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The humble and industrious dung beetle is a marvelous beast: the 6,000 species identified so far are intricately entwined with human history and scientific endeavor. these night-soil collectors of the planet have been worshiped as gods, worn as jewelery, and painted by artists. More practically, they saved Hawaii from ecological blight, and rescued Australia from plagues of flies. They fertilize soil, cleanse pastures, steer by the stars, and have a unique relationship with the African elephant (along with many other ungulates). Above all, they are the ideal subject for biological study in an evolving world. This entertaining outline of the development of science from the beetle's perspective will enchant general readers and entomologists alike.

To address the problems of limited population diversity and a tendency to converge prematurely to local optima in the original sparrow search algorithm (SSA), an improved sparrow search algorithm (ISSA) based on multi-strategy collaborative optimization is proposed. ISSA employs three strategies to enhance performance: introducing one-dimensional composite chaotic mapping SPM to generate the initial sparrow population, thus enriching population diversity; introducing the dung beetle dancing search behavior strategy to strengthen the algorithm’s ability to jump out of local optima; integrating the adaptive t-variation improvement strategy to balance global exploration and local exploitation capabilities. Through experiments with 23 benchmark test functions and comparison with algorithms such as PSO, GWO, WOA, and SSA, the advantages of ISSA in convergence speed and optimization accuracy are verified. In the application of robot path planning, compared with SSA, ISSA exhibits shorter path lengths, fewer turnings, and higher planning efficiency in both single-target point and multi-target point path planning. Especially in multi-target point path planning, as the obstacle rate increases, ISSA can more effectively find the shortest path. Its traversal order is different from that of SSA, making the planned path smoother and with fewer intersections. The results show that ISSA has significant superiority in both algorithm performance and path planning applications.

In this paper, a novel population-based technique called dung beetle optimizer (DBO) algorithm is presented, which is inspired by the ball-rolling, dancing, foraging, stealing, and reproduction behaviors of dung beetles. The newly proposed DBO algorithm takes into account both the global exploration and the local exploitation, thereby having the characteristics of the fast convergence rate and the satisfactory solution accuracy. A series of well-known mathematical test functions (including both 23 benchmark functions and 29 CEC-BC-2017 test functions) are employed to evaluate the search capability of the DBO algorithm. From the simulation results, it is observed that the DBO algorithm presents substantially competitive performance with the state-of-the-art optimization approaches in terms of the convergence rate, solution accuracy, and stability. In addition, the Wilcoxon signed-rank test and the Friedman test are used to evaluate the experimental results of the algorithms, which proves the superiority of the DBO algorithm against other currently popular optimization techniques. In order to further illustrate the practical application potential, the DBO algorithm is successfully applied in three engineering design problems. The experimental results demonstrate that the proposed DBO algorithm can effectively deal with real-world application problems.

A primary goal of community ecologists is to understand the processes underlying the spatiotemporal patterns of species distribution. Understanding the dispersal process is of great interest in ecology because it is related to several mechanisms driving community structure. We investigated the mobility of dung beetles using mark-release-recapture technique, and tested the usefulness of the current recommendation for interaction distance between baited pitfall traps in the Brazilian Atlantic Forest. We found differences in mean movement rate between Scarabaeinae species, and between species with different sets of ecological traits. Large-diurnal-tunneler species showed greater mobility than did both large-nocturnal tunneler and roller species. Our results suggest that, based on the analyses of the whole community or the species with the highest number of recaptured individuals, the minimum distance of 50 m between pairs of baited pitfall traps proposed roughly 10 years ago is inadequate. Dung beetle species with different sets of ecological traits may differ in their dispersal ability, so we suggest a new minimum distance of 100 m between pairs of traps to minimize interference between baited pitfall traps for sampling copronecrophagous Scarabaeinae dung beetles.

Beetles represent almost one-fourth of all described species, and knowledge about their relationships and evolution adds to our understanding of biodiversity. We performed a comprehensive phylogenetic analysis of Coleoptera inferred from three genes and nearly 1900 species, representing more than 80% of the world's recognized beetle families. We defined basal relationships in the Polyphaga supergroup, which contains over 300,000 species, and established five families as the earliest branching lineages. By dating the phylogeny, we found that the success of beetles is explained neither by exceptional net diversification rates nor by a predominant role of herbivory and the Cretaceous rise of angiosperms. Instead, the pre-Cretaceous origin of more than 100 present-day lineages suggests that beetle species richness is due to high survival of lineages and sustained diversification in a variety of niches.

