Metabolism, a metric of the energy cost of behavior, plays a significant role in social evolution. Body size and metabolic scaling are coupled, and a socioecological pattern of increased body size is associated with dietary change and the formation of larger and more complex groups. These consequences of the adaptive radiation of animal societies beg questions concerning energy expenses, a substantial portion of which may involve the metabolic rates of brains that process social information. Brain size scales with body size, but little is understood about brain metabolic scaling. Social insects such as ants show wide variation in worker body size and morphology that correlates with brain size, structure, and worker task performance, which is dependent on sensory inputs and information-processing ability to generate behavior. Elevated production and maintenance costs in workers may impose energetic constraints on body size and brain size that are reflected in patterns of metabolic scaling. Models of brain evolution do not clearly predict patterns of brain metabolic scaling, nor do they specify its relationship to task performance and worker ergonomic efficiency, two key elements of social evolution in ants. Brain metabolic rate is rarely recorded and, therefore, the conditions under which brain metabolism influencesmore »
- Award ID(s):
- 1953393
- Publication Date:
- NSF-PAR ID:
- 10318556
- Journal Name:
- Frontiers in Ecology and Evolution
- Volume:
- 9
- ISSN:
- 2296-701X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Despite their miniature brains, insects exhibit substantial variation in brain size. Although the functional significance of this variation is increasingly recognized, research on whether differences in insect brain sizes are mainly the result of constraints or selective pressures has hardly been performed. Here, we address this gap by combining prospective and retrospective phylogenetic-based analyses of brain size for a major insect group, bees (superfamily Apoidea). Using a brain dataset of 93 species from North America and Europe, we found that body size was the single best predictor of brain size in bees. However, the analyses also revealed that substantial variation in brain size remained even when adjusting for body size. We consequently asked whether such variation in relative brain size might be explained by adaptive hypotheses. We found that ecologically specialized species with single generations have larger brains—relative to their body size—than generalist or multi-generation species, but we did not find an effect of sociality on relative brain size. Phylogenetic reconstruction further supported the existence of different adaptive optima for relative brain size in lineages differing in feeding specialization and reproductive strategy. Our findings shed new light on the evolution of the insect brain, highlighting the importance of ecological pressuresmore »
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Current methods used to quantify brain size and compartmental scaling relationships in studies of social insect brain evolution involve manual annotations of images from histological samples, confocal microscopy or other sources. This process is susceptible to human bias and error and requires time-consuming effort by expert annotators. Standardized brain atlases, constructed through 3D registration and automatic segmentation, surmount these issues while increasing throughput to robustly sample diverse morphological and behavioral phenotypes. Here we design and evaluate three strategies to construct statistical brain atlases, or templates, using ants as a model taxon. The first technique creates a template by registering multiple brains of the same species. Brain regions are manually annotated on the template, and the labels are transformed back to each individual brain to obtain an automatic annotation, or to any other brain aligned with the template. The second strategy also creates a template from multiple brain images but obtains labels as a consensus from multiple manual annotations of individual brains comprising the template. The third technique is based on a template comprising brains from multiple species and the consensus of their labels. We used volume similarity as a metric to evaluate the automatic segmentation produced by each method againstmore »
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