Biology subjects

Landis, M. J.

Publications and source records attributed to Landis, M. J..

At least 19 recordsLinked to original sources

bioRxiv2026

On the mapping between evolutionary scenarios and governing rules in state-dependent speciation-extinction models

Biologists often wish to explain why one trait is more common than its alternatives among species in a clade: is it due to asymmetries in diversification or character evolution processes? State-dependent speciation-extinction (SSE) models provide a flexible framework for jointly modeling such processes. Here, we derive mathematical results that define functional relationships (``rulesets") among rate parameters, establish a mapping between rulesets and evolutionary scenarios, and characterize how these scenarios give rise to different long-term tip state distributions. In particular, we show that multiple evolutionary scenarios can produce identical stationary distributions of tip state frequencies. Performing inference on simulated data from extant-only trees, we find that not all scenarios are equally favored for explaining datasets constructed solely from extant species. This bias could arise from the interplay between mixing rates, elapsed time, and the conditioning inherent in tree likelihoods. As a proof of concept, we apply our framework to the binary SSE (BiSSE) model across multiple Squamate clades and conduct a literature survey of previous BiSSE analyses on Angiosperm phylogeny. We show that, to some extent, the preference for certain scenarios observed in simulations is also reflected in analyses of empirical trees. This study provides a theoretical ground to directly link data-generating process and observed tip state frequencies under an SSE framework.

evolutionary biology↗

bioRxiv2026

Gene tree patterns help answer: vicariance or dispersal?

Historical biogeography seeks to understand the drivers of species distributions over space and time. One question of interest is how, out of many possible ways, does geography drive speciation. Vicariance, where geographic barriers arise splitting populations and limiting gene flow, can lead to allopatric speciation. Founder events, where a small number of individuals disperse over a barrier, can similarly lead to allopatric speciation if the individuals remain isolated. Both of these scenarios can lead to identical ranges of and relationships between modern species. Classic biogeographic approaches often focus on the history of populations of one or few species on shallow time scales or multiple species on deep time scales. We argue that focusing exclusively on either end of this time spectrum misses a venue for investigating the biogeographic drivers of speciation, at least for certain speciation events. With simple coalescent simulations with multiple species, we show as a proof of concept that gene tree distributions vary predictably between vicariance and founder event speciation. The existence of predictable patterns warrants the development of new approaches that capitalize the gene trees to distinguish vicariance and dispersal as drivers of speciation.

evolutionary biology↗

bioRxiv2026

Summarizing Evolutionary Trajectories from Phylogenetic Character Maps of Discrete Traits

Abstract.--When reconstructing phylogenetic character histories, biologists aim to identify distinct evolutionary trajectories, or paths of character state evolution. However, biologists typically wish to summarize the information representing large numbers of potential character histories for a single phylogeny. For discrete characters, few approaches exist for summarizing the number of unique evolutionary trajectories beyond the frequency of specific events (i.e., state transition types) or the time lineages spend in each state. Here, we introduce a framework for summarizing the evolutionary trajectories of discrete character histories by compressing them into trajectory trees, where branches represent unique character-evolution pathways rather than lineages. This framework includes a novel compressed tree representation, called a scenario tree, that retains temporal information, ensuring that each root-to-tip path represents a unique, temporally explicit evolutionary trajectory. We describe and apply several approaches to summarize phylogenetic trees into transition trees. We include visual summaries - such as consensus trajectory trees, trajectory tree tanglegrams, and"trajectory-through-time plots" - to compare how unique evolutionary trajectories accumulate across lineages and state transitions. We also include quantitative summaries, such as the time spent in unique evolutionary trajectories and the number of transitions that follow unique character-state transitions. We use our new trajectory-wise summaries to evaluate the adequacy of commonly used continuous-time Markov models of character evolution, which are memoryless and consider only the rates between pairs of states. We conducted multiple simulation-based experiments demonstrating the utility of our novel trajectory-wise approaches. We also apply our new trajectory-wise approaches to Greater Antillean Anolis lizard biogeography and ecomorph evolution, and find that Anoles evolved along considerably more unique evolutionary trajectories than expected under simulations of our best-fitting character evolution model. The number of unique evolutionary paths accumulated in an "early burst" pattern relative to simulated trajectories, with this burst being more intense than expected across all character state transition events.

evolutionary biology↗

bioRxiv2026

Assessing the Efficacy of Computational Workshops and Participatory Live Coding in Evolutionary Biology

