Many aspects of visual coding have been successfully predicted by starting from the statistics of natural scenes and then asking how the stimulus could be efficiently represented. We started from the representation of color characterized by uniform color spaces, and then asked what type of color environment they implied. These spaces are designed to represent equal perceptual differences in color discrimination or appearance by equal distances in the space. The relative sensitivity to different axes within the space might therefore reflect the gamut of colors in natural scenes. To examine this, we projected perceptually uniform distributions within the Munsell, CIE ${\mathrm{L}}^{*}{\mathrm{u}}^{*}{\mathrm{v}}^{*}$ or CIE ${\mathrm{L}}^{*}{\mathrm{a}}^{*}{\mathrm{b}}^{*}$ spaces into cone-opponent space. All were elongated along a bluish-yellowish axis reflecting covarying signals along the $\mathrm{L}–\mathrm{M}$ and $\mathrm{S}–(\mathrm{L}+\mathrm{M})$ cardinal axes, a pattern typical (though not identical) to many natural environments. In turn, color distributions from environments were more uniform when projected into the CIE ${\mathrm{L}}^{*}{\mathrm{a}}^{*}{\mathrm{b}}^{*}$ perceptual space than when represented in a normalized cone-opponent space. These analyses suggest the bluish-yellowish bias in environmental colors might be an important factor shaping chromatic sensitivity, and also suggest that perceptually uniform color metrics could be derived from natural scene statistics and potentially tailored to specific environments.