Subsets of Randomly Selected Phylogenies from Existing Mega-Phylogenies.

megatrees

The goal of megatrees is to provide a collection of subset of existing mega-phylogenies (mega-trees, hence the package name) for ecological studies. For common community ecology analyses, results derived from such phylogenies are robust (Li et al. 2019). This package will save us time to repeat the effort to download and randomly select a small subset of posterior phylogenies (for some taxonomic groups). Previous studies suggested that sampling 50-100 trees is sufficient to capture the uncertainty of phylogenetic spaces (Baiser et al. 2018; Li et al. 2019; Nakagawa et al. 2019). Therefore, for taxonomic groups with multiple posterior distribution of phylogenies, a randomly selected subset of 100 phylogenies were provided here, which can be relatively large in size (e.g., 45 Mb for the 100 phylogenies of 32k fishes). The larger than normal size made it hard to host this package on CRAN.

Installation

You can install the development version of megatrees from GitHub:

if(!require("remotes")) install.packages("remotes")
remotes::install_github("daijiang/megatrees")

List of phylogenies available

Datasets with a single tree are bundled with the package and accessed directly as R objects. Datasets with 50 or 100 posterior trees are large and must be downloaded via their accessor function (marked with get_*()).

⚠️ Important update — breaking change:

Large multi-tree datasets (those with 50 or 100 posterior phylogenies) are no longer bundled with the package. They are stored as GitHub release assets and downloaded to a local cache on first use via accessor functions (e.g. get_tree_bird_n100()). This also means that we need to have internet access if we need to use those multiPhylo objects. The piggyback package is required for downloading:

install.packages("piggyback")

⚠️ Warning: this change breaks existing code. If you previously used a multiPhylo object directly (e.g. tree_bird_n100), that object no longer exists in the package. You must update your code to call the corresponding accessor function and assign the result to a variable:

# old (broken)
phy <- tree_bird_n100[[1]]

# new
tree_bird_n100 <- get_tree_bird_n100()
phy <- tree_bird_n100[[1]]

The full list of renamed accessors: get_tree_amphibian_n100(), get_tree_bee_n100(), get_tree_bird_n100(), get_tree_fish_32k_n50(), get_tree_mammal_n100_phylacine(), get_tree_mammal_n100_vertlife(), get_tree_plant_n100_Carruthers(), get_tree_reptile_n100(), get_tree_shark_ray_n100().

Contribution

Everyone is welcome to add new existing mega-trees here by sending a pull request. Or just open an issue to add links to the new mega-trees and I will add them here.

References

Baiser, B., Valle, D., Zelazny, Z., & Burleigh, J. G. (2018). Non‐random patterns of invasion and extinction reduce phylogenetic diversity in island bird assemblages. Ecography, 41(2), 361-374.

Faurby, S., Davis, M., Pedersen, R. Ø., Schowanek, S. D., Antonelli, A., & Svenning, J. C. (2018). PHYLACINE 1.2: The phylogenetic atlas of mammal macroecology. Ecology, 99(11), 2626-2626.

Henríquez-Piskulich, P.; Hugall, A.F.; Stuart-Fox; D. (2023). A supermatrix phylogeny of the world’s bees (Hymenoptera: Anthophila). bioRxiv 2023.06.16.545281. doi.org/10.1101/2023.06.16.545281.

Jetz, W., & Pyron, R. A. (2018). The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nature ecology & evolution, 2(5), 850-858.

Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012). The global diversity of birds in space and time. Nature, 491(7424), 444.

Li, D., Monahan, W. B., & Baiser, B. (2018). Species richness and phylogenetic diversity of native and non‐native species respond differently to area and environmental factors. Diversity and Distributions, 24(6), 853-864.

Li, D., Trotta, L., Marx, H. E., Allen, J. M., Sun, M., Soltis, D. E., ... & Baiser, B. (2019). For common community phylogenetic analyses, go ahead and use synthesis phylogenies. Ecology, 100(9), e02788.

Nakagawa, S., & De Villemereuil, P. (2019). A general method for simultaneously accounting for phylogenetic and species sampling uncertainty via Rubin’s rules in comparative analysis. Systematic Biology, 68(4), 632-641.

Rabosky, D. L., Chang, J., Title, P. O., Cowman, P. F., Sallan, L., Friedman, M., ... & Alfaro, M. E. (2018). An inverse latitudinal gradient in speciation rate for marine fishes. Nature, 559(7714), 392.

Smith, S. A., & Brown, J. W. (2018). Constructing a broadly inclusive seed plant phylogeny. American Journal of Botany, 105(3), 302-314.

Stein, R. W., Mull, C. G., Kuhn, T. S., Aschliman, N. C., Davidson, L. N., Joy, J. B., ... & Mooers, A. O. (2018). Global priorities for conserving the evolutionary history of sharks, rays and chimaeras. Nature ecology & evolution, 2(2), 288-298.

Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W., & Pyron, R. A. (2016). Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biological Conservation, 204, 23-31.

Upham, N. S., Esselstyn, J. A., & Jetz, W. (2019). Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS biology, 17(12), e3000494.