Francesc S. Beltran

Individuals’ spatial position is affected by social factors. The majority of studies correlating spatial position and social factors have used methods with drawbacks. A more complete method was developed by Dolado & Beltran (2011) in captive animals. The present study aimed to apply a modified version of this method in two semi-free-ranging macaque groups. The proposed method divides group’s surroundings into different subareas, selecting different points in each subarea and calculating the coordinates of these points. We filmed each group and analyzed the videos using an activated time transition recording to determine the individuals’ coordinates. With these data, we calculated spatial variables, allowing us to obtain groups’ spatial patterns. The current method improves on previous procedures and could be applied to larger study areas and groups than the method of Dolado & Beltran (2011), thus representing a viable option for studying spatial distribution patterns in semi-free-ranging macaque groups.

Studying the collective behavior of fishes often requires tracking a great number of individuals. When many fishes move together, it is common for individuals to move so close to each other that some fishes superimpose themselves on others during one or several units of time, which impacts on tracking accuracy (i.e., loss of fish trajectories, interchange of fish identities). Type 1 occlusions arise when two fishes swim so near each other that they look like one long fish, whereas type 2 occlusions occur when the fishes’ trajectories cross to create a T- or X-shaped individual. We propose an image processing method for resolving these types of occlusions when multitracking shoals in two dimensions. We assessed processing effectiveness after videorecording shoals of 20 and 40 individuals of two species that exhibit different shoal styles: zebrafish (Danio rerio) and black neon tetras (Hyphessobrycon herbertaxelrodi). Results show that, although the number of occlusions depended on both the number of individuals and the species, the method is able to effectively resolve a great deal of occlusions, irrespective of the species and the number of individuals. It also produces images that can be used in a multitracking system to detect individual fish trajectories. Compared to other methods, our approach makes it possible to study shoals with water depths similar to those seen in the natural conditions of the two species studied.

Based on previous models (Hemelrijk 1998; Puga-González, Hildenbrant & Hemelrijk 2009), we have developed an agent-based model and software, called A-KinGDom, which allows us to simulate the emergence of the social structure in a group of non-human primates. The model includes dominance and affiliative interactions and incorporates two main innovations (preliminary dominance interactions and a kinship factor), which allow us to define four different attack and affiliative strategies. In accordance with these strategies, we compared the data obtained under four simulation conditions with the results obtained in a previous study (Dolado & Beltran 2012) involving empirical observations of a captive group of mangabeys (Cercocebus torquatus). The results show that the combination of the effect of kinship on affiliative interactions and the use of ambiguity-reducing attack provide results that are the most similar to the results of the biological model (i.e., a captive group of mangabeys) used in this study.

The aim of this article is to study the relationship between the dominance hierarchy and the spatial distribution of a group of captive sooty mangabeys ( Cercocebus atys). The analysis of the spatial distribution of individuals in relation to their rank in the dominance hierarchy showed a clear linear hierarchy in which the dominant individual was located in central positions with regard to the rest of the group members. The large open enclosure where the group was living allowed them to adopt a high-risk agonistic strategy in which individuals attacked other individuals whose rank was significantly different from their own. The comparison of the results with a previous study of mangabeys showed that, although the dominance ranks of both groups were similar, the fact that they lived in facilities with different layouts caused different agonistic strategies to emerge and allowed the dominant individual to assume different spatial locations.

We empirically tested Hemelrijk’s agent-based model (Hemelrijk 1998), in which dyadic agonistic interaction between primate-group subjects determines their spatial distribution and whether or not the dominant subject has a central position with respect to the other subjects. We studied a group of captive red-capped mangabeys (Cercocebus torquatus torquatus) that met the optimal conditions for testing this model (e.g. a linear dominance hierarchy). We analyzed the spatial distribution of the subjects in relation to their rank in the dominance hierarchy and the results confirmed the validity of this model. In accordance with Hemelrijk’s model (Hemelrijk 1998), the group studied showed an ambiguity-reducing strategy that led to non-central spatial positioning on the part of the dominant subject, thus confirming the model indirectly. Nevertheless, for the model to be confirmed directly, the group has to adopt a risk-sensitive strategy so that observers can study whether dominant subjects develop spatial centrality. Our study also demonstrated that agent-based models are a good tool for the study of certain complex behaviors observed in primates because these explanatory models can help formulate suggestive hypotheses for exploring new lines of research in primatology. Keywords: Dominance-hierarchy rank; spatial distribution; Cercocebus torquatus; agent-based models

We present ACACIA, an agent-based program implemented in Java StarLogo 2.0 that simulates a two-dimensional microworld populated by agents, obstacles and goals. Our program simulates how agents can reach long-term goals by following sensorial-motor couplings (SMCs) that control how the agents interact with their environment and other agents through a process of local categorization. Thus, while acting in accordance with this set of SMCs, the agents reach their goals through the emergence of global behaviors. This agent-based simulation program would allow us to understand some psychological processes such as planning behavior from the point of view that the complexity of these processes is the result of agent–environment interaction.