Biology subjects

Morlon, H.

Publications and source records attributed to Morlon, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Deep Learning from Phylogenies for Diversification Analyses

Birth-death models are widely used in combination with species phylogenies to study past diversification dynamics. Current inference approaches typically rely on likelihood-based methods. These methods are not generalizable, as a new likelihood formula must be established each time a new model is proposed; for some models such formula is not even tractable. Deep learning can bring solutions in such situations, as deep neural networks can be trained to learn the relation between simulations and parameter values as a regression problem. In this paper, we adapt a recently developed deep learning method from pathogen phylodynamics to the case of diversification inference, and we extend its applicability to the case of the inference of state-dependent diversification models from phylogenies associated with trait data. We demonstrate the accuracy and time efficiency of the approach for the time constant homogeneous birth-death model and the Binary-State Speciation and Extinction model. Finally, we illustrate the use of the proposed inference machinery by reanalyzing a phylogeny of primates and their associated ecological role as seed dispersers. Deep learning inference provides at least the same accuracy as likelihood-based inference while being faster by several orders of magnitude, offering a promising new inference approach for deployment of future models in the field.

evolutionary biology↗

bioRxiv2022

Comparing different computational approaches for detecting long-term vertical transmission in host-associated microbiota

Long-term vertical transmissions of gut bacteria are thought to be frequent and functionally important in mammals. Several phylogenetic-based approaches have been proposed to detect, among species-rich microbiota, the bacteria that have been vertically transmitted during a host clade radiation. Applied to mammal microbiota, these methods have sometimes led to conflicting results; in addition, how they cope with the slow evolution of markers typically used to characterize bacterial microbiota remains unclear. Here, we use simulations to test the statistical performances of two widely-used global-fit approaches (ParaFit and PACo) and two event-based approaches (ALE and HOME). We find that these approaches have different strengths and weaknesses depending on the amount of variation in the bacterial DNA sequences and are therefore complementary. In particular, we show that ALE performs better when there is a lot of variation in the bacterial DNA sequences, whereas HOME performs better when there is not. Global-fit approaches (ParaFit and PACo) have higher type-I error rates (false positives) but have the advantage to be very fast to run. We apply these methods to the gut microbiota of primates and our results suggest that only a small fraction of their gut bacteria is vertically transmitted.

evolutionary biology↗

bioRxiv2022

The birth-death diffusion leading to present-day Mammal diversity

Dramatic spatial, temporal and taxonomic variation in biodiversity is ultimately explained by differences in speciation and extinction rates. Mammals represent a [~]200 My old radiation that resulted in over 6500 extant species, with stark temporal, spatial and taxonomic heterogeneity in biodiversity. Throughout their history, every mammal lineage is expected to have undergone diversification rates that vary instantaneously in time resulting from the complex interplay of context-specific extrinsic factors (e.g., K-Pg mass extinction event, rise of angiosperms) with their evolving ecologies (e.g., body size, diet). When studying the diversification history of a clade, however, mathematical and computational limitations have hindered inference of such a flexible birth-death model where speciation and extinction rates evolve continuously along a phylogenetic tree. Here we overcome these challenges by implementing a series of phylogenetic models in which speciation and extinction rates are inherited and diffuse following a latent Geometric Brownian motion process. We enable full Bayesian inference using data augmentation techniques to sample from the posterior distribution of model parameters, including augmented phylogenetic trees and validate using simulations. Using a genome-informed time-calibrated tree for over 4000 Mammals species, we are able to estimate a complete and fine-grained picture of the variation in diversification rates that captures both global and lineage specific effects. We find that, contrary to the idea of a suppressed mammalian diversification before the K-Pg mass extinction event (i.e., explosive- or delayed-rise), mammal speciation rates dramatically increased around 10-20 My before the K-Pg. Our new model opens exciting possibilities in disentangling the drivers behind variation in diversification and assaying how small-scale processes scale-up to macroevolutionary dynamics.

evolutionary biology↗

bioRxiv2022

Structure and specialization of mycorrhizal networks in phylogenetically diverse tropical communities

