The effect of surface topography on the ball-rolling ability of Kheper lamarcki (Scarabaeidae).

The most effective way to avoid intense inter- and intra-specific competition at the dung source, and to increase the distance to the other competitors, is to follow a single straight bearing. While ball-rolling dung beetles manage to roll their dung balls along nearly perfect straight paths when traversing flat terrain, the paths that they take when traversing more complex (natural) terrain are not well understood. In this study, we investigate the effect of complex surface topographies on the ball-rolling ability of Kheper lamarcki. Our results reveal that ball-rolling trajectories are strongly influenced by the characteristic scale of the surface structure. Surfaces with an increasing similarity between the average distance of elevations and the ball radius cause progressively more difficulties during ball transportation. The most important factor causing difficulties in ball transportation appears to be the slope of the substrate. Our results show that, on surfaces with a slope of 7.5 deg, more than 60% of the dung beetles lose control of their ball. Although dung beetles still successfully roll their dung ball against the slope on such inclinations, their ability to roll the dung ball sideways diminishes. However, dung beetles do not seem to adapt their path on inclines such that they roll their ball in the direction against the slope. We conclude that dung beetles strive for a straight trajectory away from the dung pile, and that their actual path is the result of adaptations to particular surface topographies.

Polarisation vision in the dark: green-sensitive photoreceptors in the nocturnal ball-rolling dung beetle Escarabaeus satyrus.

Many insects utilise the polarisation pattern of the sky to adjust their travelling directions. The extraction of directional information from this sky-wide cue is mediated by specialised photoreceptors located in the dorsal rim area (DRA). While this part of the eye is known to be sensitive to the ultraviolet, blue or green component of skylight, the latter has only been observed in insects active in dim light. To address the functional significance of green polarisation sensitivity, we define the spectral and morphological adaptations of the DRA in a nocturnal ball-rolling dung beetle-the only family of insects demonstrated to orient to the dim polarisation pattern in the night sky. Intracellular recordings revealed polarisation-sensitive green photoreceptors in the DRA of Escarabaeus satyrus. Behavioural experiments verified the navigational relevance of this finding. To quantify the adaptive value of green sensitivity for celestial orientation at night, we also obtained the polarisation properties of the night sky in the natural habitat of the beetle. Calculations of relative photon catch revealed that under a moonlit sky the green-sensitive DRA photoreceptors can be expected to catch an order of magnitude more photons compared with the UV-sensitive photoreceptors in the main retina. The green-sensitive photoreceptors - which also show a range of morphological adaptations for enhanced sensitivity - provide E. satyrus with a highly sensitive system for the extraction of directional information from the night sky.

Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

In cloud computing (CC), task scheduling allocates the task to best suitable resource for execution. This article proposes a model for task scheduling utilizing the multi-objective optimization and deep learning (DL) model. Initially, the multi-objective task scheduling is carried out by the incoming user utilizing the proposed hybrid fractional flamingo beetle optimization (FFBO) which is formed by integrating dung beetle optimization (DBO), flamingo search algorithm (FSA) and fractional calculus (FC). Here, the fitness function depends on reliability, cost, predicted energy, and makespan, the predicted energy is forecasted by a deep residual network (DRN). Thereafter, task scheduling is accomplished based on DL using the proposed deep feedforward neural network fused long short-term memory (DFNN-LSTM), which is the combination of DFNN and LSTM. Moreover, when scheduling the workflow, the task parameters and the virtual machine's (VM) live parameters are taken into consideration. Task parameters are earliest finish time (EFT), earliest start time (EST), task length, task priority, and actual task running time, whereas VM parameters include memory utilization, bandwidth utilization, capacity, and central processing unit (CPU). The proposed model DFNN-LSTM+FFBO has achieved superior makespan, energy, and resource utilization of 0.188, 0.950J, and 0.238, respectively.

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network.

Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing.

Predictions of Aeroengines' Infrared Radiation Characteristics Based on HKELM Optimized by the Improved Dung Beetle Optimizer.

