New camera reveals what life looks like through animals’ eyes with near-perfect accuracy

Cat eyes
Cat eyes

Have you ever wondered how animals perceive the colorful world around them? Scientists have long been fascinated by this question, and now, thanks to new research, we’re closer to understanding the colorful universe through the eyes of animals. A groundbreaking video camera is allowing people to replicate how various animals see the world.

The camera system, boasting over 92 percent accuracy, allows filmmakers to accurately depict colors as different animals see them. This technological advancement is not only beneficial for filmmakers but also provides scientists with a valuable tool to understand animal communication and navigation more effectively.

Animals experience the world differently from humans, thanks to their unique photoreceptors – the cells in the eye that respond to light. These photoreceptors can detect a range of colors from ultraviolet to infrared, far beyond human capability. Animals can even see polarized light, which is invisible to us. This diversity means each animal has its distinct way of perceiving color. However, there’s a catch. Our eyes and even the most advanced cameras can’t capture this vast spectrum of light. That’s where the new research comes in.

Scientists developed a cutting-edge tool that lets us see colors as various animals do, especially when they move. Movement can change the way colors look, and until now, this dynamic aspect of animal vision has been a mystery

Peacock-feathers-animal-vision-1536x864
The camera system can measure angle-dependent structural colors such as iridescence. This is illustrated here through an image of a highly iridescent peacock (Pavo cristatus) feather. The colors in this video still image represent (A) peafowl Pavo cristatus false color, where blue, green, and red quantum catches are depicted as blue, green, and red, respectively, and the UV is overlaid as magenta. Although broadly similar to a standard color video, the UV-iridescence can be seen on the blue-green barbs of the ocellus (“eyespot”). Further UV iridescence can be seen along the perimeter of the ocellus (between the outer 2 green stripes). Interestingly, the iridescence is more notable to the peafowl than to (B) humans (standard colors), (C) honeybees, or (D) dogs.

The Challenge of Capturing Animal Colors

Traditionally, researchers relied on spectrophotometry to study animal vision, a method that is often time-consuming, requires specific lighting conditions, and is unable to capture moving images. In response to these limitations, a team led by the University of Sussex developed this innovative camera and software system, capable of recording animal-view videos (see clip at end of article). 

The team then turned to multispectral photography, which captures images in different wavelength ranges, including ultraviolet and infrared. This method provides richer spatial details but still falls short in capturing the movement and temporal changes in color.

To address these limitations, researchers are now combining multispectral imaging with 3D digital modeling. This innovative approach lets them animate and study these 3D models under various simulated conditions, providing insights into how animal postures and viewpoints affect color perception.

A Revolutionary Camera System

The unique camera functions by recording videos across four color channels: blue, green, red, and ultraviolet (UV). These recordings are then processed to create videos that accurately represent how animals perceive these colors, based on our current understanding of their eye photoreceptors. The captured data is then transformed into a format that reflects how specific animals perceive these colors.

To do this, the research team designed an easy-to-use pipeline combining existing multispectral photography methods with their new hardware and software. The system uses a beam splitter to separate ultraviolet from visible light, directing these to two different cameras. The recorded footage is then processed to match the color perception of specific animals, like honeybees or UV-sensitive birds.

This method is not only groundbreaking in its scientific precision but also practical. It uses commercially available cameras and 3D-printed housing, making it accessible for wider research use. The team has ensured that all components of their system are open source, inviting further improvements and adaptations.

“We’ve long been fascinated by how animals see the world. Modern techniques in sensory ecology allow us to infer how static scenes might appear to an animal; however, animals often make crucial decisions on moving targets (e.g., detecting food items, evaluating a potential mate’s display, etc.),” says Assistant Professor Daniel Hanley from George Mason University in Virginia, in a media release. “Here, we introduce hardware and software tools for ecologists and filmmakers that can capture and display animal-perceived colors in motion.”

This approach marks a significant leap forward, as it captures the full complexity of visual signals in their natural context, with all the nuances of movement and varying light conditions.