BackgroundThe root mycobiome plays a fundamental role in plant nutrition and protection against biotic and abiotic stresses. In temperate forests or meadows dominated by angiosperms, the numerous fungi involved in root symbioses are often shared between neighboring plants, thus forming complex plant-fungus interaction networks of weak specialization. Whether this weak specialization also holds in rich tropical communities with more phylogenetically diverse sets of plant lineages remains unknown. We collected roots of 30 plant species in semi-natural tropical communities including angiosperms, ferns, and lycophytes, in three different habitat types on La Reunion island: a recent lava flow, a wet thicket, and an ericoid shrubland. We identified root-inhabiting fungi by sequencing both the 18S rRNA and the ITS2 variable regions. We assessed the diversity of mycorrhizal fungal taxa according to plant species and lineages, as well as the structure and specialization of the resulting plant-fungus networks. ResultsThe 18S and ITS2 datasets are highly complementary at revealing the root mycobiota. According to 18S, Glomeromycotina colonize all plant groups in all habitats forming the least specialized interactions, resulting in nested network structures, while Mucoromycotina (Endogonales) are more abundant in the wetland and show higher specialization and modularity compared to the former. According to ITS2, mycorrhizal fungi of Ericaceae and Orchidaceae, namely Helotiales, Sebacinales, and Cantharellales, also colonize the roots of most plant lineages, confirming that they are frequent endophytes. While Helotiales and Sebacinales present intermediate levels of specialization, Cantharellales are more specialized and more sporadic in their interactions with plants, resulting in highly modular networks. ConclusionsThis study of the root mycobiome in tropical environments reinforces the idea that mycorrhizal fungal taxa are locally shared between co-occurring plants, including phylogenetically distant plants (e.g. lycophytes and angiosperms), where they may form functional mycorrhizae or establish endophytic colonization. Yet, we demonstrate that, irrespectively of the environmental variations, the level of specialization significantly varies according to the fungal lineages, probably reflecting the different evolutionary origins of these plant-fungus symbioses. Frequent fungal sharing between plants questions the roles of the different fungi in community functioning and highlights the importance of considering networks of interactions rather than isolated hosts.

ecology↗

bioRxiv2022

Estimating clade-specific diversification rates and palaeodiversity dynamics from reconstructed phylogenies

Understanding palaeodiversity dynamics through time and space is a central goal of macroevolution. Estimating palaeodiversity dynamics has been historically addressed with fossil data because it directly reflects the past variations of biodiversity. Unfortunately, some groups or regions lack a good fossil record, and dated phylogenies can be useful to estimate diversification dynamics. Recent methodological developments have unlocked the possibility to investigate palaeodiversity dynamics by using phylogenetic birth-death models with non-homogeneous rates through time and across clades. One of them seems particularly promising to detect clades whose diversity has declined through time. However, empirical applications of the method have been hampered by the lack of a robust, accessible implementation of the whole procedure, therefore requiring users to conduct all the steps of the analysis by hand in a time-consuming and error-prone way. Here we propose an automation of Morlon et al. (2011) clade-shift model with additional features accounting for recent developments and we implement it in the R package RPANDA. We also test the approach with simulations focusing on its ability to detect shifts of diversification and to infer palaeodiversity dynamics. Finally, we illustrate the automation by investigating the palaeodiversity dynamics of Cetacea, Vangidae, Parnassiinae, and Cycadales. Simulations showed that we accurately detected shifts of diversification although false shift detections were higher for time-dependent diversification models with extinction. The median global error of palaeodiversity dynamics estimated with the automated model is low showing that the method can capture diversity declines. We detected shifts of diversification for three of the four empirical examples considered (Cetacea, Parnassiinae and Cycadales). Our analyses unveil a waxing-and-waning pattern due to a phase of negative net diversification rate embedded in the trees after isolating recent radiations. Our work makes possible to easily apply non-homogeneous models of diversification in which rates can vary through time and across clades to reconstruct palaeodiversity dynamics. By doing so, we detected palaeodiversity declines among three of the four groups tested, highlighting that such periods of negative net diversification might be common. We discuss the extent to which this approach might provide reliable estimates of extinction rates and we provide guidelines for users.

evolutionary biology↗

bioRxiv2021

Do closely related species interact with similar partners?Testing for phylogenetic signal in bipartite interaction networks

AO_SCPLOWBSTRACTC_SCPLOWWhether interactions between species are conserved on evolutionary time-scales has spurred the development of both correlative and process-based approaches for testing phylogenetic signal in interspecific interactions: do closely related species interact with similar partners? Here we use simulations to test the statistical performances of the two approaches that are the most widely used in the field: Mantel tests and the Phylogenetic Bipartite Linear Model (PBLM). Mantel tests investigate the correlation between phylogenetic distances and dissimilarities in sets of interacting partners, while PBLM is a process-based approach that relies on strong assumptions about how interactions evolve. We find that PBLM often detects a phylogenetic signal when it should not. Simple Mantel tests instead have infrequent false positives and moderate statistical power; however, they often artifactually detect that closely related species interact with dissimilar partners. Partial Mantel tests, which are used to partial out the phylogenetic signal in the number of partners, actually fail at correcting for this confounding effect, and we instead recommend evaluating the significance of Mantel tests with network permutations constraining the number of partners. We also explore the ability of simple Mantel tests to analyze clade-specific phylogenetic signals. We provide general guidelines and an application on an interaction network between orchids and mycorrhizal fungi.

