Measuring compound eye optics with microscope and microCT images

Eye morphology in four species of tiger beetles (Coleoptera: Cicindelidae)

Compound eyes undoubtedly represent the widespread eye architecture in the animal kingdom. The insects' compound eye shows a wide variety of designs, and insects use their visual capacity to accomplish several tasks, including avoiding enemies, searching for food and shelter, locating a mate, and acquiring information about the environment and its surroundings. Broad literature data support the concept that visual ability lies in the way the eyes are built. Since the resolution and sensitivity of the compound eye are partly determined by the density of the ommatidia and the size of the facets. Morphological parameters of the compound eyes could influence the function of the visual organ and its capacity to process information, also representing a sensitive indicator of different habitat demands. In this study, we compared compound eyes' parameters in four closely related species of tiger beetles to disclose differences arising from different habitats. Furthermore, to investigate whether there are consistent intersexual differences, we also compared the most relevant parameters of the eye in males and females of four selected species. Our results show sex-related and interspecific differences that occur in examined species.

Synchrotron-source micro-x-ray computed tomography for examining butterfly eyes

Comparative anatomy is an important tool for investigating evolutionary relationships among species, but the lack of scalable imaging tools and stains for rapidly mapping the microscale anatomies of related species poses a major impediment to using comparative anatomy approaches for identifying evolutionary adaptations. We describe a method using synchrotron source micro-x-ray computed tomography (syn-μXCT) combined with machine learning algorithms for high-throughput imaging of Lepidoptera (i.e., butterfly and moth) eyes. Our pipeline allows for imaging at rates of ~15 min/mm3 at 600 nm3 resolution. Image contrast is generated using standard electron microscopy labeling approaches (e.g., osmium tetroxide) that unbiasedly labels all cellular membranes in a species-independent manner thus removing any barrier to imaging any species of interest. To demonstrate the power of the method, we analyzed the 3D morphologies of butterfly crystalline cones, a part of the visual system associated with acuity and sensitivity and found significant variation within six butterfly individuals. Despite this variation, a classic measure of optimization, the ratio of interommatidial angle to resolving power of ommatidia, largely agrees with early work on eye geometry across species. We show that this method can successfully be used to determine compound eye organization and crystalline cone morphology. Our novel pipeline provides for fast, scalable visualization and analysis of eye anatomies that can be applied to any arthropod species, enabling new questions about evolutionary adaptations of compound eyes and beyond.

Evolution of compound eye morphology underlies differences in vision between closely related Drosophila species

BACKGROUND

Insects have evolved complex visual systems and display an astonishing range of adaptations for diverse ecological niches. Species of Drosophila melanogaster subgroup exhibit extensive intra- and interspecific differences in compound eye size. These differences provide an excellent opportunity to better understand variation in insect eye structure and the impact on vision. Here we further explored the difference in eye size between D. mauritiana and its sibling species D. simulans.

RESULTS

We confirmed that D. mauritiana have rapidly evolved larger eyes as a result of more and wider ommatidia than D. simulans since they recently diverged approximately 240,000 years ago. The functional impact of eye size, and specifically ommatidia size, is often only estimated based on the rigid surface morphology of the compound eye. Therefore, we used 3D synchrotron radiation tomography to measure optical parameters in 3D, predict optical capacity, and compare the modelled vision to in vivo optomotor responses. Our optical models predicted higher contrast sensitivity for D. mauritiana, which we verified by presenting sinusoidal gratings to tethered flies in a flight arena. Similarly, we confirmed the higher spatial acuity predicted for Drosophila simulans with smaller ommatidia and found evidence for higher temporal resolution.

CONCLUSIONS

Our study demonstrates that even subtle differences in ommatidia size between closely related Drosophila species can impact the vision of these insects. Therefore, further comparative studies of intra- and interspecific variation in eye morphology and the consequences for vision among other Drosophila species, other dipterans and other insects are needed to better understand compound eye structure-function and how the diversification of eye size, shape, and function has helped insects to adapt to the vast range of ecological niches.

Nanomaterial-Based Artificial Vision Systems: From Bioinspired Electronic Eyes to In-Sensor Processing Devices

High-performance robotic vision empowers mobile and humanoid robots to detect and identify their surrounding objects efficiently, which enables them to cooperate with humans and assist human activities. For error-free execution of these robots' tasks, efficient imaging and data processing capabilities are essential, even under diverse and complex environments. However, conventional technologies fall short of meeting the high-standard requirements of robotic vision under such circumstances. Here, we discuss recent progress in artificial vision systems with high-performance imaging and data processing capabilities enabled by distinctive electrical, optical, and mechanical characteristics of nanomaterials surpassing the limitations of traditional silicon technologies. In particular, we focus on nanomaterial-based electronic eyes and in-sensor processing devices inspired by biological eyes and animal visual recognition systems, respectively. We provide perspectives on key nanomaterials, device components, and their functionalities, as well as explain the remaining challenges and future prospects of the artificial vision systems.