To solve the problems of high computational cost and the long time required by the simulation and calculation of aeroengines' exhaust systems, a method of predicting the characteristics of infrared radiation based on the hybrid kernel extreme learning machine (HKELM) optimized by the improved dung beetle optimizer (IDBO) was proposed. Firstly, the Levy flight strategy and variable spiral strategy were introduced to improve the optimization performance of the dung beetle optimizer (DBO) algorithm. Secondly, the superiority of IDBO algorithm was verified by using 23 benchmark functions. In addition, the Wilcoxon signed-rank test was applied to evaluate the experimental results, which proved the superiority of the IDBO algorithm over other current prominent metaheuristic algorithms. Finally, the hyperparameters of HKELM were optimized by the IDBO algorithm, and the IDBO-HKELM model was applied to the prediction of characteristics of infrared radiation of a typical axisymmetric nozzle. The results showed that the RMSE and MAE of the IDBO-HKELM model were 20.64 and 8.83, respectively, which verified the high accuracy and feasibility of the proposed method for predictions of aeroengines' infrared radiation characteristics.

Geographical distribution and phoretic associations of the viviparous nematode Tokorhabditis atripennis with Onthophagus dung beetles in Japan.

Viviparity is generally considered to be rare in animals. In nematodes, only six species of Rhabditida are viviparous. Five of these species have been identified in association with Onthophagus dung beetles, with Tokorhabditis atripennis being repeatedly isolated from the dung beetle Onthophagus atripennis in Japan. T. atripennis is easy to culture in a laboratory setting, and its host, O. atripennis, is distributed all over Japan. Therefore, T. atripennis is an ideal candidate for ecological and evolutionary studies on viviparity. However, the extent of their distribution and relationship with dung beetles, as well as habitats, remain unclear. In the present study, we conducted field surveys and successfully isolated 27 strains of viviparous nematodes associated with tunneler dung beetles from various regions of Japan, all of which were identified as T. atripennis. T. atripennis exhibited a strong association with Onthophagus dung beetles, especially O. apicetinctus and O. atripennis. And it was predominantly found in specific anatomical locations on the beetle bodies, such as the 'groove between pronotum and elytron' and the 'back of the wings'. Our findings suggest that Onthophagus species are the primary hosts for T. atripennis, and T. atripennis exhibits a close relationship with the living environments of tunneler beetles. This association may play a significant role in the evolution of viviparity in nematodes.

Enhanced Air Quality Prediction Using a Coupled DVMD Informer-CNN-LSTM Model Optimized with Dung Beetle Algorithm.

Accurate prediction of air quality is crucial for assessing the state of the atmospheric environment, especially considering the nonlinearity, volatility, and abrupt changes in air quality data. This paper introduces an air quality index (AQI) prediction model based on the Dung Beetle Algorithm (DBO) aimed at overcoming limitations in traditional prediction models, such as inadequate access to data features, challenges in parameter setting, and accuracy constraints. The proposed model optimizes the parameters of Variational Mode Decomposition (VMD) and integrates the Informer adaptive sequential prediction model with the Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Initially, the correlation coefficient method is utilized to identify key impact features from multivariate weather and meteorological data. Subsequently, penalty factors and the number of variational modes in the VMD are optimized using DBO. The optimized parameters are utilized to develop a variationally constrained model to decompose the air quality sequence. The data are categorized based on approximate entropy, and high-frequency data are fed into the Informer model, while low-frequency data are fed into the CNN-LSTM model. The predicted values of the subsystems are then combined and reconstructed to obtain the AQI prediction results. Evaluation using actual monitoring data from Beijing demonstrates that the proposed coupling prediction model of the air quality index in this paper is superior to other parameter optimization models. The Mean Absolute Error (MAE) decreases by 13.59%, the Root-Mean-Square Error (RMSE) decreases by 7.04%, and the R-square (R2) increases by 1.39%. This model surpasses 11 other models in terms of lower error rates and enhances prediction accuracy. Compared with the mainstream swarm intelligence optimization algorithm, DBO, as an optimization algorithm, demonstrates higher computational efficiency and is closer to the actual value. The proposed coupling model provides a new method for air quality index prediction.

Diversity of dung beetle-associated yeasts from pristine environments of Botswana.

