Abstract: Through combining theoretical models and empirical data, complexity science has increased our understanding of social behavior of animals, in particular of social insects, primates, and fish. What are missing are studies of collective behavior of huge swarms of birds. Recently detailed empirical data have been collected of the swarming maneuvers of large flocks of thousands of starlings (Sturnus vulgaris) at their communal sleeping site (roost). Their flocking maneuvers are of dazzling complexity in their changes in density and flock shape, but the processes underlying them are still a mystery. Recent models show that flocking may arise by self-organization from rules of co-ordination with nearby neighbors, but patterns in these models come nowhere near the complexity of those of the real starlings. The question of this paper, therefore, is whether such complex patterns can emerge by self-organization. In our computer model, called StarDisplay, we combine the usual rules of co-ordination based on separation, attraction, and alignment with specifics of starling behavior: 1) simplified aerodynamics of flight, especially rolling during turning, 2) movement above a “roosting area” (sleeping site), and 3) the low fixed number of interaction neighbors (i.e., the topological range). Our model generates patterns that resemble remarkably not only qualitative but also quantitative empirical data collected in Rome through video recordings and position measurements by stereo photography. Our results provide new insights into the mechanisms underlying complex flocking maneuvers of starlings and other birds.

Abstract: In 1993, Les Real invented the label 'cognitive ecology'. This label was intended for work that brought cognitive science and behavioural ecology together. Real's article stressed the importance of such an approach to the understanding of behaviour. At the end of a decade in which more interdisciplinary work on behaviour has been seen than for many years, it is time to assess whether cognitive ecology is a label describing an active field.

Abstract: Data are presented on the breeding behavior of two zebra species to test whether intra- and interspecific variation in male reproductive behavior and physiology are correlated with differences in female promiscuity. In one species, plains zebra (Equus burchelli) females live in closed membership single male groups and mate monandrously. In the other species, the Grevy's zebra (E. grevyi) females live in groups whose membership is much more temporary. Typically, associations with individual males are brief and mating is polyandrous. However, some females – those having just given birth – reside with one male for long periods, mating monandrously. These differences in female mating behavior generate variability in the potential for sperm competition. We show that behavioral differences in male investment in reproductive activities correlate with the potential for sperm competition. When mating with promiscuous mares, Grevy's zebra stallions made a greater investment in reproductive behavior (calling, mounting, ejaculations) than did stallions of either species when mating with monandrous females. The evolution of large testes size in the Grevy's zebra, when compared to the congeneric plains zebra, horse, and mountain zebra, allows for this increased investment.

Abstract: Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network.

Abstract: Prey species must adapt their behavior to avoid predation. As a key prey item for lions (Panthera leo), plains zebras (Equus burchelli) were expected to respond to immediate threats posed by lions in their area. In addition, zebras were predicted to exhibit behavior tuned to reduce the potential for encounters with lions, by modifying their movement patterns in the times of day and habitats of greatest lion danger. We studied a population of approximately 600 plains zebra living in Ol Pejeta Conservancy, Kenya. We found that zebra abundance on or near a grassland patch was lower if lions had also been observed on that patch during the same day. Predation danger was highest in grassland habitat during the night, when lions were more active. Zebra sightings and global positioning system radio collar data indicated that zebras also reduced their use of grassland at night, instead using more woodland habitat. Zebras moved faster and took sharper turns in grassland at night. It is hypothesized that these more erratic movements assist zebras in avoiding detection or capture by lions.