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Silvestro, D.

Publications and source records attributed to Silvestro, D..

At least 19 recordsLinked to original sources

Empirical evidence and robustness of clock models with spikes

1Time-calibrated phylogenies provide information on past macroevolutionary history. Time calibration can be obtained from fossil ages or node calibrations in combination with a clock model describing evolutionary rates. Relaxed clocks, which allow rates of evolution to vary across lineages, are widely used in phylogenetic research but lack a mechanistic link between rate variation and the evolutionary process. A recently developed class of clock models attempts to introduce biological mechanisms by coupling speciation events with spikes of evolutionary change. However, their empirical support and overall impact on phylogenetic inference remain underexplored. Here, we evaluate the support for spike clock models across a range of empirical datasets and use simulations to quantify the effects of model misspecification and missing data across clock models. We find that spike clock models are supported as the best-fitting model in six of the seven datasets analyzed, suggesting widespread evidence of a punctuated mode of evolution. Although the choice of clock models does not strongly affect the resulting divergence time estimates, spike clock models tend to give more constrained uncertainty intervals of speciation and extinction rate estimates in some empirical analyses. We interpret this as the consequence of information transfer from sequence evolution into the inferred branching process. Our simulations show that a general clock model that incorporates both branch-specific clock rates and spikes is the most robust across simulated datasets. In particular, models with spikes are robust to missing data, capable of accurately estimating speciation and extinction rates even when fossil data is completely absent. In summary, we highlight here that evolutionary spikes leave a detectable signal in the alignment data, and correctly accounting for them leads to improved estimates of the branching parameters and tree topologies.

evolutionary biology↗

Future-proofing agrobiodiversity: climate and niche-aware conservation planning using reinforcement learning.

Despite substantial global commitments to expand protected-area networks, the strategic allocation of limited resources remains challenging. Spatial conservation planning helps identify priority regions that maximise conservation benefits per unit area. Yet, they also tend to neglect two fundamental aspects of conservation: climate-driven range shifts and the representation of environmentally distinct populations within species. Here, we propose a continental-scale conservation planning framework that explicitly accounts for both processes through novel routines implemented in the conservation planning software CAPTAIN. We apply this framework to European crop wild relatives (CWR), for which niche coverage is a focal priority, as it underpins their potential to support agricultural adaptation to future environmental stressors through breeding programs. Comparative analyses on a subset of 186 CWR associated with five focal crops show that accounting for range shifts and niche coverage leads to substantially different conservation priorities from those obtained with a baseline model based on current distributions only. These additions reduced the number of non-protected species by 64%, increased the average protected distribution range by 43%, increased mean niche coverage from 75.8% to 84.5% and reduced the number of species with less than half of their niche protected from 35 to 10. Applied to a more comprehensive checklist of 1,140 European CWRs, the final framework identifies continental-scale priority areas representing 93.5% of these taxa and includes 94.4% of its critically endangered species. Our results highlight the importance of incorporating both temporal dynamics and within-species environmental representation when designing conservation strategies under climate change. RepositoryThe repository will be made publicly accessible after publication at doi: https://10.5281/zenodo.19855597

ecology↗

Robust evidence for modest diversity loss across the K/Pg in neoselachians: Response to Guinot et al.

We reconstructed the neoselachian diversity over the past 145 million years using new occurrence dataset and DeepDive1-3. We recovered a small decline through the K/Pg following a steady increase during the Cretaceous, and a prolonged, substantial decline towards the present following a mid-Eocene peak2. Guinot et al. argue that our conclusions are compromised by problems in the underlying data and by the way extinction magnitude across the K/Pg was quantified. They cast doubt particularly on the pattern across the K/Pg, which they consider to be at odds with all previous analyses. They raise no issue with the Cretaceous trend, even though it was recovered with the same dataset and methods. We audited the alleged data issues reported in Guinot et al. and found that they mostly reflect operational choices (see Supplementary Information). However, we applied their data treatment and ran sensitivity tests to evaluate how this approach affects our results, specifically around the K/Pg. None of our tests recovered a diversity collapse for neoselachians during this interval. As such, we demonstrate that our findings are robust and consistent across different data treatments.