Computational workshops are common in evolutionary biology and are used to share discipline-specific tools and skills with researchers. Despite the perceived importance of these workshops, there is no common set of criteria for workshop success, and there are few peer-reviewed studies investigating the efficacy of workshops or assessing the value of particular instructional techniques in this context. Here, we focused on one key element of a successful workshop: its ability to increase participants motivation to use the methods and tools presented during the workshop. We analyzed the goals, perceptions, and future plans of research practitioners engaging in a workshop on phylogenetic methods of historical biogeography using pre- and post-workshop surveys. Overall, the workshop was successful at motivating participants, and survey responses provided insights into participants perceptions of different activities, including "participatory live coding". Apart from this case study, we aim to highlight the importance of developing a common set of workshop goals in collaboration with other workshop stakeholders and the need for specialized, validated tools for assessing the efficacy of computational workshops for researchers.

evolutionary biology↗

bioRxiv2026

EMPIRE: The Ellipse Model for Phylogenetic Inference of Range Evolution

AO_SCPLOWBSTRACTC_SCPLOWMany phylogenetic models of historical biogeography exist for describing how lineages move and evolve over time. Here, we present the Ellipse Model for Phylogenetic Inference of Range Evolution (O_SCPLOWEMPIREC_SCPLOW), which models the movement and splitting of species range ellipses in continuous space, summarizing important attributes of each range, such as its position, size, and orientation. The framework allows us to reconstruct ancestral range ellipses, investigate rates governing important processes like movement, expansion, and elongation, and examine the spatial context of speciation, including asymmetric range inheritance at cladogenesis. We apply O_SCPLOWEMPIREC_SCPLOW to the Australian Sphenomorphinae, a group of skinks whose diversification has coincided with substantial climatic change over the past ~36 million years. We find that speciation events are positively associated with aridification, while daughter lineages post-speciation do not tend to show evidence of ecological partitioning.

evolutionary biology↗

bioRxiv2026

Ancestral state reconstruction with discrete characters using deep learning

Ancestral state reconstruction is a classical problem of broad relevance in phylogenetics. Likelihood-based methods for reconstructing ancestral states under discrete character models, such as Markov models, have proven extremely useful, but only work so long as the assumed model yields a tractable likelihood function. Unfortunately, extending a simple but tractable phylogenetic model to possess new, but biologically realistic, properties often results in an intractable likelihood, preventing its use in standard modeling tasks, including ancestral state reconstruction. The rapid advancement of deep learning offers a potential alternative to likelihood-based inference of ancestral states, particularly for models with intractable likelihoods. In this study, we modify the phylogenetic deep learning software O_SCPLOWPHYDDLEC_SCPLOW to conduct ancestral state reconstruction. We evaluate O_SCPLOWPHYDDLEC_SCPLOWs performance under various methodological and modeling conditions, while comparing to Bayesian inference when possible. For simple models and small trees, its performance resembles the performance of Bayesian inference, but worsens as tree size increases. While O_SCPLOWPHYDDLEC_SCPLOW still performs adequately for more complex models, such as speciation and extinction models, the estimates differ more from Bayesian inference in comparison with simpler models. Lastly, we use O_SCPLOWPHYDDLEC_SCPLOW to infer ancestral states for two empirical datasets, one of the ancestral ranges of a subclade of the genus Liolaemus and ancestral locations for sequences from the 2014 Sierra Leone Ebola virus disease outbreak.

evolutionary biology↗

bioRxiv2026

Phylogenetic estimation of diversity-dependent biogeographic rates using deep learning

Ecological theory predicts that local species richness can influence biogeographic rates of speciation, extinction, and dispersal. For instance, increasing the number of competing species within a region may cause local speciation and dispersal rates to decrease but local extinction rates to increase, inducing a carrying capacity for local species richness. In this article, we introduce a fully generative, event-based phylogenetic diversification model, called DDGeoSSE, that allows diversity-dependent effects of local species richness to modulate biogeographic rates of diversification and range evolution. DDGeoSSE can accommodate and test a variety of alternative diversification scenarios that involve positive, negative, and neutral interactions among sympatric species for speciation, extinction, and dispersal. We derive mathematical and statistical properties of biogeographic outcomes generated by this model, such as the carrying capacity for a clade at equilibrium, which we validate through simulation. Because diversity-dependent phylogenetic models typically do not have tractable likelihood functions, we use deep learning with phyddle to perform parameter inference and model selection. Separately applying DDGeoSSE to Caribbean Anolis lizards and cloud forest-dwelling Viburnum plants, we find evidence that local species richness plays a significant role in shaping diversification dynamics for both clades.

