Hiding in Plain sight: the nightjar project

Using drawings and mixed media sculpture I explore the visual properties and poetics of camouflage though the dark lens of Nightjar. These are a large family of small, insect-eating birds. Usually nocturnal and often migratory, they rest during the day relying on highly evolved cryptic camouflage.  Their invisibility and strange call has earned them a place in folklore as omens of doom or spirit guides to the otherworld. My work connects with Orwell though concerns about surveillance and unease about the increasing power technology gives to the state. 

I am fascinated with the challenges of depicting camouflage, whether by man’s design or by natural evolution it is an abstract landscape. Dappled light dancing across a forest floor is one of my favourite things; the dark shadow and brilliant patches of light break up the form of fallen branches and tree trunks casting a natural camouflage pattern over the scene.  While contemplating this forest scene darker connotations spring to mind because camouflage is naturally associated with prey and predator, as well as with military concealment. It is naturally ambiguous, providing safety and/or danger depending on the user’s role. 

Making a painting featuring nightjar is difficult; sketching from life is impossible so I need to rely on photographs and museum specimens. Paint it well and the bird vanishes from the painting, destroying the conventions of traditional composition. 

Orwell championed the rights of the individual against totalitarianism and in a world where the surveillance power of the state becomes ever greater, I admire nightjar’s ability to become invisible. ‘If you’ve got nothing to hide what’s the problem?’ Well there are many lessons from History which disprove that comforting reassurance1. The military are learning from nightjar too, old fashioned simple concealment patterns are being replaced by advanced fractal designs generated by scanning natural forms and producing algorithms to design new patterns2. Following real world field trials, the most successful variants are then improved by machine learning in a form of ‘un-natural selection’.  


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