Citation: Hogan BG, Stoddard MC (2024) Hyperspectral imaging in animal coloration research: A user-friendly pipeline for image generation, analysis, and integration with 3D modeling. PLoS Biol 22(12):
e3002867.
https://doi.org/10.1371/journal.pbio.3002867
Academic Editor: Gail L. Patricelli, University of California Davis, UNITED STATES OF AMERICA
Received: December 12, 2023; Accepted: September 27, 2024; Published: December 3, 2024
Copyright: © 2024 Hogan, Stoddard. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All hyperspectral images and all code for this analysis are available on Dryad (DOI: 10.5061/dryad.j0zpc86nf) and GitHub (https://github.com/bghogan/HyperBirdOfParadise), respectively. An archived copy of the code is stored on Zenodo (DOI: 10.5281/zenodo.13749562).
Funding: This work was supported by a Packard Fellowship for Science and Engineering (to MCS), an Alfred P. Sloan Research Fellowship (to MCS), a Polymaths Award from Schmidt Sciences (to MCS), National Science Foundation grant 2029538 (to MCS), the High Meadows Environmental Institute, and general funds from Princeton University (to MCS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations:
LWS,
long wavelength-sensitive; MWS,
medium wavelength-sensitive; PCA,
principal component analysis; SWS,
short wavelength-sensitive; VS,
violet-sensitive; UMAP,
uniform manifold approximation and projection
Introduction
Investigating animal coloration has proven useful for unraveling the evolutionary processes that generate phenotypic diversity [1]. In recent years, quantifying animal color has entailed two main methods: spectrophotometry and multispectral digital photography [2–6]. Spectrophotometry permits measurements of the reflectance across many wavelengths for a single point, while multispectral photography captures detailed spatial information in a limited number of color channels. Each approach has distinct advantages, but choosing one method over the other forces a trade-off between high spectral resolution (spectrophotometry) and high spatial resolution (photography, see Table 1).
Table 1. Table highlighting some differences and similarities among spectrophotometry, multispectral photography, and hyperspectral imaging, with respect to spectral resolution, spatial resolution, sample selection, animal vision models, and cost.
Not included in the table is temporal resolution because the amount of time required to sample an individual animal is highly variable both within and among techniques. For spectrophotometry, individual measurements are collected in under a second, but typically several measurements per patch and several patches per animal are collected, resulting in a temporal resolution spanning from several seconds to minutes. For multispectral photography, capture with limited spectral sensitivity (e.g., omitting ultraviolet light) can allow for video-speed capture, but increasing spectral sensitivity typically slows capture to the range of minutes per image (but see [7] for multispectral video including ultraviolet light). For hyperspectral imaging, a similar trade-off can occur: full-frame hyperspectral imagers with limited spatial and spectral sensitivity can capture images at video-speed. The hyperspectral imager used in the current paper has high spatial and spectral resolution, but each image takes several minutes to capture.
Spectrophotometry has been the go-to tool for animal color researchers since the 1980s and 1990s, when spectrophotometers became widespread, affordable, and capable of capturing wavelengths beyond the human visible range [4,6,8]. Using this approach, reflectance measurements of animal coloration are obtained with a spectrometer and a broadband light-source connected via optical fibers to a small probe. The probe, placed on a small region of an animal’s feathers, fur, or skin, captures detailed information about the light reflected over a broad range of wavelengths. The resulting reflectance spectrum has high spectral resolution (i.e., comprising hundreds of spectral measurements), but the approach suffers from a lack of spatial resolution: spectrophotometry typically provides inadequate 2D and 3D spatial information about animal color patterning. This is because only point samples can be obtained, so color sampling across the body of an animal (for example, a taxidermy specimen) is typically sparse. Consequently, researchers might fail to measure color patches (body regions with apparently uniform color; [9]) that are visible to some animals but not to humans [10] or fall short of capturing meaningful variation within a given color patch [11].
