Software evaluation
To evaluate the software and measure the precision of the analyses and generated outputs, we recorded a total of 23 videos from 15 nesting units during two consecutive days using the nesting units as described in Fig. 1. All nesting units were placed in large flight cages (54 m2) that contained sufficient floral sources for offspring provisioning by nesting female Osmia bicornis (sown purple tansy, buckwheat and/or field mustard). A total of 24 females marked with the above described 24 unique digit-color ID tags were released into each of these flight cages and videos were recorded after initiation of nesting. Each video was recorded between 9 AM and 3 PM when flight activity was high for 2 to 4 hours. Cameras were placed with a distance of 1 m from the nesting unit with frontal view (camera placed at same height as nesting unit). From the recorded videos 8 randomly selected ones were used to train the software to this experimental setup while the remaining 15 videos were used to measure precision. Precision was assessed by visually checking 180 randomly selected events (12 events per video) for their correctness using the visualization option of the software (see above). Only events that were used for the generation of output csv files (after error correction) were inspected as described above.
For the comparability of bee health under different environmental conditions (e.g., different field sites with variable habitat quality or flight cages with/without pesticide application) a similar precision across videos is required. We therefore tested if precision varied between videos in our set of evaluated videos. Therefore, a generalized linear model with a binomial distribution was used. The correctness of the detected event (correct or wrong) was included as response variable and the video as explanatory variable. We further fitted a regression to test if the proportion of females that can be assigned to a nest cavity depends on the video recording time. The analysis was done in R 4.1. (R Core Team, 2021).