Using N-mixture models to estimate abundance and temporal trends of black rhinoceros (Diceros bicornis L.) populations from aerial counts

Abstract Inaccurate estimates of animal populations may lead to flawed management interventions, therefore, it is essential to understand the status and population trend of a species in order to plan its management efficiently. Aerial surveys are considered a useful method for estimating the population size of large conspicuous animals inhabiting large areas, but raw count data from aerial surveys usually underestimate population sizes due to imperfect detection. The use of N-mixture models with aerial count data provides a useful tool to estimate the population sizes while taking detection probability into account. As a study case we used aerial surveys conducted for monitoring black rhinoceros ( Diceros bicornis ) in Madikwe Game Reserve and Pilanesberg Nature Reserve (South Africa) during 1999–2015, and we analysed data with a dynamic extension of the N-mixture model. We estimated 0.078–0.098 and 0.139–0.142 individuals/km 2 , respectively, and we found evidence for density dependence in both reserves with a carrying capacity of 0.122 (0.102–0.142) individuals/100 km 2 . Based on simulations used to assess precision of the estimates, root-mean-square error model (RMSE) estimates was significantly smaller than those for the raw maximum counts. The N-mixture models provide a promising approach to estimate population size, trends and demographic characteristics of large conspicuous mammals such as black rhinoceroses. Such analysis can provide estimates that are more accurate than raw counts. In addition, use of model covariates that affect a species' population parameters can provide useful information for their conservation and management.