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24 results for “Insect flight”
Data for: "Continental-scale patterns in diel flight timing of high-altitude migratory insects"
<p>This dataset contains the proportional migratory insect intensity and traffic data used in Haest <em>et al.</em> (2024) to quantify patterns in diel flight periodicity of migratory insects between 50-500m above ground level during March-October 2021 using a network of seventeen vertical-looking radars across Europe. Please see the Materials and Methods section in Haest <em>et al.</em> (2024) for more details on the dataset. </p>
Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight"
<p>Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight". The cloud radar data files contains all the data used in the publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight" by Charlotte E. Wainwright, Sabrina N. Volponi, Phillip M. Stepanian, Don R. Reynolds, and David H. Richter, published in Methods in Ecology and Evolution in 2022. The MATLAB code implements the method described in the paper on the data files included.</p>
Data from: Enzyme polymorphism, oxygen and injury: a lipidomic analysis of flight-induced oxidative damage in a SDH-polymorphic insect
When active tissues receive insufficient oxygen to meet metabolic demand, succinate accumulates and has two fundamental effects: it causes ischemia-reperfusion injury while also activating the hypoxia-inducible factor pathway (HIF). The Glanville fritillary butterfly (Melitaea cinxia) possesses a balanced polymorphism in Sdhd, shown previously to affect HIF pathway activation and tracheal morphology and used here to experimentally test the hypothesis that variation in succinate dehydrogenase affects oxidative injury. We stimulated butterflies to fly continuously in a respirometer (3 min duration), which typically caused episodes of exhaustion and recovery, suggesting a potential for cellular injury from hypoxia and reoxygenation in flight muscles. Indeed, flight muscle from butterflies flown on consecutive days had lipidomic profiles similar to rested paraquat-injected butterflies, but distinct from rested untreated butterflies. Many butterflies showed a decline in flight metabolic rate (FMR) on Day 2, and there was a strong inverse relationship between the ratio of Day 2 to Day 1 FMR and the abundance of sodiated adducts of phosphatidylcholines and coenzyme Q (CoQ). This result is consistent with elevation of sodiated lipids caused by disrupted intracellular ion homeostasis in mammalian tissues after hypoxia-reperfusion. Butterflies carrying the Sdhd M allele had higher abundance of lipid markers of cellular damage, but the association was reversed in field-collected butterflies, where focal individuals typically flew for seconds at a time rather than continuously. These results indicate that Glanville fritillary flight muscles can be injured by episodes of high exertion, but injury severity appears to be determined by an interaction between SDH genotype and behavior (prolonged vs. intermittent flight).
Ancient insect vision tuned for flight amongst rocks and plants underpins natural flower colour diversity - rock, mineral, stick, bark, leaf, bird- and insect-flower petal reflectance spectra
<p>Understanding the origins of flower colour signalling to pollinators is fundamental to evolutionary biology and ecology. Flower colour evolves under pressure from visual systems of pollinators, like birds and insects, to establish global signatures among flowers with similar pollinators. However, an understanding of the ancient origins of this relationship remains elusive. Here, we employ computer simulations to generate artificial flower backgrounds assembled from real material sample spectra of rocks, leaves, and dead plant materials, against which to test flowers' visibility to birds and bees. Our results indicate how flower colours differ from their backgrounds in strength, and the distributions of salient reflectance features when perceived by these key pollinators, to reveal the possible origins of their colours. Since Hymenopteran visual perception evolved before flowers, the terrestrial chromatic context for its evolution to facilitate flight and orientation consisted of rocks, leaves, sticks, and bark. Flowers exploited these pre-evolved visual capacities of their visitors, and in response evolved chromatic features to signal to bees, and differently to birds, against a backdrop of other natural materials. Consequently, it appears that today's flower colours may be an evolutionary response to the vision of diurnal pollinators navigating their world millennia prior to the first flowers.</p>
Data from: What makes a good pollinator? Abundant and specialized insects with long flight periods transport the most strawberry pollen
