Generated output
The software will create a new sub-folder within the selected results folder for each input video. Inside each sub-folder the following outputs are stored by the software:
  1. all_events_unfiltered: Inside this csv file, all detected events are listed containing the video timestamp, the bee ID, the event type (entering or leaving) and the cavity ID. This list is completely unfiltered and may contain errors.
  2. error_corrected_events: This csv file contains all events that remain after error correction: the software identifies missing events within sequences of enter – leave – enter. Sequences with missing events are not considered for the creation of below described output files (address_book, nest_recognition, flight_list). The file contains the video timestamp, the bee ID, the cavity ID and the type of event (entering or leaving a cavity). Additionally, it is indicated for each event whether it was used for the output files address_book, nest_recognition and flight_list. Note that some events might be missing in these files due to the strict error correction of the software.
  3. address_book: This csv file contains all bees that were assigned to a nest and lists the according bee and cavity IDs. This data (assignments between individual bees and the cavity (or cavities, respectively) they are nesting in) is of interest for assessments of nesting progress and reproductive success of individual nesting females. In order to assign only cavities to females which are used for nesting (in contrast to simply probed cavities not used for nesting), a cavity is only assigned to an individual bee if (i) the bee stays inside the cavity for a time span that is minimally required by a nesting bee to unload collected pollen for offspring provision, and (ii) the bee does not enter another cavity during a time span that is minimally required by a bee to collect pollen or material such as mud for nest construction (e.g., construction of brood-cell walls). The default setting of these two time spans are both 40 s in the published open-source version of the software. These values were chosen based on over 20 h of direct observation of Osmia bicornis females nesting in a natural habitat in Switzerland (Bättig D., unpublished data). However, the species under study or experimental setting may require adjustment of these threshold values. This can be done in the “config” file of the software, which can be selected as an optional input file for the analysis (see software manual in the Supporting Information).
Nesting progress, i.e. the number of produced brood cells and offspring, can be tracked by repeatedly photographing the nest cavities (Fig. 1), e.g. before and after an assessment day. Linking this data with theaddress_book file (created form a video recorded on the same assessment day) based on cavity IDs permits to measure individual per female reproductive success for this time period.
nest_recognition: This csv file contains the number of cavities a female enters before finding its nest (i.e. number of probed “wrong” cavities before finding the “correct” nesting cavity). Besides the bee ID and the number of probed cavities, the file also lists the video timestamp.
flight_list: This csv file provides flight durations of individual females from leaving the nesting cavity until returning to it again (i.e., foraging trip or mud collection duration). Besides the bee ID and the flight duration the file also lists the video timestamp.
If of interest, flight activity can be assessed by classifying females that perform flights as active and are therefore listed in the flight_list file. For this measurement, the number of total, alive females needs to be known however, which can be assessed by taking pictures of the nest layer (Fig. 1) during the night when females are roosting inside cavities.
6. visualization: Through the “visualize results” option a video file in mp4 format can be created with all detected events visualized. This file can be used to visually check the performance of the software and to find potential errors, which can be used to retrain the software (see below) and improve the precision.