ecology↗

bioRxiv2021

Detecting the ecological footprint of selection

The structure of communities is influenced by many ecological and evolutionary processes, but the way this manifests in classic biodiversity patterns often remains unclear. Here, we aim to distinguish the ecological footprint of selection - through competition or environmental filtering - from that of neutral processes that are invariant to species identity. We build on existing Massive Eco-evolutionary Synthesis Simulations (MESS), which uses information from three biodiversity axes - species abundances, genetic diversity, and trait variation - to distinguish between mechanistic processes. In order to correctly detect and characterise competition, we add a new and more realistic form of competition that explicitly compares the traits of each pair of individuals. Our results are qualitatively different to those of previous work in which competition is based on the distance of each individuals trait to the community mean. We find that our new form of competition is easier to identify in empirical data compared to the alternatives. This is especially true when trait data are available and used in the inference procedure. Our findings hint that signatures in empirical data previously attributed to neutrality may in fact be the result of pairwise-acting selective forces. We conclude that gathering more different types of data, together with more advanced mechanistic models and inference as done here, could be the key to unravelling the mechanisms of community assembly.

ecology↗

bioRxiv2020

Fast and accurate estimation of species-specific diversification rates using data augmentation

1Diversification rates vary across species as a response to various factors, including environmental conditions and species-specific features. Phylogenetic models that allow accounting for and quantifying this heterogeneity in diversification rates have proven particularly useful for understanding clades diversification. Recently, we introduced the cladogenetic diversification rate shift model (ClaDS), which allows inferring subtle rate variations across lineages. Here we present a new inference technique for this model that considerably reduces computation time through the use of data augmentation and provide an implementation of this method in Julia. In addition to drastically reducing computation time, this new inference approach provides a posterior distribution of the augmented data, that is the tree with extinct and unsampled lineages as well as associated diversification rates. In particular, this allows extracting the distribution through time of both the mean rate and the number of lineages. We assess the statistical performances of our approach using simulations and illustrate its application on the entire bird radiation.

evolutionary biology↗

bioRxiv2020

Global drivers of eukaryotic plankton biogeography in the sunlit ocean

Eukaryotic plankton are a core component of marine ecosystems with exceptional taxonomic and ecological diversity. Yet how their ecology interacts with the environment to drive global distribution patterns is poorly understood. Here, we use Tara Oceans metabarcoding data covering all the major ocean basins combined with a probabilistic model of taxon co-occurrence to compare the biogeography of 70 major groups of eukaryotic plankton. We uncover two main axes of biogeographic variation. First, more diverse groups display stronger biogeographic structure. Second, large-bodied consumers are structured by oceanic basins, mostly via the main currents, while small-bodied phototrophs are structured by latitude, with a comparatively stronger influence of environmental conditions. Our study highlights striking differences in biogeographies across plankton groups and investigates their determinants at the global scale. One-sentence summaryEukaryotic plankton biogeography and its determinants at global scale reflect differences in ecology and body size.

ecology↗

bioRxiv2020

Diversification slowdown in an evolutionary cul-de-sac

Analyzing diversification dynamics is key to understanding the past evolutionary history of clades that led to present-day biodiversity patterns. While such analyses are widespread in well-characterized groups of species, they are much more challenging in groups which diversity is mostly known through molecular techniques. Here, we use the largest global database on the small subunit (SSU) rRNA gene of Glomeromycotina, a subphylum of microscopic arbuscular mycorrhizal fungi that provide mineral nutrients to most land plants by forming one of the oldest terrestrial symbioses, to analyze the diversification dynamics of this clade in the past 500 million years (Myr). We perform a range of sensitivity analyses and simulations to control for potential biases linked to the nature of the data. We find that Glomeromycotina tend to have low speciation rates compared to other eukaryotes. After a peak of speciations between 200 and 100 Myr ago, they experienced an important decline in speciation rates toward the present. Such a decline could be at least partially related to a shrinking of their mycorrhizal niches and to their limited ability to colonize new niches. Our analyses identify patterns of diversification in a group of obligate symbionts of major ecological and evolutionary importance and illustrate that short molecular markers combined with intensive sensitivity analyses can be useful for studying diversification dynamics in microbial groups.

evolutionary biology↗

bioRxiv2020

Prior hypotheses or regularization allow inference of diversification histories from extant timetrees

