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Tytuł pozycji:

occAssess: An R package for assessing potential biases in species occurrence data

Tytuł :
occAssess: An R package for assessing potential biases in species occurrence data
Autorzy :
Robin J. Boyd
Gary D. Powney
Claire Carvell
Oliver L. Pescott
Pokaż więcej
Temat :
bias
biological records
convenience samples
nonprobability samples
species distributions
Ecology
QH540-549.5
Źródło :
Ecology and Evolution, Vol 11, Iss 22, Pp 16177-16187 (2021)
Wydawca :
Wiley, 2021.
Rok publikacji :
2021
Kolekcja :
LCC:Ecology
Typ dokumentu :
article
Opis pliku :
electronic resource
Język :
English
ISSN :
2045-7758
Relacje :
https://doaj.org/toc/2045-7758
DOI :
10.1002/ece3.8299
Dostęp URL :
https://doaj.org/article/bd0369a1f5734610980bdcf518fe2626
Numer akcesji :
edsdoj.bd0369a1f5734610980bdcf518fe2626
Czasopismo naukowe
Abstract Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf‐nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities.

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