Biology subjects

Morlon, H.

Publications and source records attributed to Morlon, H..

At least 19 recordsLinked to original sources

bioRxiv2026

A probabilistic and phylogenetic principal component analysis for modelling high-dimensional trait evolution

Given the ever-increasing availability of highly detailed phenotypes, modelling trait evolution in a multivariate framework is becoming a challenging task. Current phylogenetic comparative methods often struggle with high-dimensional datasets because they suffer computational limitations and interpretability. Here, we propose a maximum likelihood-based approach called Probabilistic and Phylogenetic Principal Components Analysis (P3CA) to circumvent current limitations. This approach is based on a continuous latent variable model, whereby observed traits are explained by a smaller number of unobserved variables that evolve according to a given evolutionary model. We implement the approach under Pagels lambda model using an Expectation-Maximisation algorithm that makes it computationally efficient and allows missing values. Using simulations, we demonstrate that evolutionary parameters are accurately estimated, regardless of phylogenetic signal, the number of traits or the proportion of missing values. The reconstruction of the reduced space is more accurate than the one obtained using other dimensionality reduction approaches, such as phylogenetic and conventional PCA. Likewise, the estimated values for missing data are more accurate than those obtained using current phylogenetic data imputation approaches. We illustrate the approach on a 3D geometric morphometric dataset describing Crocodyliformes skull shapes and containing around 4% of missing data. Our P3CA method unlocks the possibility to analyse and more easily interpret the large-scale multivariate datasets generated in recent decades within a phylogenetic comparative framework.

evolutionary biology↗

bioRxiv2026

An Empirical Bayes approach for the study of phenotypic evolution from high-dimensional data

Multivariate phylogenetic comparative methods for modelling high-dimensional traits such as 3D shapes or gene expression proBiles have been recently developed. However, these approaches are impractical and almost impossible to use when the number of traits exceeds a few thousands, as they become computationally prohibitive. We overcome these limitations by proposing a new maximum likelihood approach based on the Empirical Bayes framework. This approach takes into account the information of the complete covariances (among species and traits) to infer parameters and compare models of trait evolution for high-dimensional datasets. Through simulations, we demonstrate that the proposed approach can accurately estimate parameters of various trait evolution models, even when the number of traits is ten times larger than the number of lineages; it requires less memory and is at least 10 times faster than currently available approaches. This fast, efBicient framework enabled us to extend the high-dimensional multivariate phylogenetic comparative toolkit by including an Ornstein-Uhlenbeck process with multiple optima to study adaptation to various selective regimes. Applying our approach to the evolution of jaw morphology in relation to dietary adaptation in mammals, we demonstrate morphological convergence in carnivorous and herbivorous lineages. The proposed Empirical Bayes framework, implemented in the R package mvMORPH, enables phylogenetic comparative methods to efBiciently handle high-dimensional datasets and complex models of trait evolution.

evolutionary biology↗

bioRxiv2026

Rapid speciation in small populations challenges the dominance of ecological speciation

Speciation - the process by which two lineages become reproductively isolated - plays a key role in the emergence and maintenance of biodiversity. Yet, our understanding of the time it takes for speciation to occur, and of the microevolutionary processes that influence this tempo, remains limited. Here, we thoroughly characterize how population size, mutation rate, local adaptation and migration are expected to influence the duration of speciation, as well as the shape of the "grey zone" of speciation. We show that the relationship between population size and speciation time is indicative of the speciation mode, as faster speciation in smaller populations only occurs in the case of non-ecological speciation. Leveraging genomic estimates of population size and speciation duration across 196 pairs of plant species, we uncover a positive association between population size and speciation duration. Taken together, these results challenge the view that ecological speciation is the source of much of species diversity.

evolutionary biology↗

bioRxiv2025

Environmental phylogenetics supports a steady diversification of crown eukaryotes starting from the mid Proterozoic