Phylogenies of extant species are widely used to study past diversification dynamics1. The most common approach is to formulate a set of candidate models representing evolutionary hypotheses for how and why speciation and extinction rates in a clade changed over time, and compare those models through their probability to have generated the corresponding empirical tree. Recently, Louca & Pennell2 reported the existence of an infinite number of congruent models with potentially markedly different diversification dynamics, but equal likelihood, for any empirical tree (see also Lambert & Stadler3). Here we explore the implications of these results, and conclude that they neither undermine the hypothesis-driven model selection procedure widely used in the field nor show that speciation and extinction dynamics cannot be investigated from extant timetrees using a data-driven procedure.

evolutionary biology↗

bioRxiv2020

No latitudinal gradients in tempo or mode of morphological evolution in birds

The latitudinal diversity gradient is one of the most striking patterns in nature yet its implications for morphological evolution are poorly understood. In particular, it has been proposed that an increased intensity of species interactions in tropical biota may either promote or constrain trait evolution, but which of these outcomes predominates remains uncertain. Here, we develop tools for fitting phylogenetic models of phenotypic evolution in which the impact of species interactions--namely, competition--can vary across lineages. Deploying these models on a global avian trait dataset to explore differences in trait divergence between tropical and temperate lineages, we find that the effect of latitude on the mode and tempo of morphological evolution is weak and clade- or trait-dependent. Our results indicate that competition does not disproportionately impact morphological evolution in tropical bird families and question the validity of previously reported patterns of slower trait evolution in the tropics.

evolutionary biology↗

bioRxiv2019

Model-based inference of punctuated molecular evolution

In standard models of molecular evolution, DNA sequences evolve through asynchronous substitutions according to Poisson processes with a constant rate (called the molecular clock) or a time-varying rate (relaxed clock). However, DNA sequences can also undergo episodes of fast divergence that will appear as synchronous substitutions affecting several sites simultaneously at the macroevolutionary time scale. Here, we develop a model combining basal, clock-like molecular evolution with episodes of fast divergence called spikes arising at speciation events. Given a multiple sequence alignment and its time-calibrated species phylogeny, our model is able to detect speciation events (including hidden ones) co-occurring with spike events and to estimate the probability and amplitude of these spikes on the phylogeny. We identify the conditions under which spikes can be distinguished from the natural variance of the clock-like component of molecular evolution and from temporal variations of the clock. We apply the method to genes underlying snake venom proteins and identify several spikes at gene-specific locations in the phylogeny. This work should pave the way for analyses relying on whole genomes to inform on modes of species diversification.

evolutionary biology↗

bioRxiv2019

Characterizing and comparing phylogenetic trait data from their normalized Laplacian spectrum

The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no non-parametric comprehensive framework for characterising and comparing patterns of phenotypic evolution. Here we build on a recently introduced approach for using the phylogenetic spectral density profile to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for non-parametric analysis of phylogenetic trait data. We show how to construct the spectral density profile of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the spectral density profile to successfully cluster phylogenetic trait data into meaningful groups and to characterise the phenotypic patterning within those groups. We furthermore demonstrate how the spectral density profile is a powerful tool for visualising phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical datasets: a comprehensive dataset of traits involved in song, plumage and resource-use in tanagers, and a high-dimensional dataset of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.

evolutionary biology↗

bioRxiv2019

Reliable Phylogenetic Regressions for Multivariate Comparative Data: Illustration with the MANOVA and Application to the Effect of Diet on Mandible Morphology in Phyllostomid Bats

Understanding what shapes species phenotypes over macroevolutionary time scales from comparative data requires the use of reliable phylogenetic regression techniques and associated tests (e.g. phylogenetic Generalized Least Squares, pGLS and phylogenetic analyses of variance and covariance, pANOVA, pANCOVA). While these tools are well established for univariate data, their multivariate counterparts are lagging behind. This is particularly true for high dimensional phenotypic data, such as morphometric data. Here we implement well-needed likelihood-based multivariate pGLS, pMANOVA and pMANCOVA, and use a recently-developed penalized likelihood framework to extend their application to the difficult case when the number of traits p approaches or exceeds the number of species n. We then focus on the pMANOVA and use intensive simulations to assess the performance of the approach as p increases, under various levels of phylogenetic signal and correlations between the traits, phylogenetic structure in the predictors, and under various types of phenotypic differences across species groups. We show that our approach outperforms available alternatives under all circumstances, with a greater power to detect phenotypic differences across species group when they exist, and a low risk to improperly detect inexistent differences. Finally, we provide an empirical illustration of our pMANOVA on a geometric-morphometric dataset describing mandible morphology in phyllostomid bats along with data on their diet preferences. Our approach, implemented in the R package mvMORPH, provides efficient multivariate phylogenetic regression tools for understanding what shapes phenotypic differences across species.