<ol> <li>Despite the importance of insect pollination to produce marketable fruits, insect pollination management is limited by insufficient knowledge about key crop pollinator species. This lack of knowledge is due in part to: 1) the extensive labour involved in collecting direct observations of pollen-transport, 2) the variability of insect assemblages over space and time, and 3) the possibility that pollinators may need access to wild plants as well as crop floral resources.</li> <li>We address these problems using strawberry in the UK as a case study. First, we compare two proxies for estimating pollinator importance: flower visits and pollen transport. Pollen-transport data might provide a closer approximation of pollination service, but visitation data are less time-consuming to collect. Second, we identify insect parameters that are associated with high importance as pollinators, estimated using each of the proxies above. Third, we estimated insects' use of wild plants as well as the strawberry crop.</li> <li>Overall, pollinator importances estimated based on easier-to-collect visitation data were strongly correlated with importances estimated based on pollen loads. Both frameworks suggest that bees <em>Apis</em> and <em>Bombus</em> and hoverflies <em>Eristalis</em> are likely to be key pollinators of strawberries, although visitation data underestimate the importance of bees.</li> <li>Moving beyond species identities, abundant, relatively specialised insects with long active periods are likely to provide more pollination service. </li> <li>Most insects visiting strawberry plants also carried pollen from wild plants, suggesting that pollinators need diverse floral resources.</li> <li>Identifying essential pollinators or pollinator parameters based on visitation data will reach the same general conclusions as those using pollen transport data, at least in monoculture crop systems. Managers may be able to enhance pollination service by preserving habitats surrounding crop fields to complement pollinators' diets and provide habitats for diverse life stages of wild pollinators.</li> </ol>
Flight data from: Acrobatics at the insect-scale: A durable, precise, and agile micro-aerial-robot
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Data from: What makes a good pollinator? Abundant and specialized insects with long flight periods transport the most strawberry pollen
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Ancient insect vision tuned for flight amongst rocks and plants underpins natural flower colour diversity - rock, mineral, stick, bark, leaf, bird- and insect-flower petal reflectance spectra
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Data from: Enzyme polymorphism, oxygen and injury: a lipidomic analysis of flight-induced oxidative damage in a SDH-polymorphic insect
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Data from: Consistent coordination patterns provide near perfect behavior decoding in a comprehensive motor program for insect flight
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Data from: Predicting forest insect flight activity: a Bayesian network approach
Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.
Data from: Low-cost automated flight intercept trap for the temporal sub-sampling of flying insects attracted to artificial light at night
<p>Sampling methods are selected depending on the targeted species or the spatial and temporal requirements of the study. However, most methods for passive sampling of flying insects have poor temporal resolution because it is time consuming, costly and/or logistically difficult. Effective sampling of flying insects attracted to artificial light at night (ALAN) requires sampling at user-defined time points (nighttime only) across well-replicated sites resulting in major time and labor-intensive survey effort or expensive automated technologies. Described here is a low-cost automated intercept trap that requires no specialist equipment or skills to construct and operate, making it a viable option for studies that require temporal sub-sampling across multiple sites. The trap can be used to address a wide range of other ecological questions that require a greater temporal and spatial scale than is feasible with previous trap technology.</p>
Dataset for "Gap junctions desynchronize a neural circuit to stabilize insect flight"
<p><strong>Data guide</strong></p> <p>For each figure, we provide one data folder. Each folder contains one overview Excel file that provides all source data that is shown in the respective figure. For some figure panels additional subfolders with original data files are provided. Please note that the data for figures 3A-J (except F and G) have been uploaded together with the analyses and simulation code and are accessible under the doi: 10.5281/zenodo.7740678</p> <p> </p> <p><strong>Data Figure 1: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 1.xlsx</li> <li>Folder Panel A: <ul> <li>Recordings shown in A and G with sorted spikes: figure 1a_original recording.smr</li> <li>Data matrix of waveforms shown in A as text file: figure 1a_waveforms_200818_561.5-566.5sec.txt</li> </ul> </li> <li>Folder Panels