Abstract

Optimal foraging theory assumes that individuals maximizing a given energy currency maximize their fitness. Though models with different currencies (and assumptions) have been successful at describing forager behavior, discriminating among currencies has proven difficult because models optimizing different currencies often make similar predictions. A field experiment was designed to use flight speeds of Black Terns (Chlidonias nigra) feeding their young to test predictions from optimal foraging models that maximize (1) the net energy gained per unit of energy expended (efficiency: EFF), (2) the net rate of energy intake (NREI), and (3) the daily delivery rate (DR), respectively. Manipulating the distance an adult had to fly between a feeding enclosure and its nest enabled us to discriminate among the three currencies. Predicted speeds of the EFF model best matched the observed flight speeds. Maximizing efficiency resulted in the lowest delivery rate to the nest, but it was also the only currency that predicted adult daily energy expenditures within the theoretical limit (DEEmax) estimated for Black Terns (206 kJ/d; Kirkwood 1983). The feeding enclosure represented an unusually profitable food source for the terns, and this may have resulted in artificially high predictions for flight speed, particularly for the NREI and DR currencies. We therefore made another set of predictions from the three models using estimates of foraging success from parents who had never foraged from the feeding enclosure, and compared these predictions with the measured airspeeds. The EFF model was again the best predictor of flight speeds even though none of the three models predicted energy expenditures in excess of DEEmax. Though the DR model resulted in the highest total daily energy delivered to the nest, this represented only a small gain relative to the NREI and EFF models. Black Terns parents may maximize EFF because this currency is least likely to result in daily energy expenditures that exceed their metabolic limit.

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