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The monarch butterfly (Danaus plexippus) performs one of the most astonishing behaviors in the animal kingdom: every fall millions of these butterflies leave their breeding grounds in North Amerika and migrate more than 4.000 km southwards until they reach their overwintering habitat in Central Mexico. To maintain their migratory direction over this enormous distance, the butterflies use a time-compensated sun compass. Beside this, skylight polarization, the Earth’s magnetic field and specific mountain ranges seem to guide the butterflies as well the south. In contrast to this fascinating orientation ability, the behavior of the butterflies in their non-migratory state received less attention. Although they do not travel long distances, they still need to orient themselves to find food, mating partners or get away from competitors. The aim of the present doctoral thesis was to investigate use of visual cues for orientation in migrating as well as non-migrating monarch butterflies. For this, field experiments investigating the migration of the butterflies in Texas (USA) were combined with experiments testing the orientation performance of non-migratory butterflies in Germany.
In the first project, I recorded the heading directions of tethered butterflies during their annual fall migration. In an outdoor flight simulator, the butterflies maintained a southwards direction as long as they had a view of the sun’s position. Relocating the position of the sun by 180° using a mirror, revealed that the sun is the animals’ main orientation reference. Furthermore, I demonstrated that when the sun is blocked and a green light stimulus (simulated sun) is introduced, the animals interpreted this stimulus as the ‘real’ sun. However, this cue was not sufficient to set the migratory direction when simulated as the only visual cue in indoor experiments. When I presented the butterflies a linear polarization pattern additionally to the simulated sun, the animals headed in the correct southerly direction showing that multiple skylight cues are required to guide the butterflies during their migration.
In the second project, I, furthermore, demonstrated that non-migrating butterflies are able to maintain a constant direction with respect to a simulated sun. Interestingly, they ignored the spectral component of the stimulus and relied on the intensity instead. When a panoramic skyline was presented as the only orientation reference, the butterflies maintained their direction only for short time windows probably trying to stabilize their flight based on optic-flow information. Next, I investigated whether the butterflies combine celestial with local cues by simulating a sun stimulus together with a panoramic skyline. Under this conditions, the animals’ directedness was increased demonstrating that they combine multiple visual cues for spatial orientation.
Following up on the observation that a sun stimulus resulted in a different behavior than the panoramic skyline, I investigated in my third project which orientation strategies the butterflies use by presenting different simulated cues to them. While a bright stripe on a dark background elicited a strong attraction of the butterflies steering in the direction of the stimulus, the inverted version of the stimulus was used for flight stabilization. In contrast to this, the butterflies maintained arbitrary directions with a high directedness with respect to a simulated sun. In an ambiguous scenery with two identical stimuli (two bright stripes, two dark stripes, or two sun stimuli) set 180° apart, a constant flight course was only achieved when two sun stimuli were displayed suggesting an involvement of the animals’ internal compass. In contrast, the butterflies used two dark stripes for flight stabilization and were alternatingly attracted by two bright stripes. This shows that monarch butterflies use stimulus-dependent orientation strategies and gives the first evidence for different neuronal pathways controlling the output behavior.
The fusion of methods from several disciplines is a crucial component of scientific development. Artificial Neural Networks, based on the principle of biological neuronal networks, demonstrate how nature provides the best templates for technological advancement. These innovations can then be employed to solve the remaining mysteries of biology, including, in particular, processes that take place on microscopic scales and can only be studied with sophisticated techniques. For instance, direct Stochastic Optical Reconstruction Microscopy combines tools from chemistry, physics, and computer science to visualize biological processes at the molecular level. One of the key components is the computer-aided reconstruction of super-resolved images. Improving the corresponding algorithms increases the quality of the generated data, providing further insights into our biology. It is important, however, to ensure that the heavily processed images are still a reflection of reality and do not originate in random artefacts.
Expansion microscopy is expanding the sample by embedding it in a swellable hydrogel. The method can be combined with other super-resolution techniques to gain additional resolution. We tested this approach on microtubules, a well-known filamentous reference structure, to evaluate the performance of different protocols and labelling techniques.
We developed LineProfiler an objective tool for data collection. Instead of collecting perpendicular profiles in small areas, the software gathers line profiles from filamentous structures of the entire image. This improves data quantity, quality and prevents a biased choice of the evaluated regions. On the basis of the collected data, we deployed theoretical models of the expected intensity distribution across the filaments. This led to the conclusion that post-expansion labelling significantly reduces the labelling error and thus, improves the data quality. The software was further used to determine the expansion factor and arrangement of synaptonemal complex data.
Automated Simple Elastix uses state-of-the-art image alignment to compare pre- and post-expansion images. It corrects linear distortions occurring under isotropic expansion, calculates a structural expansion factor and highlights structural mismatches in a distortion map. We used the software to evaluate expanded fungi and NK cells. We found that the expansion factor differs for the two structures and is lower than the overall expansion of the hydrogel.
Assessing the fluorescence lifetime of emitters used for direct Stochastic Optical Reconstruction Microscopy can reveal additional information about the molecular environment or distinguish dyes emitting with a similar wavelength. The corresponding measurements require a confocal scanning of the sample in combination with the fluorescent switching of the underlying emitters. This leads to non-linear, interrupted Point Spread Functions. The software ReCSAI targets this problem by combining the classical algorithm of compressed sensing with modern methods of artificial intelligence. We evaluated several different approaches to combine these components and found, that unrolling compressed sensing into the network architecture yields the best performance in terms of reconstruction speed and accuracy.
In addition to a deep insight into the functioning and learning of artificial intelligence in combination with classical algorithms, we were able to reconstruct the described non-linearities with significantly improved resolution, in comparison to other state-of-the-art architectures.