Yeast-insect interactions are increasingly becoming an attractive source of discovery for previously unknown, unique, diverse, and industrially relevant yeast species. Despite a wealth of studies that have recently focused on yeasts in symbiotic association with Hymenopteran insects, yeasts associated with Coleopteran insects, such as lignocellulosic-rich dung-dependent beetles, remain poorly studied. Trends in yeast discovery suggest that species richness and diversity can be attributed to the ecological niche of the insect. Here, we considered the potential of dung beetles inhabiting the extreme environments of Botswana, characterized by desert-like conditions (semi-arid to arid and hot) as well as protected pristine environments, as possible attribute niches that can shape the extremophilic and diverse life history strategies of yeasts. We obtained a total of 97 phylogenetically diverse yeast isolates from six species of dung beetles from Botswana's unexplored environments, representing 19 species belonging to 11 genera. The findings suggest that the guts of dung beetles are a rich niche for non-Saccharomyces yeast species. Meyerozyma and Pichia were the most dominant genera associated with dung beetles, representing 55% (53 out of 97) of the yeast isolates in our study. Trichosporon and Cutaneotrichosporon genera represented 32% (31 out of 97) of the isolates. The remaining isolates belonged to Apiotrichum, Candida, Diutina, Naganishia, Rhodotorula, and Wickerhamiella genera (12 out of 97). We found out that about 62% (60 out of 97) of the isolates were potentially new species because of their low internal transcribed spacer (ITS) sequence similarity when compared to the most recent optimal species delineation threshold. A single isolate was unidentifiable using the ITS sequences. Using an in silico polymerase chain reaction-restriction fragment length polymorphism approach, we revealed that there was genetic diversity within isolates of the same species. Our results contribute to the knowledge and understanding of the diversity of dung beetle-associated yeasts.

Dung beetle-associated yeasts display multiple stress tolerance: a desirable trait of potential industrial strains.

BACKGROUND: Stress-tolerant yeasts are highly desirable for cost-effective bioprocessing. Several strategies have been documented to develop robust yeasts, such as genetic and metabolic engineering, artificial selection, and natural selection strategies, among others. However, the significant drawbacks of such techniques have motivated the exploration of naturally occurring stress-tolerant yeasts. We previously explored the biodiversity of non-conventional dung beetle-associated yeasts from extremophilic and pristine environments in Botswana (Nwaefuna AE et.al., Yeast, 2023). Here, we assessed their tolerance to industrially relevant stressors individually, such as elevated concentrations of osmolytes, organic acids, ethanol, and oxidizing agents, as well as elevated temperatures. RESULTS: Our findings suggest that these dung beetle-associated yeasts tolerate various stresses comparable to those of the robust bioethanol yeast strain, Saccharomyces cerevisiae (Ethanol Red™). Fifty-six percent of the yeast isolates were tolerant of temperatures up to 42 °C, 12.4% of them could tolerate ethanol concentrations up to 9% (v/v), 43.2% of them were tolerant to formic acid concentrations up to 20 mM, 22.7% were tolerant to acetic acid concentrations up to 45 mM, 34.0% of them could tolerate hydrogen peroxide up to 7 mM, and 44.3% of the yeasts could tolerate osmotic stress up to 1.5 M. CONCLUSION: The ability to tolerate multiple stresses is a desirable trait in the selection of novel production strains for diverse biotechnological applications, such as bioethanol production. Our study shows that the exploration of natural diversity in the search for stress-tolerant yeasts is an appealing approach for the development of robust yeasts.

Fault logic and data-driven model for operation reliability analysis of the flap deflection angle.

To effectively perform the reliability analysis of the flap deflection angle, the reliability analysis framework is developed by introducing fault logic and a data-driven model. Herein, the fault logic analysis is used to study the fault mechanism and filter out the characteristic fault parameters that can be used to collect input data for data-driven modelling; the data-driven modelling is employed to establish a reliability analysis model with a small amount of input data. Under this proposed framework, the improved dung beetle optimization algorithm for back propagation (IDBO-BP) method is developed to perform the reliability modelling of the flap deflection angle. To validate the effectiveness of the proposed framework, we study the fault logic of flap symmetry and establish a surrogate model of flap deflection based on the fault parameters and the IDBO-BP algorithm. According to the predicted results of the flap deflection angle, the reliability model based on the fault mechanism can reflect the actual flap motion. At the same time, the proposed IDBO-BP algorithm has excellent modelling and simulation property by comparing with other optimization algorithms. Thus, the efforts of this study provide a new solution to the problem of reliable analysis with uncertain fault parameters. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Population structure of Phanaeus vindex (Coleoptera: Scarabaeidae) in SE Michigan.

