suggested to allow prey to survive with lower vigilance levels in larger groups only because they are less likely to be the target of a predator’s attack [27,40,41]. Each time a predator appears, we randomly select a target prey from the surviving prey of previous attacks. This is followed by a 10 time step delay between the appearance of the predator in the simulation and the actual attack, representing the time it takes for a predator to close the distance to the prey. It is during this time that prey vigilance becomes important. If the target prey is vigilant at any time during this interval, then it spots the predator and the attack only has a 10% chance of success. If the target prey is not vigilant but one or more other prey in the group are vigilant, then the other prey communicate the presence of the predator via an alarm signal or other behavioral indicator and the predator will capture the target prey 30% of the time. These probabilities are chosen based on analytical models of group vigilance [33] such that group vigilance is not as effective as individual vigilance, and models the imperfect communication between members of the group [42]. Finally, if no members of the group are vigilant while the predator is closing the distance to its target, then the entire group is unaware of the predator and the attack will succeed 100% of the time. In all cases of a successful attack, the target prey is removed from the simulation and can no longer forage to increase its fitness. Each individual prey makes the decision to forage or be vigilant every simulation time step. This decision-making process is modeled with a Markov Network (MN), which is an “artificial brain” that can stochastically make decisions based on sensory input, memory, and previous actions [20, 43, 44]. Every prey MN is encoded by a list of numbers known as its genotype, such that changes to the genotype can result in changes in the function of the MN. Because we do not provide any sensory input to the prey in this simulation, we are effectively modeling the probability of a prey taking an action every simulation time step. More information on MNs—including details on their genetic encoding, mutational operators, and functionality—is available in [14]. 7