Dung beetles do not profit from enhanced spatial heterogeneity in production forests: a large-scale forest manipulation experiment

Asch, J.; Scherer-Lorenzen, M.; Pierick, K.; Wild, C.; Rothacher, J.; Müller, J.; Decker, O.; Cesarz, S.; Daume, N.; Buse, J.; Peters, M. K.

doi:10.1101/2025.08.18.670815

bioRxiv · 10.1101/2025.08.18.670815

Dung beetles do not profit from enhanced spatial heterogeneity in production forests: a large-scale forest manipulation experiment

Asch, J.·Scherer-Lorenzen, M.·Pierick, K.·Wild, C.·Rothacher, J.·Müller, J.·Decker, O.·Cesarz, S.·Daume, N.·Buse, J.·Peters, M. K.

View original source ↗Export metadata ↓

Abstract

O_LICentral European forest management strategies promoting structurally homogeneous closed-canopy forests have led to landscape-level declines in biodiversity and ecosystem multifunctionality. Conservation-targeted management programs aim at reintroducing structural heterogeneity into production forests, however, it is not well understood if species diversity and ecosystem functions generally profit from management promoting heterogeneity in forest structure. By removing and processing mammalian dung remains, dung beetles play an integral role in ecosystem functions in forests. C_LIO_LIIn one of the largest manipulative forest experiments in Central Europe to date, we analysed the effects of enhanced structural heterogeneity in production forests, climate, and mammalian defecation on dung beetle diversity and dung removal rates at the local and landscape level. We assessed communities of dung beetles and dung removal rates on 234 study patches (50 x 50 m) in eleven paired forest landscapes across a climatic gradient in Germany. Forest landscapes were either managed to conserve a homogeneous closed canopy or to create a heterogeneous forest structure with forest patches varying in canopy coverage and dead wood availability. C_LIO_LIWe did not find that more heterogeneously managed forests had higher dung beetle species diversity and dung removal rates. Canopy openings did not increase species turnover but decreased species diversity. C_LIO_LIAlong the climate gradient, dung beetle average biomass and dung removal decreased with increasing temperature. Canopy openings in combination with higher temperatures negatively impacted all abundant dung beetle species, but especially the large species Anoplotrupes stercorosus, comprising > 90% of the total dung beetle biomass. C_LIO_LISynthesis and application: Our results suggest that dung beetles do not profit from management increasing the structural heterogeneity of forests. Due to a restricted climate and habitat niche of the current Central European dung beetle fauna, future warming and openings in forest structure might have negative effects on dung beetles and consequently on the ecosystem services they provide in Central European production forests. C_LI

Explore related subjects

ecology Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Asch, J., Scherer-Lorenzen, M., Pierick, K., Wild, C., Rothacher, J., Müller, J., Decker, O., Cesarz, S., Daume, N., Buse, J., Peters, M. K.. 2025-08-22. Dung beetles do not profit from enhanced spatial heterogeneity in production forests: a large-scale forest manipulation experiment. https://doi.org/10.1101/2025.08.18.670815

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗

bioRxiv2026

A training protocol for human classification of Asian elephant images from trail cameras

Trail cameras have become ubiquitous tools for ecological data collection over recent decades. Despite progress in the development of automated algorithms and artificial intelligence for image classification, our ability to process large volumes of data remain limited by the need for trained human observers to make refined judgements. We provide guidance on placement of trail cameras for observing Asian elephants (Elephas maximus) and outline a protocol for training and testing naive human observers in performing image classifications (age/sex class and group composition) that cannot yet be automated. This process can be used to develop a high-throughput workflow capable of extracting useful data from large volumes of images. Our training material consisted of 14,007 images collected from 6 trail cameras around Udawalawe National Park in Sri Lanka from 2017-2019. In the first stage, expert observers (n=3) trained a group of inexperienced participants (n=4), who engaged in an iterative process to develop a protocol document. The document was then tested on a second set of subjects (n=6) each of whom classified 350 test images in four separate sequential batches using quantitative measures of precision and accuracy. The test set was sampled from 54,435 images from an additional 25 cameras. When compared to expert observers, they achieved a fair level of precision (Fleiss' kappa = 0.247) and 82.6% accuracy. Our approach can usefully be extended to other species and contexts.

ecology↗

bioRxiv2026

Forest belowground productivity and carbon allocation predominantly driven by soil properties rather than climate

Forests are threatened by a multitude of stressors, including anthropogenic disturbances and climate change. Assessing how forests will respond to these stressors requires a comprehensive understanding of net primary productivity (Npp), environmental constraints on growth, and adaptive capacity. A parameter of significant uncertainty is belowground Npp (bNpp), which can account for up to 80% of total Npp but is poorly estimated and rarely measured directly. We used a cross-biome dataset of direct, field-based measurements of aboveground and belowground primary productivity and 21 climatic and soil variables to identify potential constraints on bNpp and belowground carbon allocation in boreal and cold temperate forests. Soil variables, rather than climate variables, were the main drivers of bNpp and belowground allocation across biomes. The importance of soil variables suggests that soil nutrient dynamics, especially soil nutrient pool and flux variables, must be explicitly modeled to more accurately predict feedbacks between climate, productivity, and within-tree carbon allocation. Within biomes, environmental drivers of belowground allocation varied between low versus high allocation forests, indicating that environmental drivers are site-specific and the development of within-biome, site-scale classifications for forest ecosystems could be useful. Changes in soil variables, such as increasing soil nitrogen pools, caused abrupt and large decreases in bNpp for boreal, but not cold temperate forests. Threshold-like shifts indicate that boreal forests might have lower adaptive capacity and higher sensitivity to disturbances than cold temperate forests. With 70% of boreal forests characterized by low bNpp, disturbances such as anthropogenic nitrogen deposition could cause large-scale decreases in bNpp that could push these forests beyond their adaptive capacity.

ecology↗