DOI: 10.5061/dryad.qz612jmbz
title: Data set for: Behavioral traits that define social dominance are the same that reduce social influence in a consensus task

description: Dominant individuals are often most influential in their social groups,
 affecting movement, opinion, and performance across species and contexts.
 Yet behavioral traits like aggression, intimidation, and coercion, which
 are associated with and in many cases define dominance, can be socially
 aversive. The traits that make dominant individuals influential in one
 context may therefore reduce their influence in other contexts. Here we
 examine this association between dominance and influence using the cichlid
 fish Astatotilapia burtoni, comparing the influence of dominant and
 subordinate males during normal social interactions and in a more complex
 group-consensus association task. We find that phenotypically dominant
 males are aggressive, socially central, and that these males have a strong
 influence over normal group movement, whereas subordinate males are
 passive, socially peripheral, and have little influence over normal
 movement. However, subordinate males have the greatest influence in
 generating group-consensus during the association task. Dominant males are
 spatially distant and have lower signal-to-noise ratios of informative
 behavior in the association task, potentially interfering with their
 ability to generate group-consensus. In contrast, subordinate males are
 physically close to other group members, have a high signal-to-noise ratio
 of informative behavior, and equivalent visual connectedness to their
 group as dominant males. The behavioral traits that define effective
 social influence are thus highly context-specific and can be dissociated
 with social dominance. Thus, processes of hierarchical ascension in which
 the most aggressive, competitive, or coercive individuals rise to
 positions of dominance may be counter-productive in contexts where
 group-performance is prioritized.
description: Captive <i>Astatotilapia
 burtoni</i> descended from a wild caught stock population (41) were
 maintained in stable community tanks (26°C, pH 7.5-8.0, 12:12 light:dark
 cycle) until transfer to the experimental arenas (205 liters, 108 x 54.6 x
 42.7 cm, 17 - 20cm cm water depth, 26°C, pH 7.5-8.0, 12:12 light:dark
 cycle). Groups consisted of both males and females between 40 and 70mm SL,
 though only males were used as informants since they have clear phenotypic
 indicators of social dominance, whereas females, although likely having
 social dominance hierarchies, have no reliable visual indicators of
 dominance status. In analyses of routine group interactions, we used six
 groups of ten fish (n = 60 individuals). In the association tasks, we used
 eight groups of eight fish during initial training (n = 64 individuals),
 followed by eight further groups each with either a subordinate informant
 (n = 56 additional individuals) or a dominant informant (n = 56 additional
 individuals). These informants were taken from the initial group of eight
 naïve individuals, which had now successfully responded to the association
 tasks. One of these group sets was abandoned due to technical failure and
 was not included in the final data set, leaving seven groups used in
 analyses of the association task (n = 154 individuals in association
 trials). All work was conducted in compliance with the Institutional
 Animal Care and Use Committee at The University of Texas at
 Austin.   <i>Experimental
 paradigm</i> The
 behavioral task measured the number of trials taken to reach
 group-consensus in a simple association task using a food reward and
 colored light-emitting diodes. Experiments were conducted in experimental
 arenas with two automatic fish feeders (EHEIM) mounted on opposite ends of
 each tank. The motor control pins of the feeders were rewired and
 externally controlled by a digital I/O switch slaved to an Arduino Uno
 microcontroller that also controlled one diffuse RGB LED mounted directly
 under each feeder (code available github.com/jordanlabmpi/). Four times a
 day (0830, 1130, 1430, 1730) for five consecutive days, the association
 stimulus would be remotely triggered using the Arduino microcontroller,
 which randomly assigned both LEDs to simultaneously display one of two
 colors (RGB 255,60,0 [yellow-orange] or 0,255,255 [cyan]) for three
 seconds, followed by three seconds of no stimulus; the Arduino would then
 trigger the autofeeder associated with the LED that displayed the
 yellow-orange stimulus, providing a portion of Tetramin flake food.
 Neither of these color stimuli elicits an innate response in the focal
 animals, due for example to inherent color preferences (42), allowing
 their use as conditioned stimuli in an association learning paradigm.
 However, the color of the rewarded stimulus affected the time taken for
 the association to be achieved, and we therefore kept the rewarded
 stimulus color consistent throughout all trials and randomized the
 location of colors to prevent spatial learning. A networked Logitech HD
 1080p webcam was mounted above each tank and automatically scheduled to
 record for one minute before and after each training event using iSpy open
 source security camera software.  
