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1. Data and code from: Spatially heterogeneous gene flow may hinder linking phylogeographic data to macroevolutionary patterns

   https://doi.org/10.5061/dryad.9w0vt4bsb  

   Gyllenhaal, Ethan; Musher, Lukas (March 2026, Dryad)

   Abstract: Biogeography relies on estimates of phylogeny and gene flow to test hypotheses. Established theory posits (1) gene flow varies non-randomly with respect to geography and (2) gene flow can mislead phylogenetic inference. These findings, often viewed in isolation, together suggest that spatial impacts on gene flow could bias biogeographic inference. For example, whenever a “peripheral” population is isolated and inferred as sister to a “core” clade of adjacent populations that exchange migrants, it might reflect elevated gene flow of “core” populations rather than macroevolutionary processes (e.g., direction of colonization). Given this ubiquitous but often unacknowledged biogeographic setting, it remains unknown how spatial influences on gene flow impact metrics used to assess biogeographic hypotheses (e.g., branch length or monophyly). We simulate gene flow that varies nonrandomly regarding geography and found that relatively low levels of gene flow lead to monophyly of adjacent populations, with longer branches for peripheral populations, regardless of true divergence history. We highlight an empirical example wherein antbirds (Thamnophilidae) are young in Amazonia, but older at Amazonia’s periphery. Although it is unclear if the simulated bias applies to antbirds, our results suggest a distinguishability problem for any nodes stemming from radiations in geographically heterogeneous environments. TechnicalInfo: # Data and code from: Spatially heterogeneous gene flow may hinder linking phylogeographic data to macroevolutionary patterns Last updated 4 March 2026 Repository for scripts and input files used in a study that investigates the potential role of spatially heterogeneous gene flow in shaping phylogenetic inference, with an eye towards a biogeographic pattern (the core-periphery effect). Sections 1 through 3 are written by Ethan Gyllenhaal; Section 4 is written by Lukas Musher. This README describes the scripts and data files used for the aforementioned project. Each section corresponds to a zipped directory in the Dryad repository. ### Coalescent Simulations (01_simulations.zip) core_phylo_noanc.py - Python script used to run msprime simulations, taking in a set of parameters defined in the batch script and outputting fasta format files for phylogenetic inference. Takes in parameters for output path, replicate number, number of migrants per generation (generally <1), and per population effective pop. size, time to run after colonization completes, interval of colonization events, and a string split order. Useage like python $dir/core_trial_noanc.py -r [int replicate] -m [float Nm] -n [int/float effective pop size] -e [int/float post-split evolution time] -i [int/float split interval in gens] -s [string split interval: CAB, ACB, or ABC] -o [output stem, ideally includes all variables used]. variant_fasta.sh - Custom script for reducing output fasta files to variant sites only, for space efficiency. Usage: sh variant_fasta.sh in.fasta out.fasta. full_coresim_run.slurm - SLURM bash script used for running replicates. It uses GNU parallel to iterate over replicates. Note that this did not finish in the 48-hour walltime, and had to be resumed several times. Uploaded script reflects the original run. sim_tree_output.zip - Zipped directory of all output treefiles from simulations. Used as input for the next section. ### Simulation processing (02_processing.zip) summarize_branch.py - Python script for summarizing output phylogenies run in a batch script with output from the msprime Python script (both in section 1). This uses ETE3 to process trees and output topologies and branch lengths. Takes a big list of tree files as input. Usage example: python summarize_branch.py -i 'sim_tree_output/*treefile' -o coresim_output_branch.csv coresim_output_branch.csv - Output from summarize_branch.py. Columns are as follows, (NOTE, after the first use of Anest, Bnest, ABnest, Cmono, Other, Non-monophyletic, and Poor-resolution, the full set are refered to with a *): * First column is the stem name for the parameter combination * Anest - Number of topologies with population A but not B nested in the core (C) populations * Bnest - Number of topologies with population B but not A nested in the core (C) populations * ABnest - Number of topologies with population A and B nested in the core (C) populations * Cmono - Number of topologies with a monophyletic core group (all 4 populations) * Other - number of topologies not matching the prior set, never found here, not sure it can happen but a catch for if it does * Non-monophyletic - Number of topologies where A or B were non-monophyletic (none in this set) * Poor-resolution - Number of poorly resolved topologies without a well-supported monophyletic core * *total - The SUMMED tree heights for each category, notably not a mean, needs to be divided by the count per category * *_core - The summed mean branch lengths of core populations per category * *_a - The summed branch lengths of population a per category * *_b - The summed branch lengths of population b per category * total - The summed