Parameters influencing queen body mass and their importance as determined by machine learning in honey bees (Apis mellifera carnica)

Most parameters describing queen bee quality are reflected in the queen’s body mass, which is in turn considered a robust measure and the best indicator of queen quality. State-of-the-art machine learning was used for the first time to jointly evaluate both biological and rearing parameters influenc...

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Bibliographic Details
Published in:Apidologie 2019-10, Vol.50 (5), p.745-757
Main Authors: Prešern, Janez, Smodiš Škerl, Maja Ivana
Format: Article
Language:English
Subjects:
Animal breeding
Apis mellifera
Artificial intelligence
Bees
Biomedical and Life Sciences
Body mass
Body size
Entomology
Food sources
Learning algorithms
Life Sciences
Machine learning
Mathematical models
Menopause
Original Article
Parameters
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Summary:Most parameters describing queen bee quality are reflected in the queen’s body mass, which is in turn considered a robust measure and the best indicator of queen quality. State-of-the-art machine learning was used for the first time to jointly evaluate both biological and rearing parameters influencing queen body mass. Three different models were developed using different combinations of parameters. Regardless of the model composition, we achieved high precision of classification. The parameters “ovary mass” and “breeder” were the most important factors for model predictions. Differences in rearing practices and vegetation were masked by “breeder,” demonstrating the pitfall of this method. Separate analysis confirmed the importance of the time spent in the hive after mating and the phytogeographical region as an indirect indication of food sources. Rearing practices together with phytogeographical information are not enough to explain variation in queen body mass, yet they can contribute to the prediction of queen body mass if “breeder” is excluded from the model.
ISSN:0044-8435
1297-9678
DOI:10.1007/s13592-019-00683-y