Forensic entomology is an important field of forensic science that utilizes insect evidence in criminal investigations. Blow flies (Diptera: Calliphoridae) are among the first colonizers of remains and are therefore frequently used in determining the minimum postmortem interval (mPMI). Blow fly development, however, is influenced by a variety of factors including temperature and feeding substrate type. Unfortunately, dietary fat content remains an understudied factor on the development process, which is problematic given the relatively high rates of obesity in the United States. To study the effects of fat content on blow fly development we investigated the survivorship, adult weight and development of Lucilia sericata (Meigen; Diptera: Calliphoridae) and Phormia regina (Meigen; Diptera: Calliphoridae) on ground beef with a 10%, 20%, or 27% fat content. As fat content increased, survivorship decreased across both species with P. regina being significantly impacted. While P. regina adults were generally larger than L. sericata across all fat levels, only L. sericata demonstrated a significant (P < 0.05) difference in weight by sex. Average total development times for P. regina are comparable to averages published in other literature. Average total development times for L. sericata, however, were nearly 50 hours higher. These findings provide insight on the effect of fat content on blow fly development, a factor that should be considered when estimating a mPMI. By understanding how fat levels affect the survivorship and development of the species studied here, we can begin improving the practice of insect evidence analysis in casework.

Medicolegal forensic entomology is the study of insects to aid with legal investigations (Gemmellaro, 2017). Insect evidence can be used to provide information such as the post-mortem interval (PMI). Blow flies are especially useful as these insects are primary colonizers, quickly arriving at a corpse (Malainey & Anderson, 2020). The age of blow flies found at a scene is used to calculate the PMI. Blow fly age can be estimated using weather data as these insects are poikilothermic (Okpara, 2018). Morphological analysis also can be used to estimate age; however, it is more difficult with pupal samples as the pupae exterior does not change significantly as development progresses (Bala & Sharma, 2016). Gene regulation analysis can estimate the age of samples. MicroRNAs are short noncoding RNA that regulate gene expression (Cannell et al., 2008). Here, we aim to catalog miRNAs expressed during the development of three forensically relevant blow fly species preserved in several storage conditions. Results demonstrated that various miRNA sequences were differentially expressed across pupation. Expression of miR92b increased during mid pupation, aga-miR-92b expression increased during early pupation, and bantam, miR957, and dana-bantam-RA expression increased during late pupation. These results suggest that microRNA can be used to estimate the age of pupal samples as miRNA expression changes throughout pupation. Future work could develop a statistical model to accurately determine age using miRNA expression patterns.

Studying the effects of viruses and toxins on honey bees is important in order to understand the danger these important pollinators are exposed to. Hives exist in various environments, and different colonies are exposed to varying environmental conditions and dangers. To properly study the changes and effects of seasonality and pesticides on the population dynamics of honey bees, the presence of each of these threats must be considered. This study aims to analyze how infected colonies grapple more deeply with changing, seasonal environments, and how toxins in pesticides affect population dynamics. Thus, it addresses the following questions: How do viruses within a colony affect honey bee population dynamics when the environment is seasonal? How can the effects of pesticides be modeled to better understand the spread of toxins? This project is a continuation of my own undergraduate work in a previous class, MAT 350: Techniques and Applications of Applied Mathematics, with Dr. Yun Kang, and also utilizes previous research conducted by graduate students. Original research focused on the population dynamics of honey bee disease interactions (without considering seasonality), and a mathematical modeling approach to analyze the effects of pesticides on honey bees. In order to pursue answers to the main research questions, the model for honey bee virus interaction was adapted to account for seasonality. The adaptation of this model allowed the new model to account for the effects of seasonality on infected colony population dynamics. After adapting the model, simulations with arbitrary data were run using RStudio in order to gain insight into the specific ways in which seasonality affected the interaction between a honey bee colony and viruses. The second portion of this project examines a system of ordinary differential equations that represent the effect of pesticides on honey bee population dynamics, and explores the process of this model’s formulation. Both systems of equations used as the basis for each model’s research question are from previous research reports. This project aims to further that research, and explore the applications of applied mathematics to biological issues.