Del Valle José C, Gallardo-López Antonio, Buide Mª Luisa, Whittall Justen B, Narbona Eduardo
Department of Molecular Biology and Biochemical Engineering Pablo de Olavide University Seville Spain.
Department of Biology Santa Clara University Santa Clara CA USA.
Ecol Evol. 2018 Feb 16;8(6):3064-3076. doi: 10.1002/ece3.3804. eCollection 2018 Mar.
Anthocyanin pigments have become a model trait for evolutionary ecology as they often provide adaptive benefits for plants. Anthocyanins have been traditionally quantified biochemically or more recently using spectral reflectance. However, both methods require destructive sampling and can be labor intensive and challenging with small samples. Recent advances in digital photography and image processing make it the method of choice for measuring color in the wild. Here, we use digital images as a quick, noninvasive method to estimate relative anthocyanin concentrations in species exhibiting color variation. Using a consumer-level digital camera and a free image processing toolbox, we extracted RGB values from digital images to generate color indices. We tested petals, stems, pedicels, and calyces of six species, which contain different types of anthocyanin pigments and exhibit different pigmentation patterns. Color indices were assessed by their correlation to biochemically determined anthocyanin concentrations. For comparison, we also calculated color indices from spectral reflectance and tested the correlation with anthocyanin concentration. Indices perform differently depending on the nature of the color variation. For both digital images and spectral reflectance, the most accurate estimates of anthocyanin concentration emerge from anthocyanin content-chroma ratio, anthocyanin content-chroma basic, and strength of green indices. Color indices derived from both digital images and spectral reflectance strongly correlate with biochemically determined anthocyanin concentration; however, the estimates from digital images performed better than spectral reflectance in terms of and normalized root-mean-square error. This was particularly noticeable in a species with striped petals, but in the case of striped calyces, both methods showed a comparable relationship with anthocyanin concentration. Using digital images brings new opportunities to accurately quantify the anthocyanin concentrations in both floral and vegetative tissues. This method is efficient, completely noninvasive, applicable to both uniform and patterned color, and works with samples of any size.
花青素色素已成为进化生态学的一个典型性状,因为它们通常为植物提供适应性益处。传统上,花青素是通过生化方法进行定量的,或者最近使用光谱反射率进行定量。然而,这两种方法都需要进行破坏性采样,而且对于小样本来说可能劳动强度大且具有挑战性。数字摄影和图像处理的最新进展使其成为野外测量颜色的首选方法。在这里,我们使用数字图像作为一种快速、非侵入性的方法来估计表现出颜色变化的物种中的相对花青素浓度。我们使用消费级数码相机和免费的图像处理工具箱,从数字图像中提取RGB值以生成颜色指数。我们测试了六个物种的花瓣、茎、花梗和花萼,这些物种含有不同类型的花青素色素,并呈现出不同的色素沉着模式。通过将颜色指数与生化测定的花青素浓度进行相关性评估。为了进行比较,我们还从光谱反射率计算了颜色指数,并测试了与花青素浓度的相关性。指数的表现因颜色变化的性质而异。对于数字图像和光谱反射率,花青素浓度的最准确估计来自花青素含量-色度比、花青素含量-基本色度和绿色指数强度。从数字图像和光谱反射率得出的颜色指数与生化测定的花青素浓度密切相关;然而,就平均绝对误差和归一化均方根误差而言,数字图像的估计比光谱反射率表现更好。这在一个花瓣有条纹的物种中尤为明显,但在有条纹的花萼的情况下,两种方法与花青素浓度的关系相当。使用数字图像为准确量化花和营养组织中的花青素浓度带来了新机会。这种方法高效、完全非侵入性,适用于均匀颜色和有图案的颜色,并且适用于任何大小的样本。