An XGBoost-Based Morphometric Classification System for Automatic Subspecies Identification of Apis mellifera

TL;DR

This paper introduces a fast and accurate tool using XGBoost to classify honey bee subspecies based on body measurements, aiding conservation and breeding efforts.

Contribution

A novel XGBoost-based morphometric classification system for Apis mellifera subspecies with high accuracy and interpretability.

Findings

The model achieved 98% accuracy using only 10 key morphometric traits like forewing angles and abdominal plate sizes.

SHAP analyses confirmed the importance of selected features and highlighted misclassifications in morphologically similar lineages.

The tool is portable, interpretable, and can be retrained on new datasets for other insect species.

Abstract

The reliable identification of honey bee subspecies is important for their breeding and conservation, but common approaches can be slow or expensive. We measured a compact set of routine body traits—mainly forewing angles and abdominal plate sizes—in worker bees collected under a standard protocol. Using these measurements, we built a small, easy-to-use classification tool that assigns subspecies with very high accuracy. The tool also shows which traits drive each decision so that users can understand why a specimen was assigned to a group. It runs quickly on a regular computer, accepts local data, and produces clear plots and a short list of key traits. The same steps can be retrained on new regional datasets. Our results show that routine measurements, combined with an accessible computer-based approach, can support fast screening in the lab or field and help prioritize samples for follow-up genetic testing.

The conservation and breeding of the western honey bee (Apis mellifera) is central dependent on accurate subspecies assignment, but the most commonly used methods are labor-intensive classical morphometrics and costly molecular assays. We developed an XGBoost-based classification framework using a compact set of routinely measurable characters. A curated dataset of labeled workers was measured under harmonized protocols; features were screened according to embedded importance, and model performance was assessed using five-fold cross-validation, outperforming standard machine learning baselines. The resulting model using only the top 10 characters—primarily forewing venation angles and abdominal plate metrics—achieved high performance (accuracy = 0.98; F1 = 0.99) and an area under the receiver operating characteristic curve (AUC) of 0.99 (95% CI = 0.995–0.999). SHAP analyses confirmed the discriminatory contributions of these features, while error inspection suggested that misclassifications were concentrated in morphologically overlapping lineages. The model’s performance supports its use as a rapid triage tool alongside genetic testing, providing a scalable and interpretable tool for researchers to create and deploy custom morphometric models, demonstrated here for A. mellifera but portable to other insect taxa.

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