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Bayesian phylogenetic inference requires a tree prior, which models the underlying diversification process which gives rise to the phylogeny. Existing birth-death diversification models include a wide range of features, for instance lineage-specific variations in speciation and extinction rates. While across-lineage variation in speciation and extinction rates is widespread in empirical datasets, few heterogeneous rate models have been implemented as tree priors for Bayesian phylogenetic inference. As a consequence, rate heterogeneity is typically ignored when reconstructing phylogenies, and rate heterogeneity is usually investigated on fixed trees. In this paper, we present a new BEAST2 package implementing the cladogenetic diversification rate shift (ClaDS) model as a tree prior. ClaDS is a birth-death diversification model designed to capture small progressive variations in birth and death rates along a phylogeny. Unlike previous implementations of ClaDS, which were designed to be used with fixed, user-chosen phylogenies, our package is implemented in the BEAST2 framework and thus allows full phylogenetic inference, where the phylogeny and model parameters are co-estimated from a molecular alignment. Our package provides all necessary components of the inference, including a new tree object and operators to propose moves to the MCMC. It also includes a graphical interface through BEAUti. We validate our implementation of the package by comparing the produced distributions to simulated data, and show an empirical example of the full inference, using a dataset of cetaceans.

Characterizing 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., pollinators) interaction types. Here, 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. Contrary 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 with a multiscale characterization of their structure. Mesoscale structures, such as 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. Our results clarify structural differences between antagonistic and mutualistic networks and highlight machine learning as a promising approach for characterizing interaction types in systems where it is not directly observable.

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, which impedes the prediction of future transmission scenarios. 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 codiversificationversusrecent 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.

Host-associated microbial communities can play a central role in the adaptation of their hosts to different environments during evolutionary diversifications. Yet, little is known about the deep-time evolution of these microbiota: 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 the relative abundances of microbial taxa. Here, we extend these models to the case of compositional data, thereby providing a powerful approach for estimating phylosymbiosis (i.e. the extent to which closely related host species harbor similar microbiota), ancestral microbiota composition, and integration (i.e. evolutionary covariations in bacterial abundances). We then apply our model to gut bacteria data for >200 mammal and >300 bird species. We find a significant phylosymbiosis in both mammals and birds that is not entirely explained by diet and biogeography, 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, suggesting strong constraints in gut microbiota evolution. Our results highlight the importance of host evolutionary history in shaping the gut microbiota composition of two major vertebrate groups and provide a phylogenetic comparative model for microbiota evolution broadly applicable across microbiota-host systems.Significance statementLittle is known about how host-associated microbial communities evolve as their hosts diversify. We have developed a novel approach to infer deep-time evolutionary trends in microbiota composition from the phylogenies of present-day hosts and their associated microbiota. Our approach allows testing for phylosymbiosis, i.e. whether the microbiota is evolutionary conserved, reconstructing ancestral microbiota composition, and identifying microbial taxa that vary in a concerted way. Applying our approach to the gut microbiota of mammals and birds, we find that the microbiota of both vertebrate groups is evolutionary conserved, and infer an ancestral mammalian microbiota consistent with an insectivorous diet. We observe conserved covariations in abundances among bacterial orders that suggest previously unsuspected constraints in vertebrate gut microbiota over millions of years.

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.

Diversification 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, which allows inferring multiple rate changes of small magnitude 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.[Birth–death model; data augmentation; diversification; macroevolution.]

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.

Current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species, and as such can provide key insights into processes governing biodiversity. Novel high throughput sequencing (HTS) approaches are now emerging as powerful tools to overcome limitations in the availability of arthropod biodiversity data, and hence provide insights into these processes. Here, we explored how these tools might be most effectively exploited for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first reviewed the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We considered how this could be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explored how these approaches could be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identified seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonized efforts in HTS arthropod inventory could yield significant advances in island biodiversity research., The iGON Working Group was organized by the iBioGen project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 810729.

Whether 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.

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.