evolutionary biology↗

bioRxiv2019

Estimating Diversity Through Time using Molecular Phylogenies: Old and Species-Poor Frog Families are the Remnants of a Diverse Past

Estimating how the number of species in a given group varied in the deep past is of key interest to evolutionary biologists. However, current phylogenetic approaches for obtaining such estimates have limitations, such as providing unrealistic diversity estimates at the origin of the group. Here we develop a robust probabilistic approach for estimating Diversity-Through-Time (DTT) curves and uncertainty around these estimates from phylogenetic data. We show with simulations that under various realistic scenarios of diversification, this approach performs better than previously proposed approaches. We also characterize the effect of tree size and undersampling on the performance of the approach. We apply our method to understand patterns of species diversity in anurans (frogs and toads). We find that Archaeobatrachia - a species-poor group of old frog clades often found in temperate regions - formerly had much higher diversity and net diversification rate, but the group declined in diversity as younger, nested clades diversified. This diversity decline seems to be linked to a decline in speciation rate rather than an increase in extinction rate. Our approach, implemented in the R package RPANDA, should be useful for evolutionary biologists interested in understanding how past diversity dynamics have shaped present-day diversity. It could also be useful in other contexts, such as for analyzing clade-clade competitive effects or the effect of species richness on phenotypic divergence. [phylogenetic comparative methods; birth-death models; diversity curves; diversification; extinction; anurans]

evolutionary biology↗

bioRxiv2018

Constraints upon cheating emergence in mutualistic networks: evidence in the mycorrhizal symbiosis

O_LIWhile mutualisms are widespread and essential in ecosystem functioning, the emergence of uncooperative cheaters threatens their stability, unless there are functional or evolutionary mechanisms limiting cheaters interactions.\nC_LIO_LIHere, we evaluated the constraints upon mycoheterotrophic (MH) cheating plants in the mutualistic interaction network of autotrophic (AT) plants and arbuscular mycorrhizal fungi. For this purpose, we assembled a world-scale network of >25,000 interactions in order to investigate (i) the specialization and (ii) the phylogenetic distribution of MH or AT plants and their respective fungal partners.\nC_LIO_LIWe show that MH cheating repeatedly evolved in the vascular flora, suggesting low phylogenetic constraint for plants. However, MH cheaters are significantly more specialized than AT plants, and their fungi also appear more specialized and more closely related than fungi of AT plants, which suggest that cheaters are specifically isolated into modules by functional constraints\nC_LIO_LIThis unprecedented comparison of MH vs. AT plants thus reveals that MH cheating is most likely constrained by the specialization of phylogenetically conserved cheating-susceptible fungi, which suggests mechanisms for avoidance of these fungi. Beyond the mycorrhizal symbiosis, our approach highlights an empirical multiple-partners mutualistic system illustrating that the overall persistence of mutualism can be linked to functional constraints upon cheating emergences.\nC_LI

evolutionary biology↗

bioRxiv2017

Detecting environment-dependent diversification from phylogenies: a simulation study and some empirical illustrations

Understanding the relative influence of various abiotic and biotic variables on diversification dynamics is a major goal of macroevolutionary studies. Recently, phylogenetic approaches have been developed that make it possible to estimate the role of various environmental variables on diversification using time-calibrated species trees, paleoenvironmental data, and maximum-likelihood techniques. These approaches have been effectively employed to estimate how speciation and extinction rates vary with key abiotic variables, such as temperature and sea level, and we can anticipate that they will be increasingly used in the future. Here we compile a series of biotic and abiotic paleodatasets that can be used as explanatory variables in these models and use simulations to assess the statistical properties of the approach when applied to these paleodatasets. We demonstrate that environment-dependent models perform well in recovering environment-dependent speciation and extinction parameters, as well as in correctly identifying the simulated environmental model when speciation isenvironment-dependent. We explore how the strength of the environment-dependency, tree size, missing taxa, and characteristics of the paleoenvironmental curves influence the performance of the models. Finally, using these models, we infer environment-dependent diversification in three empirical phylogenies: temperature-dependence in Cetacea,{delta} 13C-dependence in Ruminantia, and CO2-dependence in Portulacaceae. We illustrate how to evaluate the relative importance of abiotic and biotic variables in these three clades and interpret these results in light of macroevolutionary hypotheses for mammals and plants. Given the important role paleoenvironments are presumed to have played in species evolution, our statistical assessment of how environment-dependent models behave is crucial for their utility in macroevolutionary analysis.

evolutionary biology↗