High-resolution animal location data are increasingly available, requiring
analytical approaches and statistical tools that can accommodate the temporal structure and
transient dynamics (non-stationarity) inherent in natural systems. Traditional analyses often
assume uncorrelated or weakly correlated temporal structure in the velocity (net displacement)
time series constructed using sequential location data. We propose that frequency and time–
frequency domain methods, embodied by Fourier and wavelet transforms, can serve as useful
Background: Adaptive movement behaviors allow individuals to respond to fluctuations in resource quality and
distribution in order to maintain fitness. Classically, studies of the interaction between ecological conditions and
movement behavior have focused on such metrics as travel distance, velocity, home range size or patch occupancy
time as the salient metrics of behavior. Driven by the emergence of very regular high frequency data, more recently
the importance of interpreting the autocorrelation structure of movement as a behavioral metric has become
The total aerial count of elephants in Laikipia/Samburu ecosystem was carried out between 20th and 24th of June 2002, During the census, total counts of elephants, elephant carcasses and buffaloes was done. Livestock numbers (cattle and shoats) were estimated. As a MIKE site the count provided baseline data for monitoring poaching levels and elephant trends in the ecosystem.
Conserving land and ecosystem connectivity for wildlife is increasingly a global challenge as demand for infrastructure development to meet growing human population needs encroaches in many traditional wildlife areas. The survival of wildlife species in arid and semi-arid systems requires interconnected landscapes, and limiting animal movement greatly reduces the system’s ability to sustain viable wildlife populations (Vasudev et al. 2015).