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.

A questionnaire-based investigation to explore the social and legal implications derived from the use of camera traps for wildlife monitoring and conservation

“…The use of camera traps for wildlife monitoring has been practiced since the early twentieth century (Chapman 1927), but in recent years, they are becoming one of the mainstream tools used in conservation biology, ranging from the most basic level aimed at assessing species presence at a site (Kays et al 2011;Zaccaroni et al 2020), abundance estimation (Franchini et al 2022;Oberosler et al 2022), and population dynamics studies (Karanth 1995(Karanth , 2006Romani et al 2018;Anile et al 2020;Augugliaro et al 2019Augugliaro et al , 2020 up to the assessment of activity patterns (Andreoni et al 2020;Anile et al 2020) and temporal overlap of different species (Mori et al 2021). This explosion in camera trap use is reflected by the increased number of published papers in recent years.…”

How abundant is a species at the limit of its distribution range? Crested porcupine Hystrix cristata and its northern population

“…Population trend data is lacking (Amori & De Smet, 2016). To the best of our knowledge, no population densities on the border of the distribution range have been reported; only one study estimated porcupine density at the centre of the distribution area based on presence-only data (Franchini et al, 2022). Franchini et al (2022) reported population densities of 0.44 ind•km −2 at a regional scale (17,111 km 2 )…”

“…The lack of data on regional and national abundance and densities in Italy makes challenging porcupine population management (https://www.gbif.org/species/5219888). To the best of our knowledge, only one study estimated porcupine abundance in the core area of the distribution range (Franchini et al., 2022). This study used presence‐only data to estimate abundance but did not account for imperfect detection—that is species not detected despite being present—(Dorazio, 2014; Franchini et al., 2022).…”

“…org/ speci es/ 5219888). To the best of our knowledge, only one study estimated porcupine abundance in the core area of the distribution range (Franchini et al, 2022).…”

“…This study used presence-only data to estimate abundance but did not account for imperfect detection-that is species not detected despite being present- (Dorazio, 2014;Franchini et al, 2022) In this context, the use of remotely activated cameras (camera traps) has been described as a non-invasive, reliable methodology that overcame most of the limitations of previous methodologies (Rovero & Zimmermann, 2016;Wearn & Glover-Kapfer, 2019).…”