N-mixture models reliably estimate the abundance of small vertebrates

Journal article

Accurate measures of species abundance are essential to identify conservation strategies. N-mixture models are increasingly used to estimate abundance on the basis of species counts. In this study we tested whether abundance estimates obtained using N-mixture models provide consistent results with more traditional approaches requiring capture (capture-mark recapture and removal sampling). We focused on endemic, threatened species of amphibians and reptiles in Italy, for which accurate abundance data are needed for conservation assessments: the Lanza's Alpine salamander Salamandra lanzai, the Ambrosi's cave salamander Hydromantes ambrosii and the Aeolian wall lizard Podarcis raffonei. In visual counts, detection probability was variable among species, ranging between 0.14 (Alpine salamanders) and 0.60 (cave salamanders). For all the species, abundance estimates obtained using N-mixture models showed limited differences with the ones obtained through capture-mark-recapture or removal sampling. The match was particularly accurate for cave salamanders in sites with limited abundance and for lizards, nevertheless non-incorporating heterogeneity of detection probability increased bias. N-mixture models provide reliable abundance estimates that are comparable with the ones of more traditional approaches, and offer additional advantages such as a smaller sampling effort and no need of manipulating individuals, which in turn reduces the risk of harming animals and spreading diseases.

Authors: Ficetola, GF; Barzaghi, B; Melotto, A; Muraro, M; Lunghi, E

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Reports
• Volume: 8
• Issue: 1
• Pages: 10357-10357
• Publication date: 2018-07-03
• DOI: 10.1038/s41598-018-28432-8
• EISSN: 2045-2322
• ISSN: 2045-2322
• Language: English
• Keywords: Ecology; Mark and recapture; Cave; Biology; Abundance (ecology); Habitat; Salamander; Sampling (signal processing); Threatened species; Computer science; Population