A central goal of macroevolution is to understand palaeodiversity dynamics through time and space. 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 enables 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.

Analysing 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 for 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 analyse the diversification dynamics of this clade in the past 500 million years. 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 million years 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.

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.

Disentangling the relative role of environmental filtering and spatial processes in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical data sets are required to account for taxon- and context-dependency, but relevant research in understudied areas is often compromised by the taxonomic impediment. Here we used haplotype-level community DNA metabarcoding, enabled by stringent filtering of amplicon sequence variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar β diversity patterns at ASV and species (OTU, operational taxonomic unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented Quercus alnifolia habitat, we also detected a major role of spatial isolation determined by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterizing metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.

International audience; A lot of what we know about past speciation and extinction dynamics is based on statistically fitting birth–death processes to phylogenies of extant species. Despite their wide use, the reliability of these tools is regularly questioned. It was recently demonstrated that vast ‘congruent’ sets of alternative diversification histories cannot be distinguished (i.e., are not identifiable) using extant phylogenies alone, reanimating the debate about the limits of phylogenetic diversification analysis. Here, we summarize what we know about the identifiability of the birth–death process and how identifiability issues can be addressed. We conclude that extant phylogenies, when combined with appropriate prior hypotheses and regularization techniques, can still tell us a lot about past diversification dynamics.

Competition can drive macroevolutionary change, for example during adaptive radiations. However, we still lack a clear understanding of how it shapes diversification processes and patterns. To better understand the macroevolutionary consequences of competition, as well as the signal left on phylogenetic data, we developed a model linking trait evolution and species diversification in an ecological context. We find four main results: first, competition spurs trait diversity but not necessarily species richness; second, competition produces slowdowns in species diversification even in the absence of explicit ecological limits, but not in phenotypic diversification even in the presence of such limits; third, early burst patterns do not provide a reliable way of testing for adaptive radiations; and fourth, looking for phylogenetic signal in trait data and support for phenotypic models incorporating competition is a better alternative. Our results clarify the macroevolutionary consequences of competition and could help design more powerful tests of adaptive radiations in nature.

Diversification rates vary over time, yet the factors driving these variations remain unclear. Temporal declines in speciation rates have often been interpreted as the effect of ecological limits, competition, and diversity dependence, emphasising the role of biotic factors. Abiotic factors, such as climate change, are also supposed to have affected diversification rates over geological time scales, yet direct tests of these presumed effects have mainly been limited to few clades well represented in the fossil record. If warmer climatic periods have sustained faster speciation, this could explain slowdowns in speciation during the Cenozoic climate cooling. Here, we apply state-of-the art diversity-dependent and temperature-dependent phylogenetic models of diversification to 218 tetrapod families, along with constant rate and time-dependent models. We confirm the prevalence of diversification slowdowns, and find as much support for temperature-dependent than diversity-dependent models. These results call for a better integration of these two processes in studies of diversification dynamics.

Metazoan metabarcoding is emerging as an essential strategy for inventorying biodiversity,with diverse projects currently generating massive quantities of community-level data. The potential for integrating across such data sets offers new opportunities to better understand biodiversity and how it might respond to global change. However, large-scale synthesesmay be compromised if metabarcoding workflows differ from each other. There are ongoing efforts to improve standardization for the reporting of inventory data. However, harmonization at the stage of generating metabarcode data has yet to be addressed. A modular framework for harmonized data generation offers a pathway to navigate the complex structure of terrestrial metazoan biodiversity. Here, through our collective expertise as practitioners,method developers, and researchers leading metabarcoding initiatives to inventory terrestrial biodiversity, we seek to initiate a harmonized framework for metabarcode data generation, with a terrestrial arthropod module. We develop an initial set of submodules covering the 5 main steps of metabarcode data generation: (i) sample acquisition; (ii) sample processing; (iii) DNA extraction; (iv) polymerase chain reaction amplification, library preparation, and sequencing; and (v) DNA sequence and metadata deposition, providing a backbone for a terrestrial arthropod module. To achieve this, we (i) identified key points for harmonization, (ii) reviewed the current state of the art, and (iii) distilled existing knowledge within submodules, thus promoting best practice by providing guidelines and recommendations to reduce the universe of methodological options.We advocate the adoption and further development of the terrestrial arthropodmodule.We further encourage the development of modules for other biodiversity fractions as an essential step toward large-scale biodiversity synthesis through harmonization., The working group “Toward Harmonisation for the Generation of Metabarcoding Data: Soil Biodiversity and Terrestrial Arthropod modules” held in November 2020 (online) was organized by the iBioGen project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 810729. P.A. was funded through a Junior Leader Fellowship (LCF/BQ/PR21/11840006) by “la Caixa” Foundation (ID 100010434) and the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 847648.

