Crested porcupine (Hystrix cristata) abundance estimation using Bayesian methods: first data from a highly agricultural environment in central Italy
نویسندگان
چکیده
Abstract Wildlife abundance estimation is one of the key components in conservation biology. Bayesian frameworks are widely used to adjust potential biases derived by data collected field, as they can increase precision model parameter a consequence combination previous pieces knowledge (priors) combined with field produce an a-posteriori distribution. Capture-recapture most common techniques assess animal abundance. However, implementation camera traps requires that animals present unique phenotypic traits for individual-based recognition. The crested porcupine Hystrix cristata semi-fossorial rodent continuous, but patchily distribution across Italy. Despite species does not evident individual-specific traits, information gathered using presence-only obtained from traps, opportunistic observations, and road-killing events could be provide rough estimate within area. main purpose research was hence first preliminary central Italy above different monitoring methods. results estimated average minimum number 1803 individuals (SD = 26.89, CI 95% 1750–1855) area covering about 17,111 km 2 . Since considered “potentially problematic” because damages croplands riverbanks, assessing its even more important delineate adequate management actions limit trade-off effects over human activities.
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ژورنال
عنوان ژورنال: Mammal research
سال: 2022
ISSN: ['2199-241X', '2199-2401']
DOI: https://doi.org/10.1007/s13364-022-00622-w