observations can reveal omitted costs, mistaken assumptions or limits of the chosen currency.
What a learner can do afterwards
- Predicts prey acceptance by comparing net energy per handling time with the attainable rate from continuing to forage.
- Explains the reasoning behind a patch-leaving rule when returns decline with time in the patch.
- Names one cost, such as predation risk, that changes the prediction when included.
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An energy-rate model predicts choices that maximise net energy gained divided by total search, travel and handling time. Net energy is food energy minus energetic costs. Prey profitability is net energy divided by handling time. It ranks prey, but does not alone determine which prey to reject: searching for something better also takes time.
Suppose an animal can take only one of two prey before leaving. Both are available now, with no further search and other costs equal. One gives 18 net energy units in 6 handling minutes: 3 units per minute. The other gives 10 in 2 minutes: 5 units per minute. The second offers the higher rate.
Both bars measure net energy units per handling minute.
For encountered prey, compare its profitability with the attainable long-run rate from rejecting it and continuing to forage. That alternative includes search time. Under this rate model, accept prey above that rate and reject prey below it; equality gives no rate advantage either way. A lower-ranked prey may still be worth accepting when better prey are hard to find.
Compare whole patch visits using the same time boundary. Patch X gives 40 net energy units in 5 feeding minutes plus 5 travel minutes: 40/10 = 4 units per minute. Patch Y gives 30 units in 5 feeding minutes plus 10 travel minutes: 30/15 = 2. X offers the higher rate. Counting feeding time alone would miss part of each visit.
The marginal-value model considers predictable patches with declining returns. Leave when the gain rate from staying a little longer equals the best attainable long-run rate across visits, including travel. This benchmark is not an arithmetic mean of patch rates. For an unchanged declining patch, a higher alternative rate predicts an earlier exit. Changes in travel or the patch's return curve can alter the prediction.
A food-rich patch may expose a forager to predators. An energy-only model omits that survival cost. Competition or lost mating opportunities may also matter. If observations disagree, check measurements, assumptions and possible costs. Test revised predictions with new data. Continued disagreement may show that energy per time is not a suitable currency for that decision.
Compare net energy with all time costs. Prey acceptance and patch leaving depend on the attainable alternative rate. Test assumptions and possible costs when predictions disagree with observations.
Lesson, questions and study cards written for this dataset.
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What happens after a prey encounter?
A prey item can contain more energy yet offer a lower return per handling minute. Divide its net energy gain by its handling time to calculate profitability. Use the same energetic boundary for every item you compare.
Ranking prey is only part of a diet decision. When an animal encounters an item, rejecting it means returning to search. Compare the item's profitability with the attainable long-run rate from continuing to forage without taking it. Under the energy-rate model, accepting an item above that benchmark improves the rate; taking one below it reduces the rate.
Search opportunities therefore matter. A lower-ranked item may be worth taking when better prey are difficult to find. A ranking alone cannot tell you which prey types the animal should ignore.
Comparing another moment of feeding
An animal can leave a patch while food remains. What matters in the marginal-value model is the gain rate from staying a little longer, compared with the best attainable long-run rate across patch visits.
The model considers predictable patches with declining returns. Its leaving condition equates the marginal gain rate with a long-run rate that includes travel. That benchmark is not an arithmetic mean of separate patch rates.
Keep the comparison controlled. If the focal patch's return curve stays unchanged and the alternative rate rises, the predicted exit comes earlier. If travel time or the focal patch changes too, the result may differ. Describing the habitat as richer does not establish all those conditions.
For whole-visit calculations, include all relevant travel and feeding time. Use the same visit boundary for each option.
Investigating a prediction that misses
Suppose a calculation predicts feeding in an exposed patch, but animals feed under cover. Record the disagreement before deciding what explains it. Check the food-energy estimates, measured times and alternatives available to the animals.
Predator exposure is one possible omitted cost. Compare attack risk between locations and test whether including it improves predictions. Competition or lost mating opportunities may also matter, but naming a possible cost is not evidence that it explains the behaviour.
Test a revised model with observations beyond those used to build it. If measured costs and checked assumptions still leave repeated disagreements, reconsider the energy-per-time currency. A model must allow evidence to count against its predictions.
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