tree heights across categories * core - The summed mean core branch lengths across categories * a - The summed population a branch lengths across categories * b - The summed population b branch lengths across categories * name - Same as the first column, stem name of files * disp - Simulated core gene flow level in number of migrants WITHOUT THE DECIMAL (IN THIS CASE DIVIDED BY 10) for easier parsing * ne - Simulated per population effective population size * time - Simulated post-divergence time in generations * int - Simulated split interval * history - Simulated split history, named based on what orders divergences occurred in (A = population a, B = population b, C = core radiation) plot_topo_branch_heatmap.R - R script for plotting summarized phylogenetic information, primarily heat maps. Also includes code for making input used in 04_divergence_dates (Fig 3A) ### Richness Map (03_mapping.zip) plot_antbird_richness.R - R script that uses BirdLife data to plot species richness map. Note that you'll have to get your own BirdLife data: [https://datazone.birdlife.org/contact-us/request-our-data](https://datazone.birdlife.org/contact-us/request-our-data). Use the species list (below) to subset the massive input dataframe. species.txt - List of antbird species used here, based on BirdLife taxonomy. ### Divergence date estimation (04_divergence_dates.zip) area.thamno.txt - Output from R code categorizing biogeographic scores into categories for plot in Figure 3B. violin_full.csv - Input used for violin plots, generated by plot_topo_branch_heatmap.R (step 2). Most values from coresim_output_branch.csv, with some additional math from R. Columns are below: * Combination - Arbitrary number for each combination of parameters for each population's branch length * disp - Simulated core gene flow level in number of migrants WITHOUT THE DECIMAL (IN THIS CASE DIVIDED BY 10) for easier parsing * ne - Simulated per population effective population size * time - Simulated post-divergence time in generations * int - Simulated split interval * a - The summed population a branch lengths across categories * b - The summed population b branch lengths across categories * total - The summed tree heights across categories * core - The summed mean core branch lengths across categories * core_per - The summed mean core branch length per parameter combination divided by the smaller of summed population a or b branch lengths (if doing per category, would have to divide by the category count first, not relevant here) * branch.length - The mean branch length for the given population (core, A, or B) * name - Stem name of the file the data was calculated from * history - Simulated split history, named based on what orders divergences occurred in (A = population a, B = population b, C = core radiation) * count - Sum of replicates per parameter combo (20 for all here) * pop - The population these statistics were computed for, either all core populations combined or one individual peripheral population. * migration - Binary variable describing if any gene flow was included (does not separate by migration rates) T400F_Thamnophilidae.tre - Phylogeny used for divergence dates and plotting. Thamnophil_biogeographic_scoring.csv - CSV used as input for biogeographic assignment code. Columns are below: * Taxon_name_code - Name of taxon in phylogeny * Family - Family of taxon * genus - Genus of taxon * species - Species of taxon * subspecies_if_mult_sampled - Subspecies of taxon if multiple were sampled * specimen locality - Locality of specimen (only for one) * real_elevational_range - Elevational range of taxon if known (not used here) * n_america to islands - Biogeographic range of taxon (reduced to summary in area.thamno.txt). All of these variables are scored based on LJM's assessment of ranges based on available literature. In order, these refer to: North America, West Mexico, East Mexico, Central America, Northwest South America, Northern Andes, Southern Andes, Western Amazonia, Northeast Amazonia, Southeast Amazonia, Atlantic Forest of South America, Cerrado savanna ecoregion, Chaco region of South America, Caatinga biome of Brazil, Beni savanna ecoregion, Llanos grasslands, Tepui highlands, Parana river basin, Amazonian white-sand forest habitat, Temperate regions of South America, Atacama desert, Caribbean region, general lowland habitats under 250m elevation, general upland habitat from 250 to 1500m, general montane habitat from 1500m to 3500m, high montant habitat over 3500m, Amazonian flooded forest, Amazonian terra firme forest, general arid habitats, general humid habitats, and general oceanic islands. * Notes - additional notes if relevant thamnophilidae_ages.R - R code for generating the phylogeny and box plots in Figure 3 (A and B). Uses the biogeographic scoring CSV and tree, outputs area.thamno.txt 

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2. Genome-Scale Data Reveal Deep Lineage Divergence and a Complex Demographic History in the Texas Horned Lizard ( Phrynosoma cornutum ) throughout the Southwestern and Central United States

   https://doi.org/10.1093/gbe/evab260  

   Finger, Nicholas; Farleigh, Keaka; Bracken, Jason T; Leaché, Adam D; François, Olivier; Yang, Ziheng; Flouri, Tomas; Charran, Tristan; Jezkova, Tereza; Williams, Dean A; et al (January 2022, Genome Biology and Evolution)

   Baldauf, Sandra (Ed.)

