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McLean, C. J.

Publications and source records attributed to McLean, C. J..

2 recordsLinked to original sources

Multilevel-consistency of social behaviour in a cockroach

Group-living animals may show consistency in both individual behaviour and group-level behaviour. Further, while individuals show consistent differences in their responses to environmental change (plasticity), it is less clear if groups differ in plasticity. Here we exposed groups of cockroaches (Blaptica dubia) to three humidities and quantified their social networks. We repeated our design across multiple groups with individuals reassigned to different groups and used machine vision analysis of images to extract 152,800 records of associations across 16 different groups. 116 unique individuals contributed >4,500 individual social network measures and 192 measures of entire networks. Lower humidity led to individuals that had slightly better connectedness to the whole network, but otherwise did not impact individual or group-level phenotypes. Individuals showed some consistency in mean behaviours (12-39%) but scant consistency in plasticity (0-9%). In contrast, groups showed less consistency in mean behaviours than individuals (0-26%) but showed relatively higher among-group differences in plasticity compared to individuals (0-22%). Patterns of group-level variance in individual traits matched patterns of variance in characteristics of entire networks, suggesting these two approaches for quantifying group phenotypes are compatible. While consistency in individual mean behaviour is the norm across taxa, consistency in group-level plasticity is less well understood and may be an emergent phenomenon of collective dynamics that deserves further investigation. Using automated marker recognition techniques such as machine vision allows us to collect the large datasets necessary to simultaneously test hypotheses at the individual and group level and so can be more widely adopted.

animal behavior and cognition↗

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.

animal behavior and cognition↗