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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.

This paper aims to enhance the accuracy of predicting the mechanical behavior of wood subjected to thermal modification using an improved dung beetle optimization (IDBO) model. The IDBO algorithm improves the original DBO algorithm via three main steps: (1) using piece-wise linear chaotic mapping (PWLCM) to generate the initial dung beetle species and increase its heterogeneity; (2) adopting an adaptive nonlinear decreasing producer ratio model to control the number of producers and boost the algorithm’s convergence rate; and (3) applying a dimensional learning-enhanced foraging (DLF) search strategy that optimizes the algorithm’s ability to explore and exploit the search space. The IDBO algorithm is evaluated on 14 benchmark functions and outperforms other algorithms. The IDBO algorithm is then applied to optimize a back-propagation (BP) neural network for predicting five mechanical property parameters of heat-treated larch-sawn timber. The results indicate that the IDBO-BP model significantly reduces the error compared with the BP, tent-sparrow search algorithm (TSSA)-BP, grey wolf optimizer (GWO)-BP, nonlinear adaptive grouping grey wolf optimizer (IGWO)-BP and DBO-BP models, demonstrating its superiority in predicting the physical characteristics of lumber after heat treatment.

Maintaining multiple ecological functions (“multifunctionality”) is crucial to sustain viable ecosystems. To date most studies on biodiversity‐ecosystem functioning (BEF) have focused on single or few ecological functions and services. However, there is a critical need to evaluate how species and species assemblages affect multiple processes at the same time, and how these functions are interconnected. Dung beetles represent excellent model organisms because they are key contributors to several ecosystem functions. Using a novel method based on the application of 15N‐enriched dung in a mesocosm field experiment, we assessed the role of dung beetles in regulating multiple aspects of nutrient cycling in alpine pastures over appropriate spatial (up to a soil depth of 20 cm) and temporal (up to 1 yr after dung application) scales. 15N isotope tracing allowed the evaluation of multiple interrelated ecosystem functions responsible for the cycling of dung‐derived nitrogen (DDN) in the soil and vegetation. We also resolved the role of functional group identity and the importance of interactions among co‐occurring species for sustaining multiple functions by focusing on two different dung beetle nesting strategies (tunnelers and dwellers). Species interactions were studied by contrasting mixed‐species to single‐species assemblages, and asking whether the former performed multiple functions better than the latter. Dung beetles influenced at least seven ecological functions by facilitating dung removal, transport of DDN into the soil, microbial ammonification and nitrification processes, uptake of DDN by plants, herbage growth, and changes in botanical composition. Tunnelers and dwellers were found to be similarly efficient for most functions, with differences based on the spatial and temporal scales over which the functions operated. Although mixed‐species assemblages seemed to perform better than single‐species, this outcome may be dependent on the context. Most importantly though, the different functions were found to be interconnected sequentially as reveled by analyzing 15N content in dung, soil and vegetation. Taken together, our current findings offer strong support for the contention that the link between biodiversity and ecosystem functions should be examined not function by function, but in terms of understanding multiple functions and how they interact with each other.

Dung beetles provide many important ecosystem services, including dung decomposition, pathogen control, soil aeration, and secondary seed dispersal. Yet, the biology of most dung beetles remains unknown. Natural diets are poorly studied, partly because previous research has focused on choice or attraction experiments using few, easily accessible dung types from zoo animals, farm animals, or humans. This way, many links within natural food webs have certainly been missed. In this work, we aimed to establish a protocol to analyze the natural diets of dung beetles using DNA gut barcoding. First, the feasibility of gut-content DNA extraction and amplification of 12s rDNA from six different mammal dung types was tested in the laboratory. We then applied the method to beetles caught in pitfall traps in Ecuador and Germany by using 12s rDNA primers. For a subset of the dung beetles caught in the Ecuador sampling, we also used 16s rDNA primers to see if these would improve the number of species we could identify. We predicted the likelihood of amplifying DNA using gut fullness, DNA concentration, PCR primer, collection method, and beetle species as predictor variables in a dominance analysis. Based on the gut barcodes, we generated a dung beetle-mammal network for both field sites (Ecuador and Germany) and analyzed the levels of network specificity. We successfully amplified mammal DNA from dung beetle gut contents for 128 specimens, which included such prominent species as (jaguar) and (puma). The overall success rate of DNA amplification was 53%. The best predictors for amplification success were gut fullness and DNA concentration, suggesting the success rate can be increased by focusing on beetles with a full gut. The mammal dung-dung beetle networks differed from purely random network models and showed a moderate degree of network specialization ( ': Ecuador = 0.49; Germany = 0.41). We here present a reliable method of extracting and amplifying gut-content DNA from dung beetles. Identifying mammal dung DNA reference libraries, we created mammal dung-dung beetle trophic networks. This has benefits over previous methods because we inventoried the natural mammal dung resources of dung beetles instead of using artificial mammal baits. Our results revealed higher levels of specialization than expected and more rodent DNA than expected in Germany, suggesting that the presented method provides more detailed insights into mammal dung-dung beetle networks. In addition, the method could have applications for mammal monitoring in many ecosystems.