B+F: all original recordings with sorted spikes that have been analyzed for panels B and F: <ul> <li>171106_m1.SMR</li> <li>181029_m1.smr</li> <li>190522_m1.SMR</li> <li>200305_m1.smr</li> <li>200818_m1.smr</li> <li>200824_m1.smr</li> <li>200827_m1.SMR</li> <li>201005_m1.smr</li> </ul> </li> <li>Folder Panel C: all original recordings as .smr (Spike2) files that went into the analyses shown in figure 1c. <ul> <li>100 recordings in folders “Fly 1 to Fly 100”</li> <li>1 recording in folder “single animal”</li> <li>the waveforms of these 101 animals are also as text in file Figure 1.xlsx</li> </ul> </li> <li>Folder Panel D <ul> <li>The original data file of the recording shown in figure 1D as axon file: 20128002_OUT1_firing.abf</li> <li>The selected waveforms shown in figure 1D are also as text in file Figure 1.xlsx</li> </ul> </li> <li>All recordings that underlie the analysis in figure 1E are as number matrices in the file Figure 1.xlsx</li> <li>The spike events shown in figure 1G are as number matrices in the file Figure 1.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 2: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 2.xlsx</li> <li>The number matrices for all traces shown in figure 2A are as number matrices in the file Figure 2.xlsx</li> <li>Panel B: all number matrices of the event times that underlie the analyses shown in figure 2B: <ul> <li>Folder “control”: .txt of event times of the 7 control animals</li> <li>Folder “ShakB-RNAi”: .txt of event times of the 7 ShakB-RNAi-kd animals</li> <li>Folder “ShakB-oe”: .txt of event times of the 7 ShakB-oe (overexpression) animals</li> </ul> </li> <li>Panel C original confocal image stack for image data shown in figure 2C: <ul> <li>original Leica file: figure 2c_image stack.lif</li> <li>Folder “tiff” with same image stack saved as tiff</li> </ul> </li> </ul> <p> </p> <ul> <li>Panel D: The number matrices underlying the waveforms of the traces shown in Figure 2D are contained in the file Figure 2.xlsx</li> <li>Panel E: The number matrices underlying the waveforms of the traces shown in Figure 2E are contained in the file Figure 2.xlsx</li> <li>Panel F: The number matrices of all pre- and postsynaptic voltage areas of paired MN recordings and the analyses thereof for Figure 2F (left) are contained in the file Figure 2.xlsx. The number matrices of all pre- and postsynaptic voltage amplitudes of paired MN recordings and the analyses thereof for Figure 2F (right) are contained in the file Figure 2.xlsx</li> <li>Panel G: The number matrices underlying the waveforms of the traces shown in Figure 2G are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2H are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2I are contained in the file Figure 2.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 3: </strong>(all data for Fig. 3 except f and g are at doi: 10.5281/zenodo.7740678)</p> <ul> <li>Overview Excel file with all source data for Figures 3G and F: Figure 3.xlsx</li> <li>Panel F: The number matrices for all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel F: The number matrices for the waveforms of all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel G: The number matrices for the waveforms of all traces shown in figure 3G are contained in the file Figure 3.xlsx <ul> <li>original data file from which the traces in figure 3G (top) were taken: control MN4_3_0s-87s.SMR</li> <li>original data file from which the traces in figure 3G (bottom) were taken: UAS shab81 2022_10_07_0s-87s.SMR</li> </ul> </li> </ul> <p> </p> <p><strong>Data Figure 4: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 4.xlsx</li> <li>Panel A: The number matrices for all event signals and all waveform traces shown in figure 4A are as number matrices in the file Figure 4.xlsx</li> <li>Figure 4B (top): <ul> <li>The number matrices for the waveforms (voltage recording and fit) shown in Figure 4b (top) are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms of the voltage recordings that were fitted to obtain the averages shown in Figure 4B top are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (top) are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4B (middle): <ul> <li>The number matrices for the waveforms (calcium imaging and fit) shown in Figure 4b (middle) are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (middle) are contained in the file Figure 4.xlsx</li> <li>The original imaging data for this analysis is contained in movie S4</li> </ul> </li> <li>Figure 4B (bottom): <ul> <li>The number matrices for the waveforms (wingbeat frequency from events and fit) shown in Figure 4b (bottom) are contained in the file Figure 4.xlsx</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4C: <ul> <li>The number matrices for the waveform shown in Figure 4C are contained in the file Figure 4.xlsx</li> <li>The original calcium imaging movie as .avi file: DLM6_Imaging.avi</li> <li>The original calcium imaging movie as tiff stack: file DLM6_imaging_tiff</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4E: <ul> <li>The number matrices for the waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms that resulted in the average waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The original data files for analyses in Figure 4E are contained as text files in the folders ShakB_oe (7 text files) and control (7 text files)</li> </ul> </li> </ul>