 <i>Association
 task</i> Using the protocol described above,
 groups of eight<i> A. burtoni</i> (4 males, 4 females)
 underwent the training four times a day for five days. Animals remained in
 the experimental tanks described above for the entire five days, and were
 not disturbed or moved between association trials. Behavior and
 interactions were recorded for 20 minutes prior to, during, and 10 minutes
 following the stimulus onset. Group behavioral response to the task during
 all trials was scored as the proportion of individuals in the group that
 responded to the light stimulus by swimming towards it in the three
 seconds of LED stimulus prior to the three second pause and subsequent
 delivery of food. A successful group response was defined as seven or more
 of the eight group members swimming directly toward the positive stimulus
 in less than one second of stimulus onset, in two or more consecutive
 trials. The second successful trial was used as the value in subsequent
 analyses (e.g. if a group responded successfully in trials 11 and 12, we
 recorded a successful response for that group at trial 12).
   Within the
 five-day training period, all groups showed a behavioral shift from a lack
 of coordinated movement to a consensus movement toward the conditioned
 stimulus. After five days, all initially naïve groups reached consensus
 movement towards that correct cue, and subsequently one dominant male
 (“<i>dominant</i>”) and one subordinate male
 (“<i>subordinate</i>”) were placed into new groups (3 males, 4
 females; total group size 8 individuals) that were naïve to the
 association task (Figure 2a). For groups with <i>dominant</i>
 males, all three other males were smaller than the
 <i>dominant</i>, while for groups with
 <i>subordinate</i> males, at least one male was larger than
 the <i>subordinate</i>. We did not observe any dominance
 shifts (i.e. a <i>dominant</i> becoming a
 <i>subordinate</i> in a new group, or vice-versa) in these
 group transitions. Seven groups each with either a <i>dominant or
 subordinate </i>informant were then placed in identical training
 protocols as previously and the time taken to group-consensus
 measured.   <i>Deep-learning based automated
 tracking and analysis of behavior</i> We trained
 an implementation of a Mask and Region-based Convolution Neural Network,
 or Mask R-CNN (43, 44), on a small set of 34 manually labeled images.
 Given the simplicity of this highly specific segmentation task and using
 standard image augmentations such as rotation, horizontal or vertical
 flipping, this allowed the accurate detection and segmentation of
 individual fish in each of the video frames. In terms of raw data, Mask
 R-CNN predictions resulted in binary pixel masks for each frame and
 individual respectively. These masks were then skeletonized into 1 px
 midlines along each mask’s long axis using morphological image
 transformations. Subsequently, this allowed the estimation of fish spine
 poses (45) as seven equidistantly spaced points along these midlines. The
 second spine point represents an individual’s head position and the vector
 pointing from the second to the first spine point its orientation. This
 positional data was then used to automatically reconstruct continuous fish
 trajectories using a simple, distance-based identity assignment approach.
 Accuracy and high detection frequency were visually verified with a
 Python-based GUI (45) developed within the lab, that was also used to
 manually correct false identity assignments and losses. Mask R-CNN
 predictions resulted in a mean coverage of 96.3% throughout all analyzed
 videos and automatic trajectory assignment in an average of 14 losses per
 individual. 1.6% of all detections were false positives or poorly
 segmented, resulting in a mean coverage of 94.8% in the manually corrected
 trajectories. See SI Appendix Video 1 for a visualization of the tracking
 pipeline. All code is available at
 https://github.com/jordanlabmpi/social-influence. <i>Behavioral, visual, and spatial
 connectivity analyses</i> In order to
 examine baseline differences in the behavior of dominant and subordinate
 males in social contexts, we placed six additional groups of 10
 individuals in identical tanks as described above and filmed their
 behavior for 5 minutes in the absence of external stimuli (‘routine social
 context’). We calculated the behavioral, visual, and spatial interactions
 between all fish of each group. To estimate the number of behavioral
 interactions that dominant and subordinate males had with other group
 members, trajectory data was used to determine events with elevated
 swimming speed (above the 95<sup>th</sup> percentile of the
 speed distribution). The first two individuals passing this threshold in
 such events were treated as event initiator and responder, and a delay
 time between the two individuals was calculated (Figure 1c, d). All other
 group members passing the threshold while either the initiator or the
 responder were still at elevated speed were considered to be part of the
 same event, but not counted as direct responders. Each separate event
 lasted until the speed of all initiators or responders fell below the
 threshold again.   From these
 dyadic initiator-responder events, we created behavioral interaction
 networks using the ’networkx’ package (46) in Python. Here, the count of
 the directed, pairwise events between each pair of network nodes defines
 the weight of the respective edge (directed from initiator to responder).