RNA-binding proteins (RBPs) play crucial roles in gene regulation. Their dysregulation has been increasingly linked to neurodegenerative diseases, liver cancer, and lung cancer. Although experimental methods like CLIP-seq accurately identify RNA–protein binding sites, they are time-consuming and costly. To address this, we propose RMDNet—a deep learning framework that integrates CNN, CNN-Transformer, and ResNet branches to capture features at multiple sequence scales. These features are fused with structural representations derived from RNA secondary structure graphs. The graphs are processed using a graph neural network with DiffPool. To optimize feature integration, we incorporate an improved dung beetle optimization algorithm, which adaptively assigns fusion weights during inference. Evaluations on the RBP-24 benchmark show that RMDNet outperforms state-of-the-art models including GraphProt, DeepRKE, and DeepDW across multiple metrics. On the RBP-31 dataset, it demonstrates strong generalization ability, while ablation studies on RBPsuite2.0 validate the contributions of individual modules. We assess biological interpretability by extracting candidate binding motifs from the first-layer CNN kernels. Several motifs closely match experimentally validated RBP motifs, confirming the model’s capacity to learn biologically meaningful patterns. A downstream case study on YTHDF1 focuses on analyzing interpretable spatial binding patterns, using a large-scale prediction dataset and CLIP-seq peak alignment. The results confirm that the model captures localized binding signals and spatial consistency with experimental annotations. Overall, RMDNet is a robust and interpretable tool for predicting RNA–protein binding sites. It has broad potential in disease mechanism research and therapeutic target discovery. The source code is available https://github.com/cskyan/RMDNet .

Studies investigating how biodiversity affects ecosystem functioning increasingly focus on multiple functions measured simultaneously (\"multifunctionality\"). However, few such studies assess the role of species interactions, particularly under alternative environmental scenarios, despite interactions being key to ecosystem functioning. Here we address five questions of central importance to ecosystem multifunctionality using a terrestrial animal system. (1) Does the contribution of individual species differ for different ecosystem functions? (2) Do inter-species interactions affect the delivery of single functions and multiple functions? (3) Does the community composition that maximizes individual functions also maximize multifunctionality? (4) Is the functional role of individual species, and the effect of interspecific interactions, modified by changing environmental conditions? (5) How do these roles and interactions change under varying scenarios where ecosystem services are weighted to reflect different societal preferences? We manipulated species' relative abundance in dung beetle communities and measured 16 functions contributing to dung decomposition, plant productivity, nutrient recycling, reduction of greenhouse gases, and microbial activity. Using the multivariate diversity–interactions framework, we assessed how changes in species identity, composition, and interspecific interactions affected these functions in combination with an environmental driver (increased precipitation). This allowed us to identify key species and interactions across multiple functions. We then developed a desirability function approach to examine how individual species and species mixtures contribute to a desired state of overall ecosystem functioning. Species contributed unequally to individual functions, and to multifunctionality, and individual functions were maximized by different community compositions. Moreover, the species and interactions important for maintaining overall multifunctionality depended on the weight given to individual functions. Optimal multifunctionality was context-dependent, and sensitive to the valuation of services. This combination of methodological approaches allowed us to resolve the interactions and indirect effects among species that drive ecosystem functioning, revealing how multiple aspects of biodiversity can simultaneously drive ecosystem functioning. Our results highlight the importance of a multifunctionality perspective for a complete assessment of species' functional contributions.

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.

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.

This study aims to determine whether the application of cattle manure and associated bioturbation by dung beetles and other invertebrates can increase oak seedling establishment in the high biodiversity Mediterranean silvopastoral systems known as “dehesas”, which are comprised of scattered oaks and a grassland layer and are used for livestock rearing. A two-year study was conducted in a representative dehesa in southern Spain. Firstly, the effect of cattle dung pads on acorn consumption by livestock and wild predators was monitored, along with dung beetle abundance and plot bioturbation. Secondly, the indirect effects of bioturbation on acorn burial, acorn predation by rodents and oak seedling establishment were assessed under the condition of livestock exclusion. The results consistently demonstrate a high positive indirect effect of the presence of cattle manure on the persistence of acorns, as well as on seedling establishment, as a result of reduced predation by rodents and improved microhabitat conditions for the acorns and seedlings. These processes were mediated by bioturbation. Tunneler dung beetles, which move a great amount of soil material, were dominant in our records, which suggest their importance for passive acorn burial, without disregarding the influence of other edaphic invertebrates present. We conclude that the presence of cattle manure significantly improves oak seedling establishment in Mediterranean dehesas through the passive burial of acorns by bioturbation, which acts to protect them from predation by rodents, desiccation and other damage. Passive acorn burial by dung beetles and other invertebrates thus emerges as an important ecosystem service that has been little-studied to date. Appropriate rotational management of livestock could therefore contribute to addressing the bottleneck of oak regeneration in Mediterranean dehesas.

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.