Assessing among‐lineage variability in phylogenetic imputation of functional trait datasets

Molina‐Venegas, Rafael; Moreno-Saiz, Juan Carlos; Parga, Isabel Castro; Davies, T. Jonathan; Peres‐Neto, Pedro R.; Rodrı́guez, Miguel Á.

doi:10.1111/ecog.03480

Cited by 29 publications

(46 citation statements)

References 48 publications

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“…Traits were simulated under a Brownian model of evolution, with a Gaussian distribution of values ranging from zero to 10 to mimic the distribution of real trait data on a logarithmic scale (a transformation often used in comparative studies). The impact of phylogenetic signal strength on imputation performance was already tested by Kim et al (2018) and Molina‐Venegas et al (2018); therefore, we standardized Pagel’s λ between the phylogeny and traits at approximately one. The response was simulated as a product of a trait, rather than through the phylogeny, and has a Gaussian distribution ranging from zero to 10.…”

Section: Methodsmentioning

confidence: 99%

Handling missing values in trait data

Johnson

Isaac

Paviolo

et al. 2020

Global Ecol. Biogeogr.

122

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Aim Trait data are widely used in ecological and evolutionary phylogenetic comparative studies, but often values are not available for all species of interest. Traditionally, researchers have excluded species without data from analyses, but estimation of missing values using imputation has been proposed as a better approach. However, imputation methods have largely been designed for randomly missing data, whereas trait data are often not missing at random (e.g., more data for bigger species). Here, we evaluate the performance of approaches for handling missing values when considering biased datasets. Location Any. Time period Any. Major taxa studied Any. Methods We simulated continuous traits and separate response variables to test the performance of nine imputation methods and complete‐case analysis (excluding missing values from the dataset) under biased missing data scenarios. We characterized performance by estimating the error in imputed trait values (deviation from the true value) and inferred trait–response relationships (deviation from the true relationship between a trait and response). Results Generally, Rphylopars imputation produced the most accurate estimate of missing values and best preserved the response–trait slope. However, estimates of missing data were still inaccurate, even with only 5% of values missing. Under severe biases, errors were high with every approach. Imputation was not always the best option, with complete‐case analysis frequently outperforming Mice imputation and, to a lesser degree, BHPMF imputation. Mice, a popular approach, performed poorly when the response variable was excluded from the imputation model. Main conclusions Imputation can handle missing data effectively in some conditions but is not always the best solution. None of the methods we tested could deal effectively with severe biases, which can be common in trait datasets. We recommend rigorous data checking for biases before and after imputation and propose variables that can assist researchers working with incomplete datasets to detect data biases and minimize errors.

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Section: Methodsmentioning

confidence: 99%

Handling missing values in trait data

Johnson

Isaac

Paviolo

et al. 2020

Global Ecol. Biogeogr.

122

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“…We assumed that the trait evolved in a steady manner throughout the phylogeny and consistently we assigned a single pair of values (a and ϕ), but it is possible to assign specific pairs of parameters to each node in a given phylogeny [22]. Under pure Brownian motion ( purely neutral evolution), a = 0 and the expected changes are proportional to the square root of the branch lengths [22,50]. By contrast, when a = 1, evolutionary change occurs at a fixed rate irrespective of branch length [22,50].…”

Section: (Iv) Phylogenetic Eigenvector Mapsmentioning

confidence: 99%

Were the synapsids primitively endotherms? A palaeohistological approach using phylogenetic eigenvector maps