evolutionary biology↗

bioRxiv2025

The Goldilocks effect drives plant diversification on middle-aged Hawaiian islands

Islands are natural laboratories for studying dispersal, speciation, and extinction, yet the historical dynamics of many insular radiations remain poorly understood because of the difficulty aligning the tempo and mode of diversification with paleogeography during phylogenetic inference. We introduce a phylogenetic modeling framework, named TimeFIG, that combines data about species ranges and genetic variation with paleogeographic information to simultaneously infer divergence times, ancestral ranges, and historical diversification rates, without relying on fossils to time-calibrate the phylogeny. Applying TimeFIG to the spectacular radiation of Hawaiian Kadua (Rubiaceae) points to colonization of either modern Kaua'i or ancient now-eroded islands, with island isolation as the strongest correlate of dispersal, and with a tendency for net diversification to be highest on "middle-aged" islands. Modeling diversification within an explicit spatiotemporal context, while accounting for historical uncertainty regarding the timing and placement of phylogenetic and biogeographic events, enables the rigorous testing of foundational hypotheses in island biology and evolutionary theory for clades beyond Kadua.

evolutionary biology↗

bioRxiv2025

Seventy-five Years of Systematic Biology: Looking Back, Moving Forward

AbstractWhat does "systematic biology" mean today, where has it been in the past, and where is it going? We explore these questions by considering five elements - collaboration, integration, discourse, infrastructure, and society - that we think have allowed systematic biology to adapt to change and sustain growth without losing its unique identity. In the spirit of celebrating the 75th anniversary of our flagship journal, we generated a comprehensive dataset for all Systematic Biology and Systematic Zoology articles that we could locate (N = 5,150) and used bibliometric and textual analyses to illustrate ways in which our field has transformed over time. We offer our humble opinions on how our community can ensure that systematic biologists inherit an enlightening, dynamic, and enduring future.

evolutionary biology↗

bioRxiv2024

A Phylogenetic Model of Established and Enabled Biome Shifts

Where each species actually lives is distinct from where it could potentially survive and persist. This suggests that it may be important to distinguish established from enabled biome affinities when considering how ancestral species moved and evolved among major habitat types. We introduce a new phylogenetic method, called RFBS, to model how anagenetic and cladogenetic events cause established and enabled biome affinities (or, more generally, other discrete realized versus fundamental niche states) to shift over evolutionary timescale. We provide practical guidelines for how to assign established and enabled biome affinity states to extant taxa, using the flowering plant clade Viburnum as a case study. Through a battery of simulation experiments, we show that RFBS performs well, even when we have realistically imperfect knowledge of enabled biome affinities for most analyzed species. We also show that RFBS reliably discerns established from enabled affinities, with similar accuracy to standard competing models that ignore the existence of enabled biome affinities. Lastly, we apply RFBS to Viburnum to infer ancestral biomes throughout the tree and to highlight instances where repeated shifts between established affinities for warm and cold temperate forest biomes were enabled by a stable and slowly-evolving enabled affinity for both temperate biomes.

evolutionary biology↗

bioRxiv2024

phyddle: software for phylogenetic model exploration with deep learning

Phylogenies contain a wealth of information about the evolutionary history and process that gave rise to the diversity of life. This information can be extracted by fitting phylogenetic models to trees. However, many realistic phylogenetic models lack tractable likelihood functions, prohibiting their use with standard inference methods. We present phyddle, pipeline-based software for performing phylogenetic modeling tasks on trees using likelihood-free deep learning approaches. phyddle has a flexible command-line interface, making it easy to integrate deep learning approaches for phylogenetics into research workflows. phyddle coordinates modeling tasks through five pipeline analysis steps (Simulate, Format, Train, Estimate, and Plot) that transform raw phylogenetic datasets as input into numerical and visual model-based output. We conduct three experiments to compare the accuracy of likelihood-based inferences against deep learning-based inferences obtained through phyddle. Benchmarks show that phyddle accurately performs the inference tasks for which it was designed, such as estimating macroevolutionary parameters, selecting among continuous trait evolution models, and passing coverage tests for epidemiological models, even for models that lack tractable likelihoods. Learn more about phyddle at https://phyddle.org.

