Predicting the diet breadth and habitat use of an optimal forager


Animals looking for and capturing food can be considered to make economic decisions, where food types or locations (‘patches’) are included if they increase profit. The animal ‘spends’ time or energy to gain nutrients, especially those that provide energy. Natural selection will act on the behaviour of animals to make them behave approximately like they are making optimal decisions that depend on these costs and benefits.


In this model, all food types contain the same amount of energy, but take different amounts of time to find, and to capture and eat. As more food types or patches are included in the diet the time to find something to eat decreases. As the forager has to have more diverse skills, the capture and eating time will increase. The food types can be ranked in order of preference by the difference between search time and capture and eating time. An animal should add less appropriate items to its diet only if the search time decreases more than the capture and eating time increases.


The predictions of the model are that animals should have specialist, narrow diet if (1) food is higher density, (2) the forager is more mobile, (3) prey are harder to catch once found. Competition for food should have no effect. Predictions can be made about use of different patches in a similar way. Specialism of patch use should increase if (1) patches are larger relative to the animal, (2) the prey are harder to catch once found, (3) there is more competition.


Behavioural ecology

Subject Group

Zoology and Ecology


optimal foraging

patch use

diet breadth

food type

Posted by


on Thu Oct 19 2017

Article ID


Details of original research article:

MacArthur RH, Pianka ER. On optimal use of a patchy environment. American Naturalist. 1966;100:603-609.

Followed by:

To better understand the behaviour of animals we need to combine two approaches: understanding why they make decisions and how they make decisions

Posted by: AndrewDHigginson Posted Fri Oct 27 2017


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