Data from: Predicting forest insect flight activity: a Bayesian network approach
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Data from: Genome-wide transcriptional signatures of migratory flight activity in a globally invasive insect pest
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Data from: Low-cost automated flight intercept trap for the temporal sub-sampling of flying insects attracted to artificial light at night
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Data from: Reanalyzing the Palaeoptera problem - the origin of insect flight remains obscure
The phylogenetic relationships of the winged insect lineages – mayflies (Ephemeroptera), damselflies and dragonflies (Odonata), and all other winged insects (Neoptera) – are still controversial with three hypotheses supported by different datasets: Palaeoptera, Metapterygota and Chiastomyaria. Here, we reanalyze available phylogenomic data with a focus on detecting confounding and alternative signal. In this context, we provide a framework to quantitatively evaluate and assess incongruent molecular phylogenetic signal inherent in phylogenomic datasets. Despite overall support for the Palaeoptera hypothesis, we also found considerable signal for Chiastomyaria, which is not easily detectable by standardized tree inference approaches. Analyses of the accumulation of signal across gene partitions showed that signal accumulates gradually. However, even in case signal only slightly supported one over the other hypothesis, topologies inferred from large datasets switch from statistically strongly supported Palaeoptera to strongly supported Chiastomyaria. From a morphological point of view, Palaeoptera currently appears to be the best-supported hypothesis; however, recent analyses were restricted to head characters. Phylogenetic approaches covering all organ systems including analyses of potential functional or developmental convergence are still pending so that the Palaeoptera problem has to be considered an open question in insect systematics.
Data from: Cascading effects of host plant inbreeding on the larval growth, muscle molecular composition, and flight capacity of an adult herbivorous insect.
A primary function of adult winged insects is dispersal. Limiting larval dietary intake (partial starvation) has been shown to affect the flight muscle metabolism of adult moths reared on artificial diet, but a more ecologically relevant question is whether natural variation in host plant quality can lead to differences in the flight capacity of adult insects. Recent studies have shown that inbreeding compromises plant anti-herbivore defenses. We created inbred and outbred progeny from locally collected horsenettle (Solanum carolinense L.) and examined how host plant inbreeding affects the growth, development, and flight muscle physiology of tobacco hornworm (Manduca sexta L.), a specialist herbivore on Solanaceae. We tested the hypothesis that within population genetic variation in host plant quality, resulting from inbreeding, can create significant changes to the larval development and flight physiology of an adult insect. We found that Manduca larvae reared on inbred horsenettle plants grew faster and developed into larger pupae compared to larvae reared on outbred plants. Adult flight metabolic rate was greater in adults reared on inbred plants compared to outbred plants, and this elevation was independent of body mass when we excluded one plant family that produced small, low metabolic rate moths regardless of breeding regime. Differences in mass-specific flight metabolism were associated with changes in alternative splicing of Troponin t, a flight muscle protein that regulates muscle contraction. These results show that host plant inbreeding can create effects that cascade through larval and pupal development to affect dispersal-related traits of the adult stage. Hence, plant inbreeding may also impact herbivore population dynamics, particularly their ability to spread away from, and possibly into, isolated patches of inbred plants creating increased herbivore pressure on these plant populations. More generally, our findings reveal that changes in population biology at one trophic level can affect the metabolic physiology and flight capacity of an animal at a higher trophic level.
Data from: Reanalyzing the Palaeoptera problem - the origin of insect flight remains obscure
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Data from: Flight loss linked to faster molecular evolution in insects
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OpenNeuro
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