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Keywords:

  • colour measurement;
  • eggshell;
  • reflectance spectrophotometer;
  • sampling error

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

1. The evolution of coloration has generated some of the most diverse and variable phenotypes, both within and between species, in animals and plants. The objective quantitative assessment of the physical, chemical and perceptual basis of coloration has been greatly improved by the application of portable full-spectrum spectrophotometers to measure reflectance data. Yet, the analysis and interpretation of physical measures of colour spectra must be conducted within the constraints of the uncertainty regarding the relative impacts of methodological vs. biological sources of variation.

2. Here, we characterise the components of variation in data on reflectance spectra, related to sample storage and measurement equipment, to characterise colourful pigmentation of eggshells (Class: Aves) of two Turdus species. We quantified longitudinal shifts in reflectance occurring over repeated measurements of the same sets of avian eggshells. Shells were sampled at the time of collection and again after 5 years of dark storage using the same equipment. These data were then compared with spectra obtained from the same eggs after the storage with a different model of spectrophotometer for three colour metrics [blue-green chroma (BGC), UV chroma and brightness].

3. Blue-green chroma and brightness of the same eggs varied systematically between years at a similar magnitude to the biological variation among different eggs. This suggests the need for future research into the extent of chemical and physical deterioration of eggshell appearance even during relatively short-term storage. The variation introduced by using two different spectrophotometers also was significant but relatively small compared with biological levels of variation for UV and brightness.

4. Our results confirm quantitatively that museum eggshell specimens are suitable for interspecific comparative analyses, but also highlight the requirement to account explicitly for variation in storage duration and measurement equipment, when ‘objectively’ comparing biological variation in coloration across individuals in space and also in time.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Portable reflectance spectrophotometers have transformed the quantitative study of the colourful appearance, displays and signals of animals and plants and have provided an arguably objective, repeatable and physical approach for the measurement of light-reflectance-based colour (Bennett & Thery 2007). As the initial biological application of spectrophotometers to characterise reptile scales (Norris & Lowe 1964) and chicken eggshells (Poole 1965), the use of reflectance spectra to quantify variation in phenotypic traits has greatly expanded to include taxonomic groups as diverse as cyanobacteria (Karnieli & Sarafis 1996), plants (Lacey & Herr 2005), insects (Prum, Quinn & Torres 2006; Davis et al. 2008) and non-human and human primates (Sumner & Mollon 2000; Shekar et al. 2008). The application of spectrophotometers has also spanned diverse habitat types and included the study of both terrestrial and aquatic vertebrate and invertebrate species (e.g. Marshall 2000; Siefferman & Hill 2003; Davis et al. 2008; Detto & Backwell 2009). Accordingly, many questions regarding the evolution and adaptive value of a species’ colourful appearance can no longer be addressed convincingly without the use of spectrophotometers (e.g. Shawkey et al. 2003). However, amidst this success of quantitative reflectance measurements, it is easy to overlook that, underlying the measurement of the physical dimensions of colour, there persist diverse causes and potential confounds regarding the mechanical, chemical and biological aspects of generating, perceiving and measuring colour.

Spectrophotometers function by accepting light energy transmitted through single-strand optical fibre and dispersing it via a fixed grating across a linear charge-coupled device (CCD) array detector, which is responsive across a range of wavelengths. This range includes the perceivable wavelengths to humans and many other animals, commonly from the ultraviolet through the (human) visible and near-infrared. Most studies in different laboratories, and sometimes across time in the same laboratory, are conducted with different spectrophotometer models and associated light sources, but even identical models differ subtly in the grating of their monochromator and the width (optical resolution) of the entrance slit. Even the same equipment might differ through internal changes of the cables and light source, and with continued and repeated usage. Such variation in equipment could introduce further and, thus far, in the published literature, unaccounted differences in colour measurements of biological tissues. The resulting effects are of particular importance for comparisons between the studies conducted with different combinations and models of equipment.

Most animal colours are based on chemical pigments (e.g. McGraw 2005), including structural colours which are the result of specific arrangements of compounds that generate highly organised optical interfaces (Shawkey & Hill 2005; Shawkey et al. 2006). As such, animal colours are susceptible to deterioration with age as chemical reactions of pigments may result in the alteration of structural arrangements and optical properties, including the resulting reflectance spectra. This effect is particularly relevant when historic specimens recovered from the field or stored in collections, rather than fresh or live samples, are used (McNett & Marchetti 2005). For many animal and plant species in situ, live or fresh-sampling is the only feasible method to collect biologically relevant reflectance spectra. In turn, for many other taxa (e.g. terrestrial vertebrates, insects and marine hard-shelled molluscs) historic specimens have been used consistently and successfully in quantitative and comparative studies (e.g. Huxley 1975; Doucet & Hill 2009; Cassey et al. 2010b).

