Lightning Talk: Biopython (bio) Geography Module

For Google Summer of Code 2009/NESCENT Phyloinformatics Summer of Code 2009, I built a Geography module for Biopython. The purpose of the module is to search, download, and process biogeographical data from GBIF, much as Biopython currently accesses Genbank. Application of the tool to a historical biogeography study on bivalves will be illustrated.

As required by Google Summer of Code and Biopython, the code is open access and is released under the Biopython License:
"http://www.biopython.org/DIST/LICENSE":http://www.biopython.org/DIST/LICENSE

The module is described, and a tutorial is presented, on the Biopython wiki:
http://biopython.org/wiki/BioGeography

The page contains links to the source hosted on Github; here is the direct link:
"http://github.com/nmatzke/biopython/tree/Geography":http://github.com/nmatzke/biopython/tree/Geograph

Whole mitochondrial genome sequencing of domestic horses reveals incorporation of extensive wild horse diversity during domestication

<p>Abstract</p> <p>Background</p> <p>DNA target enrichment by micro-array capture combined with high throughput sequencing technologies provides the possibility to obtain large amounts of sequence data (e.g. whole mitochondrial DNA genomes) from multiple individuals at relatively low costs. Previously, whole mitochondrial genome data for domestic horses (<it>Equus caballus</it>) were limited to only a few specimens and only short parts of the mtDNA genome (especially the hypervariable region) were investigated for larger sample sets.</p> <p>Results</p> <p>In this study we investigated whole mitochondrial genomes of 59 domestic horses from 44 breeds and a single Przewalski horse (<it>Equus przewalski</it>) using a recently described multiplex micro-array capture approach. We found 473 variable positions within the domestic horses, 292 of which are parsimony-informative, providing a well resolved phylogenetic tree. Our divergence time estimate suggests that the mitochondrial genomes of modern horse breeds shared a common ancestor around 93,000 years ago and no later than 38,000 years ago. A Bayesian skyline plot (BSP) reveals a significant population expansion beginning 6,000-8,000 years ago with an ongoing exponential growth until the present, similar to other domestic animal species. Our data further suggest that a large sample of wild horse diversity was incorporated into the domestic population; specifically, at least 46 of the mtDNA lineages observed in domestic horses (73%) already existed before the beginning of domestication about 5,000 years ago.</p> <p>Conclusions</p> <p>Our study provides a window into the maternal origins of extant domestic horses and confirms that modern domestic breeds present a wide sample of the mtDNA diversity found in ancestral, now extinct, wild horse populations. The data obtained allow us to detect a population expansion event coinciding with the beginning of domestication and to estimate both the minimum number of female horses incorporated into the domestic gene pool and the time depth of the domestic horse mtDNA gene pool.</p

The Morphology of Steve

This report is part of Project Steve. Project Steve is, among other things, the first scientific analysis of the sex, geographic location, and body size of scientists named Steve. We performed this research for the best of all reasons: we discovered that we had lots of data. No scientist can resist the opportunity to analyze data, regardless of where that data came from or why it was gathered

ENMTools 1.0: an R package for comparative ecological biogeography

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages

Data from: Model selection in historical biogeography reveals that founder-event speciation is a crucial process in island clades

Founder-event speciation, where a rare jump dispersal event founds a new genetically isolated lineage, has long been considered crucial by many historical biogeographers, but its importance is disputed within the vicariance school. Probabilistic modeling of geographic range evolution creates the potential to test different biogeographical models against data using standard statistical model choice procedures, as long as multiple models are available. I re-implement the Dispersal-Extinction-Cladogenesis (DEC) model of LAGRANGE in the R package BioGeoBEARS, and modify it to create a new model, DEC+J, which adds founder-event speciation, the importance of which is governed by a new free parameter, j. The identifiability of DEC and DEC+J is tested on datasets simulated under a wide range of macroevolutionary models where geography evolves jointly with lineage birth/death events. The results confirm that DEC and DEC+J are identifiable even though these models ignore the fact that molecular phylogenies are missing many cladogenesis and extinction events. The simulations also indicate that DEC will have substantially increased errors in ancestral range estimation and parameter inference when the true model includes +J. DEC and DEC+J are compared on 13 empirical datasets drawn from studies of island clades. Likelihood ratio tests indicate that all clades reject DEC, and AICc model weights show large to overwhelming support for DEC+J, for the first time verifying the importance of founder-event speciation in island clades via statistical model choice. Under DEC+J, ancestral nodes are usually estimated to have ranges occupying only one island, rather than the widespread ancestors often favored by DEC. These results indicate that the assumptions of historical biogeography models can have large impacts on inference and require testing and comparison with statistical methods

SuppData2_all_BioGeoBEARS_inferences

This zipfile contains a folder and a script for each of the 53 empirical BioGeoBEARS analyses presented in the paper. Users will have to edit working directories etc. to re-run these analysis. See the BioGeoBEARS wiki page for updates to the basic example script

Supplemental Data 1: Excel file showing DEC and DEC+J weights calculations

This excel file shows the DEC and DEC+J weights calculations for a three-area problem. Users may change the value of the j parameter and see the resulting change in the individual per-event weights of y (sympatry), s (subset sympatry), v (vicariance), and j (jump dispersal/founder-event speciation). The conditional probabilities of each individual event, used in the calculation of the likelihood, are the individual weight divided by the sum of the weights of events allowed, conditional on a particular ancestral range

Supplemental Text, Tables, and Figures

All supplemental text, tables, and figures, as one PDF

Data from: Inferring node dates from tip dates in fossil Canidae: the importance of tree priors

Tip-dating methods are becoming popular alternatives to traditional node calibration approaches for building time-scaled phylogenetic trees, but questions remain about their application to empirical datasets. We compared the performance of the most popular methods against a dated tree of fossil Canidae derived from previously published monographs. Using a canid morphology dataset, we performed tip-dating using BEAST v. 2.1.3 and MrBayes v. 3.2.5. We find that for key nodes (Canis, approx. 3.2 Ma, Caninae approx. 11.7 Ma) a non-mechanistic model using a uniform tree prior produces estimates that are unrealistically old (27.5, 38.9 Ma). Mechanistic models (incorporating lineage birth, death and sampling rates) estimate ages that are closely in line with prior research. We provide a discussion of these two families of models (mechanistic versus non-mechanistic) and their applicability to fossil datasets

Date 2013-07-27

Description BioGeoBEARS allows probabilistic inference of both historical biogeography (ancestral geographic ranges on a phylogeny) as well as comparison of different models of range evolution. It reproduces the model available in LAGRANGE (Ree and Smith 2008), as well as making available numerous additional models. For example, LAGRANGE as typically run has two free parameters, d (dispersal rate, i.e. the rate of range addition along a phylogenetic branch) and e (extinction rate, really the rate of local range loss along a phylogenetic branch). LAGRANGE also has a fixed cladogenic model which gives equal probability to a number of allowed range inheritance events, e.g.: (1) vicariance, (2) a new species starts in a subset of the ancestral range, (3) the ancestral range is copied to both species; in all cases, at least one species must have a starting range of size 1. LAGRANGE assigns equal probability to each of these events, and zero probability to other events. BioGeoBEARS adds an additional cladogenic event: founder-event speciation (the new species jumps to a range outside of the ancestral range), and also allows the relative weighting of the different sorts of events to be made into free parameters, allowing optimization and standard model choice procedures to pick the best model. The relative probability of differen