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https://doi.org/10.5281/zenodo.17956786 

Body size is a salient functional trait in bees, with implications for reproductive fitness, pollination ecology, and responses to environmental change. Methods for quantifying bee body size commonly rely on indirect estimates and vary widely across studies, particularly in studies of the large carpenter bees (Xylocopa Latreille) (Apidae: Xylocopinini). We evaluate the robustness of three common body size parameters (intertegular distance, head width, and costal vein length) as predictors of dry body mass within and among 11 species of Xylocopa (and 5 subspecies). We found that all three size measurements provide robust body size estimates, accounting for 92–93% of intraspecific variation in body mass. Within species, however, these measurements were considerably less predictive of body mass, explaining on average only 36.8% (intertegular distance), 57.4% (head width), and 38.8% (costal vein length) of the variation in body mass. We also highlight a novel application of photogrammetry and 3D modeling to estimate surface area and volume across species, and comment on the utility of these methods for body size estimates in Xylocopa and in insects more broadly. These findings provide practical guidelines for body size estimation methods within and among carpenter bee species.  

Abstract An animal’s diet breadth is a central aspect of its life history, yet the factors determining why some species have narrow dietary breadths (specialists) and others have broad dietary breadths (generalists) remain poorly understood. This challenge is pronounced in herbivorous insects due to incomplete host plant data across many taxa and regions. Here, we develop and validate machine learning models to predict pollen diet breadth in bees, using a bee phylogeny and occurrence data for 682 bee species native to the United States, aiming to better understand key drivers. We found that pollen specialist bees made an average of 72.9% of their visits to host plants and could be predicted with high accuracy (mean 94%). Our models predicted generalist bee species, which made up a minority of the species in our dataset, with lower accuracy (mean 70%). The models tested on spatially and phylogenetically blocked data revealed that the most informative predictors of diet breadth are plant phylogenetic diversity, bee species’ geographic range, and regional abundance. Our findings also confirm that range size is predictive of diet breadth and that both male and female specialist bees mostly visit their host plants. Overall, our results suggest we can use visitation data to predict specialist bee species in regions and for taxonomic groups where diet breadth is unknown, though predicting generalists may be more challenging. These methods can thus enhance our understanding of plant-pollinator interactions, leading to improved conservation outcomes and a better understanding of the pollination services bees provide. 

ABSTRACT Community or volunteer participation in research has the potential to significantly help mobilize the wealth of biodiversity and functional ecological data housed in natural history collections. Many such projects recruit community scientists to transcribe specimen label data from images; a next step is to task community scientists with conducting straightforward morphological measurements (e.g., body size) from specimen images. We investigated whether community science could be an effective approach to generating significant body size datasets from specimen images generated by museum digitization initiatives. Using the community science platform Notes from Nature, we engaged community scientists in a specimen measurement task to estimate body size (i.e., intertegular distance) from images of bee specimens. Community scientists showed high engagement and completion of this task, with each user measuring 43.6 specimens on average and self‐reporting successful measurement of 98.0% of the images. Community scientist measurements were significantly larger than measurements conducted by trained researchers, though the average measurement error was only 2.3%. These results suggest that community science participation could be an effective approach for bee body size measurement, for descriptive studies or for research questions where this degree of expected error is deemed acceptable. For larger‐bodied organisms (e.g., vertebrates), where modest measurement errors represent a smaller proportion of body size, community science approaches may be particularly effective. Methods we present here may serve as a blueprint for future projects aimed at engaging the public in biodiversity and collections‐based research efforts.