Social network analysis in pigs: impacts of significant dyads on general network and centrality parameters

Abstract

In general, one animal is considered dominant over another animal if it has won more fights than its opponent. Whether this difference in won and lost fights is significant is neglected in most studies. Thus, the present study evaluates the impact of two different calculation methods for dyadic interactions with a significant asymmetric outcome on the results of social network analysis regarding agonistic interactions of pigs in three different mixing events (weaned piglets, fattening pigs and gilts). Directly after mixing, all animals were video recorded for 17 (fattening pigs, gilts) and 28 h (weaned piglets), documenting agonistic interactions. Two calculation methods for significant dyads, that is, dyadic interactions with a clear dominant subordinate relationship in which one animal has won significantly more fights than its encounter, were proposed: pen individual limits were calculated by a sign test considering the differences of won and lost fights of all dyadic interactions in each pen; dyad individual limits were determined by a one-sided sign test for each individual dyad. For all data sets (ALL, including all dyadic interactions; PEN or DYAD, including only significant dyads according to pen or dyad individual limits), networks were built based on the information of initiator and receiver with the pigs as nodes and the edges between them illustrating attacks. General network parameters describing the whole network structure and centrality parameters describing the position of each animal in the network were calculated. Both pen and dyad individual limits revealed only a small percentage of significant dyads for weaned piglets (12.4% or 8.8%), fattening pigs (4.2% or 0.6%) and gilts (3.6% or 0.4%). The comparison between the data sets revealed only high Spearman’s rank correlation coefficients (rS) for the density, that is, percentage of possible edges that were actually present in the network, whereas the centrality parameters showed only moderate rS values (0.37 to 0.75). Thus, the rank order of the animals changed due to the exclusion of insignificant dyads, which shows that the results obtained from social network analysis are clearly influenced if insignificant dyads are excluded from the analyses. Due to the fact that the pen individual limits consider the overall level of agonistic interactions within each pen, this calculation method should be preferred over the dyad individual limits. Otherwise, too many animals in the group became isolated nodes with zero centrality for which no statement about their position within the network can be made.

Social Network Analysis of Agonistic Behaviour and Its Association with Economically Important Traits in Pigs

“…In this context, the SNA showed the potential of obtaining a comprehensive insight into agonistic behaviour of pigs. SNA transformed these records to a network graph representing the social relationships among animals within each pen/group, giving an overview of the agonistic relationships among pen-mates [ 37 ]. It can be utilized to identify the animals that are frequently involved in agonistic behaviour and those that avoid any interaction with their pen-mates, i.e., nodes that are not attached to any edges.…”

Systematic review of animal-based indicators to measure thermal, social, and immune-related stress in pigs

“…Also, it is well known that the pigs are social animals that prefer to live in well-established hierarchical groups [ 139 ]; therefore, changes in these social orders may induce aggressive behaviors when accessing resources (food, water, or resting places), which can cause injuries and physiological reactions that decrease animal welfare [ 139 , 140 ]. This situation is frequent during the weaning process and other regrouping events that frequently occur in standard pig production systems [ 10 , 13 , 140 – 146 ]. Social stress in pigs can increase the concentration of cortisol, acute phase protein levels, immune activity, and affect several hematological parameters.…”

Social Network Analysis in Farm Animals: Sensor-Based Approaches

“…Investigations into the dominance behaviour and group interactions in pigs highlighted the importance of a nuanced understanding of social interactions [31]. Using video recording, Büttner et al [32,33] were able to demonstrate the harmonizing effects of aggressive behaviour and physical conflict on the group of pigs. This effect has been systematically overlooked, with agricultural employees traditionally separating animals who display aggressive behaviour due to safety concerns.…”

“…Combined with social monitoring technology and analysis software, sensor data can be collected in greater amounts, which would lead to an increase in meaningful contributions to enhancing animal welfare [17,23,39]. While the technology is in continuous development and is making breakthroughs in animal husbandry research, analysts acknowledge that the scope of the current sensor-based technological assessment is limited and more research and innovation is needed for continuation and improvement of farm-specific SNA research [31][32][33]40].…”