English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/71815
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

A Novel Method to Handle the Effect of Uneven Sampling Effort in Biodiversity Databases

AuthorsPardo Guereño, Iker ; Pata, M. P.; Gómez García, Daniel ; García González, María Begoña
Issue Date2013
PublisherPublic Library of Science
CitationPLoS ONE 8: (2013)
Abstract[EN] How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses. © 2013 Pardo et al.
Publisher version (URL)http://dx.doi.org/10.1371/journal.pone.0052786
URIhttp://hdl.handle.net/10261/71815
DOI10.1371/journal.pone.0052786
Identifiersdoi: 10.1371/journal.pone.0052786
issn: 1932-6203
Appears in Collections:(IPE) Artículos
Files in This Item:
File Description SizeFormat 
Pardo_et_al_journal.pone.2013.pdf397,51 kBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.