Until now, little is known about the population structure and mobility of temperate dung beetles including the rainbow scarab, Phanaeus vindex (MacLeay 1819), although this knowledge is essential for their conservation as pastures become increasingly rare and the landscape fragmented by monocultures and urbanization. Here, we estimated population size, longevity, and dispersal within and between pastures. For 3 yr, we life-trapped beetles every week on 2 adjacent farms in SE Michigan, determined their sex, male morph, and size, and marked their elytra with individual tattoo patterns before releasing them. We marked a total of 470 rainbow scarabs of which 14 were recaptured once and 2 were recaptured twice. The sex ratio was not significantly sex-biased but fluctuated between months with no apparent uniformity between years. While the minor to major male ratios were unbiased in 2019 and 2020, they were marginally minor-biased in 2021. The gross population estimates for the 2 farms were 458-491 and 217 rainbow scarabs, respectively. Beetles traveled distances of up to 178 m within farms. No beetles dispersed between farms. One large female was recaptured after 338 days documenting the first cold hardiness and long lifespan of a cold-temperate dung beetle species in the wild. The low population estimates on both farms indicate 2 vulnerable populations with no or extremely limited connectivity. Supplementary funding for the land stewardship of small-scale cattle farmers could stabilize populations of native dung beetles and maintain their ecosystem services.

Optimization of Tungsten Heavy Alloy Cutting Parameters Based on RSM and Reinforcement Dung Beetle Algorithm.

Tungsten heavy alloys (WHAs) are an extremely hard-to-machine material extensively used in demanding applications such as missile liners, aerospace, and optical molds. However, the machining of WHAs remains a challenging task as a result of their high density and elastic stiffness which lead to the deterioration of the machined surface roughness. This paper proposes a brand-new multi-objective dung beetle algorithm. It does not take the cutting parameters (i.e., cutting speed, feed rate, and depth of cut) as the optimization objects but directly optimizes cutting forces and vibration signals monitored using a multi-sensor (i.e., dynamometer and accelerometer). The cutting parameters in the WHA turning process are analyzed through the use of the response surface method (RSM) and the improved dung beetle optimization algorithm. Experimental verification shows that the algorithm has better convergence speed and optimization ability compared with similar algorithms. The optimized forces and vibration are reduced by 9.7% and 46.47%, respectively, and the surface roughness Ra of the machined surface is reduced by 18.2%. The proposed modeling and optimization algorithms are anticipated to be powerful to provide the basis for the parameter optimization in the cutting of WHAs.

Monthly runoff prediction based on variational modal decomposition combined with the dung beetle optimization algorithm for gated recurrent unit model.

Highly accurate monthly runoff forecasts play a pivotal role in water resource management and utilization. This article proposes a coupling of variational modal decomposition (VMD) and the dung beetle optimization algorithm (DBO) with the gated recurrent unit (GRU) to establish a new monthly runoff forecasting model: the VMD-DBO-GRU. Initially, historical runoff data are decomposed via VMD. Subsequently, the parameters of the GRU are optimized using the DBO, and the decomposed monthly runoff components are inputted into the GRU neural network. Finally, the predictions for each component are consolidated to provide monthly runoff predictions. The model is then validated using monthly runoff data from the Ansha reservoir in Fujian, collected from 1980 to 2020. The results demonstrate a higher prediction accuracy of the VMD-DBO-GRU model compared to BP, SVM, GRU, VMD-GRU, DBO-GRU, and EMD-GRU models, providing a new alternative for conducting monthly runoff prediction.

Nutritional and metabolic process of the dung beetle Phelotrupes auratus depends on the plant ingredients that the herbivores eat.

BACKGROUND: The dung beetle Phelotrupes auratus is a holometabolous insect belonging to the order Coleoptera, and it is widely distributed in Japan. The P. auratus habitat depends on herbivores. P. auratus eats the dung of the herbivores and carries it underground for its young. In this process, herbivore droppings disappear from the ground, not only keeping the ground hygienic but also maintaining good soil conditions for plant growth. In this way, a rich ecosystem is maintained. In recent years, the population of P. auratus has decreased, and the main cause has been the decrease in grazing land. It seems that Japanese dung beetles are mainly dependent on herbivores for nutrient sources. However, the physiological relationship between herbivores and P. auratus has not been well investigated. Here, we investigated the nutritional metabolism system of P. auratus by performing whole gene expression analysis of individuals collected from two areas where the ecosystem is occupied by different herbivores. RESULTS: We obtained 54,635 transcripts from P. auratus from Nara Park and Cape Toi and identified 2,592 differentially expressed genes in the fat bodies of the Nara Park and Cape Toi groups. We annotated P. auratus transcripts using Homo sapiens and Drosophila melanogaster genes as references; 50.5% of P. auratus transcripts were assigned to H. sapiens genes, and 54.0% of P. auratus transcripts were assigned to D. melanogaster genes. To perform gene set enrichment analysis, we chose H. sapiens genes for P. auratus transcript annotation. Principal component analysis and gene set enrichment analysis revealed that the nutritional metabolism of P. auratus from Cape Toi might differ from that of P. auratus from Nara Park. CONCLUSION: We analyzed the nutritional metabolism system of P. auratus from Cape Toi and Nara Park and found that the characteristics of the nutritional metabolism process might depend on the plants consumed by the herbivores. Our findings will contribute to elucidating the relationships among habitat plants, herbivores, and dung decomposers and may aid in the maintenance of sustainable land health cycles.

