DOI: 10.5061/dryad.hx3ffbgnm
title: Investigating historical drivers of latitudinal gradients in polyploid plant biogeography: A multi-clade perspective

description: Premise of the Study The proportion of polyploid plants in a community
 increases with latitude, and different hypotheses have been proposed about
 which factors drive this pattern. Here, we aim to understand the
 historical causes of the latitudinal polyploidy gradient using a
 combination of ancestral state reconstruction methods. Specifically, we
 assess whether (1) polyploidization enables movement to higher latitudes
 (i.e., polyploidization precedes occurrences in higher latitudes) or (2)
 higher latitudes facilitate polyploidization (i.e., occurrence in higher
 latitudes precedes polyploidization). Methods We reconstruct the ploidy
 states and ancestral niches of 1,032 angiosperm species at four
 paleoclimatic time slices ranging from 3.3 million years ago to the
 present, comprising taxa from four well-represented clades: Onagraceae,
 Primulaceae, Solanum (Solanaceae), and Pooideae (Poaceae). We use
 ancestral niche reconstruction models alongside a customized discrete
 character evolution model to allow reconstruction of states at specific
 time slices. Patterns of latitudinal movement are reconstructed and
 compared in relation to inferred ploidy shifts. Key Results We find that
 no single hypothesis applies equally well across all analyzed clades.
 While significant differences in median latitudinal occurrence were
 detected in the largest clade, Poaceae, no significant differences were
 detected in latitudinal movement in any clade. Conclusions Our preliminary
 study is the first to attempt to connect ploidy changes to continuous
 latitudinal movement, but we cannot favor one hypothesis over another.
 Given that patterns seem to be clade-specific, a larger number of clades
 must be analyzed in future studies for generalities to be drawn.
description: Phylogenies were collected from the following sources: Onagraceae
 (Freyman and Höhna 2019), Primulaceae (De Vos et al. 2014), Solanaceae
 (Särkinen et al. 2013), and Poaceae (Spriggs et al. 2014). The phylogenies
 of Solanaceae and Poaceae were pruned to retain just those species in
 <em>Solanum </em>and Pooideae respectively. Occurrence points
 for species in each clade were downloaded from GBIF, and were cleaned for
 inaccuracies following protocols similar to those in Boyko et al. (2023).
 Ploidy data were gathered from the supplementary data of Rice et al.
 (2019). The ancestral ranges of taxa in each clade were
 reconstructed using the program machuruku (Guillory and Brown 2021) to
 time periods available from PaleoClim data (Brown et al. 2018): the Last
 Interglacial (LIG, c. 130 ka), Marine Isotope Stage 19 (MIS19, c. 787 ka),
 the mid-Pliocene Warm Period (mPWP, c. 3.205 Ma), and Marine Isotope Stage
 M2 (M2, c. 3.3 Ma). Ploidy was reconstructed using corHMM (Beaulieu et al.
 2013; Boyko and Beaulieu 2021) with modified functions that allow for
 ancestral state reconstruction at specific time slices rather than at
 nodes. Finally, the data gathered from these analyses
 were examined using a variety of statistical techniques.
description: The proportion of polyploid plants in a community increases with latitude,
 and different hypotheses have been proposed about which factors drive this
 pattern. Here, we aim to understand the historical causes of the
 latitudinal polyploidy gradient using a combination of ancestral state
 reconstruction methods. Specifically, we assess whether (1)
 polyploidization enables movement to higher latitudes (i.e.,
 polyploidization precedes occurrences in higher latitudes) or (2) higher
 latitudes facilitate polyploidization (i.e., occurrence in higher
 latitudes precedes polyploidization). We reconstruct the ploidy states and
 ancestral niches of 1,032 angiosperm species at four paleoclimatic time
 slices ranging from 3.3 million years ago to the present, comprising taxa
 from four well-represented clades: Onagraceae, Primulaceae, Solanum
 (Solanaceae), and Pooideae (Poaceae). We use ancestral niche
 reconstruction models alongside a customized discrete character evolution
 model to allow reconstruction of states at specific time slices. Patterns
 of latitudinal movement are reconstructed and compared in relation to
 inferred ploidy shifts. We find that no single hypothesis applies equally
 well across all analyzed clades. While significant differences in median
 latitudinal occurrence were detected in the largest clade, Poaceae, no
 significant differences were detected in latitudinal movement in any
 clade. Our preliminary study is the first to attempt to connect ploidy
 changes to continuous latitudinal movement, but we cannot favor one
 hypothesis over another. Given that patterns seem to be clade-specific, a
 larger number of clades must be analyzed in future studies for
 generalities to be drawn. ###### Description of the data and file
 structure Contained within the The dataset also contains six code files,
 meant to be run in order using inputs provided in this repository (order
 indicated by the beginnings of R file names, 01 through 06).