1.Molecular clock and preservation of early microfossil assemblages suggest that eukaryotes were present and already diverse more than [~]1600 million years ago (Ma). Yet, the earliest identifiable eukaryotic crown group fossil only appeared around 1050 Ma, leaving a [~]600 million years gap in the Mesoproterozoic during which the evolution and diversification of early eukaryotes remain poorly understood. Here, we inferred a timeline of eukaryote evolution using molecular clock and birth-death diversification models, including the great diversity of environmental sequences to take into account the uncultured majority of microbial life. Our analyses, based on a unique dataset of 75,975 non-redundant Operational Taxonomic Units and 77 well-supported fossil calibrations, indicate a steady diversification of crown group eukaryotes during the Proterozoic after the Last Eukaryotic Common Ancestor (LECA). We show that Archaeplastida was one of the earliest diversifying supergroups and the most diverse throughout the Proterozoic, suggesting that the first successful plastid endosymbiosis gave Archaeplastida a measurable evolutionary advantage. Discoba, Amoebozoa and Rhizaria followed Archaeplastida in phylogenetic diversity throughout the Proterozoic, suggesting that crown eukaryotes were already thriving in this Era. These results contrast with the common view that the geologically stable Proterozoic Era experienced little eukaryotic evolutionary innovation, and indicate that it was already at the dawn of eukaryote evolution that all current supergroups were established.

evolutionary biology↗

bioRxiv2025

Darwin's entangled bank through deep time: Structural stability of mutualistic networks over evolutionary timescales

Mutualistic interactions form species-rich, complex networks that play essential roles for ecosystem function. Over macroevolutionary time scales, global- and continental-level networks change as species emerge and go extinct, yet the stability of their structural organization remains poorly understood. Here, we develop ELEFANT, a novel method for reconstructing ancestral interaction networks based on the hypothesis that species interactions are shaped by unobserved traits that evolve over evolutionary time. We show that ancestral interaction networks can be reliably reconstructed from present-day phylogenetic and interaction data. We infer the ancestral networks of plant mutualisms involving arbuscular mycorrhizal fungi, bat pollinators, and bird seed dispersers at large biogeographic scales. We find that these mutualistic networks exhibit a modular structure that seems to have persisted for millions of years, in part maintained by the evolutionary conservatism of species interactions. As species diversify, they tend to show limited shifts in mutualistic partners, which results in a remarkable long-term stability of mutualistic network structure at large spatial scales.

evolutionary biology↗

bioRxiv2025

The Occurrence Birth-Death Diffusion Process: Unraveling Diversification Histories with Fossils and Heterogeneous Rates

AO_SCPLOWBSTRACTC_SCPLOWPhylogenetic diversification analyses often overlook the wealth of information contained in fossils that cannot be directly integrated into phylogenetic trees (called fossil occurrences), as well as fine-grained variations in speciation and extinction rates across lineages. Addressing both of these shortcomings, we develop the Occurrence Birth-Death Diffusion (OBDD) process. This process incorporates the sampling of fossil occurrences by extending the recently developed Fossilized Birth-Death Diffusion (FBDD) process, thereby taking advantage of a clades entire fossil record to infer its evolutionary history. To mitigate the computational challenges associated with the OBDD process, we employ Bayesian data augmentation methods. We illustrate our approach with an empirical analysis of cetacean diversification. Our analyses show that the integration of fossils is essential to recover diversification rates and richness trajectories. More specifically, all types of fossils are highly informative about past diversification dynamics, but fossil occurrences do not provide significant additional information when the phylogeny already contains many fossils. We make the OBDD available in the Julia package Tapestree.