Faure‐Brac

Cubo

2020

Phil. Trans. R. Soc. B

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Cite this article: Faure-Brac MG, Cubo J. 2020 Were the synapsids primitively endotherms? A palaeohistological approach using phylogenetic eigenvector maps. Phil. Trans. R. Soc. B 375: 20190138. http://dx.One contribution of 15 to a theme issue 'Vertebrate palaeophysiology'.The acquisition of mammalian endothermy is poorly constrained both phylogenetically and temporally. Here, we inferred the resting metabolic rates (RMRs) and the thermometabolic regimes (endothermy or ectothermy) of a sample of eight extinct synapsids using palaeohistology, phylogenetic eigenvector maps (PEMs), and a sample of 17 extant tetrapods of known RMR (quantified using respirometry). We inferred high RMR values and an endothermic metabolism for the anomodonts (Lystrosaurus sp., Oudenodon bainii) and low RMR values and an ectothermic metabolism for Clepsydrops collettii, Dimetrodon sp., Edaphosaurus boanerges, Mycterosaurus sp., Ophiacodon uniformis and Sphenacodon sp. A maximum-likelihood ancestral states reconstruction of RMRs performed using the values inferred for extinct synapsids, and the values measured using respirometry in extant tetrapods, shows that the nodes Anomodontia and Mammalia were primitively endotherms. Finally, we performed a parsimony optimization of the presence of endothermy using the results obtained in the present study and those obtained in previous studies that used PEMs. For this, we assigned to each extinct taxon a thermometabolic regime (ectothermy or endothermy) depending on whether the inferred values were significantly higher, lower or not significantly different from the RMR value separating ectotherms from endotherms (1.5 ml O 2 h −1 g −0.67 ). According to this optimization, endothermy arose independently in Archosauromorpha, Sauropterygia and Therapsida.This article is part of the theme issue 'Vertebrate palaeophysiology'.

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“…In the area of phylogenetic methods for data imputation, PWR could be an especially useful new approach. By optimizing the bandwidth that informs the local regression, PWR might overcome some of the shortfalls of existing phylogenetic imputation methods, which often have low prediction accuracy (Molina‐Venegas et al, ), and imputation is already embedded within the cross‐validation heuristic of the method.…”

Section: Discussionmentioning

confidence: 99%

Phylogenetically weighted regression: A method for modelling non‐stationarity on evolutionary trees

Davies

Regetz

Wolkovich

et al. 2018

Global Ecol Biogeogr

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Aim Closely related species tend to resemble each other in their morphology and ecology because of shared ancestry. When exploring correlations between species traits, therefore, species cannot be treated as statistically independent. Phylogenetic comparative methods (PCMs) attempt to correct statistically for this shared evolutionary history. Almost all such approaches, however, assume that correlations between traits are constant across the tips of the tree, which we refer to as phylogenetic stationarity. We suggest that this assumption of phylogenetic stationarity might be often violated and that relationships between species traits might evolve alongside clades, for example, owing to the effects of unmeasured traits or other latent variables. Specific examples range from shifts in allometric scaling relationships between clades (e.g., basal metabolic rate and body mass in endotherms, and tree diameter and biomass in trees) to the differing relationship between leaf mass per area and shade tolerance in deciduous versus evergreen trees and shrubs. Innovation Here, we introduce an exploratory modelling framework, phylogenetically weighted regression, which represents an extension of geographically weighted regression (GWR) used in spatial studies, to allow non‐stationarity in model parameters across a phylogenetic tree. We demonstrate our approach using empirical data on flowering time and seed mass from a well‐studied plant community in southeastern Sweden. Our model reveals strong, diverging trends across the phylogeny, including changes in the sign of the relationship between clades. Main conclusions By allowing for phylogenetic non‐stationarity, we are able to detect shifting relationships among species traits that would be obscured in traditional PCMs; thus, we suggest that PWR might be an important exploratory tool in the search for key missing variables in comparative analyses.

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Assessing among‐lineage variability in phylogenetic imputation of functional trait datasets

Cited by 29 publications

References 48 publications

Handling missing values in trait data

Handling missing values in trait data

Were the synapsids primitively endotherms? A palaeohistological approach using phylogenetic eigenvector maps

Phylogenetically weighted regression: A method for modelling non‐stationarity on evolutionary trees

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