| Summary: | The effectiveness of animal camouflage critically depends on the properties of the environment and so any changes in habitat structure can interfere with an animal’s camouflage influencing the landscape of fear. Human activity globally alters the appearance of habitats by altering community composition, geometry and lighting through direct management, and indirectly from climate change and pollution. Even subtle changes to geometry and atmospheric conditions might severely impact camouflage. Yet, how human activity affects camouflage is still poorly understood as habitat interactions with camouflage are muddled by the diversity of animal phenotypes and alternative selective drivers, e.g., signalling and thermoregulation. Within this thesis, I investigated the broad effects of background geometry and lighting on camouflage and used populations of threatened Northern lapwing (Vanellus vanellus) in Southeast England as a case study for how management can affect these background features. By photographing and 3D scanning habitats under different lighting conditions and evolving artificial targets with a custom-built genetic algorithm, I was able to demonstrate the multidimensional effects of direct lighting on camouflage. Not only did direct lighting promote distinct camouflage strategies from diffuse lighting, selecting for more contrasting and directional patterns as well as countershading, but it also increased capture time within more 3D variable environments due to the formation of complex shadows. These results indicate that changes in background 3D composition and lighting regime are likely to have a large effect on the effectiveness of animal camouflage. Within the populations I monitored, lapwing selected local nest sites that better matched the appearance of their eggs (luminance, colour and pattern) and had greater 3D complexity than random sites. Furthermore, the shape of the lapwing nests makes the clutches highly occluded from the viewing angles of their natural predators. Surprisingly intensive ...
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