Spatiotemporal clustering and ecological niche modeling of Varroa destructor infestations in honey bee colonies across Türkiye.

Abstract

Varroa destructor is the most important ectoparasite affecting honey bee (Apis mellifera) colonies worldwide and represents a major threat to apiculture through its direct pathogenic effects and its role in the transmission of viral diseases. Despite its widespread occurrence in Türkiye, information regarding its large-scale spatial distribution and environmental determinants remains limited. This study aimed to investigate the spatiotemporal distribution patterns of V. destructor infestations across Türkiye using retrospective Space-Time Permutation Scan Statistics (SaTScan) and ecological niche modeling based on the Maximum Entropy (MaxEnt) algorithm. A total of 545 confirmed varroasis records collected from 1,196 colonies in 400 apiaries across 40 provinces during 2018-2019 were analyzed. Space-time cluster analyses identified statistically significant infestation hotspots under a 30-day temporal window and 20% spatial window. Nine clusters were detected in 2018, eleven in 2019, and thirteen in the combined 2018-2019 dataset. Persistent hotspot regions were primarily located in the Mediterranean, Eastern Anatolia, Southeastern Anatolia, Central Black Sea, and Northwestern Anatolia regions. Ecological niche modeling was performed using 166 unique occurrence records and nine bioclimatic variables. The final optimized MaxEnt model (RM = 1.5) showed moderate predictive performance (mean test AUC = 0.705). Mean temperature of the driest quarter (BIO9), precipitation of the warmest quarter (BIO18), precipitation seasonality (BIO15), and precipitation of the coldest quarter (BIO19) were identified as among the most influential climatic predictors. The predicted climatic suitability map indicated relatively high climatic suitability in Thrace, northwestern Türkiye, the Eastern Black Sea region, and selected parts of the Mediterranean and Eastern Anatolian regions. Visual comparison suggested that several statistically significant clusters occurred within climatically suitable areas predicted by the MaxEnt model. However, because no formal quantitative overlap analysis was performed, this correspondence should be interpreted cautiously. The integration of spatiotemporal cluster analysis and ecological niche modeling provides a useful framework for identifying current hotspots and climatically suitable areas that may support risk-based surveillance and targeted control strategies for varroosis.