Over the last 15 years, multispectral digital photography has emerged as a complementary approach to spectrophotometry. Multispectral photography typically involves using a consumer camera that has been modified for broadband light sensitivity (to permit ultraviolet sensitivity, for example). External lens filters are sequentially attached to the camera, allowing for a series of images to be captured across multiple color channels encompassing (human) visible and ultraviolet wavelengths [4,5]. These images can then be stacked and processed (i.e., controlling for nonlinearity in raw pixel values, the spectral sensitivity of the particular model of camera, and lighting conditions) using specialized software. The result is a digital multispectral image comprising multiple channels: typically ultraviolet, blue, green, and red, or sometimes more (see [12]). Ultimately, multispectral photography generates images with high spatial resolution [4,5] because each pixel in the image represents a measurement of color. However, each of these pixels contains only a few spectral measurements (one for each channel), severely limiting spectral resolution. Overall, multispectral photography resolves some of the challenges of spectrophotometry—it captures detailed spatial information (including patterning) and permits dense sampling across an animal’s body—but at the expense of spectral resolution.
To quantify animal color accurately and comprehensively, it is desirable to capture both high spectral resolution and high spatial resolution [13]. Although reflectance spectra (high spectral resolution) and multispectral images (high spatial resolution) are both amenable to visual modeling and can be used to address a range of questions about how animals perceive color signals (reviewed in [14,15]), for many studies researchers must choose one approach in favor of the other. Reflectance spectra are essential when the goal is to identify metamers—two colors that look identical to a viewer but in fact have different spectral properties [16]. Moreover, knowing the shape and features of reflectance spectra can be especially valuable for addressing questions about the physical properties of an animal’s color, such as those related to the mechanisms underlying color production [17–19] or to non-signaling functions of color like thermoregulation [20,21] and mechanical strength [22]. In these cases—in which a direct link to a particular visual system is often unimportant or unknown—analyses of reflectance spectra are usually the most appropriate (reviewed in [14]). On the other hand, for questions related to spatial information—such as those involving the distribution of patches and fine-scale patterning on an animal’s body (see [9,23])—multispectral images have the clear advantage [3–5]. Multispectral photography can also be combined with photogrammetry to produce multispectral 3D models, allowing for integrative studies of color and 3D body morphology [24].
Hyperspectral imaging is a compelling alternative to spectrophotometry and multispectral photography: it offers the best of both worlds by providing both high spectral resolution and high spatial resolution (Table 1). Hyperspectral imagers work by simultaneously capturing high-resolution color information at many locations, in essence taking a photograph with hundreds of channels of color information instead of relatively few as in traditional (RGB) or multispectral photography (see Fig 1 and Fig D in S1 Appendix). A hyperspectral imaging system separates the light spectrum into a series of narrow bands, each of which corresponds to a small wavelength range (Fig 1). The end result is a data array or stack (often termed a data-cube), recording radiance along two spatial dimensions (width, height) and one spectral dimension (wavelength). Each pixel in a hyperspectral image can correspond to a full reflectance spectrum (Fig 1C), and an image of the brightness of the whole specimen can be produced for any desired wavelength (by taking a slice of the data array, see Fig 1A and 1D). While hyperspectral imaging has become an important tool in agriculture, geology, medicine, archeology, art history, and human color science [16,25,26], applications of hyperspectral imaging to animal color quantification are in their relative infancy. To study animal color, early adopters of hyperspectral imaging used the technique to investigate the tuning of dichromatic visual pigment sensitivities to real forest and underwater scenes [27], surface color in butterfly wings [28], iridescent spiders [29], beetle wing cases [30], and frog skin [31], and camouflage in cuttlefish [32] and crabs on natural substrates [33]. Additionally, researchers have used hyperspectral imaging to simulate avian perception of warning colors on lepidopteran wings [34], to measure color and simulate warning coloration for newts [35], and to measure bioluminescence in sharks [36]. Kim and colleagues [37] combined hyperspectral imaging with 3D scanning to produce the first—to our knowledge—hyperspectral 3D model of an animal museum specimen, a colorful Papuan lorikeet (Charmosyna papou goliathina; now considered Stella’s lorikeet, Charmosyna stellae).