The issue of chemical and physical alteration and degradation of pigments with time has been acknowledged explicitly as a possible limitation to research on collection material (Starling et al. 2006, Doucet et al., 2009), especially regarding the quantitative comparison of stored specimens with fresh samples (Cassey et al. 2009, 2010a). Some studies have specifically investigated the effect of long-term storage on the physical appearance of animal materials for colours generated by diverse mechanisms (e.g. Doucet et al., 2009, McNett & Marchetti 2005, Pohland & Mullen 2006). Such studies typically rely on comparing a cross-section of different aged specimens in their data set and/or comparing different, fresh or stored, samples. In all cases, the results yielded substantial differences in the measured reflectance spectra of fresh and older specimens, even on the scale of just a few days, and may serve specific behavioural functions, including the recognition of foreign eggs laid by intraspecific brood parasites into nests of other birds (Riehl 2010).

Some of the differences found between museum-stored samples are almost certainly because of spurious but unclear effects related to the wear and tear of initial and in-storage handling, and the time since collection. Thus, from a methodological perspective, two important questions for the use of historic specimens need to be answered: (i) What effect does storage have on the measurable dimensions of animal coloration? and (ii) How marked is the temporal shift of physical measures of colours within the same specimens? Only longitudinal studies using repeated measurements of the same specimens can address these critical questions.

The increasingly large number of publications based on reflectance data used to quantify aspects of coloration of organisms in different biological lineages would provide substantial opportunity for taxonomically comparative analyses and quantitative meta-analyses, yet these approaches require that the data generated by different studies serve as comparable. We emphasise that for such studies, the above methodological concerns remain, despite the effort to scale, all measurements against calibrated commercial standards, which may themselves be individually different and undergo temporal shifts in reflectance and physical–chemical degradation. It is therefore critical to establish and quantitatively partition, which aspects of variation in reflectance measurements of animal coloration are caused by genuine and likely biologically relevant differences between the samples, by time since collection between and within samples and by variation because of differences in the equipment.

Here, we used avian eggshell coloration, as an example of what has been assumed to be a slowly perishable biological phenotype (e.g. Starling et al. 2006; Igic et al. 2010), to quantify relative sources of variation at which differences in measurements of colourful appearance occur. To this end, we measured the same eggshells twice (5 years apart) with the identical spectrophotometer and light source and analysed in detail the statistical differences in the measurements. In addition, we conducted reflectance measurements of stored eggshells with a second, different model of spectrophotometer, to compare the effects of temporal differences with those of equipment-based variation in the appearance of eggshell colours. These analyses provide an illustration of a general approach to conduct a detailed statistical analysis to assign biological, temporal and technical sources of variation in colour data.

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Data Sources

We collected 25 eggs of song thrush, Turdus philomelos, and 21 eggs of blackbird, Turdus merula, from different clutches of introduced wild populations in Benneydale, New Zealand (38°30′49″S, 175°24′13″E) during September and October 2005. The two pigments that are known to make up egg coloration are heme derivates (Gorchein, Lim, & Cassey 2009; Wang et al. 2009). Song thrush and blackbird eggs are both characterised by a blue-green background colour of varying intensity (Fig. 1). The eggs of the two species differ in the density and appearance of their speckling, with sparser, darker brown and strongly demarcated spots in song thrush and more rufus and softly defined spots in blackbirds that can densely cover most of the egg (Lack 1958). Eggs were selected randomly with respect to their known laying order. Details of the nest finding protocol and the New Zealand study site are provided in (Cassey et al. 2008). After collection, all eggs were transported to a field station, washed in laboratory grade water and split lengthwise to remove yolk and albumen. The eggshell halves were then washed again and air-dried in the dark for a minimum of 2 days before reflectance measurements were taken.

image

Figure 1.  Representative reflectance spectra from the eggshell equator for (a) song thrush; Turdus philomelos and (b) blackbird; T. merula eggs. Two replicate measures were taken from each egg. Black lines were measured in 2005 on an OceanOptics USB2000 (see Methods). Grey and red lines, respectively, were measured in 2010 on the same egg using an OceanOptics USB4000 (see Methods) and the same spectrophotometer as in 2005.

