Motion anti-predation strategies and the visual system

Abstract

Predator vision is finely tuned to detect movement, which often circumvents camouflage. However, literature suggests that motion can be used as camouflage, e.g.: (1) Motion Signal Minimization - animals adapt their motion to resemble their surroundings; (2) Protean motion - unpredictable motion hinders a predator’s estimation of prey location; (3) Punctuated motion - when prey stop and start motion unpredictably. However, the effectiveness of these strategies has not been quantified or directly compared.

The central purpose of the thesis to achieve this is to quantitively assess whether documented proposed motion camouflage strategies function as effective camouflage, and if so, how effective they are. By measuring how these different motion types affect human estimation of movement direction and tracking ability, this research provides a direct comparison of their effectiveness, ultimately determining which strategies are most successful in enhancing camouflage.

It is important to consider animals in dynamic environments and the implications for camouflage techniques and visual search, as real-world surroundings are rarely static. Therefore, virtual environments were utilized in the experiments of this thesis to curate a balance between ecological validity and experimental control.

This thesis explores the interaction between movement type and environmental complexity through the use of custom-developed 3D-modeled forest scenes and ecologically valid virtual reality (VR) environments. Across three experimental chapters, the influence of motion type and background complexity on target detection and tracking difficulty is examined. Additionally, target properties such as saliency and speed are evaluated to further understand their impact on camouflage effectiveness in the final experimental chapter.

Each experiment demonstrates motion as camouflage is quantifiably successful. The extent of its effectiveness in minimising predation is shown to be varied across experimental tasks and combinations of factors with which they operate.

Date of Award	1 Jul 2025
Original language	English
Awarding Institution	University of St Andrews
Supervisor	Julie Harris (Supervisor) & Justin Ales (Supervisor)