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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.
Patterns of resource use by animals can clarify how ecological communities have assembled in the past, how they currently function and how they are likely to respond to future perturbations. Bumble bees (Hymentoptera: Bombus spp.) and their floral hosts provide a diverse yet tractable system in which to explore resource selection in the context of plant–pollinator networks. Under conditions of resource limitation, the ability of bumble bees species to coexist should depend on dietary niche overlap. In this study, we report patterns and dynamics of floral morphotype preferences in a mountain bumble bee community based on ~13 000 observations of bumble bee floral visits recorded along a 1400 m elevation gradient. We found that bumble bees are highly selective generalists, rarely visiting floral morphotypes at the rates predicted by their relative abundances. Preferences also differed markedly across bumble bee species, and these differences were well-explained by variation in bumble bee tongue length, generating patterns of preference similarity that should be expected to predict competition under conditions of resource limitation. Within species, though, morphotype preferences varied by elevation and season, possibly representing adaptive flexibility in response to the high elevational and seasonal turnover of mountain floral communities. Patterns of resource partitioning among bumble bee communities may determine which species can coexist under the altered distributions of bumble bees and their floral hosts caused by climate and land use change.