A dynamic habitat selection game.

Area of Science:

• Behavioral Ecology
• Game Theory
• Evolutionary Biology

Background:

• Optimal foraging theory (OFT) traditionally models individual foraging as a "1-person" game.
• OFT assumes fixed conspecific densities, overlooking their dynamic influence on foraging.
• A comprehensive game theoretic approach is needed to incorporate conspecific interactions.

Purpose of the Study:

• To formulate and analyze a patch selection game that includes the effects of conspecifics.
• To compare the predictions of this game theory model with traditional optimal foraging theory.
• To develop a computational method for solving the n-person game.

Main Methods:

• Formulation of a patch selection game with H patches, incorporating food density, value, foraging cost, predation risk, and conspecific density.
• Game theoretic analysis of organismal strategy, considering the behavior of conspecifics.
• Application of an iterative algorithm to compute the dynamic evolutionary stable strategy (ESS).

Main Results:

• The game theory model predicts different optimal patch choices compared to traditional OFT.
• Conspecific density significantly influences food finding, food sharing, and predation risk.
• The iterative algorithm successfully computes the n-person game solution.

Conclusions:

• Incorporating conspecific interactions into foraging models provides a more realistic framework than OFT.
• The developed game theory model and computational approach offer new insights into collective foraging behavior.
• Experimental validation using rodents and seed trays is planned to support the theoretical findings.