evolutionary biology↗

bioRxiv2025

Graph Neural Networks for Likelihood-Free Inference in Diversification Models

A common approach to infer the processes that gave rise to past speciation and extinction rates across taxa, space and time is to formulate hypotheses in the form of probabilistic diversification models and estimate their parameters from extant phylogenies using Maximum Likelihood or Bayesian inference. A drawback of this approach is that likelihoods can easily become computationally intractable, limiting our ability to extend current diversification models with new hypothesized mechanisms. Neural networks have been proposed as a likelihood-free alternative for parameter inference of stochastic models, but so far there is little experience in using this method for diversification models, and the quality of the results is likely to depend on finding the right network architecture and data representation. As phylogenies are essentially graphs, graph neural networks (GNNs) appear to be the most natural architecture but previous results on their performance are conflicting, with some studies reporting poor accuracy of GNNs in practice. Here, we show that this underperformance was likely caused by optimization issues and inappropriate pooling operations that flatten the information along the phylogeny and make it harder to extract relevant information about the diversification parameters. When equipped with PhyloPool, a new time-informed pooling procedure, GNNs show similar or better performance compared to all other architectures and data representations (including Maximum Likelihood Estimation) that we tested for two common diversification models, the Constant Rate Birth-Death and the Binary State Speciation and Extinction. We conclude that GNNs could serve as a generic tool for estimating diversification parameters of complex diversification models with intractable likelihoods.

evolutionary biology↗

bioRxiv2025

The Fossilized Birth Death Process with heterogeneous diversification rates unravels the link between diversification and specialisation to a carnivorous diet in Nimravidae (Carnivoraformes)

Bayesian phylogenetic inference uses more and more complex diversification models as tree priors to test new macroevolutionary hypotheses. However, those models are usually developed in a neontological framework, despite the increasing number of datasets covering both extant and fossil taxa, as well as the fact that many clades are entirely extinct. In this paper, we develop the F-ClaDS model, a Fossilized-Birth-Death (FBD) version of the cladogenetic diversification rate shift (ClaDS) model, in BEAST2. ClaDS estimates partially inherited branch-specific rates from a phylogeny, providing a nuanced and detailed perspective of the variations in diversification across the tree. Our extension allows the integration of fossil samples directly into the phylogeny. We apply our new implementation to a dataset of 36 Nimravidae, a fully extinct carnivoraform clade that spanned from the Early Eocene to the Late Miocene, which species had different degrees of specialisation to a carnivorous diet. We show that using different tree priors does not affect substantially the topology of the inferred trees, but affects the ages of nodes and tips, as well as branch-lengths. F-ClaDS also recovers more species as sampled ancestors than the homogeneous FBD model. The branches with the highest speciation and extinction rates are those corresponding to the hypercarnivorous clades (Hoplophoneus and the barbourofelins), supporting the view that specialization to a hypercarnivorous diet can spur speciation, but also increase extinction risk, especially during times of global ecosystem change, potentially due to a high position in the trophic chain.

evolutionary biology↗

bioRxiv2025

Questioning the Evidence for Host-Symbiont Codiversification in Mycorrhizal Symbioses

The vast majority of plants form mycorrhizal symbioses. The ecological importance of these mutualistic interactions has sparked interest in their long evolutionary history. By examining interaction networks and phylogenetic trees, several studies have suggested that plants codiversify with their associated mycorrhizal fungi. However, recent research has demonstrated that phylogenetic congruence (interpreted as codiversification when divergence times match) is often conflated with another pattern called cophylogenetic signal (i.e., closely related plants interacting with closely related fungi and vice versa), which may arise from different biological processes. We performed cophylogenetic analyses on 29 diverse mycorrhizal networks to reevaluate the evidence for codiversification in mycorrhizal symbioses. We found significant cophylogenetic signal but no phylogenetic congruence: closely related plants often interact with closely related fungi, but their phylogenies do not match. Instead of codiversification, this finding is consistent with trait matching between plants and mycorrhizal fungi, where compatible, evolutionarily conserved plant and fungal traits govern their interactions. Our work highlights the importance of appropriately interpreting the cophylogenetic methods used to study the macroevolution of plants and their mycorrhizal fungi. It suggests that previous evidence of codiversification in mycorrhizal symbioses actually detected cophylogenetic signal, since across the multiple and diverse networks analyzed here, there is no evidence that codiversification occurred during the evolution of mycorrhizal symbioses.