Fig 1. Hyperspectral imaging data captures spectral and spatial information.
(a) Montage of selected slices across the spectral dimension of the hyperspectral data-cube, each indicating in grayscale the reflectance of one specimen (Magnificent bird-of-paradise dorsal view) at the wavelength indicated in the inset colored boxes. The blue, orange, green, and red dots indicate the plumage regions sampled (see also b and c). Note that intervals of 20 nm were chosen for plotting; the actual data were acquired in intervals of ~2.5 nm. (b) RGB image of the dorsal view of the specimen generated using 3 select slices (425 nm, 550 nm, 600 nm) with plumage regions sampled again indicated with colored dots. (c) Plot of the reflectance of each of the plumage samples shown in (a) and (b); each pixel in a hyperspectral image corresponds to a full reflectance spectrum. The dotted vertical line indicates the lower sensitivity bound of the hyperspectral image; reflectance values below this wavelength were extrapolated. (d) The birds-of-paradise measured in this study reflect relatively little ultraviolet light (300–400 nm). For comparison, shown here are hyperspectral images of the lateral view of a Gouldian finch (Chloebia gouldiae), which reflects substantial ultraviolet light in the breast patch. The data underlying this figure can be found at https://doi.org/10.5061/dryad.j0zpc86nf [99].
Despite these applications, widespread adoption of hyperspectral imaging in animal color research has been slow. There are three main obstacles. First, hyperspectral imaging systems are expensive and require substantial computational power and memory for data processing [37]. Second, hyperspectral imagers that are sensitive to the entire portion of the visible spectrum relevant to animals have not typically been commercially available [38]; for many animals, this range is 300 nm to 700 nm [39], with 300 nm to 400 nm comprising ultraviolet light. Finally, we lack a strong conceptual framework—and corresponding software tools—for analyzing hyperspectral data in a way that is convenient for animal color research. Fortunately, the first two of these challenges are quickly fading. Hyperspectral imaging systems are becoming increasingly affordable and computationally practical, and some imagers now have sensitivity across wavelengths (including ultraviolet) relevant to animal visual systems [34]. Thus, the stage is set for wider uptake of hyperspectral imaging, which could be a game-changing tool in animal color research. All of the advantages to multispectral photography [2,5] apply to hyperspectral images, but with the additional benefit of high-resolution spectral data. For example, a single data set of hyperspectral images might now be used in two ways: (i) to ask questions about an animal’s coloration and patterning (i.e., the images can be processed using models of animal vision and analyzed using existing tools for multispectral images); and (ii) to ask questions about the physical properties of the colors themselves (i.e., by comparing the shape and features of the reflectance spectra to those of known pigments and structural colors).
We currently lack a user-friendly, step-by-step pipeline for obtaining and analyzing hyperspectral images for animal color research. To address this gap, we developed such a pipeline and demonstrated how it can be used to visualize and compare animal colors in hyperspectral images. We used a commercially available Resonon Pika NUV imager (Resonon, Montana, United States of America), but the pipeline is general to hyperspectral animal coloration data captured by any hyperspectral imager. Our pipeline includes: imaging, sampling reflectance spectra from the images, and embedding the resulting colors into receiver-independent and receiver-dependent color spaces. To illustrate how hyperspectral imaging can provide new, detailed insights about animal color, we apply our pipeline to a study of the plumage of a rare hybrid bird-of-paradise, with only around 25 male museum specimens known in the world [40]. Birds, the most colorful land vertebrates, are especially amenable to hyperspectral imaging and analysis. Their colors are produced by diverse mechanisms (pigments and structural colors), they have tetrachromatic color vision, and bird specimens are readily available in museum collections [3]. Overall, hyperspectral imaging may represent a new gold standard for many avenues of animal coloration research.
