Date Published: January 26, 2017
Publisher: Public Library of Science
Author(s): Vitor Passos Rios, Roberto André Kraenkel, Dante R. Chialvo.
Groups in nature can be formed by interactions between individuals, or by external pressures like predation. It is reasonable to assume that groups formed by internal and external conditions have different dynamics and structures. We propose a computational model to investigate the effects of individual recognition on the formation and structure of animal groups. Our model is composed of agents that can recognize each other and remember previous interactions, without any external pressures, in order to isolate the effects of individual recognition. We show that individual recognition affects the number and size of groups, and the modularity of the social networks. This model can be used as a null model to investigate the effects of external factors on group formation and persistence.
In this paper, we define group as “a spatial aggregation of conspecifics”, regardless of presence or absence interactions between the individuals it comprises. We choose to use a purely spatial definition as we intend to study the effects interaction have on grouping, and including interaction directly on the definition would be troublesome for this purpose. We purposefully avoid using terms like society, colony, band, flock and others as they are loaded with meaning, and can imply a defined group structure. Group structure here refers to “the pattern of social behaviors between individuals in the group” and group stability is “group persistence trough time”.
We analyze the resulting groups from two different perspectives, spatial and social. Since our definition of group is spatial, we use a spatial clustering algorithm, DBSCAN , to investigate whether simulations with IR result in different spatial patterns. DBSCAN gives us the number, size and composition of spatial groups. This allows us to compare whether groups survive in time using the MONIC algorithm , which compares group composition in successive moments. Thus, our spatial metrics are number of groups, average group size, and average group lifespan. We predict that these three metrics will all be larger in simulations with IR.
To approach the question “how do memory and individual recognition affect group structure and stability” we face serious problems: it is difficult to isolate the effects that memory has on social behavior from those caused by other factors, such as resource availability, age and reproductive state. It can also be extremely difficult to determine whether a species exhibits individual recognition or is merely able to determine whether a conspecific falls into a general class (see  for a treatment of this problem). Further, if we were to examine the effects of IR, it would be tremendously useful to be able to turn it off and on, to compare the effects of its presence with those of its absence. Though there are methods to experimentally alter the levels of affiliation individuals from a given species exhibit , there is currently no way to do this with individual recognition, and doing so could raise ethical questions about the use of transgenic animals. Thus, we use computational modelling to investigate memory and individual recognition, instead of using traditional experiments with live animals.