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First reflectance measure

Eggshell reflectance was measured using an Ocean Optics USB2000 Miniature Fiber Optic Spectrophotometer illuminated by a DT mini-lamp (Table S1). Before the measurements were taken, the lamp was turned on for 30 min to ensure consistent light production. A custom-built light-proof cap was fitted over the probe to maintain a consistent angle (90°) between the eggshell and the measuring fibre optics. Spectra were recorded in ∼0·4 nm steps and were expressed relative to a white Ocean Optics WS-1 diffuse reflectance standard. Two measurements of the background shell colour at the equator of the larger, better preserved half of each eggshell were collected by a single observer (PC). For these measurements, specific care was taken to avoid visible maculation so as to quantify the background shell colour only. Spectra were visually inspected during sampling to ensure accurate measurements. Spurious spectra were removed, and the egg background was re-sampled. To minimise instrument error, dark and white standard reflectance calibration measures were made regularly during sampling. After measurements, eggshell samples were stored in dry and completely dark individual containers.

Second and third reflectance measures

In July 2010, PC measured eggshell reflectance of the same samples again at two locations of the equatorial region of the eggshells: (i) with the same Ocean Optics USB2000 Miniature Fiber Optic Spectrophotometer used 5 years earlier and (ii) with an Ocean Optics USB4000 Miniature Fiber Optic Spectrophotometer and a PX-2 lamp (Table S1). The USB4000 is the commercial ‘successor’ of the USB2000 in Ocean Optic’s range and is distinguished by faster processing electronics, a more advanced detector, and improved electrical dark correction (manufacturer’s information). This second spectrophotometer’s probe was also fitted with a custom-built light-proof cap to ensure a 90° measurement angle. As before, a white Ocean Optics WS-1 diffuse reflectance standard was used to calibrate the equipment regularly during measurements with both spectrophotometers. In the period between the first and the second set of measurements with the USB2000 (5 years), the spectrophotometer and its light source had been used for a total of 408 h in a variety of laboratory and field studies (e.g. Thorogood et al. 2008; Cassey et al. 2009).

Analyses

Reflection curves were truncated, to include only the avian visible wavelength, between 300 and 700 nm (Cherry & Bennett 2001). We used an interpolated average to calculate mean reflectance values at 5 nm steps, an approach we recommend to ensure comparable curve-smoothing between studies using spectrometers with slightly different sampling slit-widths. To characterise physical measures of eggshell coloration, we used our reflectance spectra data to calculate three commonly used metrics of biological colour applied in the literature on avian plumage and eggshell appearance. Brightness (sensuMontgomerie 2006) was calculated as the total area under the reflectance curve divided by the total wavelength. Both blackbird and song thrush eggshells reflected maximally in the blue-green (human visible) region of the analysed wavelength regions (Fig. 1). Blue-green chroma (BGC) was calculated as the proportion of the wavelength representing the region of least absorbance for the blue-green eggshell pigment biliverdin, i.e., Σλ400–575/Σλ300–700 (Siefferman, Navara, & Hill 2006). Similarly, UV chroma (UVC) was calculated from the same literature as the proportion of the total reflectance curve that lay in the UV region of the wavelength (Σλ300–410/Σλ300–700).

We calculated Pearson’s correlation coefficients across average metrics of eggshell colour and conducted nested analysis of variance (anova) to determine the proportion of the total variability for all three colour traits that could be attributed to the difference between sampling years and different spectrophotometers among replicated spectra within the egg and among individual eggs. We used generalised linear mixed models (accounting for egg identity as a repeated random effect) to test whether differences between the two sampling time points, and between the two spectrophotometers, were significant (α = 0·05). Wavelength reflectance modelling and statistical analyses were conducted in sas v9.2 (SAS Institute Inc., Cary, NC, USA).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

For the three spectral metrics analysed from our samples of both species, the positive correlations between repeated average measurements (Fig. 2) were stronger for measurements taken at the same time point (2010) using the two different spectrophotometers than for average measurements taken at different time points (on the same spectrophotometer), for all relationships, except brightness in blackbirds (Fig. 2e–f). However, correlations between average measurements taken at the two time points (on the same spectrophotometer) were still positive and significant in all cases, except UVC in song thrush (Fig. 2d).

image

Figure 2.  Bivariate correlations for average measures (two replicates per egg) of (a, b) blue-green chroma, (c, d) ultraviolet chroma, and (e, f) brightness for song thrush; Turdus philomelos (hollow loci □; n = 24) and blackbird; T. merula (solid loci ▮; n = 21) eggs. (a), (c), and (e) display the x-y correlations between measurements on an OceanOptics USB2000 and an OceanOptics USB4000 taken in 2010 (same measurement period but different spectrophotometer). (b), (d) and (f) display the x-y correlations between measurements on the same OceanOptics USB2000 in 2005 and again in 2010 (same spectrophotometer but different measurement period). Pearson's correlation coefficients for song thrush (upper left) and blackbird (lower right) are presented in bold (5 out of 6) if statistically significant (α = 0.05).