Study system: Birds-of-paradise
The birds-of-paradise (family: Paradisaeidae), native to New Guinea and Australia, are a diverse and charismatic group known for their elaborate multimodal courtship displays [41]. The family comprises 44 species in 16 or 17 genera [40,42] (Fig 2). Different species exhibit a strong tendency to hybridize, with hybridization known or suspected among very different-looking species [43,44]. One rare bird-of-paradise hybrid—with just ~25 male museum specimens in the world [40]—is the King of Holland’s bird-of-paradise (Cicinnurus magnificus x C. regius) [45–48]. Initially thought to be a new species (by Meyer in 1875 [47]), the King of Holland’s bird-of-paradise was later described as a hybrid of the Magnificent bird-of-paradise (C. magnificus, following the convention used in [42]; also known as Diphyllodes magnificus) and the King bird-of-paradise (C. regius). In 1927, Berlioz [45] described the King of Holland’s bird-of-paradise as the perfect combination of its parents’ phenotypes, writing (in French) that “an artist who tried to imagine a hybrid could not compose a more faithful reproduction.” More recently, Thörn and colleagues [49] confirmed genetically that a male King of Holland’s bird-of-paradise specimen was indeed an F1 hybrid. However, they also found that a male specimen originally identified as a King of Holland’s bird-of-paradise was genetically closer to a Magnificent bird-of-paradise, while a female specimen thought to be a King of Holland’s bird-of-paradise was more similar to a King bird-of-paradise.
Fig 2. The birds-of-paradise examined in this study.
Top row: Illustrations of the King bird-of-paradise, the hybrid King of Holland’s bird-of-paradise, and the Magnificent bird-of-paradise. The King and Magnificent illustrations are reproduced from Levaillant and Barraband (1806, [50]), which is in the public domain. The hybrid illustration is reproduced from Gould and colleagues (1875, [51]), which is in the public domain. Middle row: Icons and colors representing the parent species and hybrid throughout the paper. Bottom row: Phylogenetic reconstruction of the subfamily Paradisaeinae, based on our replication of the phylogeny by Irestedt and colleagues [52]. All individual specimens and all genera other than Cicinnurus and Paradisaea are collapsed for clarity. See Table B and Fig J in S1 Appendix for details of the phylogenetic reconstruction.
The male King, Magnificent, and King of Holland’s birds-of-paradise exhibit plumage colors produced by diverse plumage mechanisms. Avian plumage coloration is generated through two broad mechanisms, pigmentary and structural color, or a combination of the two [19]. Pigmentary color is generated by the deposition of colorful substances within the feather, whereas structural color arises from nano-scale physical structures in the feather barbs and barbules. Pigmentary mechanisms commonly generate a range of plumage colors, including gray and black (melanin), brown and rufous (pheomelanin), and yellow, orange, and red (carotenoids). Structural mechanisms are typically responsible for blue and iridescent colors, and green can be generated by a combination of yellow carotenoid pigmentation and structural blue. White plumage typically results from lack of pigmentation. Although there are few descriptions of color-production mechanisms in the King, Magnificent, and King of Holland’s birds-of-paradise (but see [53]), pigmentary [54] and structural colors have been described in a range of other bird-of-paradise species [55–57]. Females of both the Magnificent and King birds-of-paradise are less colorful, with a brown head, shoulders, back and wings, and barred undersides [40]. The plumage of the female hybrid King of Holland’s bird-of-paradise is likely to be drab; at least one specimen labeled as a female hybrid was recently found to be a King bird-of-paradise, though the existence of female hybrids is evidenced by at least one apparent backcross between a fertile female King of Holland’s bird-of-paradise and a male King bird-of-paradise [49].
How hybridization affects coloration is not well understood, especially where coloration is non-uniform (i.e., consisting of distinct patches) across an animal’s body. In birds, it is common for even adjacent patches of coloration to be generated by different structural or pigmentary mechanisms [19]. In some species, a hybrid may have a mosaic of color patches that individually match one or the other parent species [58,59], or each patch may in itself appear to be intermediate to the parent species’ patches [60,61], or both [62,63]. In other cases, combinations of parental genotypes can produce apparently novel phenotypes, as in the crown patch of hybrid offspring of snow-capped (Lepidothrix nattereri) and opal-crowned manakins (L. iris [64]), and in hummingbird throat patches [65]. To what extent are the hybrid King of Holland’s bird-of-paradise plumage patch colors intermediate composites of its parent species? Are individual patches intermediate, or is the hybrid a mosaic of the patches of its parents? Are the colors intermediate in terms of physical properties (reflectance spectra)? Would they be perceived as intermediate by a relevant signal receiver, such as another bird-of-paradise? To determine—quantitatively, using objective color measurements—whether the hybrid plumage patches are intermediate, refined spectral and spatial color quantification is required. This is therefore an ideal system for the application and testing of hyperspectral imaging.
