Optimal foraging theory: an introduction

Optimal Foraging Theory (OFT) uses techniques of mathematical optimization to make predictions about foraging behavior which is a fundamental aspect of animal behavior. As it has just turned 50, it is timely to review its foundations, what it has achieved and where it is headed.

As an introduction to OFT, I discuss the classic model of patch exploitation, developed by Eric Charnov in 1973, which considers how long a forager should spend exploiting a patch before it moves to a fresh one. A graphical and mathematical approach to this problem led to predictions that were supported by early empirical studies, thus fueling enthusiasm for the optimality approach to understanding animal foraging behavior. OFT, from the outset, also considered foraging decisions regarding patch choice, diet and movements.

OFT views foraging behavior as the outcomes of a set of decisions, made continuously, assumes fundamentally that animal foraging decision-making has evolved to the point that biological fitness (i.e., ability to contribute to the next generation) of an individual forager has been maximized, and seeks to understand such decision-making by matching predicted and observed behavior as closely as possible. OFT must therefore be based on models that mathematically describe the foraging processes involved.

Because biological fitness is difficult to measure directly, it is generally necessary to adopt a surrogate ‘currency’ and to find the behavior that maximizes it. Examples include the net rate of energy intake and the likelihood of meeting total energy requirements during available foraging time.

The classic OFT models have been expanded and extended in many ways. Expansions have included allowing for lack of perfect information, the risk of becoming someone else’s meal, and for decisions to vary with changes in an animal’s state. Extensions have occurred where OFT models have been incorporated into studies of related biological phenomena that involve foraging, such as population dynamics, food webs, and co-evolutionary relationships between nectar-feeding animals and the plants they visit.

OFT has also been applied to areas outside the realm of animal feeding behavior and acted as inspiration for solving various optimization problems in human technology.

Through all of this OFT has grown enormously and been successful in terms of qualitative predictions, but less so quantitatively. It has demonstrated its usefulness and emerged as a strong theory of behavior and ecology