paleontology↗

Bayesian neural networks enable inference of complex phylodynamic processes

Phylogenetic branching patterns carry essential information about population and diversification dynamic processes, including speciation, extinction, and epidemiological transmission. Phylodynamic models offer a rigorous mathematical framework for quantifying these dynamics from phylogenetic trees. Extensions of these models enable the incorporation of external covariates as predictors of phylodynamic parameters, for instance allowing us to link traits or environmental variables with changes in speciation, extinction, or transmission rates. However, the dependencies between predictors and phylodynamic parameters are typically restricted to linear, additive effects and may thus fail to capture complex, potentially non-linear relationships underlying evolutionary dynamics. To address this limitation, we propose a new framework, BELLA, which leverages unsupervised Bayesian neural networks (BNNs) to flexibly model functional relationships between key phylodynamic parameters and a broad set of predictors, including categorical traits, quantitative variables, and time series data. Based on these covariates, the BNN weights are estimated through Markov chain Monte Carlo and can be inferred jointly with the phylogenetic tree topology and branching times, obtained directly from sequence alignment data. Using extensive simulations, we demonstrate that this approach accurately recovers predictor-parameter relationships, mitigates overfitting, and remains robust across both macroevolutionary and epidemiological contexts. By incorporating tools from explainable artificial intelligence, we further show that our framework reliably identifies the most influential predictors and yields interpretable descriptions of their impact on phylodynamic rates. Finally, we apply our method to two empirical analyses: linking SARS-CoV-2 migration dynamics with travel data during its early spread in Europe, and inferring trait and time-dependent speciation and extinction rates in the Cenozoic diversification of platyrrhines. Our unsupervised BNN framework substantially expands the capabilities of phylodynamic inference providing a powerful and flexible approach to model complex macroevolutionary and epidemiological processes. Significance statementUnderstanding how species diversify or pathogens spread requires linking phylogenetic trees to biological traits and environmental characteristics. We present BELLA, an unsupervised Bayesian neural network that learns complex, nonlinear links between predictors--such as traits or environmental variables--and key rates such as speciation, extinction, transmission, and migration directly from sequence data--which in turn inform on the phylogenetic relationships. BELLA does not require any training data, enabling inferences that were previously not tractable. Across simulated epidemics and macroevolutionary histories, BELLA is more accurate than state-of-the-art linear models relating predictors and rates, while remaining interpretable through explainable artificial intelligence. Empirical applications in epidemiology reveal nonlinear effects of travel among countries on the early spread of SARS-CoV-2 in Europe, while a macroevolutionary application shows selective extinction events in the diversifcation history of New World monkeys. The BELLA framework expands the reach of phylodynamics in biology and public health.

evolutionary biology↗

Global gaps and priorities for shark and ray conservation: Integrating threat, function, and evolutionary distinctiveness

Elasmobranchs (sharks, rays, and skates) face unprecedented extinction risk, with over one-third of species threatened primarily by overfishing. While marine protected areas (MPAs) are essential tools to help safeguard marine ecosystems and the species that inhabit them, we lack a comprehensive understanding of how well elasmobranchs are protected globally. Here, we (1) evaluate the current level of protection for elasmobranchs, (2) employ CAPTAIN reinforcement learning algorithm to identify areas that optimise conservation based on species level of threat and their functional and evolutionary distinctiveness, and (3) examine conservation conflicts and opportunities by identifying areas where conservation priorities overlap with high and low fishing pressures. Our analysis revealed severe protection deficits, with elasmobranchs having on average only 3% of their range covered by no-take MPAs and 64% of species under-represented relative to random expectations. Areas that optimise conservation priorities consistently converged in Australian, Southeast Asia, the Indian Ocean, Atlantic Africa, European Atlantic coasts, the Caribbean, and South American Atlantic regions. However, areas of highest conservation priority often coincided with intensive fishing, particularly in East Asian seas, highlighting conservation conflicts requiring strategic management. Our results provide actionable insights for policymakers to optimise conservation strategies and maximise biodiversity retention. TeaserReinforcement learning identifies priority areas to expand ocean protection for unique and irreplaceable elasmobranchs.