evolutionary biology↗

bioRxiv2025

The rise, decline and fall of clades

How and why species diversity varies over geological time scales remains disputed. We analyzed the evolutionary history of 27 radiations with phylogenetic information incorporating extinct and extant species under a new fossilized birth-death diffusion model that provides a detailed characterization of past diversification and resulting diversity dynamics. Clade diversity trajectories follow rise and decline dynamics, with fast accumulation following recurrent speciation while slowdowns and losses are modulated primarily by changes in extinction. Diversity dynamics do not appear to be governed by clade-level processes expected from adaptive radiations or diversity equilibria. Rather, these patterns emerge from dynamics at the species-level, where lineages tend to become increasingly vulnerable to extinction and less likely to speciate with time. Those species that counteract this trend create and maintain biodiversity through deep time. One Sentence Summary

evolutionary biology↗

bioRxiv2025

Integrating fossils samples with heterogeneous diversification rates: a combined Multi-Type Fossilized Birth-Death model

Birth-death models are widely used to describe the diversification process which leads to the observed species and phylogenies. When integrated into Bayesian phylogenetic inference, birth-death models allow the joint inference of the phylogeny and the diversification parameters from molecular information. Two major classes of extensions of the birth-death process are considered in this article. The first extends the phylogenetic tree to include fossil samples alongside extant species, allowing the inference to integrate information about the past diversity. The second extension models diversification rates which can vary between lineages, and is thus able to infer patterns of variation in speciation or extinction rates. In this work, we combine these two types of extension into a multi-type fossilized birth-death (MTFBD) process, which can perform the joint inference of a phylogeny including extinct and extant samples, and lineage-specific diversification and fossilization rates in a Bayesian framework. The MTFBD model is implemented as part of the phylogenetic inference framework BEAST2. Using simulated and empirical datasets, we demonstrate the performance and accuracy of the new model compared to a model with rate heterogeneity but using only extant samples, and compared to a model without rate variation including fossils. We demonstrate that including fossils improves the accuracy of the phylogeny and diversification rates, especially extinction rates, provided that the inference includes morphological information to accurately place the fossil samples. When this information is not available however, MTFBD estimates are strongly driven by the priors and are thus no better or even worse than estimates obtained only with extant samples. With informative fossil characters, the MTFBD model provides accurate phylogenies, and precisely characterizes how speciation, extinction and fossilization rates vary as diversification proceeds.

evolutionary biology↗

bioRxiv2024

A theoretical framework linking the birth-death and protracted birth-death models, with implications for the phylogenetic analysis of diversification

Standard birth-death processes used in macroevolutionary studies assume instantaneous speciation, an unrealistic premise that limits the interpretation of speciation and extinction rates. The protracted birth-death (PBD) model instead assumes that speciation involves two steps: initiation and completion. In order to understand their respective influence on macroevolutionary speciation rates, we compute a standard time-varying birth-death scenario that is "equivalent" to the PBD model in terms of speciation and extinction probabilities. First, we find a sharp decline in the equivalent birth rate near the present, indicating that rates estimated at the tips of phylogenies may not accurately reflect the underlying speciation process. Second, the completion rate controls the timing of the decay rather than the asymptotic equivalent rates. The equivalent birth rate in the past scales with the speciation initiation rate, with a scaling factor depending mostly on the population extinction rate. Our results suggest that the rates of population formation and extinction may often play a larger role than the speed of accumulation of reproductive isolation in modulating speciation rates. Our study establishes a theoretical framework for understanding how microevolutionary processes combine to explain the diversification of species on macroevolutionary time scales.