evolutionary biology↗

bioRxiv2023

PhyloJunction: a computational framework for simulating, developing, and teaching evolutionary models

We introduce PhyloJunction, a computational framework designed to facilitate the prototyping, testing, and characterization of evolutionary models. PhyloJunction is distributed as an open-source Python library that can be used to implement a variety of models, through its flexible graphical modeling architecture and dedicated model specification language. Model design and use are exposed to users via command-line and graphical interfaces, which integrate the steps of simulating, summarizing, and visualizing data. This paper describes the features of PhyloJunction - which include, but are not limited to, a general implementation of a popular family of phylogenetic diversification models - and, moving forward, how it may be expanded to not only include new models, but to also become a platform for conducting and teaching statistical learning.

bioinformatics↗

bioRxiv2023

Testing relationships between multiple regional features and biogeographic processes of speciation, extinction, and dispersal

The spatial and environmental features of regions where clades are evolving are expected to impact biogeographic processes such as speciation, extinction, and dispersal. Any number of regional features (such as altitude, distance, area, etc.) may be directly or indirectly related to these processes. For example, it may be that distances or differences in altitude or both may limit dispersal rates. However, it is difficult to disentangle which features are most strongly related to rates of different processes. Here, we present an extensible Multi-feature Feature-Informed GeoSSE (MultiFIG) model that allows for the simultaneous investigation of any number of regional features. MultiFIG provides a conceptual framework for incorporating large numbers of features of different types, including categorical, quantitative, within-region, and between-region features, along with a mathematical framework for translating those features into biogeographic rates for statistical hypothesis testing. Using traditional Bayesian parameter estimation and reversible-jump Markov chain Monte Carlo, MultiFIG allows for the exploration of models with different numbers and combinations of feature-effect parameters, and generates estimates for the strengths of relationships between each regional feature and core process. We validate this model with a simulation study covering a range of scenarios with different numbers of regions, tree sizes, and feature values. We also demonstrate the application of MultiFIG with an empirical case study of the South American lizard genus Liolaemus, investigating sixteen regional features related to area, distance, and altitude. Our results show two important feature-process relationships: a negative distance/dispersal relationship, and a negative area/extinction relationship. Interestingly, although speciation rates were found to be higher in Andean versus non-Andean regions, the model did not assign significance to Andean- or altitude-related parameters. These results highlight the need to consider multiple regional features in biogeographic hypothesis testing.

evolutionary biology↗

bioRxiv2022

The build-up of the present-day tropical diversity of tetrapods

The extraordinary number of species in the tropics when compared to the extra-tropics is probably the most prominent and consistent pattern in biogeography, suggesting that overarching processes regulate this diversity gradient. A major challenge to characterizing which processes are at play relies on quantifying how the frequency and determinants of tropical and extra-tropical speciation, extinction and dispersal events shaped evolutionary radiations. We address this question by developing and applying spatio-temporal phylogenetic and paleontological models of diversification for tetrapod species incorporating paleoenvironmental variation. Our phylogenetic model results show that area, energy or species richness did not uniformly affect speciation rates across tetrapods and dispute expectations of a latitudinal gradient in speciation rates. Instead, both neontological and fossil evidence coincide in underscoring the role of extra-tropical extinctions and the outflow of tropical species in shaping biodiversity. These diversification dynamics accurately predict present-day levels of species richness across latitudes and uncover temporal idiosyncrasies but spatial generality across the major tetrapod radiations.

