Crested porcupine (Hystrix cristata) abundance estimation using Bayesian methods: first data from a highly agricultural environment in central Italy

Abstract

Wildlife abundance estimation is one of the key components in conservation biology. Bayesian frameworks are widely used to adjust the potential biases derived by data collected in the field, as they can increase the precision of model parameter as a consequence of the combination of previous pieces of knowledge (priors) combined with data collected in the field to produce an a-posteriori distribution. Capture-recapture is one of the most common techniques used to assess animal abundance. However, the implementation with camera traps requires that animals present unique phenotypic traits for individual-based recognition. The crested porcupine Hystrix cristata is a semi-fossorial rodent with a continuous, but patchily distribution across Italy. Despite the species does not present evident individual-specific phenotypic traits, the information gathered using presence-only data obtained from camera traps, opportunistic observations, and road-killing events could be used to provide a rough estimate of the species abundance within an area. The main purpose of the present research was hence to provide the first preliminary estimate of the abundance of the crested porcupine in central Italy using presence-only data obtained from the above different monitoring methods. The results obtained estimated an average minimum number of 1803 individuals (SD = 26.89, CI 95% = 1750–1855) within an area covering about 17,111 km2. Since the porcupine is considered as “potentially problematic” because of damages to croplands and riverbanks, assessing its abundance is even more important to delineate adequate conservation and management actions to limit the potential trade-off effects over human activities.