Discussion
The hyperspectral imaging pipeline we presented here provides the tools necessary to harness the power of off-the-shelf hyperspectral imagers in animal color research. We have demonstrated how hyperspectral imagers can overcome the spatial limitations of spectrophotometers and the spectral limitations of multispectral photography to provide a versatile and powerful tool for quantifying animal coloration. We applied visual system-independent and visual system-dependent methods to quantify the diversity of plumage colors present in a rare hybrid and its parent species—and to explore the extent to which the hybrid’s plumage colors are intermediate composites of those of its parent species.
The plumage colors of the hybrid are generally intermediate to those of the King and Magnificent birds-of-paradise (Figs 5A, 5F, 6A and 6F). However, the degree of intermediacy appears to differ by patch. The back and shoulder of the hybrid are more similar to the King bird-of-paradise (Fig 5D, 5E, 5I and 5J), whereas the belly/vent is more similar to the Magnificent bird-of-paradise (Fig 5C and 5H). In both the breast and tail, we found that the hybrid was strikingly intermediate: this is most clear when inspecting the spectral shapes of the reflectance of tail feathers (Fig 7D). The hybrid’s plumage patches span the gamut of avian color production from carotenoid and melanin pigments to structural coloration, so differences in intermediacy among patches may reflect different genetic and developmental effects of hybridization across coloration mechanisms. By applying avian visual models, we show that some but not all of the differences in color among the hybrid, King, and Magnificent birds-of-paradise are likely to be perceptually discriminable to birds and therefore potentially behaviorally relevant (see Fig I in S1 Appendix, at least when modeled using only a single spectrum per patch, also see Fig Q in S1 Appendix). This observation highlights the importance of tailoring the approach to the scientific question (i.e., modeling with respect to visual signal receiver or not), which is possible even after data collection due to the versatility of hyperspectral images. Further, we found that in general, patches are not homogenous: summarizing plumage using single or average color spectra may obscure substantial differences within patches. Overall, we showed that hyperspectral imaging is a feasible, efficient, and effective method for collecting detailed color information from museum specimens. Animal color research need not be limited by the trade-off between spectral and spatial resolution, and hyperspectral imaging represents a promising avenue for future researchers.
Hyperspectral imaging: Future challenges and opportunities
Moving forward, several challenges and opportunities remain for developing methods for hyperspectral imaging of animal coloration. Angle-dependent (iridescent or any non-Lambertian) colors are difficult to quantify [79]. For these colors, the angle of observation, the angle of the specimen’s surface (surface normal), and the angle/s of incident illumination (together termed the viewing geometry) determine in large part the perceived (and measured) color [74]. In spectrophotometry, this problem is often approached either by integrating across all possible viewing geometries (using an integrating sphere) or by systematically taking many measurements using various viewing geometries [80]. Generally, in multispectral imaging (and in the current paper, with hyperspectral imaging) this problem is approached by diversifying the viewing geometries used in color measurement by taking multiple measurements of the same patch (for instance, by imaging the sample at two or more angles e.g., [41,81]). An extension of this approach is to more precisely understand the viewing geometries within each image. Any image of a 3D (non-flat) iridescent or angle-dependent colored sample will contain a variety of viewing geometries because the surface normal of the sample is not uniform. In this paper, we piloted a method to recover information about the viewing geometries contained within an image through registration with a separately generated 3D model. In this way, we supplemented our hyperspectral images with surface normal information to better explore the angle dependency of the breast patch in the three birds-of-paradise (see Fig 4). This approach could easily be adapted for use with multispectral images. A remaining goal is to better control, diversify, and record the direction of illumination for color measurements in hyperspectral images of animal colors. Kim and colleagues [37] introduced a related approach: the authors collected 3D surface information concurrent with hyperspectral imaging, allowing registration between the 3D structure of a bird specimen while color measurements were taken. However, this approach required specialized and highly customized equipment [37]. Further development of these, or similar, approaches will allow animal color researchers to move away from methods that implicitly assume that all animal colors are Lambertian (perfectly diffusely reflective) and to thereby assess animal colors in a more realistic way.