Disentangling the relative role of environmental filtering and dispersal limitation in driving metacommunity structure across mountainous regions remains challenging, as the way we quantify spatial connectivity in topographically and environmentally heterogeneous landscapes can influence our perception of which process predominates. More empirical datasets are required to account for taxon- and context-dependency but relevant research is often compromised by coarse taxonomic resolution. We here employed haplotype-level community DNA metabarcoding, enabled by stringent filtering of Amplicon Sequence Variants (ASVs), to characterize metacommunity structure of soil microarthropod assemblages across a mosaic of five forest habitats on the Troodos mountain range in Cyprus. We found similar β diversity patterns at ASV and species (OTU, Operational Taxonomic Unit) levels, which pointed to a primary role of habitat filtering resulting in the existence of largely distinct metacommunities linked to different forest types. Within-habitat turnover was correlated to topoclimatic heterogeneity, again emphasizing the role of environmental filtering. However, when integrating landscape matrix information for the highly fragmented Golden Oak habitat, we also detected a major role of dispersal limitation imposed by patch connectivity, indicating that stochastic and niche-based processes synergistically govern community assembly. Alpha diversity patterns varied between ASV and OTU levels, with OTU richness decreasing with elevation and ASV richness following a longitudinal gradient, potentially reflecting a decline of genetic diversity eastwards due to historical pressures. Our study demonstrates the utility of haplotype-level community metabarcoding for characterising metacommunity structure of complex assemblages and improving our understanding of biodiversity dynamics across mountainous landscapes worldwide.

Metabarcoding of DNA extracted from community samples of whole organisms (whole organism community DNA, wocDNA) is increasingly being applied to terrestrial, marine and freshwater metazoan communities to provide rapid, accurate and high resolution data for novel molecular ecology research. The growth of this field has been accompanied by considerable development that builds on microbial metabarcoding methods to develop appropriate and efficient sampling and laboratory protocols for whole organism metazoan communities. However, considerably less attention has focused on ensuring bioinformatic methods are adapted and applied comprehensively in wocDNA metabarcoding. In this study we examined over 600 papers and identified 111 studies that performed COI metabarcoding of wocDNA. We then systematically reviewed the bioinformatic methods employed by these papers to identify the state-of-the-art. Our results show that the increasing use of wocDNA COI metabarcoding for metazoan diversity is characterised by a clear absence of bioinformatic harmonisation, and the temporal trends show little change in this situation. The reviewed literature showed (i) high heterogeneity across pipelines, tasks and tools used, (ii) limited or no adaptation of bioinformatic procedures to the nature of the COI fragment, and (iii) a worrying underreporting of tasks, software and parameters. Based upon these findings we propose a set of recommendations that we think the wocDNA metabarcoding community should consider to ensure that bioinformatic methods are appropriate, comprehensive and comparable. We believe that adhering to these recommendations will improve the long-term integrative potential of wocDNA COI metabarcoding for biodiversity science.

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 form of competition to MESS that explicitly compares the traits of each pair of individuals, allowing us to distinguish between inter- and intra-specific competition. Our results are qualitatively different to those of previous work that only compares each individual’s trait to the community mean. We find that neutral forces receive much less support from real systems when trait data is available and incorporated into the inference algorithm. We conclude that gathering more different types of data could be the key to unravelling the mechanisms of community assembly.

