| Summary: | Background/Question/Methods The survival of animal populations is strongly influenced by the individuals’ ability to forage efficiently, yet there are few studies of how populations respond when disturbances cause animals to deviate from their natural foraging behavior. Animals that respond to disturbances by moving away are prevented from accessing the food in the disturbed areas and may also be prevented from dispersing among areas where food is available at different times of the year. Such disturbance effects may play a particularly large role for marine mammals that live in environments that are increasingly exposed to noise from ships, wind turbines, etc. In the present study we investigate how the dynamics of the harbor porpoise population (Phocoena phocoena) in the inner Danish waters is influenced by disturbances using an agent- based simulation model. In the model animal movement, and hence the animals’ ability to forage efficiently and to sustain their energy intake, is influenced by noise emitted from wind turbines and ships. The energy levels in turn affect their survival. The fine-scale movements of the simulated animals was governed by a spatial memory, which allowed the model to produce realistic movement patterns in scenarios where animals were not exposed to noise. Results/Conclusions The main results of the study were that the effects of disturbances were highly dependent on how fast the food recovered after being eaten, but that the long-term survival of the population was not jeopardized even when disturbances were simulated to have a relatively large and persistent effect on the behavior of individual animals. Porpoises were simulated to move away from noisy objects, preventing them from returning to the known food patches in that area. This resulted in decreasing energy reserves and an increasing risk of starvation. When food was simulated to recover slowly, the population effects of the disturbances was small because little food would have been available to porpoises in the known food ...
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