Animal coloration is often patchy (i.e., a given animal’s color is distributed across the body in patches), which complicates analysis of overall color difference and similarity between individuals or species. In general, researchers define color patches a priori, allowing them to compare homologous locations—an approach we took here. It is becoming possible, however, to generate patches for animal colors automatically or semi-automatically. For example, powerful clustering algorithms are being developed for quantifying animal coloration patterns [2]; other algorithms are being used to segregate a sample from the background [82,83] and to find physically homologous body locations in images of animals [84,85]. It seems likely that soon some combination of these efforts will result in a robust means by which to identify discrete patches in an objective and automated way, free of human biases.
At present, hyperspectral imaging typically has relatively poor temporal resolution (Table 1). Hyperspectral imaging of live animals moving in the wild (at least with high spectral and spatial resolution, and broad wavelength sensitivity) is not yet practical because images take minutes to generate. To some extent, this problem is also true of multispectral photography, although a study recently introduced the use of a beam splitting mirror to photograph a scene using ultraviolet- and visible light-sensitive cameras simultaneously (typically at framerates up to 30 per second), allowing the collection of multispectral video [7]. Both hyperspectral imaging and multispectral photography may benefit from better integration of imaging with 3D modeling and animation, where dynamic animal motion may be introduced and investigated in silico ([24,86]). For now, hyperspectral imaging for animal coloration as implemented here may be largely limited to museum specimens. However, museum specimens do not always perfectly represent the colors of live animals. For example, the physical posture for the specimen chosen during preparation can strongly influence the colors visible on the animal surface. Further, as specimens age, their color can change or fade, especially if the specimen is exposed to sunlight [3]. Lastly, complete metadata is often unavailable for historical specimens, sometimes limiting their use for study. Overall, hyperspectral imaging is currently primed for imaging animal specimens, but in situ animal color measurements will have to wait for advances in hardware (such as video-speed capture) to allow rapid collection of full-frame hyperspectral data with high spatial and spectral resolution and broad wavelength sensitivity.
Although our study focused on plumage colors, hyperspectral imaging holds great promise for the study of animal color in diverse invertebrate and vertebrate taxa, including butterflies [28,34], beetles [30], spiders [29], and cephalopods [32], to name a few. Hyperspectral imaging is likely to yield new insights about the evolution of color and pattern in bird eggs, bird nests, fruit, and flowers—and could reveal rich details about animal color in the contexts of urbanization and climate change. Similar insights might be gleaned from spectrophotometry and multispectral imaging, too—but only hyperspectral imaging provides high-resolution spectral and spatial data simultaneously. Hyperspectral imaging systems remain expensive. Resonon is currently selling its ultraviolet-sensitive imaging system for about $55,000 USD, and the light source and lens used here increase the total cost to around $75,000 USD. However, we expect that costs will continue to decrease in the future. As hyperspectral imaging becomes increasingly mainstream in animal coloration research, we hope that researchers will collaborate to establish shared tools, software, and “best practices,” all of which could help to fuel large-scale efforts to digitize and characterize diverse animal specimens. Future software should ideally be freely available, highly computationally efficient (and amenable to cloud computing), and inter-operable with existing tools for multispectral image analysis (such as MICA/QCPA [2,5]) and spectral analysis (such as the R package pavo [70]).