Although several methods exist for extracting and sequencing historical DNA originating from dry-preserved insect specimens deposited in natural history museums, no consensus exists as to what is the optimal approach. We demonstrate that a customized, low-cost archival DNA extraction protocol (~[euro]10 per sample), in combination with Ultraconserved Elements (UCEs), is an effective tool for insect phylogenomic studies. We successfully tested our approach by sequencing DNA from scarab dung beetles preserved in both wet and dry collections, including unique primary type and rare historical specimens from internationally important natural history museums in London, Paris and Helsinki. The focal specimens comprised of enigmatic dung beetle genera (Nesosisyphus, Onychothecus and Helictopleurus) and varied in age and preservation. The oldest specimen, the holotype of the now possibly extinct Mauritian endemic Nesosisyphus rotundatus, was collected in 1944. We obtained high-quality DNA from all studied specimens to enable the generation of a UCE-based dataset that revealed an insightful and well-supported phylogenetic tree of dung beetles. The resulting phylogeny propounded the reclassification of Onychothecus (previously incertae sedis) within the tribe Coprini. Our approach demonstrates the feasibility and effectiveness of combining DNA data from historic and recent museum specimens to provide novel insights. The proposed archival DNA protocol is available at DOI 10.17504/protocols.io.81wgbybqyvpk/v3.

Accurate estimation of the state-of-health (SOH) of lithium-ion batteries is a crucial reference for energy management of battery packs for electric vehicles. It is of great significance in ensuring safe and reliable battery operation while reducing maintenance costs of the battery system. To eliminate the nonlinear effects caused by factors such as capacity regeneration on the SOH sequence of batteries and improve the prediction accuracy and stability of lithium-ion battery SOH, a prediction model based on Variational Modal Decomposition (VMD) and Dung Beetle Optimization -Support Vector Regression (DBO-SVR) is proposed. Firstly, the VMD algorithm is used to decompose the SOH sequence of lithium-ion batteries into a series of stationary mode components. Then, each mode component is treated as a separate subsequence and modeled and predicted directly using SVR. To address the problem of difficult parameter selection for SVR, the DBO algorithm is used to optimize the parameters of the SVR model before training. Finally, the predicted values of each subsequence are added and reconstructed to obtain the final SOH prediction. In order to verify the effectiveness of the proposed method, the VMD-DBO-SVR model was compared with SVR, Empirical Mode Decomposition-Support Vector Regression (EMD-SVR), and VMD-SVR methods for SOH prediction of batteries based on the NASA dataset. Experimental results show that the proposed model has higher prediction accuracy and fitting degree, with prediction errors all within 1% and better robustness.

Dung beetles are important ecosystem engineers as they play an important role in recycling faces from animals. Dung beetles have evolved different behaviors, including dung ball rolling for their egg and developing offspring. Ball rolling is a complex behavior that varies between species. In some species, males roll the dung ball and females choose partners based on this, while in other species, males and females work together to form the ball. Competition can be fierce with fighting, and ball stealing is common. Gymnopleurus sturmi is a ball rolling species that exhibits gregarious behavior with adults congregating on a dung source. This study assesses sex‐related roles in ball rolling as well as the impact of varying sex ratios on the number of balls produced, either left at the surface or buried and fertilized, and emergence rates of the offspring. The theoretical number of offspring per female was used as a measure of fitness. Results show that both males and females can produce dung balls, and higher numbers were obtained when males and females were separated. Female‐biased sex ratio produced mostly buried and fertilized balls, while male‐biased sex ratio produced more unburied balls left on the surface. When females were alone, they produced the maximum number of total dung balls compared to the rest of the treatments. On the other hand, emergence rate was found to be higher when more males were present. When females were alone, emergence rate was extremely low, suggesting reduced sperm storage. Using the theoretical number of offspring per female, no difference in fitness was observed when males and females were both present. In a gregarious species like G. sturmi, finding a partner would be easier than for other dung beetle species, which could explain an increasing competition between males and reducing the need to store sperm for the longer term. This study highlights the diversity of behaviors present in this species. This study examines the sex‐specific roles and the impact of sex ratios on dung ball production and offspring emergence in the dung beetle species Gymnopleurus sturmi. It finds that both males and females can produce dung balls, with females producing the most when alone, while male presence increases offspring emergence rates. The research suggests that in this gregarious species, easy access to mates may lead to increased male competition and reduced need for long‐term sperm storage in females.

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.

The dung beetle optimization (DBO) algorithm, a swarm intelligence-based metaheuristic, is renowned for its robust optimization capability and fast convergence speed. However, it also suffers from low population diversity, susceptibility to local optima solutions, and unsatisfactory convergence speed when facing complex optimization problems. In response, this paper proposes the multi-strategy improved dung beetle optimization algorithm (MDBO). The core improvements include using Latin hypercube sampling for better population initialization and the introduction of a novel differential variation strategy, termed “Mean Differential Variation”, to enhance the algorithm’s ability to evade local optima. Moreover, a strategy combining lens imaging reverse learning and dimension-by-dimension optimization was proposed and applied to the current optimal solution. Through comprehensive performance testing on standard benchmark functions from CEC2017 and CEC2020, MDBO demonstrates superior performance in terms of optimization accuracy, stability, and convergence speed compared with other classical metaheuristic optimization algorithms. Additionally, the efficacy of MDBO in addressing complex real-world engineering problems is validated through three representative engineering application scenarios namely extension/compression spring design problems, reducer design problems, and welded beam design problems.