Data from: Accounting for extinction dynamics unifies the geological and biological histories of Indo-Australian Archipelago
Data files
Aug 20, 2024 version files 132.14 KB
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A10_36_finaltree.out.tre
47.98 KB
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aics_calculation.R
1.18 KB
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distrib.csv
1.41 KB
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distribution_pseu.csv
843 B
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Figure_ADE.R
4.83 KB
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main_paphiopedilum.R
4.70 KB
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main_pseuduvaria.R
4.40 KB
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Paph-ITS_trnL_F_atpB-com-new-exclude_gap.trees
63.78 KB
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README.md
3.02 KB
Abstract
Biogeographical reconstructions of the Indo-Australian Archipelago (IAA) have suggested recent spread across the Sunda and Sahul shelves of lineages with diverse origins, which appears to be congruent with a geological history of recent tectonic uplift in the region. However, this scenario is challenged by new geological evidence suggesting that the Sunda shelf was never submerged prior to the Pliocene, casting doubt on the interpretation of recent uplift and the correspondence of evidence from biogeography and geology. A mismatch between geological and biogeographical data may occur if analyses ignore the dynamics of extinct lineages, because this may add uncertainty to timing and origin of clades in biogeographical reconstructions. We revisit the historical biogeography of multiple IAA taxa and explicitly allow for the possibility of lineage extinction. In contrast to models assuming zero extinction, we find that all of these clades, including plants, invertebrates, and vertebrates, have a common and widespread geographic origin, and each has spread and colonized the region much earlier than previously thought. The results for the eight clades re-examined in this paper suggest that they diversified and spread during the early Eocene, which helps to unify the geological and biological histories of IAA.
README: Code and data for: Accounting for extinction dynamics unifies the geological and biological histories of Indo-Australian Archipelago
https://doi.org/10.5061/dryad.0vt4b8h70
Code to re-run the analysis in the manuscript entitled: "Accounting for extinction dynamics unifies the geological and biological histories of Indo-Australian Archipelago". It also includes some datasets re-visited in the manuscript. We have permission by the original authors of those datasets to make them public.
Geologists and biogeographers have worked together to characterize the consequences of large-scale events on species formation and extinction. Until very recently, complementary evidence from both disciplines has been used to describe the evolution of species diversity in Southeast Asia. However, fresh geological findings show a completely different scenario of island connectivity that sets an evolutionary stage impossible to match the biological inferences made with standard biogeographic approaches. In this paper we show that modelling species extinction, one of the key elements of any evolutionary process but often neglected, is crucial to reconcile macroevolutionary patterns with our best current geological understanding of the region. We are convinced that the interdisciplinary view and message of this manuscript will be appealing to a large audience
Description of the data and file structure
We include two R scripts which will import data (two phylogenetic trees and two .csv files with geographic information) into R and perform the biogeographic analysis.
Step 1
main_paphiopedilum.R is used to a) import the file Paph-ITS+trnL+F+atpB-com-new-exclude gap.trees and also distrib.csv which are the phylogenetic tree and the geographic distribution data respectively and b) this R script will run the lemad analysis for paphiopedilum group. The output could be stored in memory.
main_pseuduvaria.R is used to a) import the file A10_36_finaltree.out.tre and also distribution_pseu.csv which are the phylogenetic tree and the geographic distribution data respectively and b) this R script will run the lemad analysis for pseuduvaria group. The output could be stored in memory.
Step 2
One can plot a figure for the ancestral geographic reconstruction using FigureADE.R which will use the tree, the distribution file and the output from lemad analysis stored in memory.
Step 3
I include a simple R script to compute and compare across different models using AIC (aic_calculation.R).
Sharing/Access information
The two datasets included in here were kindly provided by the authors of those original datasets. They granted us permission to make them public.
Code/Software
We use the R package LEMAD (https://github.com/leonelhalsina/lemad) to carry out our analysis analysis. Vignettes to use it are available at the same GitHub.
Please use R version > 3 for this.