ecology↗

Splendid isolation with migration: Diversity dynamics of South American mammals

Biodiversity dynamics following encounters between long-isolated faunas provide natural experiments to examine how diversity dependence and ecological interactions shape diversification patterns at macroevolutionary time scales. The Cenozoic arrival of African and North American mammals in South America likely had transformative effects on one of the worlds most unique and previously isolated continental faunas. However, the extent and persistence of these effects remain debated. Here, we assess the impact of immigrant mammal groups on native South American mammal diversity by combining an extensive fossil dataset (6,214 occurrences; 1,739 species) with deep learning methods and time-series analyses to model dynamics of mammalian biodiversity throughout the Cenozoic. The arrival of African and North American lineages increased the continents overall species richness but also contributed to a decline in native diversity. Additionally, our models suggest that the negative effect of immigrant lineages on native diversity was strongest immediately after their arrival and initial diversification, but this effect lessened over time. Overall, these findings support a scenario where immigration simultaneously enriched South American faunas and triggered a time-decaying replacement of native lineages, reshaping continental biodiversity.

evolutionary biology↗

Predicting Ecosystem Resilience Using Multi-Agent Reinforcement Learning

Twin models of natural ecosystems hold great promise for informing real-world decisions on sustainable land use and biodiversity conservation. However, existing simulations of animal behavior often rely on manually crafted rules, limiting their scalability and practical utility. Here, we present a flexible and scalable agent-based modeling approach that uses reinforcement learning--instead of hand-coded rules--to simulate animal behavior. We validate this approach across ten alpine ecosystems featuring wolves, chamois, and vegetation. By comparing model outputs with empirical data, we show that the simulations reproduce realistic ecological and behavioral patterns, including population dynamics, life history traits, and social interactions. We then use the model to assess ecosystem resilience under scenarios of habitat degradation, game hunting, and heat stress. Our framework paves the way for realistic simulations advancing our ability to predict ecosystem responses to disturbance and tipping points leading to biodiversity loss, in order to support conservation planning and guide the sustainable use of natural resources.

ecology↗

Environmental fluctuations trigger faster evolution and turnover

Biodiversity patterns and species communities emerge from the interactions between organisms and their environment and from processes spanning across different spatial and temporal scales. Understanding the dynamics of these interactions remains difficult as it requires mapping how microevolutionary processes scale up to macroevolutionary outcomes. Here, we use an individual-based simulation framework to model how populations respond to environmental fluctuations in a spatially explicit context. Our approach tracks evolutionary processes over thousands of generations to quantify the emerging macroevolutionary patterns under varying degrees of environmental instability modeled as shifting environmental gradients. We found that increased instability across time and space leads to elevated rates of species-level extinctions leading to homogenization of species communities, while promoting high rates of phenotypic evolution. Emerging extinction patterns indicate a selective process where species with low migration ability, narrow environmental tolerance, and limited evolvability are disproportionally affected by phases of rapid environmental change. Understanding the impact of environmental variation on organisms and species evolution remains challenging but can be explored through simulations to generate expectations and testable hypotheses about evolutionary processes across different scales.

evolutionary biology↗

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↗

Using artificial intelligence to optimize ecological restoration for climate and biodiversity