 00_utility_functions.R contains functions necessary to execute the code in
 other files. The other code files are as follows: *
 01_assembling_ploidy_data.R: Assembles clean ploidy data from the raw
 files (included in this dataset)  * 02_organizing_files.R: Assembles
 datasets for occurrence points and ploidy for each of the four clades
 included in our study; also prunes phylogenies for taxa in each clade that
 also possess ploidy data as well as sufficient occurrence data  *
 03_present_day_sprich.R: Assembles species richness raster map from
 occurrence points.  * 04_running_machuruku.R: Runs machuruku range
 reconstructions to paleoclimatic time slices based on the climatic
 variables that characterize the distribution of each lineage included in
 the input phylogeny.  * 05_running_machuruku_part2.R: Runs modified corHMM
 reconstructions of ploidy to each paleoclimatic time slice; also runs
 machuruku reconstructions for individual taxa such that the median
 latitude and longitude can be extracted from each individual reconstructed
 range.  * 06_making_plots.R: Code to create the plots included in our
 manuscript. The folder “CE_out” contains 113 raw genus-level ploidy data
 CSV files, with one file per genus (e.g., AegilopsPloidy.csv,
 AgropyronPloidy.csv, etc.). Each genus falls within one of the four clades
 of interest in our manuscript. Ploidy inferences used in our manuscript
 come from the "Ploidy inference" column. The folder
 "gbif_points" contains four CSV files (one for each of our four
 clades of interest) containing occurrence points downloaded from the
 Global Biodiversity Information Facility (GBIF). Each row represents a
 single occurrence point, with column entries for latitude and longitude.
 The folder "Paleoclim" contains files needed for reconstructing
 climatic data at various time slices. There is one folder for each time
 slice, as follows: 1_cur_CHELSA_V1_2B_r10m (present-day),
 2_LGM_chelsa_v1_2B_r10m (the Last Glacial Maximum c. 21 thousand years ago
 [ka]), 3_LIG_v1_10m (Last Interglacial c. 130 ka), 4_787ka_MIS19_v1_r10m
 (Marine Isotope Stage 19 c. 787 ka), 5_3.205Ma_mPWP_v1_r10m (mid-Pliocene
 Warm Period c. 3.205 Ma), and 6_3.3Ma_M2_v1_r10m (Marine Isotope Stage M2
 c. 3.3 Ma). The folder "ploidy" contains four CSV files (one for
 each of our four clades of interest) containing ploidy inferences for
 individual species. This is a cleaned up version of the single-genus
 folders contained within the folder "CE_out." The folder
 "Trees" contains four .tre files (one for each of our four
 clades of interest) containing molecular phylogenies of each clade. ######
 Sharing/access information Data derived from other sources are listed
 below: 1. Ploidy data (Rice et al. 2019; “The global biogeography of
 polyploid plants”) 2. GBIF data GBIF.org (23 July 2021) GBIF Occurrence
 Download
 ([https://doi.org/10.15468/dl.pw2qns](https://doi.org/10.15468/dl.pw2qns);
 GBIF.org (23 July 2021) GBIF Occurrence Download
 ([https://doi.org/10.15468/dl.yesy2v](https://doi.org/10.15468/dl.yesy2v);
 GBIF.org (23 July 2021) GBIF Occurrence Download
 ([https://doi.org/10.15468/dl.vqm9q3](https://doi.org/10.15468/dl.vqm9q3);
 GBIF.org (23 July 2021) GBIF Occurrence Download
 ([https://doi.org/10.15468/dl.gqu424](https://doi.org/10.15468/dl.gqu424);
 GBIF.org (23 July 2021) GBIF Occurrence Download
 ([https://doi.org/10.15468/dl.3ucjgk](https://doi.org/10.15468/dl.3ucjgk);
 GBIF.org (23 July 2021) GBIF Occurrence Download
 ([https://doi.org/10.15468/dl.78shpr](https://doi.org/10.15468/dl.78shpr);
 GBIF.org (23 July 2021) GBIF Occurrence Download
 ([[https://doi.org/10.15468/dl.f9pq57](https://doi.org/10.15468/dl.f9pq57)]. 3. Onagraceae phylogeny (Freyman and Höhna 2019: “Stochastic character mapping of state-dependent diversification reveals the tempo of evolutionary decline in self-compatible Onagraceae lineages”) 4. Primulaceae phylogeny (De Vos et al. 2014; “Small and ugly? Phylogenetic analyses of the “selfing syndrome” reveal complex evolutionary fates of monomorphic primrose flowers”) 5. Solanaceae phylogeny (Särkinen et al. 2013; “A phylogenetic framework for evolutionary study of the nightshades (Solanaceae): a dated 1000-tip tree” 6. Poaceae phylogeny (Spriggs et al. 2014; “C4 Photosynthesis Promoted Species Diversification during the Miocene Grassland Expansion”) 7. PaleoClim data from the LIG (Last Interglacial, c. 130 ka), MIS19 (Marine Isotope Stage 19, c. 787 ka), mPWP (mid-Pliocene Warm Period, c. 3.205 Ma), and M2 (Marine Isotope Stage M2, c. 3.3 Ma), all using the spatial resolution of 10 arc-minutes (Brown et al. 2018; “PaleoClim, high spatial resolution paleoclimate surfaces for global land areas”) ###### Code/Software To run the two code files, you will need the following packages (versions used during the production of this dataset are also provided): 1\. raster (3.6.26) 2\. ape (5.6.2) 3\. taxize (0.9.100) 4\. rgbif (3.7.3) 5\. maptools (1.1.4) 6\. sp (1.5.0) 7\. rgeos (0.5.9) 8\. rworldmap (1.3.6) 9\. data.table (1.14.2) 10\. terra (1.7.55) 11\. dismo (1.3.9) 12\. usdm (1.1.18) 13\. phytools (1.2.0) 14\. stringr (1.5.0) 15\. stringi (1.7.8) 16\. machuruku (1.8.3) 17\. corHMM (2.8) 18\. geiger (2.0.10) 19\. parallel (4.2.1) 20\. MASS (7.3.58.1) 21\. Peacock.test (1.0) 22\. dispRity (1.7.0) 23\. paleotree (3.4.5) 24\. dplyr (1.1.3) 25\. castor (1.7.3)