Age-related habitat selection by brown forest skinks(Sphenomorphus indicus)

In reptiles, habitat selection is the process whereby suitable habitat is selected that optimizes physiological functions and behavioral performance. Here, we used the brown forest skink（Sphenomorphus indicus） as a model animal and examined whether the frequency of active individuals, environmental temperature, illumination of activity area, and habitat type vary with different age classes. We surveyed the number of active individuals and measured environmental variables at Baiyunshan Mountain in Lishui, Zhejiang, China. We found no difference in the activity frequency of adult and juvenile S. indicus; the activity pattern of active individuals was bimodal. The mean environmental temperature selected by adults was higher than that selected by juveniles. The environmental temperature of active areas measured at 0900-1000 h and 1100-1200 h was higher than at 1400-1500h; illumination of the active area at 1000-1200 h was also higher than at 1400h-1600 h. The number of active individuals, the environmental temperature and illumination of activity areas showed pairwise positive correlation. There was a difference in habitat type between juveniles and adults whereby juveniles prefer rock habitats. We predict that active S. indicus select optimal habitats with different environmental temperatures and types to reach the physiological needs particular to their age classes.

Performance analysis of two EM-based measurement bias estimation processes for tracking systems

In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization（EM）algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly,we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements.These results are confirmed by our simulations.