Biology subjects

Martinet, K. M.

Publications and source records attributed to Martinet, K. M..

4 recordsLinked to original sources

bioRxiv2026

Unlocking a flexible set of phylogenetic models for discrete and continuous trait evolution using discretized stochastic diffusion

The practical utility of many modern phylogenetic comparative methods can depend on how accurately mathematical models capture the evolutionary process of traits. Boucher and Demery (2016) described a new quantitative trait model for testing hypotheses about constraint on phenotypic character evolution: Brownian motion with reflective limits. Since their analytic solution for the probability function under this bounded scenario was intractable for reasonably-sized trees, Boucher and Demery (2016) also identified a creative technique for computing the likelihood of their model. The basis of this method derives from the convergence of an equal-rates, symmetric, ordered Markov chain and continuous stochastic diffusion in the limit as the number of lattice states in the chain goes to{infty} (or, alternatively, as their widths decrease towards zero). We realized that this general approach had the potential to unlock a surprisingly large number of additional models for the phylogenetic comparative analysis of discrete and continuous trait data, and we explore several of these in the present article. Specifically, we examine application of this discretized diffusion approximation to the threshold model from evolutionary quantitative genetics, to a new "semi-threshold" trait evolution model, to a joint model where the rate of evolution for a continuous trait depends on the value of a co-evolving discrete character, along with a separate model where precisely the converse is true, and to a discrete-character-dependent multi-trend trended continuous trait evolution model. We conclude with some context for the origins of our article and discussion of other possible applications of this powerful approach.

evolutionary biology↗

bioRxiv2026

Organotypic artery-graft culture enables label-free multiphoton tracking of remodeling that links to long-term graft microarchitecture

The long-term performance of tissue-engineered scaffolds, particularly small-diameter vascular grafts, is shaped by remodeling events at the tissue-graft interface, yet these processes remain difficult to resolve longitudinally and at microstructural resolution in conventional implantation models. Here we develop an organotypic artery-graft model that preserves cylindrical vessel geometry and enables non-destructive label-free multiphoton monitoring of interface remodeling. Using second harmonic generation and two-photon excited fluorescence, we capture evolving fibrillar collagen architecture and cellularization over time, demonstrate compatibility with multiple biomaterial classes, and show integration with rat and mouse explants, live-cell dyes, and fluorescent reporter tissues. The platform resolved distinct remodeling responses to transforming growth factor-{beta} isoforms (TGF-{beta}1, -{beta}2, and -{beta}3), with differential shifts in collagen-fiber distributions, accompanied by changes in matrix-remodeling and contractile gene expression. Across two graft designs, culture-derived remodeling phenotypes, collagen fiber distributions, and initial trajectories agreed with those observed in long-term 6-month interpositional explants. Together, these results establish an accessible intermediate platform for interrogating artery-graft remodeling, tracking these trajectories, and prioritizing graft designs through interface-resolved outcomes before and alongside animal implantation studies.

bioengineering↗

bioRxiv2025

Is Evolution Predictable? Experiments in an Evolutionary Video Game

The outcome of evolution sometimes appears to be predictable, as in the evolution of the same characteristics independently in convergent evolution. Other times, evolutions path depends on starting conditions or chance events, and some forms evolve just once and never appear again. Is convergence, and by implication, predictability, a common characteristic of the evolutionary process? We aimed to answer this question by using an evolutionary video game titled Project Hastur as a study system. In this video game, the enemies evolve traits to help them combat the players strategy. We determined whether the same environmental pressures (in this case, player strategy) lead to predictable evolution. We conducted a series of experiments with three different playing styles and four different evolutionary treatments with varying kinds of selection pressure, each with several replicates. For each replicate, enemies from the first and final generations were categorized into types using k-means clustering. The Euclidean distance between the cluster centroids and the sum of squares values were recorded for each replicate and compared among evolutionary treatments using Wilcoxon rank sum tests. Evolutionary predictability was evaluated using permutation tests. We found that fitness functions in Project Hastur led to incredible, but unpredictable, diversity. The fitness landscape changes between replicates, even within the same experimental treatment and regardless of player strategy, resulting in enemies with an unpredictable array of trait values.

evolutionary biology↗

bioRxiv2025

SSARP: An R Package for Easily Creating Species- and Speciation- Area Relationships Using Web Databases

A universal method of quantifying patterns of biodiversity on islands is the species-area relationship (SAR). SARs visualize the relationship between species richness (the number of species) and the area of the land mass on which they live. An extension of this visualization, the speciation-area relationship (SpAR), helps researchers determine trends in speciation rate over a set of land masses. Comparing these relationships across island systems globally is an extremely difficult task because gathering and processing a large amount of species occurrence data and island data often requires researchers to conduct lengthy literature searches and combine datasets from several different sources. Here we present SSARP (Species/Speciation-Area Relationship Projector), an R package that provides a simple workflow for creating SARs and SpARs. The SSARP workflow allows users to gather occurrence data from GBIF, use mapping tools to determine whether the GPS points in the occurrence data refer to valid land masses, associate those land masses with their areas using a built-in dataset of island names and areas, and create SARs using linear and segmented regression. SSARP also provides multiple functions for estimating speciation rates for use in creating a SpAR. Using SSARP allows researchers to dramatically increase the scope of their biodiversity research through the creation of SARs and SpARs with data from island systems across the globe.

evolutionary biology↗