Improving the interpretation of protein marking data to better study insect movement

A valuable technique in the study of insect movement is protein marking, a quantitative method where individuals are determined to be marked or unmarked based on the amount of protein detected by an enzyme-linked immunosorbent assay (ELISA). Whether an individual is considered marked or not is dependent on a threshold chosen by the experimenter. The traditionally employed method of choosing the threshold accepts some risk of false positives, wherein an unmarked individual is misclassified as marked. While some risk of false positives is inevitable, it is important to estimate the false positive rate so that the resulting dispersal data can be corrected. In long-distance dispersal studies where both the likelihood of capturing marked individuals is low and where the proportion of unmarked to marked insects is high, false positives can significantly affect estimates of dispersal rates and result in incorrect estimates of insect movement abilities. Using simulations, we demonstrate that the standard method for choosing a threshold results in a higher than expected false positive rate. We introduce new statistical procedures for choosing a threshold that decrease the incidence of false positives and allow data to be more accurately corrected for anticipated rates of false positives. This methodology should enhance researcher confidence in the data generated from dispersal studies using protein marking techniques.