   Abstract The southwestern and central United States serve as an ideal region to test alternative hypotheses regarding biotic diversification. Genomic data can now be combined with sophisticated computational models to quantify the impacts of paleoclimate change, geographic features, and habitat heterogeneity on spatial patterns of genetic diversity. In this study, we combine thousands of genotyping-by-sequencing (GBS) loci with mtDNA sequences (ND1) from the Texas horned lizard (Phrynosoma cornutum) to quantify relative support for different catalysts of diversification. Phylogenetic and clustering analyses of the GBS data indicate support for at least three primary populations. The spatial distribution of populations appears concordant with habitat type, with desert populations in AZ and NM showing the largest genetic divergence from the remaining populations. The mtDNA data also support a divergent desert population, but other relationships differ and suggest mtDNA introgression. Genotype–environment association with bioclimatic variables supports divergence along precipitation gradients more than along temperature gradients. Demographic analyses support a complex history, with introgression and gene flow playing an important role during diversification. Bayesian multispecies coalescent analyses with introgression (MSci) analyses also suggest that gene flow occurred between populations. Paleo-species distribution models support two southern refugia that geographically correspond to contemporary lineages. We find that divergence times are underestimated and population sizes are overestimated when introgression occurred and is ignored in coalescent analyses, and furthermore, inference of ancient introgression events and demographic history is sensitive to inclusion of a single recently admixed sample. Our analyses cannot refute the riverine barrier or glacial refugia hypotheses. Results also suggest that populations are continuing to diverge along habitat gradients. Finally, the strong evidence of admixture, gene flow, and mtDNA introgression among populations suggests that P. cornutum should be considered a single widespread species under the General Lineage Species Concept. 

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3. Gene Flow Complicates Phylogenetic Inference in an Archipelago Radiation

   https://doi.org/10.1093/sysbio/syaf081  

   Gyllenhaal, Ethan F; Klicka, Lukas B; DeCicco, Lucas H; Weeks, Brian C; Moyle, Robert G; Andersen, Michael J (November 2025, Systematic Biology)

   Carstens, Bryan (Ed.)

   Allopatric divergence is a fundamental component of most traditional models of biogeography and community assembly. Gene flow between allopatric populations should be influenced by the nature of geographic barriers and can have a profound impact on adaptation, the speciation process, and phylogenetic inference. Superspecies—monophyletic groups of taxa with species-level differences in phenotype or genotype that are found exclusively in allopatry or parapatry—present an opportunity to characterize the effects of gene flow on the divergence process. Here, we investigate patterns of gene flow, population structure, and inferred phylogenetic relationships for members of an avian superspecies, the Solomons Monarchs (Aves: Symposiachrus barbatus complex) occupying the Solomon Islands. We found that gene flow among allopatric species matches predictions based on geography, but phylogenetic relationships were not concordant with the most likely colonization history based on a stepping-stone colonization model. Notably, the most isolated island, Makira, has a species that was inferred to be sister to the taxa on all other islands in concatenated phylogenetic analyses, despite Makira being farthest from the presumed original source of immigrants. We use population genetic simulations to demonstrate that such a result could be driven by bias resulting from low levels of gene flow, reflecting a challenge in phylogeographic inference that results when one population is differentially isolated. These simulated findings demonstrate a distinguishability issue in phylogeographic inference, where gene flow and colonization history can be difficult to disentangle. 

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4. Pushed waves, trailing edges, and extreme events: Eco‐evolutionary dynamics of a geographic range shift in the owl limpet, Lottia gigantea

   https://doi.org/10.1111/gcb.17414  

   Nielsen, Erica S; Walkes, Samuel; Sones, Jacqueline L; Fenberg, Phillip B; Paz‐García, David A; Cameron, Brenda B; Grosberg, Richard K; Sanford, Eric; Bay, Rachael A (July 2024, Global Change Biology)