evolutionary biology↗

bioRxiv2022

Bayesian inference of admixture graphs on Native American and Arctic populations

Admixture graphs are mathematical structures that describe the ancestry of populations in terms of divergence and merging (admixing) of ancestral populations as a graph. An admixture graph consists of a graph topology, branch lengths, and admixture proportions. The branch lengths and admixture proportions can be estimated using numerous numerical optimization methods, but inferring the topology involves a combinatorial search for which no polynomial algorithm is known. In this paper, we present a reversible jump MCMC algorithm for sampling high-probability admixture graphs and show that this approach works well both as a heuristic search for a single best-fitting graph and for summarizing shared features extracted from posterior samples of graphs. We apply the method to 11 Native American and Siberian populations and exploit the shared structure of high-probability graphs to address the relationship between Saqqaq, Inuit, Koryaks, and Athabascans. Our analyses show that the Saqqaq is not a good proxy for the previously identified gene flow from Arctic people into the Na-Dene speaking Athabascans. Author SummaryOne way of summarizing historical relationships between genetic samples is by constructing an admixture graph. An admixture graph describes the demographic history of a set of populations as a directed acyclic graph representing population splits and mergers. The inference of admixture graphs is currently done via greedy search algorithms that may fail to find the global optimum. We here improve on these approaches by developing a novel MCMC sampling method, AdmixtureBayes, that can sample from the posterior distribution of admixture graphs. This enables an efficient search of the entire state space as well as the ability to report a level of confidence in the sampled graphs. We apply AdmixtureBayes to a set of Native American and Arctic genomes to reconstruct the demographic history of these populations and report posterior probabilities of specific admixture events. While some previous studies have identified the ancient Saqqaq culture as a source of introgression into Athabascans, we instead find that it is the Siberian Koryak population, not the Saqqaq, that serves as the best proxy for gene flow into Athabascans.

genetics↗

bioRxiv2021

RevGadgets: an R Package for visualizing Bayesian phylogenetic analyses from RevBayes

O_LIStatistical phylogenetic methods are the foundation for a wide range of evolutionary and epidemiological studies. However, as these methods grow increasingly complex, users often encounter significant challenges with summarizing, visualizing, and communicating their key results. C_LIO_LIWe present RevGadgets, an R package for creating publication-quality figures from the results of a large variety of phylogenetic analyses performed in RevBayes (and other phylogenetic software packages). C_LIO_LIWe demonstrate how to use RevGadgets through a set of vignettes that cover the most common use cases that researchers will encounter. C_LIO_LIRevGadgets is an open-source, extensible package that will continue to evolve in parallel with RevBayes, helping researchers to make sense of and communicate the results of a diverse array of analyses. C_LI [Bayesian phylogenetics, data visualization, R, RevBayes]

evolutionary biology↗

bioRxiv2021

Evolution of butterfly-plant networks over time, as revealed by Bayesian inference of host repertoire

The study of herbivorous insects underpins much of the theory that concerns the evolution of species interactions. In particular, Pieridae butterflies and their host plants have served as a model system for studying evolutionary arms-races. To learn more about the coevolution of these two clades, we reconstructed ancestral ecological networks using stochastic mappings that were generated by a phylogenetic model of host-repertoire evolution. We then measured if, when, and how two ecologically important structural features of the ancestral networks (modularity and nestedness) evolved over time. Our study shows that as pierids gained new hosts and formed new modules, a subset of them retained or recolonized the ancestral host(s), preserving connectivity to the original modules. Together, host-range expansions and recolonizations promoted a phase transition in network structure. Our results demonstrate the power of combining network analysis with Bayesian inference of host-repertoire evolution to understand changes in complex species interactions over time.

ecology↗

bioRxiv2019

Modeling phylogenetic biome shifts on a planet with a past

The spatial distribution of biomes has changed considerably over deep time, so the geographical opportunity for an evolutionary lineage to shift into a new biome may depend on how the availability and connectivity of biomes has varied temporally. To better understand how lineages shift between biomes in space and time, we developed a phylogenetic biome shift model in which each lineage shifts between biomes and disperses between regions at rates that depend on the lineages biome affinity and location relative to the spatiotemporal distribution of biomes at any given time. To study the behavior of the biome shift model in an empirical setting, we developed a literature-based representation of paleobiome structure for three mesic forest biomes, six regions, and eight time strata, ranging from the Late Cretaceous (100 Ma) through the present. We then fitted the model to a time-calibrated phylogeny of 119 Viburnum species to compare how the results responded to various realistic or unrealistic assumptions about paleobiome structure. Ancestral biome estimates that account for paleobiome dynamics reconstructed a warm temperate (or tropical) origin of Viburnum, which is consistent with previous fossil-based estimates of ancestral biomes. Imposing unrealistic paleobiome distributions led to ancestral biome estimates that eliminated support for tropical origins, and instead inflated support for cold temperate ancestry throughout the warmer Paleocene and Eocene. The biome shift model we describe is applicable to the study of evolutionary systems beyond Viburnum, and the core mechanisms of our model are extensible to the design of richer phylogenetic models of historical biogeography and/or lineage diversification. We conclude that biome shift models that account for dynamic geographical opportunities are important for inferring ancestral biomes that are compatible with our understanding of Earth history.

evolutionary biology↗