Measuring the effect of RFID and Marker Recognition tags on cockroach behaviour using AI aided tracking

RFID technology and marker recognition algorithms can offer an efficient and non-intrusive means of tracking animal positions. As such, they have become important tools for invertebrate behavioural research. Both approaches require fixing a tag or marker to the study organism, and so it is useful to quantify the effects such procedures have on behaviour before proceeding with further research. However, frequently studies do not report doing such tests. Here, we demonstrate a time-efficient and accessible method for quantifying the impact of tagging on individual movement using open-source automated video tracking software. We tested the effect of RFID tags and tags suitable for marker recognition algorithms on the movement of Argentinian wood roaches (Blapicta dubia) by filming tagged and untagged roaches in laboratory conditions. We employed DeepLabCut on the resultant videos to track cockroach movement and extract measures of behavioural traits. We found no statistically significant differences between RFID tagged and untagged groups in average speed over the trial period, the number of unique zones explored, and the number of discrete walks. However, groups that were tagged with labels for marker recognition had significantly higher values for all three metrics. We therefore support the use of RFID tags to monitor the behaviour of B. dubia but note that the effect of using labels suitable for label recognition to identify individuals should be taken into consideration when measuring B.dubia behaviour. We hope that this study can provide an accessible and viable roadmap for further work investigating the effects of tagging on insect behaviour.