   As climatic variation re‐shapes global biodiversity, understanding eco‐evolutionary feedbacks during species range shifts is of increasing importance. Theory on range expansions distinguishes between two different forms: “pulled” and “pushed” waves. Pulled waves occur when the source of the expansion comes from low‐density peripheral populations, while pushed waves occur when recruitment to the expanding edge is supplied by high‐density populations closer to the species' core. How extreme events shape pushed/pulled wave expansion events, as well as trailing‐edge declines/contractions, remains largely unexplored. We examined eco‐evolutionary responses of a marine invertebrate (the owl limpet,Lottia gigantea) that increased in abundance during the 2014–2016 marine heatwaves near the poleward edge of its geographic range in the northeastern Pacific. We used whole‐genome sequencing from 19 populations across >11 degrees of latitude to characterize genomic variation, gene flow, and demographic histories across the species' range. We estimated present‐day dispersal potential and past climatic stability to identify how contemporary and historical seascape features shape genomic characteristics. Consistent with expectations of a pushed wave, we found little genomic differentiation between core and leading‐edge populations, and higher genomic diversity at range edges. A large and well‐mixed population in the northern edge of the species' range is likely a result of ocean current anomalies increasing larval settlement and high‐dispersal potential across biogeographic boundaries. Trailing‐edge populations have higher differentiation from core populations, possibly driven by local selection and limited gene flow, as well as high genomic diversity likely as a result of climatic stability during the Last Glacial Maximum. Our findings suggest that extreme events can drive poleward range expansions that carry the adaptive potential of core populations, while also cautioning that trailing‐edge extirpations may threaten unique evolutionary variation. This work highlights the importance of understanding how both trailing and leading edges respond to global change and extreme events. 

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5. Geogenomic Predictors of Genetree Heterogeneity Explain Phylogeographic and Introgression History: A Case Study in an Amazonian Bird ( Thamnophilus aethiops )

   https://doi.org/10.1093/sysbio/syad061  

   Musher, Lukas J; Del-Rio, Glaucia; Marcondes, Rafael S; Brumfield, Robb T; Bravo, Gustavo A; Thom, Gregory (October 2023, Systematic Biology)

   Burbrink, Frank (Ed.)

   Abstract Can knowledge about genome architecture inform biogeographic and phylogenetic inference? Selection, drift, recombination, and gene flow interact to produce a genomic landscape of divergence wherein patterns of differentiation and genealogy vary nonrandomly across the genomes of diverging populations. For instance, genealogical patterns that arise due to gene flow should be more likely to occur on smaller chromosomes, which experience high recombination, whereas those tracking histories of geographic isolation (reduced gene flow caused by a barrier) and divergence should be more likely to occur on larger and sex chromosomes. In Amazonia, populations of many bird species diverge and introgress across rivers, resulting in reticulated genomic signals. Herein, we used reduced representation genomic data to disentangle the evolutionary history of 4 populations of an Amazonian antbird, Thamnophilus aethiops, whose biogeographic history was associated with the dynamic evolution of the Madeira River Basin. Specifically, we evaluate whether a large river capture event ca. 200 Ka, gave rise to reticulated genealogies in the genome by making spatially explicit predictions about isolation and gene flow based on knowledge about genomic processes. We first estimated chromosome-level phylogenies and recovered 2 primary topologies across the genome. The first topology (T1) was most consistent with predictions about population divergence and was recovered for the Z-chromosome. The second (T2), was consistent with predictions about gene flow upon secondary contact. To evaluate support for these topologies, we trained a convolutional neural network to classify our data into alternative diversification models and estimate demographic parameters. The best-fit model was concordant with T1 and included gene flow between non-sister taxa. Finally, we modeled levels of divergence and introgression as functions of chromosome length and found that smaller chromosomes experienced higher gene flow. Given that (1) genetrees supporting T2 were more likely to occur on smaller chromosomes and (2) we found lower levels of introgression on larger chromosomes (and especially the Z-chromosome), we argue that T1 represents the history of population divergence across rivers and T2 the history of secondary contact due to barrier loss. Our results suggest that a significant portion of genomic heterogeneity arises due to extrinsic biogeographic processes such as river capture interacting with intrinsic processes associated with genome architecture. Future phylogeographic studies would benefit from accounting for genomic processes, as different parts of the genome reveal contrasting, albeit complementary histories, all of which are relevant for disentangling the intricate geogenomic mechanisms of biotic diversification. [Amazonia; biogeography; demographic modeling; gene flow; gene tree; genome architecture; geogenomics; introgression; linked selection; neural network; phylogenomic; phylogeography; reproductive isolation; speciation; species tree.] 

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