Journal: eLife
Article Title: Social-like responses are inducible in asocial Mexican cavefish despite the exhibition of strong repetitive behavior
doi: 10.7554/eLife.72463
Figure Lengend Snippet: ( A, B ) Measurements of nearest neighbour distance (NND) ( A ) and interindividual distance (IID) ( B ). Surface fish are shown as black lines, and cavefish are shown as orange lines (N = 18 groups of four each). The means ± standard errors of the mean (s.e.m.) in each frame are shown. Smaller panels on the right side of each of ( A ) and ( B ) show averages of NND or IID sampled every 30 s of the 300 s assays. n.s.: not significant. ( C ) An example frame of recorded surface fish video. Coloured lines represent trajectories of individual fish detected by idTracker software (1 s/20-frame trajectories). A red-labeled fish was followed by a blue-labelled fish. ( D ) Distribution of IID across all frames of 18 surface fish groups, where each group yields six IIDs from each pair among the four fish ( 4 C 2 = 6, black line: means ± 95 % confidence intervals (CIs)). Similarly, the distribution of IIDs was calculated from 1,280 simulated randomly sampled groups (blue line: means ± 95% CI). IID between 3 and 6 cm showed significant separation between actual and simulated IIDs (yellow shaded areas). Inset shows the magnified 3–6 cm interval. *: p < 0.05, t -test between the actual and simulated data adjusted by the Holm correction within the yellow shaded area. ( E ) Inverse cumulative distribution of the nearby-event durations. The inverse cumulative distributions for the event durations whose IIDs were less than 5 cm are shown, where the black lines represent the results from the actual 18 surface fish groups (means ± 95% CI) and the blue lines represent the results of the 1,280 simulated groups (means ± 95% CI). In the actual surface fish data, the nearby-interaction durations between 3 and 8 s were significantly higher than the simulated surface fish data. **: p < 0.01, t -test between the actual and simulated data adjusted by the Holm correction within the yellow shaded area. ( F ) Swimming velocity during or out of nearby interactions. The mean swimming speeds (1) during the 4 s before the nearby-interaction bout, (2) during the bout, (3) during 4 s after the bout, and (4) during out-of-bout periods are shown for surface fish (left) and cavefish (right) by using the actual and simulated dataset (N = 72 from 18 groups for each of surface fish and cavefish and for each of the actual and randomly sampled data). Swimming speeds are lower in the cavefish during the bout in the actual data. ( G ) Pirate plots depicting nearby-interaction durations detected by the newly developed method. The plot of the actual data is shown in black and white, and the simulated data are shown in light blue (N = 72 from 18 groups for each of the surface fish and cavefish). The thicknesses of the beans represent data density. *: p < 0.05, ***: p < 0.001. All statistical scores are available in . The new detection protocol for determining nearby interactions is described in Materials and methods and – . The involvement of the lateral line sensory system in the nearby interaction was tested, and the data are presented in . Figure 2—source data 1. Surface fish’s and cavefish’s inter-individual distance (cm) in every 0.05 s. Figure 2—source data 2. Surface fish’s and cavefish’s inter-individual distance (cm) in every 0.05 s. Figure 2—source data 3. Event numbers counted under the cut-off distances between pairs of fish in the actual and simulated surface fish data. Figure 2—source data 4. Nearby event numbers shorter than 5 cm distance counted more than cut-off duration between pairs of fish in the actual and simulated surface fish data. Figure 2—source data 5. Speed (cm/s) during the nearby interaction events and other periods, and the detected nearby interaction duration (s) comparing between the actual and simulated random data.
Article Snippet: Using these background-subtracted videos, the X-Y coordinates of each fish were extracted by tracking each fish’s ID under the ID-detection algorithm of idTracker (idTracker-beta running under MATLAB release 2019a or above; https://github.com/idTracker/idTracker/tree/Beta ; ; The MathWorks Inc, Natick, MA, USA).
Techniques: Software, Labeling