Claudia Canedoli

Dr. Claudia Canedoli

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Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2018 in Conservation Biology
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Gentile Francesco FicetolaGentile Francesco Ficetola
Gentile Francesco Ficetola
CCClaudia Canedoli
Claudia Canedoli
Fabio StochFabio Stoch
Fabio Stoch

9 July 2018 in Scientific Reports
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Journal Article
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Gentile Francesco FicetolaGentile Francesco Ficetola
Gentile Francesco Ficetola
BBBenedetta Barzaghi
Benedetta Barzaghi
AMAndrea Melotto
Andrea Melotto
MMMartina Muraro
Martina Muraro
Enrico LunghiEnrico Lunghi
Enrico Lunghi
Raoul ManentiRaoul Manenti
Raoul Manenti
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.


31 October 2019 in Urban Ecosystems
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Journal Article
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CCClaudia Canedoli
Claudia Canedoli
CFChiara Ferrè
Chiara Ferrè
DKDavide Abu El Khair
Davide Abu El Khair
EPEmilio Padoa-Schioppa
Emilio Padoa-Schioppa
RCRoberto Comolli
Roberto Comolli

12 July 2018 in Scientific Reports
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Gentile Francesco FicetolaGentile Francesco Ficetola
Gentile Francesco Ficetola
Enrico LunghiEnrico Lunghi
Enrico Lunghi
CCClaudia Canedoli
Claudia Canedoli
EPEmilio Padoa-Schioppa
Emilio Padoa-Schioppa
RPRoberta Pennati
Roberta Pennati
Raoul ManentiRaoul Manenti
Raoul Manenti
The extent to which closely related species share similar niches remains highly debated. Ecological niches are increasingly analysed by combining distribution records with broad-scale climatic variables, but interactions between species and their environment often occur at fine scales. The idea that macroscale analyses correctly represent fine-scale processes relies on the assumption that average climatic variables are meaningful predictors of processes determining species persistence, but tests of this hypothesis are scarce. We compared broad- and fine-scale (microhabitat) approaches by analyzing the niches of European plethodontid salamanders. Both the microhabitat and the macroecological approaches identified niche differences among species, but the correspondence between micro- and macroecological niches was weak. When exploring niche evolution, the macroecological approach suggested a close relationship between niche and phylogenetic history, but this relationship did not emerge in fine-scale analyses. The apparent pattern of niche evolution emerging in broad-scale analyses likely was the by-product of related species having closely adjacent ranges. The environment actually experienced by most of animals is more heterogeneous than what is apparent from macro-scale predictors, and a better combination between macroecological and fine-grained data may be a key to obtain robust ecological generalizations.

20 October 2021 in Landscape Ecology
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Journal Article
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CCClaudia Canedoli
Claudia Canedoli
GFGentile Francesco Ficetola
Gentile Francesco Ficetola
DCDavide Corengia
Davide Corengia
PTPaola Tognini
Paola Tognini
AFAndrea Ferrario
Andrea Ferrario
Emilio Padoa SchioppaEmilio Padoa Schioppa
Emilio Padoa Schioppa