Foraging is a complex behavior vital for survival regularly involving memory anddecision-making to optimize costs and benefits. How long should an animal stay near a foodsource, and when should it move to a better food patch? In this poster, we investigate pigeons’foraging behavior testing predictions from the marginal value theorem (MVT) in a well-controlled environment with 3D pose tracking. We used DeepLabCut to analyze the behaviorof twelve homing pigeons in a 4sqm arena with two separate food patches. We manipulatedthe elevation of the food patches to investigate foraging strategies conditioned on theaccessibility and the travel modality between food patches (bipedal/flight). Using behaviortracking approaches we automated the quantification of task-relevant behaviors without theneed of manual coding, providing detailed kinematic features of pigeons’ foraging behavior toquantify the cost of travel between patches. We further quantified patch depletion to calculatethe absolute and marginal values of food patches and to obtain a model of pigeons’ foragingbehavior that predicts durations and latencies of travels as well as the number and duration ofpatch visits. By analyzing pigeons’ gaze direction, we operationalized covert decision-makingbehavior, differentiating between activity on patch and suppressed travel decisions.Preliminary results indicate consistent foraging performance, with the longer latencies inasymmetric elevation conditions and longer visit duration on elevated patches. Our studyextends beyond traditional measures of time and length of travel by using video tracking tomodel costs and benefits of flight in a context of optimal foraging.