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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.

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.

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.

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.

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO–TCN–GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO–TCN–GRU model, OTDBO–TCN–GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO–TCN–GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO–TCN–GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO–TCN–GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.

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.

Roller dung beetles play a pivotal role in the nutrient distribution in soil and secondary dispersal of seeds. Dung beetles are sampled either using a dung-baited pitfall trap or an exposed dung pile on the ground. While the former method is useful for a rapid survey of dung beetles, information on the ecology and behaviour of dung beetles can be lost, which the latter method provides, but underestimates species diversity due to its inefficiency in trapping rollers. Efficiency of a new method for sampling rollers—installing guarding pitfall traps around dung piles—is assessed in three habitats—contiguous tropical rainforests, fragmented forests, and disturbed used home gardens—and two diel periods—day and night. Five guarding pitfall traps were installed at 50 cm radius around dung piles. About 98% of the total rollers were sampled in pitfall traps. The habitats were similar when the roller catches of only dung piles—conventional approach—were analyzed, but were different when the rollers of guarding pitfall traps were considered. The roller abundance was negatively affected by forest fragmentation and land-use change. About 98% of the rollers were collected at daytime. Using guarding pitfall traps around dung piles is highly recommended for dung beetle diversity studies.

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 .

The South American Pleistocene–Holocene transition has been characterized by drastic climatic and diversity changes. These rapid changes induced one of the largest and most recent extinctions in the megafauna at the continental scale. However, examples of the extinction of small animals (e.g., insects) are scarce, and the underlying causes of the extinction have been little studied. In this work, a new extinct dung beetle species is described from a late Pleistocene sequence (~15.2 k cal yr BP) at the paleoarcheological site Pilauco, Chilean Northern Patagonia. Based on morphological characters, this fossil is considered to belong to the genus Onthophagus Latreille, 1802 and named Onthophagus pilauco sp. nov. We carried out a comprehensive revision of related groups, and we analyzed the possible mechanism of diversification and extinction of this new species. We hypothesize that Onthophagus pilauco sp. nov. diversified as a member of the osculatii species-complex following migration processes related to the Great American Biotic Interchange (~3 Ma). The extinction of O. pilauco sp. nov. may be related to massive defaunation and climatic changes recorded in the Plesitocene-Holocene transition (12.8 k cal yr BP). This finding is the first record of this genus in Chile, and provides new evidence to support the collateral-extinction hypothesis related to the defaunation.

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.