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Spatially explicit capture recapture (SECR) models have gained enormouspopularity to solve abundance estimation problems in ecology. In this study, wedevelop a novel Bayesian SECR model that disentangles the process of animalmovement through a detector from the process of recording data by a detector inthe face of imperfect detection. We integrate this complexity into an advancedversion of a recent SECR model involving partially identified individuals(Royle, 2015). We assess the performance of our model over a range of realisticsimulation scenarios and demonstrate that estimates of population size $N$improve when we utilize the proposed model relative to the model that does notexplicitly estimate trap detection probability (Royle, 2015). We confront andinvestigate the proposed model with a spatial capture-recapture data set from acamera trapping survey on tigers (\textit{Panthera tigris}) in Nagarahole,southern India. Trap detection probability is estimated at 0.489 and thereforejustifies the necessity to utilize our model in field situations. We discusspossible extensions, future work and relevance of our model to otherstatistical applications beyond ecology.

A spatially explicit capture recapture model for partially identified individuals when trap detection rate is less than one