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Regarding the reflectance spectra output from the different spectrophotometers (in the same period), the variability in measurements (BGC, UVC, brightness) was greatest among different eggs (Table 1). In contrast, for reflectance spectra recorded across different time points (using the same spectrophotometer), the variability in measurements was greatest between measurement taken at the two time points from the same eggs. In both cases (for all three colour metrics), the variability between two different (replicate) spectra within the same egg accounted for <20% of the total variation (average = 11%).

Table 1.   Nested analysis of variance of the percentage of variability occurring at different levels of repeated measurement for three metrics of reflectance-based eggshell coloration (blue-green chroma, ultraviolet chroma and brightness) in two species of Turdus thrush
 Blackbird (n = 21)Song thrush (n = 24)
BGCUVCBrightnessBGCUVCBrightness
  1. BGC, Blue-green chroma; UVC, UV chroma.

Among eggs87·1643·0073·7050·0261·3072·52
Between spectrophotometers (same year)3·2238·2015·1044·3428·6110·00
Error (two replicate spectra within an egg)9·6118·8011·205·6310·0817·48
Among eggs33·566·5529·550·0023·791·92
Between years (same spectrophotometer)55·4879·6160·0495·6466·3485·42
Error (two replicate spectra within an egg)10·9613·8410·414·369·8712·67

For both species, measures of BGC and brightness (but not UVC) were significantly different between the two time points of measurement (Table 2). In turn, measures of UVC and brightness (but not BGC), for both species, were significantly different between the two different spectrophotometers (Table 2).

Table 2. T-statistics (and P-values) for differences between and within spectrophotometers. Generalised linear mixed models accounting for individual egg identity as a repeated random effect. Significant effects (α = 0·05) are in bold
 Blackbird (n = 21)Song thrush (n = 24)
BGCUVCBrightnessBGCUVCBrightness
  1. BGC, Blue-green chroma; UVC, UV chroma.

Different spectrophotometers (same year)0·58 (0·569)5·68 (0·001)−3·25 (0·004)1·00 (0·329)3·93 (0·001)−2·92 (0·008)
Different years (same spectrophotometer)−6·31 (0·001)0·21 (0·835)−4·23 (0·001)−6·46 (0·001)0·05 (0·96)−3·48 (0·002)

The average difference between measurements was greatest between different time points of sampling for the measurements of brightness (Table 2). In this case, the average value of brightness decreased by approximately 12% in both species, between 2005 and 2010. Average brightness also differed significantly between the two spectrophotometers in the same sampling period (Table 2), with the average value of brightness approximately 6% greater, in both species, in the older and more extensively used USB2000 model than the newer and less used USB4000 spectrophotometer.

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Our study revealed that for physical measures of reflectance-based avian eggshell coloration, both the relatively short time (5 years) in storage and the use of two different reflectance spectrophotometers affected the variability in measurements of the same biological samples, although these effects were not uniform across the different physical metrics of colour and from the different biological samples of two closely related bird species. The finding that, in 5 years, temporal effects led to a detectable change in different aspects of coloration was surprising for a bio-material considered to preserve well, such as the avian eggshell. While different biological pigments will be dynamic in their time-scale of degradation (e.g. Grande, Negro, & Torres 2004), we a priori considered that eggshells would provide an excellent model for persistent animal coloration because of previous work on museum-stored specimens collected over dozens of years prior to measurements (Starling et al. 2006; Cassey et al. 2010a). Our findings, thus, highlight the need to take even short-term storage duration into account when studying other animal tissues regardless of how well it is assumed or may appear to the human observer that they would maintain their appearance.

