Sampling bias inverts ecogeographical relationships in island reptiles

Gentile Francesco Ficetola, Massimo Cagnetta, Emilio Padoa-Schioppa, Anita Quas, Edoardo Razzetti, Roberto Sindaco and Anna Bonardi
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Gentile Francesco Ficetola: UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca, LECA - Laboratoire d'Ecologie Alpine - USMB [Université de Savoie] [Université de Chambéry] - Université Savoie Mont Blanc - CNRS - Centre National de la Recherche Scientifique - Fédération OSUG - Observatoire des Sciences de l'Univers de Grenoble - UGA - Université Grenoble Alpes
Massimo Cagnetta: UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca
Emilio Padoa-Schioppa: UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca
Edoardo Razzetti: UNIPV - Università degli Studi di Pavia [Italia] = University of Pavia [Italy] = Université de Pavie [Italie]
Roberto Sindaco: Museo Civico di Storia Naturale di Carmagnola
Anna Bonardi: UNIMIB - Università degli Studi di Milano-Bicocca = University of Milano-Bicocca

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Abstract: Aim Species richness is one of the commonest measures of biodiversity, and is a basis for analyses at multiple scales. Data quality may affect estimations of species richness, but most broad-scale studies do not take sampling biases into account. We analysed reptile richness on islands that have received different sampling efforts, and assessed how inventory completeness affects the results of ecogeographical analyses. We also used simulations to evaluate under what circumstances insufficient sampling can bias the outcome of biodiversity analyses. Location Mediterranean islands. Methods We gathered data on reptile richness from 974 islands, assuming better sampling in islands with specific inventories. We used Moran's eigenvector mapping to analyse the factors that determine whether an island has been surveyed, and to identify the relationships between reptile richness, geographical parameters and anthropic parameters. We simulated islands, mimicking patterns of true data, and sampled them with varying effort. Simulated richness was analysed using the same approach used for real-world data. Results The probability that islands were sampled for reptiles was higher in large, human-populated islands. The relationship between human impact and reptile richness was negative in well-surveyed islands, but positive in islands that had not been systematically surveyed, because densely populated and accessible islands receive better sampling. In simulations, analyses successfully retrieved the relationships between species richness and human presence only if the average species detection probability was ≥75%. Poorer sampling resulted in biased regression results. Main conclusions Human activities may strongly affect biodiversity, but human presence and accessibility improve sampling effort and thus the quality of biodiversity information. Therefore, regressing known species richness on parameters representing human presence may result in apparent positive relationships. These two facets of human presence (positive on biodiversity knowledge, negative on actual biodiversity) represent a major challenge for ecogeographical studies, as not taking them into account would bias analyses and underestimate human impact.

Keywords: Accessibility; biodiversity surveys; detection probability; herpetological atlas; island biogeography; virtual ecologist; Wallacean shortfall; zero-inflated models (search for similar items in EconPapers)
Date: 2014-11-01
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Published in Global Ecology and Biogeography, 2014, 23 (11), 1303-1313 p. ⟨10.1111/geb.12201⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02313221

DOI: 10.1111/geb.12201

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