Data from: Optimal background matching camouflage

Background matching is the most familiar and widespread camouflage strategy: avoiding detection by having a similar colour and pattern to the background. Optimizing background matching is straightforward in a homogeneous environment, or when the habitat has very distinct sub-types and there is divergent selection leading to polymorphism. However, most backgrounds have continuous variation in colour and texture, so what is the best solution? Not all samples of the background are likely to be equally inconspicuous, and laboratory experiments on birds and humans support this view. Theory suggests that the most probable background sample (in the statistical sense), at the size of the prey, would, on average, be the most cryptic. We present an analysis, based on realistic assumptions about low-level vision, that estimates the distribution of background colours and visual textures, and predicts the best camouflage. We present data from a field experiment that tests and supports our predictions, using artificial moth-like targets under bird predation. Additionally, we present analogous data for humans, under tightly controlled viewing conditions, searching for targets on a computer screen. These data show that, in the absence of predator learning, the best single camouflage pattern for heterogeneous backgrounds is the most probable sample.

背景匹配是最常见且应用最为广泛的伪装策略：通过与背景保持相似的色彩与图案，从而避免被侦测发现。在均质环境中，或是当栖息地存在特征鲜明的亚型且存在导致多态性的歧化选择时，优化背景匹配策略的过程十分直观。然而，绝大多数背景的色彩与纹理均呈现连续变化的特征，此时最优的伪装方案是什么？并非所有背景样本都能达到同等的隐蔽效果，针对鸟类与人类开展的实验室实验也佐证了这一观点。理论研究表明，以猎物的尺寸为参照，统计学意义上概率最高的背景样本，平均而言将是隐蔽性最强的伪装方案。我们基于针对低级视觉（low-level vision）的现实假设开展了一项分析，该分析可估算背景色彩与视觉纹理的分布，并预测最优伪装方案。我们通过一项野外实验获取了数据：该实验以鸟类捕食情境下的人工蛾类模拟靶标为对象，对我们的预测进行了检验与佐证。此外，我们还获取了在严格控制的视觉条件下，人类在电脑屏幕上搜寻靶标时的类比实验数据。上述数据表明，在捕食者未发生学习行为的前提下，针对异质背景的最优单一伪装图案，正是统计学意义上概率最高的背景样本。