Forensic entomotoxicology is a subdiscipline that utilizes necrophagous insects as bioindicators for detecting drugs and toxicants in decomposed remains, particularly in cases where conventional biological matrices are no longer available. Toxic substances can profoundly alter insect development, physiology, and community succession, potentially impacting the accuracy of postmortem interval (PMI) estimation. This review systematically summarizes the effects of various xenobiotics, including pesticides, illicit drugs, sedatives, heavy metals, and antibiotics on larval growth, physiological traits, and gut microbial composition in forensically relevant flies. However, most studies to date have relied primarily on phenotypic observations, with limited insight into underlying molecular mechanisms. Significant interspecies and dose-dependent variability also exists in the absorption, metabolism, and physiological responses to xenobiotics. We highlight recent advances in multi-omics technologies that facilitate the identification of molecular biomarkers associated with xenobiotic exposure, particularly within the insect detoxification system. Key components such as cytochrome P450 monooxygenases (P450s), glutathione S-transferases (GSTs), and ATP-binding cassette (ABC) transporters play essential roles in xenobiotic metabolism and insecticide resistance. Additionally, the insect fat body serves as a central hub for detoxification, hormonal regulation, and energy metabolism. It integrates signals related to xenobiotic exposure and modulates larval development, making it a promising model for future mechanistic studies in insect toxicology. Altogether, this review offers a comprehensive and reliable framework for understanding the complex interactions between toxic substance exposure, insect ecology, and decomposition in forensic investigations.

Accurate species identification of necrophagous flies is fundamental to forensic entomology, particularly for postmortem interval (PMI) estimation in decomposed remains. Here, we conducted a targeted carrion-baited survey along the Shandong Peninsula and documented 15 Sarcophaga species, including the first regional records of S. cinerea, S. pingi, and S. pterygota. We established an expert-validated image dataset for automated identification. We then developed a parameter-efficient identification framework by fine-tuning a pretrained Vision Transformer with Low-Rank Adaptation (ViT-LoRA) on this custom dataset. Compared with conventional CNN-based models, ViT-LoRA achieved 98.50% species-level accuracy while updating only ~0.16 M trainable parameters, and it converged rapidly and stably within ~10 epochs, demonstrating efficient adaptation under limited training data. This study provides faunistic and distributional data on carrion-associated Sarcophaga species in the coastal Shandong Peninsula, characterizes their regional distribution patterns, and offers a scalable image-based identification approach for forensically important sarcophagid flies.