evolutionary biology↗

bioRxiv2023

Exploring congruent diversification histories with flexibility and parsimony

AO_SCPLOWBSTRACTC_SCPLOWO_LIUsing phylogenies of present-day species to estimate diversification rate trajectories - speciation and extinction rates over time - is a challenging task due to non-identifiability issues. Given a phylogeny, there exists an infinite set of trajectories that result in the same likelihood; this set has been coined a congruence class. Previous work has developed approaches for sampling trajectories within a given congruence class, with the aim to assess the extent to which congruent scenarios can vary from one another. Based on this sampling approach, it has been suggested that rapid changes in speciation or extinction rates are conserved across the class. Reaching such conclusions requires to sample the broadest possible set of distinct trajectories. C_LIO_LIWe introduce a new method for exploring congruence classes, that we implement in the R package CRABS. Whereas existing methods constrain either the speciation rate or the extinction rate trajectory, ours provides more flexibility by sampling congruent speciation and extinction rate trajectories simultaneously. This allows covering a more representative set of distinct diversification rate trajectories. We also implement a filtering step that allows selecting the most parsimonious trajectories within a class. C_LIO_LIWe demonstrate the utility of our new sampling strategy using a simulated scenario. Next, we apply our approach to the study of mammalian diversification history. We show that rapid changes in speciation and extinction rates need not be conserved across a congruence class, but that selecting the most parsimonious trajectories shrinks the class to concordant scenarios. C_LIO_LIOur approach opens new avenues both to truly explore the myriad of potential diversification histories consistent with a given phylogeny, embracing the uncertainty inherent to phylogenetic diversification models, and to select among these different histories. This should help refining our inference of diversification trajectories from extant data. C_LI

evolutionary biology↗

bioRxiv2023

Distinguishing cophylogenetic signal from phylogenetic congruence clarifies the interplay between evolutionary history and species interactions

Interspecific interactions, including host-symbiont associations, can profoundly affect the evolution of the interacting species. Given the phylogenies of host and symbiont clades and knowledge of which host species interact with which symbiont, two questions are often asked: "Do closely related hosts interact with closely related symbionts?" and "Do host and symbiont phylogenies mirror one another?". These questions are intertwined and can even collapse under specific situations, such that they are often confused one with the other. However, in most situations, a positive answer to the first question, hereafter referred to as "cophylogenetic signal", does not imply a close match between the host and symbiont phylogenies. It suggests only that past evolutionary history has contributed to shaping present-day interactions, which can arise, for example, through present-day trait matching, or from a single ancient vicariance event that increases the probability that closely related species overlap geographically. A positive answer to the second, referred to as "phylogenetic congruence", is more restrictive as it suggests a close match between the two phylogenies, which may happen, for example, if symbiont diversification tracks host diversification or if the diversifications of the two clades were subject to the same succession of vicariance events. Here we apply a set of methods (ParaFit, PACo, and eMPRess), which significance is often interpreted as evidence for phylogenetic congruence, to simulations under three biologically realistic scenarios of trait matching, a single ancient vicariance event, and phylogenetic tracking with frequent cospeciation events. The latter is the only scenario that generates phylogenetic congruence, whereas the first two generate a cophylogenetic signal in the absence of phylogenetic congruence. We find that tests of global-fit methods (ParaFit and PACo) are significant under the three scenarios, whereas tests of event-based methods (eMPRess) are only significant under the scenario of phylogenetic tracking. Therefore, significant results from global-fit methods should be interpreted in terms of cophylogenetic signal and not phylogenetic congruence; such significant results can arise under scenarios when hosts and symbionts had independent evolutionary histories. Conversely, significant results from event-based methods suggest a strong form of dependency between hosts and symbionts evolutionary histories. Clarifying the patterns detected by different cophylogenetic methods is key to understanding how interspecific interactions shape and are shaped by evolution.

evolutionary biology↗

bioRxiv2023

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

O_LICharacterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology, and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g., herbivory) and mutualistic (e.g., pollination) interaction types. C_LIO_LIHere, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. C_LIO_LIContrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso-scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over-representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These characteristics can be used to predict interaction types with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g., pollination, parasitism). C_LIO_LIOur results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy. C_LI

ecology↗

bioRxiv2023

Recent evolutionary origin and localized diversity hotspots of mammalian coronaviruses

