Journal: Methods in Ecology and Evolution

Abbreviation

Methods Ecol. Evol.

Publisher

Wiley-Blackwell

Journal Volumes

ISSN

2041-210X
2041-2096

Description

Filters

2012 - 20154
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Publications 1 - 4 of 4
  • Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data
    Item type: Journal Article
    Bengtsson-Palme, Johan; Ryberg, Martin; Hartmann, Martin; et al. (2013)
    Methods in Ecology and Evolution
  • A method for analysing replicated point patterns in ecology
    Item type: Journal Article
    Bagchi, Robert; Illian, Janine B. (2015)
    Methods in Ecology and Evolution
  • REDDcalculator.com: a web-based decision-support tool for implementing Indonesia's forest moratorium
    Item type: Journal Article
    Koh, Lian Pin; Gibbs, Holly K.; Potapov, Peter V.; et al. (2012)
    Methods in Ecology and Evolution ~ REDDcalculator.com: a web-based decision-support tool for implementing Indonesia's forest moratorium
  • Measurement errors should always be incorporated in phylogenetic comparative analysis
    Item type: Journal Article
    Silvestro, Daniele; Kostikova, Anna; Litsios, Glenn; et al. (2015)
    Methods in Ecology and Evolution
    Summary: 1. The evolution of continuous traits is the central component of comparative analyses in phylogenetics, and the comparison of alternative models of trait evolution has greatly improved our understanding of the mechanisms driving phenotypic differentiation. Several factors influence the comparison of models, and we explore the effects of random errors in trait measurement on the accuracy of model selection. 2. We simulate trait data under a Brownian motion model (BM) and introduce different magnitudes of random measurement error. We then evaluate the resulting statistical support for this model against two alternative models: Ornstein–Uhlenbeck (OU) and accelerating/decelerating rates (ACDC). 3. Our analyses show that even small measurement errors (10%) consistently bias model selection towards erroneous rejection of BM in favour of more parameter-rich models (most frequently the OU model). Fortunately, methods that explicitly incorporate measurement errors in phylogenetic analyses considerably improve the accuracy of model selection. 4. Our results call for caution in interpreting the results of model selection in comparative analyses, especially when complex models garner only modest additional support. 5. Importantly, as measurement errors occur in most trait data sets, we suggest that estimation of measurement errors should always be performed during comparative analysis to reduce chances of misidentification of evolutionary processes.
Publications 1 - 4 of 4