Abstract of Thesis Presented to the Graduate School
 of the University of Florida in Partial Fulfillment of the
 Requirements for the Degree of Master of Science

 ESTIMATING SLOTH BEAR ABUNDANCE FROM REPEATED
 PRESENCE-ABSENCE DATA IN NAGARAHOLE-
 BANDIPUR NATIONAL PARKS, INDIA

 By

 Arjun Mallipatna Gopalaswamy

 December 2006

Chair: Melvin Sunquist
Major Department: Wildlife Ecology and Conservation

 It is notoriously difficult to estimate the abundance of bears in general and most methods

that are currently available are too consumptive of time and effort. Sloth bears (Melursus

ursinus) pose very similar challenges to field biologists trying to estimate their abundances.

 I investigated the possibility of estimating abundance of sloth bears using presence-

absence data from repeated samples from camera traps. The simulation results generated from

the likelihood estimator for small sample sizes showed a positive bias for A, the mean abundance

per site.

 To more effectively use data with small sample sizes, a Bayesian approach to the problem

was developed so that an informative prior could influence the parameter values to a reasonable

range. A Bayesian Markov Chain Monte Carlo simulation procedure using the Gibbs sampler

algorithm was developed.

 Data were analyzed using two ideas of bear movement that are incorporated into the

model. Data were first analyzed with the intention of maintaining the idea of site independence

to ensure that a bear will not occur in two sites during the sampling period. This restricted the

data set and the uncertainties in the parameter estimates were found to be very high. To