Horizontal gene transfer (HGT) is an important source of novelty in eukaryotic genomes. This is particularly true for the ochrophytes, a diverse and important group of algae. Previous studies have shown that ochrophytes possess a mosaic of genes derived from bacteria and eukaryotic algae, acquired through chloroplast endosymbiosis and from HGTs, although understanding of the time points and mechanisms underpinning these transfers has been restricted by the depth of taxonomic sampling possible. We harness an expanded set of ochrophyte sequence libraries, alongside automated and manual phylogenetic annotation, in silico modeling, and experimental techniques, to assess the frequency and functions of HGT across this lineage. Through manual annotation of thousands of single-gene trees, we identify continuous bacterial HGT as the predominant source of recently arrived genes in the model diatom Phaeodactylum tricornutum. Using a large-scale automated dataset, a multigene ochrophyte reference tree, and mathematical reconciliation of gene trees, we note a probable elevation of bacterial HGTs at foundational points in diatom evolution, following their divergence from other ochrophytes. Finally, we demonstrate that throughout ochrophyte evolutionary history, bacterial HGTs have been enriched in genes encoding secreted proteins. Our study provides insights into the sources and frequency of HGTs, and functional contributions that HGT has made to algal evolution.

Circulating in the sunlit ocean Marine plankton, which lie at the base of oceanic food chains, drive global biogeochemical fluxes, and knowledge of their distribution is key to understanding the response of oceans to environmental changes. Sommeria-Klein et al . explored the patterns and drivers of biogeography in eukaryotic plankton using a probabilistic model of taxon co-occurrence to compare the biogeography of 70 major groups, including a variety of size fractions and ecologies. The analysis is based on metabarcoding data from 129 stations in several oceanic provinces worldwide. Samples are from sunlit surface waters and, in about half of the stations, from the deep chlorophyll maximum. An essential message is that small phototrophs distribute mostly by latitude and bigger consumers are partitioned by ocean basin. —CA

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.

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.

International audience; How ecological interaction networks emerge on evolutionary time scales remains unclear. Here we build an individual-based eco-evolutionary model for the emergence of mutualistic, antagonistic and neutral bipartite interaction networks. Exploring networks evolved under these scenarios, we find three main results. First, antagonistic interactions tend to foster species and trait diversity, while mutualistic interactions reduce diversity. Second, antagonistic interactors evolve higher specialisation, which results in networks that are often more modular than neutral ones; resource species in these networks often display phylogenetic conservatism in interaction partners. Third, mutualistic interactions lead to networks that are more nested than neutral ones, with low phylogenetic conservatism in interaction partners. These results tend to match overall empirical trends, demonstrating that structures of empirical networks that have most often been explained by ecological processes can result from an evolutionary emergence. Our model contributes to the ongoing effort of better integrating ecological interactions and macroevolution.

Much ecological and evolutionary theory predicts that interspecific interactions often drive phenotypic diversification and that species phenotypes in turn influence species interactions. Several phylogenetic comparative methods have been developed to assess the importance of such processes in nature; however, the statistical properties of these methods have gone largely untested. Focusing mainly on scenarios of competition between closely-related species, we assess the performance of available comparative approaches for analyzing the interplay between interspecific interactions and species phenotypes. We find that many currently used statistical methods often fail to detect the impact of interspecific interactions on trait evolution, that sister-taxa analyses are particularly unreliable in general, and that recently developed process-based models have more satisfactory statistical properties. Methods for detecting predictors of species interactions are generally more reliable than methods for detecting character displacement. In weighing the strengths and weaknesses of different approaches, we hope to provide a clear guide for empiricists testing hypotheses about the reciprocal effect of interspecific interactions and species phenotypes and to inspire further development of process-based models. [Character displacement; competition; interspecific interactions; phylogenetic comparative methods; trait evolution.]