HONEYBEE PERSPECTIVE: Here, we show the application of UV-blocking sunscreen in honeybee false colors. As in other depictions, we show the honeybee’s UV, blue, and green photoreceptor responses as blue, green, and red, respectively. Note that the light toned skin (DH) appears similar in honeybee false colors as in human vision, because skin reflectance increases progressively at longer wavelengths. The sunscreen appears white to our eye because it reflects broadly over the human visible range, but it appears yellow in honeybee false colors because it absorbs UV light. (Credit: PLoS Biology)
HONEYBEE PERSPECTIVE: The camera system is capable to capture naturally occurring behaviors in their original context. This is illustrated with an image that depict bees foraging and fighting in their natural environment. The image is shown in honeybee false colors (displaying the honeybee’s UV, blue, and green photoreceptor responses as blue, green, and red, respectively). (Credit: PLoS Biology)
northern mockingbirds interacting in a tree, in avian false colors.
BIRD’S EYE VIEW: Here, we illustrate 2 northern mockingbirds interacting in a tree, in avian false colors. Specifically, we show blue, green, and red quantum catches as blue, green, and red, respectively, and UV quantum catches are overlaid as magenta. While the 80 mm lens is not designed for imaging distant subjects, the system captures avian-view imagery well and shows the “avian white” (reflective from the UV through the visible portions of the spectrum) patches of their feathers. It also illustrates that the sky as predominantly UV-colored (i.e., appearing magenta), due to shorter wavelengths being subjected to increased Rayleigh scattering. Thus, while the sky may appear blue to our eyes, it would appear UV-blue to many other organisms. (Credit: PLoS Biology).
HONEYBEE PERSPECTIVE: Conceal and reveal displays can pose a problem for spectroscopy and standard multispectral photography. Here, we show a still shot of a black swallowtail Papilio polyxenes caterpillar displaying its osmeteria. We illustrate this image in honeybee false colors such that UV, blue, and green quantum catches are shown as blue, green, and red, respectively. The (human) yellow osmeteria as well as the yellow spots along the caterpillar’s back both reflect strongly in the UV and appear magenta when the colors are shifted into honeybee false colors (as the strong responses on the honeybee’s UV-sensitive and green-sensitive photoreceptors are depicted as blue and red, respectively). Many predators of caterpillars perceive UV, and accordingly, this coloration might be an effective aposematic signal. (Credit: PLoS Biology)
HONEYBEE PERSPECTIVE: Image depicting 2 eastern leaf-footed bug (Leptoglossus phyllopus) eggs. These eggs are approximately 2 mm in diameter and were on the base of a small leaf, which is shown blowing in the wind in honeybee false color (photoreceptors sensitive to UV, blue, and green light are shown as blue, green, and red, respectively). This demonstrates the capability of the system to image small items close up. (Credit: PLoS Biology)
Museum specimen of a Phoebis philea butterfly in avian RNL false colors.
BIRD’S EYE VIEW: Museum specimen of a Phoebis philea butterfly in avian RNL false colors. Another potentially useful application of the system is the fast digitization of museum specimens. This butterfly possesses both pigmentary and structural UV coloration. Bright magenta colors highlight the predominantly UV-reflective areas, while the areas appearing purple reflect similar amounts of UV and long wavelength light. The specimen is mounted on a stand, showcasing how the iridescent colors change depending on viewing angle. (Credit: PLoS Biology)
Certain scenes would be challenging to measure using traditional methods and would appear entirely distinct to different animal viewers
Certain scenes would be challenging to measure using traditional methods and would appear entirely distinct to different animal viewers. As an illustration of this point, we show the same rainbow in (A) mouse, (B) honeybee, and (C) avian false colors, alongside a (D) video with standard colors (i.e., human colors)

Implications and Future Directions

This advancement opens up numerous possibilities for researchers. Now, they can study how animals perceive dynamic visual signals in their natural habitats, a crucial aspect of understanding animal behavior and communication.

For instance, researchers can now capture rapid changes in color signals in real-world settings, like a leaf fluttering in the wind or a bird moving through undergrowth. This was previously impossible with static imaging techniques.

This new tool promises to revolutionize our understanding of sensory ecology. By revealing the unseen world of animal vision, scientists can explore new frontiers in the study of animal behavior, communication, and evolution.

The study is published in the journal PLoS Biology.

Source: Study Finds

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