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Updated: Feb 8, 2026

Characterization of Biological Absorption Spectra Spanning the Visible to the Short-Wave Infrared
Published on: January 10, 2025
The microstructure of white feathers predicts their visible and near-infrared reflectance properties
Devi Stuart-Fox1, Elizabeth Newton1, Raoul A Mulder1
1School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia.
Abstract:
Research on the optical properties of animal integuments, including fur, feather, skin and cuticle, has focussed almost exclusively on animal-visible wavelengths within the narrow range of 300-700 nm. By contrast, the near-infrared (NIR) portion of direct sunlight, spanning 700-2600 nm, has been largely ignored despite its potentially important thermal consequences. We quantified variation in visible and NIR reflectance and transmission for white body contour feathers of 50 bird species, and examined how well they are predicted by feather macro- and micro-structural morphology. Both visible and NIR reflectance of the feathers varied substantially across species. Larger, thicker, and sparser feathers that are characteristic of larger species, and feathers with rounder barbs and more closely spaced barbules, had high average reflectance, particularly within avian-visible wavelengths (300-700 nm). Feathers with rounder barbs and more closely situated barbules also had high average reflectance, particularly for NIR wavelengths. Barb roundness and barbule density were the only predictors of NIR reflectance after accounting for variation in visible reflectance and body size. Our results highlight the potential for adaptive variation in NIR reflectance mediated by feather structure, which may inform the design of functional materials to control light and heat.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

