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Dung beetles are important actors in the self-regulation of ecosystems by driving nutrient cycling, bioturbation, and pest suppression. Urbanization and the sprawl of agricultural areas, however, destroy natural habitats and may threaten dung beetle diversity. In addition, climate change may cause shifts in geographical distribution and community composition. We used a space-for-time approach to test the effects of land use and climate on α-diversity, local community specialization (H\(_2\)′) on dung resources, and γ-diversity of dung-visiting beetles. For this, we used pitfall traps baited with four different dung types at 115 study sites, distributed over a spatial extent of 300 km × 300 km and 1000 m in elevation. Study sites were established in four local land-use types: forests, grasslands, arable sites, and settlements, embedded in near-natural, agricultural, or urban landscapes. Our results show that abundance and species density of dung-visiting beetles were negatively affected by agricultural land use at both spatial scales, whereas γ-diversity at the local scale was negatively affected by settlements and on a landscape scale equally by agricultural and urban land use. Increasing precipitation diminished dung-visiting beetle abundance, and higher temperatures reduced community specialization on dung types and γ-diversity. These results indicate that intensive land use and high temperatures may cause a loss in dung-visiting beetle diversity and alter community networks. A decrease in dung-visiting beetle diversity may disturb decomposition processes at both local and landscape scales and alter ecosystem functioning, which may lead to drastic ecological and economic damage.
Interactive effects of climate and land use on pollinator diversity differ among taxa and scales
(2022)
Changes in climate and land use are major threats to pollinating insects, an essential functional group. Here, we unravel the largely unknown interactive effects of both threats on seven pollinator taxa using a multiscale space-for-time approach across large climate and land-use gradients in a temperate region. Pollinator community composition, regional gamma diversity, and community dissimilarity (beta diversity) of pollinator taxa were shaped by climate-land-use interactions, while local alpha diversity was solely explained by their additive effects. Pollinator diversity increased with reduced land-use intensity (forest < grassland < arable land < urban) and high flowering-plant diversity at different spatial scales, and higher temperatures homogenized pollinator communities across regions. Our study reveals declines in pollinator diversity with land-use intensity at multiple spatial scales and regional community homogenization in warmer and drier climates. Management options at several scales are highlighted to mitigate impacts of climate change on pollinators and their ecosystem services.
The monitoring of species and functional diversity is of increasing relevance for the development of strategies for the conservation and management of biodiversity. Therefore, reliable estimates of the performance of monitoring techniques across taxa become important. Using a unique dataset, this study investigates the potential of airborne LiDAR-derived variables characterizing vegetation structure as predictors for animal species richness at the southern slopes of Mount Kilimanjaro. To disentangle the structural LiDAR information from co-factors related to elevational vegetation zones, LiDAR-based models were compared to the predictive power of elevation models. 17 taxa and 4 feeding guilds were modeled and the standardized study design allowed for a comparison across the assemblages. Results show that most taxa (14) and feeding guilds (3) can be predicted best by elevation with normalized RMSE values but only for three of those taxa and two of those feeding guilds the difference to other models is significant. Generally, modeling performances between different models vary only slightly for each assemblage. For the remaining, structural information at most showed little additional contribution to the performance. In summary, LiDAR observations can be used for animal species prediction. However, the effort and cost of aerial surveys are not always in proportion with the prediction quality, especially when the species distribution follows zonal patterns, and elevation information yields similar results.