Comprehensive mapping of transposable elements reveals distinct patterns of element accumulation on chromosomes of wild beetles.

Over the past decades, transposable elements (TEs) have been shown to play important roles shaping genome architecture and as major promoters of genetic diversification and evolution of species. Likewise, TE accumulation is tightly linked to heterochromatinization and centromeric dynamics, which can ultimately contribute to speciation. Despite growing efforts to characterize the repeat landscape of species, few studies have focused on mapping the accumulation profiles of TEs on chromosomes. The few studies on repeat accumulation profiles in populations are biased towards model organisms and inbred lineages. Here, we present a cytomolecular analysis of six mobilome-extracted elements on multiple individuals from a population of a species of wild-captured beetle, Dichotomius schiffleri, aiming to investigate patterns of TE accumulation and uncover possible trends of their chromosomal distribution. Compiling TE distribution data from several individuals allowed us to make generalizations regarding variation of TEs at the gross chromosome level unlikely to have been achieved using a single individual, or even from a whole-genome assembly. We found that (1) transposable elements have differential accumulation profiles on D. schiffleri chromosomes and (2) specific chromosomes have their own TE accumulation landscape. The remarkable variability of their genomic distribution suggests that TEs are likely candidates to contribute to the evolution of heterochromatin architecture and promote high genetic variability in species that otherwise display conserved karyotypes. Therefore, this variation likely contributed to genome evolution and species diversification in Dichotomius.

Contrasting Volatilomes of Livestock Dung Drive Preference of the Dung Beetle Bubas bison (Coleoptera: Scarabaeidae).

Volatile cues can play a significant role in the location and discrimination of food resources by insects. Dung beetles have been reported to discriminate among dung types produced by different species, thereby exhibiting behavioral preferences. However, the role of volatile organic compounds (VOCs) in dung localization and preference remains largely unexplored in dung beetles. Here we performed several studies: firstly, cage olfactometer bioassays were performed to evaluate the behavioral responses of Bubas bison (Coleoptera: Scarabaeidae) to VOCs emanating from fresh horse, sheep, and cattle dung; secondly, concurrent volatilome analysis was performed to characterize volatilomes of these dung types. Bubas bison adults exhibited greater attraction to horse dung and less attraction to cattle dung, and they preferred dung from horses fed a pasture-based diet over dung from those fed lucerne hay. Volatilomes of the corresponding dung samples from each livestock species contained a diverse group of alkanes, alkenes, alkynes, alcohols, aldehydes, ketones, esters, phenols, and sulfurous compounds, but the composition and abundance of annotated VOCs varied with dung type and livestock diet. The volatilome of horse dung was the most chemically diverse. Results from a third study evaluating electroantennogram response and supplementary olfactometry provided strong evidence that indole, butyric acid, butanone, p-cresol, skatole, and phenol, as well as toluene, are involved in the attraction of B. bison to dung, with a mixture of these components significantly more attractive than individual constituents.

How Dung Beetles Steer Straight.

Distant and predictable features in the environment make ideal compass cues to allow movement along a straight path. Ball-rolling dung beetles use a wide range of different signals in the day or night sky to steer themselves along a fixed bearing. These include the sun, the Milky Way, and the polarization pattern generated by the moon. Almost two decades of research into these remarkable creatures have shown that the dung beetle's compass is flexible and readily adapts to the cues available in its current surroundings. In the morning and afternoon, dung beetles use the sun to orient, but at midday, they prefer to use the wind, and at night or in a forest, they rely primarily on polarized skylight to maintain straight paths. We are just starting to understand the neuronal substrate underlying the dung beetle's compass and the mystery of why these beetles start each journey with a dance.

Sisyphus and his rock: Quasi-random walk inspired by the motion of a ball transported by a dung beetle on combined terrain.

