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Abstract Behavior is shaped by genes, environment, and evolutionary history in different ways. Nest architecture is an extended phenotype that results from the interaction between the behavior of animals and their environment. Nests built by ants are extended phenotypes that differ in structure among species and among colonies within a species, but the source of these differences remains an open question. To investigate the impact of colony identity (genetics), evolutionary history (species), and the environment on nest architecture, we compared how two species of harvester ants, Pogonomyrmex californicus and Veromessor andrei, construct their nests under different environmental conditions. For each species, we allowed workers from four colonies to excavate nests in environments that differed in temperature and humidity for seven days. We then created casts of each nest to compare nest structures among colonies, between species, and across environmental conditions. We found differences in nest structure among colonies of the same species and between species. Interestingly, however, environmental conditions did not have a strong influence on nest structure in either species. Our results suggest that extended phenotypes are shaped more strongly by internal factors, such as genes and evolutionary history, and are less plastic in response to the abiotic environment, like many physical and physiological phenotypes.
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Multi-Attribute Subset Selection enables prediction of representative phenotypes across microbial populations
Abstract The interpretation of complex biological datasets requires the identification of representative variables that describe the data without critical information loss. This is particularly important in the analysis of large phenotypic datasets (phenomics). Here we introduce Multi-Attribute Subset Selection (MASS), an algorithm which separates a matrix of phenotypes (e.g., yield across microbial species and environmental conditions) into predictor and response sets of conditions. Using mixed integer linear programming, MASS expresses the response conditions as a linear combination of the predictor conditions, while simultaneously searching for the optimally descriptive set of predictors. We apply the algorithm to three microbial datasets and identify environmental conditions that predict phenotypes under other conditions, providing biologically interpretable axes for strain discrimination. MASS could be used to reduce the number of experiments needed to identify species or to map their metabolic capabilities. The generality of the algorithm allows addressing subset selection problems in areas beyond biology.
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- PAR ID:
- 10523256
- Publisher / Repository:
- Nature
- Date Published:
- Journal Name:
- Communications Biology
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2399-3642
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s42003-024-06093-w
