Factors influencing seasonal and monthly changes in the group size of chital or axis deer in southern India

Chital or axis deer (Axis axis) form fluid groups that change in size temporally and in relation to habitat. Predictions of hypotheses relating animal density, rainfall, habitat structure, and breeding seasonality, to changes in chital group size were assessed simultaneously using multiple regression models of monthly data collected over a 2 yr period in Guindy National Park, in southern India. Over 2,700 detections of chital groups were made during four seasons in three habitats (forest, scrubland and grassland). In scrubland and grassland, chital group size was positively related to animal density, which increased with rainfall. This suggests that in these habitats, chital density increases in relation to food availability, and group sizes increase due to higher encounter rate and fusion of groups. The density of chital in forest was inversely related to rainfall, but positively to the number of fruiting tree species and availability of fallen litter, their forage in this habitat. There was little change in mean group size in the forest, although chital density more than doubled during the dry season and summer. Dispersion of food items or the closed nature of the forest may preclude formation of larger groups. At low densities, group sizes in all three habitats were similar. Group sizes increased with chital density in scrubland and grassland, but more rapidly in the latter—leading to a positive relationship between openness and mean group size at higher densities. It is not clear, however, that this relationship is solely because of the influence of habitat structure. The rutting index (monthly percentage of adult males in hard antler) was positively related to mean group size in forest and scrubland, probably reflecting the increase in group size due to solitary males joining with females during the rut. The fission-fusion system of group formation in chital is thus interactively influenced by several factors. Aspects that need further study, such as interannual variability, are highlighted.

System Reliability Analysis of Flexible Pavements

The uncertainty in material properties and traffic characterization in the design of flexible pavements has led to significant efforts in recent years to incorporate reliability methods and probabilistic design procedures for the design, rehabilitation, and maintenance of pavements. In the mechanistic-empirical (ME) design of pavements, despite the fact that there are multiple failure modes, the design criteria applied in the majority of analytical pavement design methods guard only against fatigue cracking and subgrade rutting, which are usually considered as independent failure events. This study carries out the reliability analysis for a flexible pavement section for these failure criteria based on the first-order reliability method (FORM) and the second-order reliability method (SORM) techniques and the crude Monte Carlo simulation. Through a sensitivity analysis, the most critical parameter affecting the design reliability for both fatigue and rutting failure criteria was identified as the surface layer thickness. However, reliability analysis in pavement design is most useful if it can be efficiently and accurately applied to components of pavement design and the combination of these components in an overall system analysis. The study shows that for the pavement section considered, there is a high degree of dependence between the two failure modes, and demonstrates that the probability of simultaneous occurrence of failures can be almost as high as the probability of component failures. Thus, the need to consider the system reliability in the pavement analysis is highlighted, and the study indicates that the improvement of pavement performance should be tackled in the light of reducing this undesirable event of simultaneous failure and not merely the consideration of the more critical failure mode. Furthermore, this probability of simultaneous occurrence of failures is seen to increase considerably with small increments in the mean traffic loads, which also results in wider system reliability bounds. The study also advocates the use of narrow bounds to the probability of failure, which provides a better estimate of the probability of failure, as validated from the results obtained from Monte Carlo simulation (MCS).