Early Behavioral Indicators of Mortality Risk in Pyrethroid-Exposed Bees Using Explainable Artificial Intelligence.
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| Title: | Early Behavioral Indicators of Mortality Risk in Pyrethroid-Exposed Bees Using Explainable Artificial Intelligence. |
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| Authors: | YILDIZ, Berkant İsmail1 berkantyildizz@gmail.com |
| Source: | Kafkas Üniversitesi Veteriner Fakültesi Dergisi. Jan/Feb2026, Vol. 32 Issue 1, p141-146. 6p. |
| Document Type: | Article |
| Subjects: | Behavioral assessment, Artificial intelligence, Pollinators, Bees, Mortality risk factors, Pesticide pollution |
| Author-Supplied Keywords: | Behavioral biomarkers Explainable AI Pollinator health Pyrethroid ecotoxicity Sublethal effects |
| Abstract: | Pollinator populations, which play a critical role in maintaining global ecosystem health, have been experiencing marked declines worldwide due to widespread pesticide usage. However, early behavioral indicators of lethal stress induced by chemical exposure remain insufficiently characterized, largely because conventional ecotoxicological assessments predominantly focus on mortality-based endpoints. In this study, we evaluated the potential to predict mortality risk at an early stage using behavioral markers, based on 1.506 behavioral observation records collected from seven bee species exposed to lambdacyhalothrin. To this end, we implemented explainable artificial intelligence models, including Random Forest, XGBoost, and LightGBM, and interpreted the model outputs using SHAP analysis. Among these models, Random Forest and XGBoost demonstrated the strongest performance in distinguishing high mortality risk, achieving an accuracy of 0.873 on an independent test dataset. SHAP-based model interpretation revealed a temporal behavioral progression associated with elevated mortality risk: cramps and apathy emerged as early warning indicators (2-4-hour window), uncoordinated movement represented the intermediate phase, and the dorsal recumbent position characterized the terminal collapse stage. These findings demonstrate that behavioral early-warning signals of lethal pesticide stress can be reliably detected prior to mortality and highlight the potential of explainable artificial intelligence as a robust decision-support tool for pollinator health monitoring and pesticide risk assessment. [ABSTRACT FROM AUTHOR] |
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| Author Affiliations: | 1Akdeniz University, Faculty of Agriculture, Department of Agricultural Biotechnology, TR-07058 Antalya - TÜRKİYE |
| ISSN: | 1300-6045 |
| DOI: | 10.9775/kvfd.2025.35628 |
| Accession Number: | 191676596 |
| Database: | Veterinary Source |
| Abstract: | Pollinator populations, which play a critical role in maintaining global ecosystem health, have been experiencing marked declines worldwide due to widespread pesticide usage. However, early behavioral indicators of lethal stress induced by chemical exposure remain insufficiently characterized, largely because conventional ecotoxicological assessments predominantly focus on mortality-based endpoints. In this study, we evaluated the potential to predict mortality risk at an early stage using behavioral markers, based on 1.506 behavioral observation records collected from seven bee species exposed to lambdacyhalothrin. To this end, we implemented explainable artificial intelligence models, including Random Forest, XGBoost, and LightGBM, and interpreted the model outputs using SHAP analysis. Among these models, Random Forest and XGBoost demonstrated the strongest performance in distinguishing high mortality risk, achieving an accuracy of 0.873 on an independent test dataset. SHAP-based model interpretation revealed a temporal behavioral progression associated with elevated mortality risk: cramps and apathy emerged as early warning indicators (2-4-hour window), uncoordinated movement represented the intermediate phase, and the dorsal recumbent position characterized the terminal collapse stage. These findings demonstrate that behavioral early-warning signals of lethal pesticide stress can be reliably detected prior to mortality and highlight the potential of explainable artificial intelligence as a robust decision-support tool for pollinator health monitoring and pesticide risk assessment. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13006045 |
| DOI: | 10.9775/kvfd.2025.35628 |