 This allowed the calculation of out-edges Katz centrality (47) as a
 measure of behavioral influence in standard conditions (Figure 1). Related
 to this, we also calculated the initiator count for each fish as the
 number of events in which an individual was the initiator. Additionally,
 the ratio of the total time spent in these events above the speed
 threshold to the full duration of the recording was calculated for each
 fish, constituting individual hypothetical noise frequency in the social
 training context (fast, directed movement in absence of LED
 stimulus).   Spatial
 connectivity between group members was calculated as their mean pairwise
 distances. We then computed the visual connectivity as the mean angular
 area subtended by each individual on the retinas of all other group
 members, utilizing the contours of the Mask R-CNN detection results as
 occluding objects in a ray-casting approach (Figure 3a). Casting rays from
 both eyes of a focal fish towards these contours (including the focal
 individual), we modelled the nearly complete field of view known from
 other freshwater fishes (48). These measures generated three connectivity
 scores for each dominant and subordinate group member: a behavioral
 (‘interaction’) connectivity, spatial (‘association’) connectivity, and
 visual connectivity. Finally, we conducted a principal component analysis
 (PCA; Figure 3e, See SI Appendix Figure 5a) on the speed threshold event
 ratio (noise frequency) and connectivity scores. After determining the
 appropriate number of clusters using silhouette analysis (49), we
 performed a clustering analysis of the principal components (50)
 (<i>k</i>-means clustering, <i>k</i> = 2; See SI
 Appendix Figure 5b) to assess the overall consistency of metrics derived
 from trajectory and network analyses with the phenotypic indicators of
 dominance that we used to identify dominant from subordinate
 individuals.   <i>Data analysis and
 statistics</i> For time-to-consensus movement analysis,
 we conducted a Kaplan-Meier Survival Analysis (51) with the ‘survival’
 package in R (52), using the first of two consecutive trials in which
 seven or more individuals responded to the stimulus onset as time to
 criterion, and right-censoring groups that did not complete the social
 learning task. We then fit a Cox Proportional-Hazards model with the
 absence or social status of the informed individual as the single
 covariate. This allowed the comparison of the survival estimates between
 the groups that were initially naïve to the stimulus during the training
 regime and the groups with both dominant and subordinate informants.
 Further, we validated the proportional hazard assumption for each of the
 groups using the same package in R.  
 For
 comparisons of the baseline behavioral traits of dominant and subordinate
 fish, we either performed network randomization tests in Python or linear
 models in R. In the case of network centrality, the mean angular area
 subtended on the retina, and the mean pairwise distance, network
 randomization tests were necessary because these metrics, by definition,
 are non-independent for individuals of the same group and social network.
 Further, the initiator count is also based on dyadic interactions and
 should be considered non-independent within the networks. Therefore, we
 preformed network randomization (i.e. node randomization by assigning the
 dominance status to an individual that was randomly drawn from the group,
 n = 1000) for each of the six ’routine social context’ groups to construct
 null models in which social dominance is detached from the respective
 response variable (53). For each randomization of the six networks and for
 each response variable, an estimate was calculated as the mean difference
 of the respective metric between the assigned dominant individual and the
 mean of the remaining, assigned subordinate individuals. These estimates
 can be considered as null distributions for the test statistics, and were
 used to calculate two-tailed <i>p</i>-values for the actually
 observed differences between dominant and subordinate individuals.
 Accordingly, the null hypotheses that the observed differences were drawn
 from the respective null models were rejected when the corresponding
 <i>p</i>-value was smaller than, or equal to, the significance
 level α = .05. See SI Appendix Figures 1-3 for visualizations of the
 network randomization tests.   By
 contrast, the noise frequency (ratio of time spent in above speed
 threshold events to trial duration) is not dependent between individuals
 of a group. Here, we fit a linear regression model with social status as
 predictor and noise frequency as response variable. Further, we tested the
 model’s assumptions of normality of residuals with the Shapiro-Wilk test
 and homoscedasticity with the Breusch-Pagan test (54).
description: Dataset for: Behavioral traits that define social dominance
 are the same that reduce social influence in a consensus
 task<br style="color:rgb(255, 255, 255);font-family:Helvetica;font-size:12px;font-style:normal;font-weight:normal;letter-spacing:normal;text-align:start;text-indent:0px;white-space:normal;text-decoration:none;"> <br style="color:rgb(255, 255, 255);font-family:Helvetica;font-size:12px;font-style:normal;font-weight:normal;letter-spacing:normal;text-align:start;text-indent:0px;white-space:normal;text-decoration:none;"> - "delay_times.csv",
 "learning.csv" and "social_parameters.csv" were used
 in statistical analyses<br style="color:rgb(255,
 255, 255);font-family:Helvetica;font-size:12px;font-style:normal;font-weight:normal;letter-spacing:normal;text-align:start;text-indent:0px;white-space:normal;text-decoration:none;"> - "tracks_T[3, 4, 5, 6, 7, 12].csv" provide
 simplified tracking data (only head position per individual per
 frame)<br style="color:rgb(255, 255,
 255);font-family:Helvetica;font-size:12px;font-style:normal;font-weight:normal;letter-spacing:normal;text-align:start;text-indent:0px;white-space:normal;text-decoration:none;"> <br style="color:rgb(255, 255, 255);font-family:Helvetica;font-size:12px;font-style:normal;font-weight:normal;letter-spacing:normal;text-align:start;text-indent:0px;white-space:normal;text-decoration:none;"> All analysis, raw data and further
 scripts can be found at https://github.com/jordanlabmpi/social-influence