## 4 Results
### Performance  
In homogeneous backgrounds, Ethoflow achieved a median rate of 32.5 fps. This rate is a satisfactory processing speed compared to other tracking software that does not use AI in their algorithms (e.g., idTracker = 5.5 fps; ToxTrac = 28.6 fps; Tracktor = 25.7 fps) (Figure 9).  
Statistical interaction was observed between the variables video resolution and group size in fps rate (F 1, 130 = 12.81, \(p\) = 0.0005, Figure 10A). The heterogeneous environment quantification was not influenced by the video resolution or number of individuals (F 1, 28 = 0.81, \(p\) = 0.37, Figure 10B), and the fps rate in a heterogeneous environment (0.386) was lower than in homogeneous backgrounds. The fps decreased with an increase in the centrality of individuals (F 1, 38 = 81.24, \(p\) < 0.0001, Figure 10C). There was no significant effect on the number of individuals (F 1, 37 = 0.009, \(p\) = 0.93), and no interaction was observed between the centrality and individuals (F 1, 36 = 1.62, \(p\) = 0.21). Besides, the software exhibited high detection rates with significant interaction between the number of individuals and type of background (F 1, 94 = 137.85, \(p\) < 0.0001, Figure 10D), where an increase in the number of individuals had a greater influence on the heterogeneous environments.  
Figure 7: Performance of the AI models used in Ethoflow. (**A**) Animals’ detection in heterogeneous environments based on instance segmentation (IS). The high average precision (left panel; \(n=100\)) implies that the model precisely detects real animals in the scenes with no false positives, as demonstrated by (right panel) the detected animals (marked _Melipona quadrificaita_ bees with masks in random colors) in different heterogeneous backgrounds. (**B**) The training process of the CNN model (top panel) and validation (percentage confusion matrix; bottom left panel) (\(n=127\)) for the monitoring of trophallaxis (green circles) in bees (bottom right panel).  
Figure 8: Behavioral assessments conducted using the Ethoflow software. (**A**) Association between centrality and group dynamics polarization (top panel) and milling (bottom panel) (\(n=37\)). The 2D density plots and network diagrams showed that a higher interaction exists among individuals in more polarized bee groups, while this interaction is reduced in the milling groups. In the networks, the circles represent individuals, and connections correspond to the edges, where their widths are proportional to the frequency of interactions. (**B**) Meandering behavior is associated with resting proportions (left panel) (\(n=37\)) and histograms of polar coordinates (rays and azimuth angles) for the two bee species (right panel). (**C**) The tracked distance of the assessed bee species (\(n=37\)). In group representative tracks, the track color reflects the individual identity (right panel). (**D**) Trophallaxis alteration in _Melipona quadrifascita_ after pesticide exposure (\(n=60\)). \({}^{*}p<0.05\) in the generalized linear model.  
Figure 10: Quantification of the performance of Ethoflow. (**A**) Frames per seconds (fps) response to the video resolution (in pixels) and the number of individuals in homogeneous backgrounds; the dots (\(n=134\)) represent the raw data. (**B**) Histogram of the fps in heterogeneous environments (\(n=30\)). The box plot indicates the median and range of dispersion (lower and upper quartiles and outliers). (**C**) Fps in response to centrality. The proportion of group interaction per frame was used to quantify the centrality (\(n=40\)). (**D**) Accuracy of the software as a function of the interaction between the number of individuals and type of environment (homogeneous and heterogeneous); the symbols represent the raw data (circles; \(n=98\)).  
Figure 9: Distribution of frames per second rate exhibited for tracking software. The vertical lines within the density curves show the median.