The extent to which an individual interacts with other members of a population underlies the structure of a species' social network. Animal social networks describe the level of interaction between individuals via the quantification of the frequency, strength, and weight of social bonds. The position of an individual within their respective social network can have important implications in terms of individual fitness. Despite being studied extensively, the impact of external factors on the structure of animal social networks, such as seasonality, remains understudied. This research project analysed the social network structure of a reticulated giraffe (Giraffa reticulata) population in the Laikipia District of Kenya, using the network centrality metrics degree centrality, betweenness centrality, and the clustering coefficient, which demonstrated changes in social network structure induced by seasonal variation. Across time, the degree centrality of individuals increased, in line with predictions; however, no difference was observed for betweenness centrality. Similarly, the clustering coefficient was higher in the wet season compared to the dry season, corresponding to previous studies. Additionally, sex-related and age-related differences in degree centrality, betweenness centrality, and parasite load were established for each seasonal period, however, there were no significant differences. Finally, social network parameters were correlated to the fitness proxies triiodothyronine (T3) and glucocorticoid (GC) concentrations. No correlation was found for either degree centrality or betweenness centrality, however, the increased group size correlated with an increase in T3 concentration and a decrease in GC concentration. Better understanding the social structure of populations via social network analysis provides further insight into how the position of an individual affects fitness level, particularly when correlating network centrality metrics with fitness level proxies. Therefore, applying social network analysis to conservation can enable more targeted and effective management strategies.
|Date of Award||1 Aug 2023|
- The University of Manchester
|Supervisor||Catherine Walton (Supervisor) & Susanne Shultz (Supervisor)|