Microbiomes of a specialist caterpillar are consistent across different habitats but also resemble the local soil microbial communities

BACKGROUND

Insect-associated microorganisms can provide a wide range of benefits to their host, but insect dependency on these microbes varies greatly. The origin and functionality of insect microbiomes is not well understood. Many caterpillars can harbor symbionts in their gut that impact host metabolism, nutrient uptake and pathogen protection. Despite our lack of knowledge on the ecological factors driving microbiome assemblages of wild caterpillars, they seem to be highly variable and influenced by diet and environment. Several recent studies have shown that shoot-feeding caterpillars acquire part of their microbiome from the soil. Here, we examine microbiomes of a monophagous caterpillar (Tyria jacobaeae) collected from their natural host plant (Jacobaea vulgaris) growing in three different environments: coastal dunes, natural inland grasslands and riverine grasslands, and compare the bacterial communities of the wild caterpillars to those of soil samples collected from underneath each of the host plants from which the caterpillars were collected.

RESULTS

The microbiomes of the caterpillars were dominated by Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes. Only 5% of the total bacterial diversity represented 86.2% of the total caterpillar's microbiome. Interestingly, we found a high consistency of dominant bacteria within the family Burkholderiaceae in all caterpillar samples across the three habitats. There was one amplicon sequence variant belonging to the genus Ralstonia that represented on average 53% of total community composition across all caterpillars. On average, one quarter of the caterpillar microbiome was shared with the soil.

CONCLUSIONS

We found that the monophagous caterpillars collected from fields located more than 100 km apart were all dominated by a single Ralstonia. The remainder of the bacterial communities that were present resembled the local microbial communities in the soil in which the host plant was growing. Our findings provide an example of a caterpillar that has just a few key associated bacteria, but that also contains a community of low abundant bacteria characteristic of soil communities.

Plant community composition steers grassland vegetation via soil legacy effects

Soil legacy effects are commonly highlighted as drivers of plant community dynamics and species co-existence. However, experimental evidence for soil legacy effects of conditioning plant communities on responding plant communities under natural conditions is lacking. We conditioned 192 grassland plots using six different plant communities with different ratios of grasses and forbs and for different durations. Soil microbial legacies were evident for soil fungi, but not for soil bacteria, while soil abiotic parameters did not significantly change in response to conditioning. The soil legacies affected the composition of the succeeding vegetation. Plant communities with different ratios of grasses and forbs left soil legacies that negatively affected succeeding plants of the same functional type. We conclude that fungal-mediated soil legacy effects play a significant role in vegetation assembly of natural plant communities.

Real time binocular detection of horizontal vertical and torsional eye movements by an infra red video-eye tracker

So far it has only been possible to detect horizontal and vertical eye movements during real time; torsional eye movements have to be calculated from video-tape that have been processed several times, and each investigation takes considerable time. We developed a real time binocular infra red video eye tracker for routine clinical examination which allows real time graphical visualization of horizontal, vertical and torsional eye movements with a bandwidth of 12 Hz (25 Hz refresh frequency). Eye movements in three dimensions can now be obtained as easy as has been done before with electronystagmography for the horizontal component. In contrast to previous systems described, we analyze only those parts of the video-frame that are essential for detection of all three eye movements. This results in a huge data-reduction accompanied by a speed-up of analysis. The black pupil centre is detected by an iterative process of analyzing several horizontal and vertical lines of the even video field. The algorithm searches for regions of minimal intensity. Horizontal and vertical eye rotation is then calculated and transformed into Fick's coordinates. Based upon the centre of the pupil four concentric segments are defined in the iris to detect torsion and to enable correction for false rotation. Torsion is detected by cross-correlation of each segment and estimated from the outcome of the correlation coefficients. All transformations are established by predefined stored transformation matrices thus making real time calculations for transformation unnecessary. After corrections for eccentricity and assuming that fixation of the subjects tested was perfect, accuracy appears to be within 0.3 degrees-1.0 degrees for all three dimensions. Linearity after all corrections is within 4-9% within a visual field of 60 x 40 degrees (horizontal x vertical).