Background complexity and the detectability of camouflaged targets by birds and humans

Abstract

Remaining undetected is often key to survival, and camouflage is a widespread solution. However, extrinsic to the animal itself, the complexity of the background may be important. This has been shown in laboratory experiments using artificially patterned prey and backgrounds, but the mechanism remains obscure (not least because 'complexity' is a multifaceted concept). In this study, we determined the best predictors of detection by wild birds and human participants searching for the same cryptic targets on trees in the field. We compared detection success to metrics of background complexity and 'visual clutter' adapted from the human visual salience literature. For both birds and humans, the factor that explained most of the variation in detectability was the textural complexity of the tree bark as measured by a metric of feature congestion (specifically, many nearby edges in the background). For birds, this swamped any effects of colour match to the local surroundings, although for humans, local luminance disparities between the target and tree became important. For both taxa, a more abstract measure of complexity, entropy, was a poorer predictor. Our results point to the common features of background complexity that affect visual search in birds and humans, and how to quantify them.

Quantifying camouflage: how to predict detectability from appearance

“…Our use of tree-bark backgrounds photographed under diffuse lighting conditions may also have influenced our findings, and qualitatively different results could be possible against alternative backgrounds, and under different lighting conditions. A number of studies have demonstrated the importance of background complexity in affecting detectability [24, 38, 43], so our findings may not hold against simple backgrounds with low levels of achromatic complexity. Xiao and Cuthill found that feature congestion best predicted human and bird detection of artificial moths [24].…”

“…A number of studies have demonstrated the importance of background complexity in affecting detectability [24, 38, 43], so our findings may not hold against simple backgrounds with low levels of achromatic complexity. Xiao and Cuthill found that feature congestion best predicted human and bird detection of artificial moths [24]. This metric combines local achromatic and chromatic changes, and edge orientation changes.…”

“…While we found that all of these metrics predicted capture times in line with Xiao & Cuthill, they were not as effective as other methods, possibly because they do not consider the prey’s interaction with its background. Future work should compare the effectiveness of these models with images of natural prey, and in wholly natural systems to establish how wide-ranging these findings are to detection times in alternative contexts and with non-human visual systems [24]. In addition, models should be developed that can integrate chromatic cues; experiments on colour discrimination typically involve comparisons between flat colour patches rather than the complex and varied colours encountered in natural search tasks.…”

“…using fast Fourier transforms, or Gabor filters), then compare the information at these different scales between the prey and background. Other methods search for shapes or features found in the prey that are similar to those in their backgrounds [22, 24]. For an overview see Table 1 and Additional file 1.…”

“…Background complexity is also known to influence detection times [38]. For example, artificial moths were more difficult to find for humans and birds alike when the surrounding bark had higher levels of luminance contrast and edge orientation changes [24]. However, in this study we aimed to focus on metrics that investigate the interaction between target and background, rather than assess the general properties of backgrounds that affect concealment.…”