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Advisor(s)
Abstract(s)
DeepWings© is a software that uses Machine Learning for fully automated identification
of Apis mellifera subspecies based on wing geometric morphometrics (WGM). Here, we
examined the performance of DeepWings© under realistic conditions by processing
14,782 wing images with varying quality and produced by different operators. These
images represented 2,593 colonies covering the native ranges of A. m. iberiensis (Portugal,
Spain and historical introduction in the Azores), A. m. mellifera (Belgium, France, Ireland,
Poland, Russia, Sweden, Switzerland, UK) and A. m. carnica (Croatia, Hungary, Romania).
The classification probability obtained for the colonies was contrasted with the endemic
subspecies distribution. Additionally, the association between WGM classification and that
inferred from microsatellites and SNPs was evaluated for 1,214 colonies. As much as 94.4%
of the wings were accepted and classified by DeepWings©. In the Iberian honey bee native
range, 92,6% of the colonies were classified as A. m. iberiensis with a median probability
of 91.88 (IQR = 22.52). In the Azores, 85.7% of colonies were classified as A. m. iberiensis,
with a median probability of 84.16 (32.40). In the Dark honey bee native range, 41.1 % of
the colonies were classified as A. m mellifera with a median probability of 99.36 (8.02). The
low percentage of colonies matching the native subspecies was mainly due to the low
values registered in Avignon (20.0%), Poland (32.9%), and Wales (41.2%). In contrast, most
of the colonies analyzed in other locations of the native range of A. m. mellifera matched
this subspecies: Belgium (100.0%), Groix (63.9%), Ouessant (72.7%), Ireland (78.0%), Russia
(96.2%), Sweden (84.2%) and Switzerland (55.6%). In the colonies from Croatia, Hungary,
and Romania, 88.0% of the samples were classified as A. m. carnica, with a median
probability of 98.49 (6.76). The association between WGM and molecular data was highly
significant but not very strong (Spearman r = 0.31, p < 0.0001). A good agreement between
morphological and molecular methods was registered in samples originating from highly
conserved M-lineage populations whereas in populations with historical records of foreign queen importations the agreement was weaker. In general, DeepWings© showed good
performance when tested under realistic conditions. It is a valuable tool that can be used
not only for honey bee breeding and conservation but also for research purposes.
Description
Keywords
Wing Geometric Morphometrics Apis mellifera subspecies classification Honey bee conservation
Citation
Yadró, Carlos; Rodrigues, Pedro João; Adam, Tofilski; Elen, Dylan; McCormack, Grace P.; Henriques, Dora; Pinto, M. Alice (2022). DeepWings: a machine learning tool for identification of honey bee subspecies. In Eurbee 9: 9th European Conference of Apidology. Belgrade
Publisher
Estonian University of Life Science