Phylogenetic eigenvector maps: a framework to model and predict species traits

Guénard, Guillaume; Legendre, Pierre; Peres‐Neto, Pedro R.

doi:10.1111/2041-210x.12111

Cited by 107 publications

(160 citation statements)

References 54 publications

(105 reference statements)

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“…Unless the new statistical models significantly improve the accuracy of PTP at low phylogenetic signals (Guénard et al, 2013;Elliot and Mooers, 2014;Revell, 2014), other methods should be used instead.…”

Section: Discussionmentioning

confidence: 99%

Predicting microbial traits with phylogenies

Goberna

Verdú²

2015

The ISME Journal

154

159

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Phylogeny reflects genetic and phenotypic traits in Bacteria and Archaea. The phylogenetic conservatism of microbial traits has prompted the application of phylogeny-based algorithms to predict unknown trait values of extant taxa based on the traits of their evolutionary relatives to estimate, for instance, rRNA gene copy numbers, gene contents or tolerance to abiotic conditions. Unlike the 'macrobial' world, microbial ecologists face scenarios potentially compromising the accuracy of trait reconstruction methods, as, for example, extremely large phylogenies and limited information on the traits of interest. We review 990 bacterial and archaeal traits from the literature and support that phylogenetic trait conservatism is widespread through the tree of life, while revealing that it is generally weak for ecologically relevant phenotypic traits and high for genetically complex traits. We then perform a simulation exercise to assess the accuracy of phylogeny-based trait predictions in common scenarios faced by microbial ecologists. Our simulations show that ca. 60% of the variation in phylogeny-based trait predictions depends on the magnitude of the trait conservatism, the number of species in the tree, the proportion of species with unknown trait values and the mean distance in the tree to the nearest neighbour with a known trait value. Results are similar for both binary and continuous traits. We discuss these results under the light of the reviewed traits and provide recommendations for the use of phylogeny-based trait predictions for microbial ecologists.

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Section: Discussionmentioning

confidence: 99%

Predicting microbial traits with phylogenies

Goberna

Verdú²

2015

The ISME Journal

154

159

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“…Calculation of PEMs and of the species scores used to make predictions is described in [12]; here, we will only summarize the method. These calculations involve the singular value decomposition of a weighted influence matrix (i.e.…”

Section: (C) Modelling Approachmentioning

confidence: 99%

Using phylogenetic information and chemical properties to predict species tolerances to pesticides

et al. 2014

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Direct estimation of species' tolerance to pesticides and other toxic organic substances is a combinatorial problem, because of the large number of species-substance pairs. We propose a statistical modelling approach to predict tolerances associated with untested species-substance pairs, by using models fitted to tested pairs. This approach is based on the phylogeny of species and physico-chemical descriptors of pesticides, with both kinds of information combined in a bilinear model. This bilinear modelling approach predicts tolerance in untested species-compound pairs based on the facts that closely related species often respond similarly to toxic compounds and that chemically similar compounds often have similar toxic effects. The three tolerance models (median lethal concentration after 96 h) used up to 25 aquatic animal species and up to nine pesticides (organochlorines, organophosphates and carbamates). Phylogeny was estimated using DNA sequences, while the pesticides were described by their mode of toxic action and their octanol-water partition coefficients. The models explained 77-84% of the among-species variation in tolerance (log 10 LC 50 ). In cross-validation, 84-87% of the predicted tolerances for individual species were within a factor of 10 of the observed values. The approach can also be used to model other species response to multivariate stress factors.

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“…It involved the computation of a multiple regression model in which swimming cost descriptors were used together with phylogenetic eigenvectors. Phylogenetic eigenvectors are linearly independent variables obtained from the structure of the phylogenetic tree (topology and branch lengths; Diniz-Filho et al 1998, Desdevise et al 2003, Guénard et al 2013; they allow one to represent phylogenetic patterns of variation, for instance in multiple regression. We preferred to use phylogenetic eigenvectors rather than other alternatives (e.g., Martins and Hansen 1997, Garland and Ives 2000, Bruggeman et al 2009) because of the relative simplicity of that approach and its ability to assess interaction between the phylogeny and swimming cost descriptors.…”

Section: Modeling Approachmentioning

confidence: 99%

“…All calculations other than sequence alignment and tree estimation were performed using the R language for statistical computing (R Development Core Team 2014) and R packages ape (Paradis et al 2004), MASS (Venables and Ripley 2002), and MPSEM (Guénard et al 2013).…”

Section: Modeling Approachmentioning

confidence: 99%

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Phylogenetics to help predict active metabolism

2015

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Abstract. This paper shows how to build predictive models involving phylogenetic information to estimate metabolic traits such as active metabolic costs. Fish swimming cost is often estimated from body mass and swimming speed. The parameters of the relationships between these variables and swimming cost vary among species because each species has its own morphology and physiology. It is now widely recognized that traits are phylogenetically structured. Using new statistical approaches, it is possible to both correct swimming cost models for statistical phylogenetic non-independence and use the inherent phylogenetic signal to improve models. With these models one can extend, to a larger set of species, empirical knowledge about traits that are difficult to obtain; swimming cost is one such trait.Swimming cost accounts for a large and variable component of a fish energy budget, yet models have only been developed from observations performed on a few species, thereby constraining the scope of bioenergetic models. Here, we propose a method where body mass and swimming speed are used together with phylogeny to predict swimming cost. The resulting model explained a large proportion of the variation (90%) in the forced swimming cost of 16 fish species submitted to forced swimming experiments. We also compared phylogenetically-explicit predictions for forced swimming experiments with experimental results of routine swimming for five species, among which one was not used to build the model. Results confirmed that forced swimming underestimates the cost of unsteady swimming. The phylogenetic modeling could be used to estimate other variables of interest in bioenergetic studies.

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Phylogenetic eigenvector maps: a framework to model and predict species traits

Cited by 107 publications

References 54 publications

Predicting microbial traits with phylogenies

Predicting microbial traits with phylogenies

Using phylogenetic information and chemical properties to predict species tolerances to pesticides

Phylogenetics to help predict active metabolism

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