A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Cayuela, Luis; Cruz Rot, Marcelino de la y Ruokolainen, Kalle (2011). A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data. "Ecography", v. 34 (n. 1); pp. 94-102. ISSN 1600-0587. https://doi.org/10.1111/j.1600-0587.2009.05899.x.

Descripción

Título: A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data
Autor/es:
  • Cayuela, Luis
  • Cruz Rot, Marcelino de la
  • Ruokolainen, Kalle
Tipo de Documento: Artículo
Título de Revista/Publicación: Ecography
Fecha: Febrero 2011
Volumen: 34
Materias:
Escuela: E.U.I.T. Agrícolas (UPM)
Departamento: Biología Vegetal [hasta 2014]
Licencias Creative Commons: Reconocimiento

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  • A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data. (deposited 02 Abr 2012 08:40) [Mostrada Ahora]

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Resumen

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re-assign non identified species in each site to any of the other species found in the remaining sites. After each re-assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re-assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus-resolution, family-resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.

Más información

ID de Registro: 10618
Identificador DC: http://oa.upm.es/10618/
Identificador OAI: oai:oa.upm.es:10618
Identificador DOI: 10.1111/j.1600-0587.2009.05899.x
URL Oficial: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0587
Depositado por: PTU Marcelino De la Cruz Rot
Depositado el: 02 Abr 2012 08:40
Ultima Modificación: 20 Abr 2016 18:52
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