The majority of biologically inspired dynamic problems are essentially defined by the complexity of the contact surface where such motion takes place. From a statistical point of view, such a surface in many biological problems is typically a combination of a universal scale invariant (fractal) component and a well-defined component having a characteristic scale. If the biological object, here a dung ball, or its parts have a size comparable to the dimensions of the surface peculiarities, one can expect a strong influence on the motion. To avoid competition for the same food resource, some dung-feeding insect species form a dung ball and roll it away from the dung pile. In order to quickly escape competition, dung beetles seem to strictly follow an initial bearing. On flat terrain, they manage to roll a dung ball along a nearly perfect straight path. However, on a more realistic terrain, which normally includes both components mentioned above, the motion is more complex. In this study, we numerically model the ball transportation on terrain with different scales of surface profile. A strong correlation is observed between effective ball transportation (time, distance, work) and the ratio of the size of the ball relative to the size of the terrain roughness. Surface irregularities, with a characteristic size comparable to the ball diameter, are negatively correlated to the efficiency of ball transportation. In addition a strong correlation is found between the quasi random noise, numerically simulating the activity of a dung beetle trying to escape from a valley in which it is trapped, and the success in ball transportation.

Diverse mobilome of Dichotomius (Luederwaldtinia) schiffleri (Coleoptera: Scarabaeidae) reveals long-range horizontal transfer events of DNA transposons.

Transposable elements (TEs) are mobile DNA sequences that are able to move from one genomic location to another. These selfish elements are known as genomic parasites, since they hijack the host molecular machinery to generate new copies of themselves. The mobilization of TEs can be seen as a natural mutagen because new TE copies can insert into different loci and impact host genomic structure through different mechanisms. Although our knowledge about TEs is improving with new genomes available, there is still very limited data about the mobilome of species from the Coleoptera order, the most diverse order of insects, including species from the Scarabaeidae family. Therefore, the main goal of this study was to characterize the mobilome of D. (Luederwaldtinia) schiffleri, based on low-coverage genome sequencing, and reconstruct their evolutionary history. We used a combination of four different approaches for TE characterization and maximum likelihood phylogenetic analysis to study their evolution. We found a large and diverse mobilome composed of 38 TE superfamilies, 20 DNA transposon and 18 retrotransposons, accounting for 21% of the genome. Moreover, we found a number of incongruences between the TE and host phylogenetic trees in three DNA transposon TE superfamilies, which represents five TE families, suggesting possible horizontal transfer events between highly divergent taxa. In summary, we found an abundant and diverse mobilome and a number of horizontal transfer events that have shaped the evolutionary history of this species.

Life stages differ in plasticity to temperature fluctuations and uniquely contribute to adult phenotype in Onthophagus taurus dung beetles.

Adaptive thermal plasticity allows organisms to adjust their physiology to cope with fluctuating environments. However, thermal plasticity is rarely studied in response to thermal variability and is often measured in a single life stage. Plasticity in response to thermal variability likely differs from responses to constant temperature or acute stress. In addition, life stages likely differ in their plasticity, and responses in one stage may be affected by the experiences in a previous stage. Increasing the resolution with which we understand thermal plasticity in response to thermal variation across ontogeny is crucial to understanding how organisms cope with the thermal variation in their environment and to estimating the capacity of plasticity to mitigate costs of rapid environmental change. We wanted to know whether life stages differ in their capacity for thermal plasticity under temperature fluctuations. We reared Onthophagus taurus dung beetles in either low or high temperature fluctuation treatments and quantified thermal plasticity of metabolism of pupae and adults. We found that adults were thermally plastic and pupae were not. Next, we tested whether the plasticity observed in the adult life stage was affected by the thermal conditions during development. We again used low and high temperature fluctuation treatments and reared individuals in one condition through all egg to pupal stages. At eclosion, we switched half of the individuals in each treatment to the opposite fluctuation condition and, later, measured thermal plasticity of metabolism in adults. We found that temperature conditions experienced during the adult stage, but not egg to pupal stages, affect adult thermal plasticity. However, temperature fluctuations during development affect adult body size, suggesting that some aspects of the adult phenotype are decoupled from previous life stages and others are not. Our data demonstrate that life stages mount different responses to temperature variability and uniquely contribute to the adult phenotype. These findings emphasize the need to broadly integrate the life cycle into studies of phenotypic plasticity and physiology; doing so should enhance our ability to predict organismal responses to rapid global change and inform conservation efforts.