Cooperative multi-robot missions require team of robots to traverse environments where adversaries or hazards with stochastic dynamics induce time-varying traversal risk. While support coordination--where robots assist teammates in traversing risky regions--can significantly reduce mission costs, its effectiveness depends on the team's ability to anticipate future risk. We formulate support-based multi-robot graph traversal problem with stochastically moving adversaries, where future risky regions become uncertain as adversaries move through the environment. When adversaries remain stationary, our formulation reduces to the static risky-edge setting. To address the stochastic case, we model individual adversaries as first-order Markov stay-move processes over graph edges and propagate their occupancy distributions over a finite planning horizon to obtain time-indexed edge-risk forecasts. These forecasts inform the support candidate selection and joint robot path planning. Experimental results show that forecast-informed support decisions consistently lower expected team cost relative to evaluated baselines in stochastic motion settings.

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