Spatial statistics for modeling of abundance and distribution of wildlife species in the Masai Mara ecosystem, Kenya

This study illustrates the use of modern statistical procedures for better wildlife management by addressing three key issues: determination of abunda nce, modeling of animal distributions and variability of diversity in space and time. Prior information in Markov Chain Monte Carlo (MCMC) methods is used to improve estimates of abundance. Measures of autocorrelation are inc luded when modeling distributions of animal counts, and a diversity index t o indicate species abundance and richness for large herbivores is developed . Data from the Masai Mara ecosystem in Kenya are used to develop and demon strate these procedures. The new abundance estimates are up to 35% more acc urate than those obtained by existing methods. Significant temporal changes in spatial patterns are found from a space-time analysis of elephant count s over a 20-year period, with strong interactions over 5 km and 6 months sp ace and time separations, respectively. The new diversity index is sensitiv e to both high abundance and species richness and is also able to capture y ear to year variation. It indicates an overall marginal decrease in diversi ty for large herbivores in the Mara ecosystem. The space-time analyses and diversity index can easily be computed thereby providing tools for rapid de cision making.