Phylogenetic supergraphs

Wheeler, Ward C.

doi:10.1111/cla.12485

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…By combining ease and speed of use, low distortion of functional space, low sensitivity to outliers, and comparability with PD measures, the use of NJ seems a promising approach. More broadly, there are other methods available for building trees that are not considered here, but that could also provide new and advantageous ways to represent functional diversity (Wheeler 2022). As such, we would argue that further exploration and testing of alternatives to the commonly used functional tree construction approaches (e.g.…”

Section: Discussionmentioning

confidence: 99%

Calculating functional diversity metrics using neighbor‐joining trees

Cardoso,

Guillerme,

Mammola

et al. 2024

Ecography

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The study of functional diversity (FD) provides ways to understand phenomena as complex as community assembly or the dynamics of biodiversity change under multiple pressures. Different frameworks are used to quantify FD, either based on dissimilarity matrices (e.g. Rao entropy, functional dendrograms) or multidimensional spaces (e.g. convex hulls, kernel‐density hypervolumes), each with their own strengths and limits. Frameworks based on dissimilarity matrices either do not enable the measurement of all components of FD (i.e. richness, divergence, and regularity), or result in the distortion of the functional space. Frameworks based on multidimensional spaces do not allow for comparisons with phylogenetic diversity (PD) measures and can be sensitive to outliers.We propose the use of neighbor‐joining trees (NJ) to represent and quantify FD in a way that combines the strengths of current frameworks without many of their weaknesses. Importantly, our approach is uniquely suited for studies that compare FD with PD, as both share the use of trees (NJ or others) and the same mathematical principles.We test the ability of this novel framework to represent the initial functional distances between species with minimal functional space distortion and sensitivity to outliers. The results using NJ are compared with conventional functional dendrograms, convex hulls, and kernel‐density hypervolumes using both simulated and empirical datasets.Using NJ, we demonstrate that it is possible to combine much of the flexibility provided by multidimensional spaces with the simplicity of tree‐based representations. Moreover, the method is directly comparable with taxonomic diversity (TD) and PD measures, and enables quantification of the richness, divergence and regularity of the functional space.

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Section: Discussionmentioning

confidence: 99%

Calculating functional diversity metrics using neighbor‐joining trees

Cardoso,

Guillerme,

Mammola

et al. 2024

Ecography

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show abstract

“…By combining ease and speed of use, low distortion of functional space, low sensitivity to outliers, and comparability with PD measures, the use of NJ seems a promising approach for the future. More broadly, there are other methods available for building trees that are not considered here, but that could also provide new and advantageous ways to represent functional diversity (e.g., Wheeler, 2021). As such, we would argue that further exploration and testing of alternatives to the commonly used functional tree construction approaches (e.g., UPGMA) will likely prove rewarding in the study of functional diversity going forward.…”

Section: Discussionmentioning

confidence: 99%

Calculating functional diversity metrics using neighbor-joining trees

Cardoso

Guillerme

Mammola

et al. 2022

Preprint

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The study of functional diversity (FD) provides ways to understand phenomena as complex as community assembly or the dynamics of biodiversity change under multiple pressures. Different frameworks are used to quantify FD, either based on dissimilarity matrices (e.g., Rao entropy, functional dendrograms) or multidimensional spaces (e.g. convex hulls, kernel-density hypervolumes). While the first does not enable the measurement of FD within a richness/divergence/regularity framework, or results in the distortion of the functional space, the latter does not allow for comparisons with phylogenetic diversity (PD) measures and can be extremely sensitive to outliers. We propose the use of neighbor-joining trees (NJ) to represent and quantify functional diversity in a way that combines the strengths of current FD frameworks without many of their weaknesses. Our proposal is also uniquely suited for studies that compare FD with PD, as both share the use of trees (NJ or others) and the same mathematical principles. We test the ability of this novel framework to represent the initial functional distances between species with minimal functional space distortion and sensitivity to outliers. The results using NJ are compared with conventional functional dendrograms, convex hulls, and kernel-density hypervolumes using both simulated and empirical datasets. Using NJ we demonstrate that it is possible to combine much of the flexibility provided by multidimensional spaces with the simplicity of tree-based representations. Moreover, the method is directly comparable with PD measures, and enables quantification of the richness, divergence and regularity of the functional space.

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“…Furthermore, individual trees can be generated for data partitions and combined to yield either initial trees or network graphs (via the methods in Miyagi and Wheeler, 2019; Wheeler, 2022). This allows for the disparate phylogenetic information found in multiple data blocks to be explored in either tree or network analysis.…”

Section: Search Optionsmentioning

confidence: 99%

PhylogeneticGraph (PhyG) a new phylogenetic graph search and optimization program

Wheeler,

Washburn,

Crowley

2023

Cladistics

Self Cite

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We present Phylogenetic Graph (PhyG), an open‐source, phylogenetic search tool for diverse data types and graphs, including softwired and hardwired networks, in addition to trees. This allows for analysis of horizontal transfer and hybridization scenarios, as well as the necessary vertical inheritance of trees. PhyG is the successor to POY5 in performing combined data tree‐alignment with enhancements in heuristic optimality (up to 7% in example data) and execution time (up to a factor of 200). Input data may exhibit a practically unlimited number of character states in qualitative or sequence (aligned and unaligned) types. Novel graph construction and refinement algorithms have been implemented and integrated into a variety of search procedures. Currently, PhyG implements parsimony and No‐Common‐Mechanism Likelihood optimization.

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Phylogenetic supergraphs

Cited by 3 publications

References 33 publications

Calculating functional diversity metrics using neighbor‐joining trees

Calculating functional diversity metrics using neighbor‐joining trees

Calculating functional diversity metrics using neighbor-joining trees

PhylogeneticGraph (PhyG) a new phylogenetic graph search and optimization program

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