Insights into the plumage color of a hybrid bird-of-paradise
We found evidence of intermediacy (in terms of spectral properties) across patches of color generated by both pigmentary (back and shoulder, Fig 5M and 5N) and structural (breast and tail, Figs 5K and 7D) mechanisms in the hybrid. In birds, yellow and red colors are typically generated by carotenoids, which are metabolized from dietary sources for deposition in the integument [87,88]. Research has shown that intermediate orange pigmentation in the hybrid offspring of red and yellow birds can correlate with greater concentrations of incompletely oxidized carotenoid products [89–92]. In addition, the genetic basis of carotenoid coloration in birds is becoming clearer [87,88,93–95]. Future work might explore intermediacy in carotenoid coloration in the hybrid bird-of-paradise through characterization of the carotenoid composition of their feathers as well as investigation of hybrid genetics at relevant loci.
We also described intermediacy in the color of the hybrid’s structurally colored breast and tail patches (Figs 5K and 7D), a finding that contrasts with those of Barrera-Guzmán and colleagues [64] and Eliason and colleagues [65], who found —in other avian taxa— non-intermediate coloration in patches of structural coloration in hybrid offspring. Here, we found that the structural colors from both parent species instead gave rise to intermediate hybrid breast and tail colors. Some nanostructures in the Magnificent bird-of-paradise have been investigated; for example, melanosomes in the breast patch are known to be solid (as opposed to air-filled, though they appear to be porous [96], as cited in [97]), round and rod-like, about 130 nm in diameter, and arranged in double layers apparently unique to Paradisaeidae [97]. Less is known about the melanosomes or their arrangement in the King bird-of-paradise, though there are hints that its melanosomes are also round and solid [97]. Whether and which nano- or micro-structural parameters of the hybrid breast and tail patches are intermediate to that of the parents could be investigated directly using microscopy.
Mysteries about the hybrid King of Holland’s bird-of-paradise remain. For example, the Magnificent bird-of-paradise is strongly fluorescent under a blacklight (pers. comm. Glenn Seeholzer). We confirmed this: fluorescence is especially prominent in the highly reflective yellow shoulder/cape patch (see Fig V in S1 Appendix). Neither the King nor the hybrid bird-of-paradise shows significant fluorescence: whatever the mechanism, we do not see evidence of intermediacy in the hybrid. We note that strong fluorescence can complicate the measurement of animal color; the light produced by fluorescence can introduce errors in inferred reflectance (pers. comm. Jolyon Troscianko). Exactly what mechanism generates fluorescence in the Magnificent bird-of-paradise, and why it appears absent in the hybrid, could be investigated by sectioning and imaging feather samples—and by testing for any influence of specimen preparation methods [98]. Further characterization of the fluorescence, including specifics of the excitation and emission wavelengths, might also be studied using a hyperspectral imager in concert with spectral filters and/or a monochromator. Another question relates to bare-part coloration across these birds. One disadvantage of avian museum specimens is that bare-skin colored parts do not retain their colors from life. Both the King and Magnificent birds-of-paradise have dark blue legs in life [40]. Additionally, the inside of the mouth and the tongue of the Magnificent bird-of-paradise are a striking pale green, while the inside of the King’s mouth is pale aqua-green [40]. Since the hybrid is only known from specimens, how and whether these bare-part colors compare may never be known unless live hybrids can be found.
In summary, we have shown that hyperspectral imaging is becoming a practical and advantageous alternative to spectrophotometry and multispectral photography for measuring animal colors. Hyperspectral images have high spectral and spatial resolution, allowing for detailed visual system-independent and visual system-dependent analyses of animal colors. Using the example of the birds-of-paradise, we provide a guide for capturing and analyzing hyperspectral data, which we hope other researchers will adopt and update. We also provide example code showing how hyperspectral data can be easily incorporated into existing spectra- and image-based color analysis toolkits (pavo: [70], MICA/QCPA: [2,5]). Overall, hyperspectral imaging may help galvanize high-throughput projects that allow for a more complete description of animal color, thereby providing new insights into the mechanistic, evolutionary, and developmental processes that drive phenotypic diversity.
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