Several coronaviruses infect humans, with three, including the SARS-CoV2, causing diseases. While coronaviruses are especially prone to induce pandemics, we know little about their evolutionary history, host-to-host transmissions, and biogeography. One of the difficulties lies in dating the origination of the family, a particularly challenging task for RNA viruses in general. Previous cophylogenetic tests of virus-host associations, including in the Coronaviridae family, have suggested a virus-host codiversification history stretching many millions of years. Here, we establish a framework for robustly testing scenarios of ancient origination and codiversification versus recent origination and diversification by host switches. Applied to coronaviruses and their mammalian hosts, our results support a scenario of recent origination of coronaviruses in bats and diversification by host switches, with preferential host switches within mammalian orders. Hotspots of coronavirus diversity, concentrated in East Asia and Europe, are consistent with this scenario of relatively recent origination and localized host switches. Spillovers from bats to other species are rare, but have the highest probability to be towards humans than to any other mammal species, implicating humans as the evolutionary intermediate host. The high host-switching rates within orders, as well as between humans, domesticated mammals, and non-flying wild mammals, indicates the potential for rapid additional spreading of coronaviruses across the world. Our results suggest that the evolutionary history of extant mammalian coronaviruses is recent, and that cases of long-term virus-host codiversification have been largely over-estimated.

evolutionary biology↗

bioRxiv2023

A Comparison of Deep Learning Architectures for Inferring Parameters of Diversification Models from Extant Phylogenies

AO_SCPLOWBSTRACTC_SCPLOWTo infer the processes that gave rise to past speciation and extinction rates across taxa, space and time, we often formulate hypotheses in the form of stochastic diversification models and estimate their parameters from extant phylogenies using Maximum Likelihood or Bayesian inference. Unfortunately, however, likelihoods can easily become intractable, limiting our ability to consider more complicated diversification processes. Recently, it has been proposed that deep learning (DL) could be used in this case as a likelihood-free inference technique. Here, we explore this idea in more detail, with a particular focus on understanding the ideal network architecture and data representation for using DL in phylogenetic inference. We evaluate the performance of different neural network architectures (DNN, CNN, RNN, GNN) and phylogeny representations (summary statistics, Lineage Through Time or LTT, phylogeny encoding and phylogeny graph) for inferring rates of the Constant Rate Birth-Death (CRBD) and the Binary State Speciation and Extinction (BISSE) models. We find that deep learning methods can reach similar or even higher accuracy than Maximum Likelihood Estimation, provided that network architectures and phylogeny representations are appropriately tuned to the respective model. For example, for the CRBD model we find that CNNs and RNNs fed with LTTs outperform other combinations of network architecture and phylogeny representation, presumably because the LTT is a sufficient and therefore less redundant statistic for homogenous BD models. For the more complex BiSSE model, however, it was necessary to feed the network with both topology and tip states information to reach acceptable performance. Overall, our results suggest that deep learning provides a promising alternative for phylogenetic inference, but that data representation and architecture have strong effects on the inferential performance.

ecology↗

bioRxiv2023

Phylogenetic comparative approach reveals evolutionary conservatism, ancestral composition, and integration of vertebrate gut microbiota

How host-associated microbial communities evolve as their hosts diversify remains equivocal: How conserved is their composition? What was the composition of ancestral microbiota? Do microbial taxa covary in abundance over millions of years? Multivariate phylogenetic models of trait evolution are key to answering similar questions for complex host phenotypes, yet they are not directly applicable to relative abundances, which usually characterize microbiota. Here, we extend these models in this context, thereby providing a powerful approach for estimating phylosymbiosis (the extent to which closely related host species harbor similar microbiota), ancestral microbiota composition, and integration (evolutionary covariations in bacterial abundances). We apply our model to the gut microbiota of mammals and birds. We find significant phylosymbiosis that is not entirely explained by diet and geographic location, indicating that other evolutionary-conserved traits shape microbiota composition. We identify main shifts in microbiota composition during the evolution of the two groups and infer an ancestral mammalian microbiota consistent with an insectivorous diet. We also find remarkably consistent evolutionary covariations among bacterial orders in mammals and birds. Surprisingly, despite the substantial variability of present-day gut microbiota, some aspects of their composition are conserved over millions of years of host evolutionary history.

evolutionary biology↗