The eggshell pigments biliverdin and protoporphyrin have both been successfully extracted from colourful eggshell fragments of extinct bird species >650 years old (Igic et al. 2010) and thus may constitute a little perishable type of pigmentation in the animal kingdom. It is, therefore, likely that our findings on the blue-green eggshells of thrushes provide a moving temporal target for estimating the magnitude of the structural changes and chemical degradation of pigments that takes place even over a short storage period. It is noteworthy, in our results, that sample age is likely to affect both overall brightness (i.e. the cumulative height of the reflectance curve) and also the relative contributions of different wavelengths (i.e. the reflectance curve’s shape and chromatic purity).

An alternative explanation for the temporal component of colour shifts that we detected is the unavoidable potential confound that our instrument’s sensitivity has changed over time, whether because of extensive use between the two time periods and/or because of the simple passage of time and deterioration of its parts and components. For example, temporal differences in spectra taken by the same instrument could be because of the equipment suffering specific damage or regular wear and tear either in the field or during transportation between projects (and laboratories on different hemispheres). This possibility remains, even though the instrumentation sources of variability between measurements, obtained from the different spectrophotometers at the same time period, was considerably smaller than that between the two time periods. Accordingly, when samples of different ages (and possibly storage conditions) are analysed, the difference in collection year, and the associated biological and structural changes in the samples, will likely contribute to the larger source of variation in the analysis than differences in instrumental effects.

Critically, the differences in reflectance spectra data revealed in our analyses, associated with the use of different spectrophotometers, were a priori unpredictable in terms of both its direction and its prevalence across the different components (BGC vs. UV) of the sampled light spectrum. Regarding the instrumentation-related sources of variation, our results are likely to underestimate the full extent of the variability caused by different equipment, as both of our spectrophotometers were sourced from the same manufacturer and have been maintained by the same research group. Technical differences between the USB2000 and its successor the USB4000 model were specified mainly in the detector sensitivity and dark level corrections, improvements which are unlikely to affect quantitative or directional differences in the measurements. Nonetheless, it is important to note that differences in the UV measurements were found only between spectrophotometers (and not between sampling times), suggesting an unexplained source of potential spectral variability between the two models, and different light sources.

Overall, we conclude that shifts in reflectance-based measures of avian eggshell coloration, which are associated with time in storage, exceeded those caused by variation in the measurements from different spectrophotometers on the same biological specimens. Therefore, particular attention should be paid, when comparing studies, to ensure that findings of small differences (e.g. in the contribution of UV based coloration) are not confounded by different equipment sources.

Rather than preclude the usage of spectrophotometers in studies of biological coloration using museum collections, our study highlights the need for care in formulating suitable research questions regarding spectral data, comparable sample sets for spectral measurements, and explicit accounting for biological, temporal and equipment differences in the collected data. In particular, it is important to take the storage duration into account explicitly when comparing pigmentation. Museum egg collections, with their precise records of collection time and circumstances, allow the inclusion of this information as covariates in the analysis (Starling et al. 2006). In addition, whenever plausible, researchers should use eggs of similar storage duration when inter- and intra-population level trends in shifts of coloration are compared (Avilés et al. 2007; Mccormack & Berg 2010). If this is not possible, then storage duration should be controlled for statistically. This approach becomes more difficult, when the differences in shell appearance at different times are explicitly the subject of the specific research questions, as in studies of the effects of climate variation or changes in pollutants (Avilés et al. 2007). In these cases, studying multiple species with diverse egg coloration or comparing the rates of change across different time scales may help to separate the effects of storage and the environment.

It is important to note that despite the variability within a species, biologically likely relevant differences in coloration metrics were consistently detected across all sampling sets. For example, the chromatic region of functional significance in eggshell discrimination, BGC (Honza et al. 2007, Cassey et al. 2008, 2009), was always separable in our data sets between song thrush and blackbirds (Fig. 2). This is remarkable, considering the close taxonomic relationship between the two study-species. In particular, the tremendous intra- and interspecific variation in eggshell coloration and patterning in birds requires that the variability in the specific metrics chosen for study be assessed prior to analysing differences within and between species (Kilner 2006, Cassey et al. 2009, 2010a). Thus, while comparative studies on colourful biological materials should attempt to account for the variability in specimen ages, or samples from similar periods, historic collections should continue to provide a powerful resource for interspecific studies (Green & Scharlemann 2003; Cassey et al. 2010a,b).

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Funding was provided by the Royal Society, Leverhulme Trust Fund and the Human Frontier Science Program. We are grateful to Rob Freckleton and Dan Hanley, plus three anonymous reviewers, for constructive comments that greatly improved the manuscript.

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  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Table S1. Settings and technical specifications of the two Spectrophotometers used for comparative measurements.

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