The restoration of degraded ecosystems is critical for mitigating climate change and reversing biodiversity loss. Depending on the primary objective - such as maximizing carbon sequestration or protecting threatened species - and within the boundaries of budget constraints, different spatial priorities have been identified at global and regional scales. Funding mechanisms to support such work comprise public sources, philanthropy, and the private sector, including the sales of carbon and biodiversity credits. However, effectively exploring tradeoffs among restoration objectives and estimating the price of biodiversity and carbon credits to design financially viable projects remain challenging. Here we harness the power of artificial intelligence in our software CAPTAIN, which we further develop to identify spatial priorities for ecological restoration that maximize multiple objectives at once and to allow a robust evaluation of biodiversity and climate outcomes. We find through a series of simulations that even low-to-moderate consideration of biodiversity in restoration projects leads to the selection of restored areas that disproportionately improve the conservation of threatened species, while resulting in a relatively smaller total amount of carbon captured. We propose a data-driven valuation of biodiversity credits in relation to carbon credits, enabling the design of a bundled financial model that could support restoration efforts even in areas previously excluded for economic reasons. Applying our methodology to plant diversity in the Atlantic Forest of eastern South America, one of the most biodiverse and threatened ecosystems globally, we demonstrate its practical utility in guiding real-world restoration and quantifying the essential trade-offs between climate and nature outcomes. Our study provides a robust, scalable methodological pathway to optimize the outcomes of restoration efforts for climate and nature.

bioinformatics↗

Widespread selection relaxation in aquatic mammals

While mammals are predominantly terrestrial, several lineages within them have independently transitioned to aquatic environments and exhibit a great variety of evolutionary changes, which ultimately lead to irreversible transitions to aquatic lifestyle. Such changes should also be detectable at the molecular level and, for instance, olfactory genes have been found to evolve under reduced selection and functionality in whales. However, the presence of a molecular signature related to aquatic transitions across other genes remains unknown as is the degree to which this affects only fully aquatic groups or also semiaquatic ones. Here, we use a Bayesian framework to investigate differences in the strength of purifying selection among terrestrial, semiaquatic and fully aquatic mammals using a set of 1000 orthologous genes, while accounting for shared ancestry, and controlling for body mass and effective population size. We found that selection relaxation linked to aquatic transitions is not only occurring in olfactory genes, but also detected across 20% of the other genes analysed here. This consistent trend of genes under weaker selection is inferred in both semi- and fully aquatic mammals although the pattern is stronger in the latter. While most of the genes analysed here likely remain functional in all mammals, their evolution is inferred to be neutral in a substantial number of aquatic species, consistently with genes that might be losing their functionality. The inferred widespread relaxation of selection among genes is consistent with a macroevolutionary scenario where secondary transitions to aquatic environment eventually become irreversible.

evolutionary biology↗

DeepDiveR - A software for deep learning estimation of palaeodiversity from fossil occurrences

O_LIThe incompleteness of the fossil record, in particular variation in preservation and sampling through space and time, presents a barrier to estimating changes in biodiversity which standard statistical methods struggle to account for. C_LIO_LIHere we present DeepDiveR, an R package for the DeepDive program enabling estimation of biodiversity from fossil occurrence data. The method uses a simulation-trained deep neural network to generate predictions of biodiversity change through time, while accounting for temporal, spatial and taxonomic heterogeneities in preservation. C_LIO_LIDeepDiveR can be readily used to explore the extinct biodiversity of different clades. We demonstrate the pipeline to build and customise analyses, including consideration of changes in biogeography. We also further develop the model to integrate information about modern diversity in the case of extant clades and introduce a function that automatically adjusts the parameterization of the simulations to generate training data that reflect the distribution of empirical datasets. C_LIO_LITo demonstrate the software, we analyse the fossil record of the order Carnivora through the Cenozoic, finding a peak in diversity in the Late Miocene and a 37% species loss since the Pleistocene. Our implementation includes the generation summary statistics and plots that allow for an evaluation of the model performance and diversity estimations and a configuration file that captures all parameters required to guarantee the full reproducibility of the results. C_LI

paleontology↗

Challenges in estimating species age from phylogenetic trees

AimSpecies age, the elapsed time since origination, can give an insight into how species longevity might influence eco-evolutionary dynamics and has been hypothesized to influence extinction risk. Traditionally, species ages have been measured in the fossil record. However, recently, numerous studies have attempted to estimate the ages of extant species from the branch lengths of time-calibrated phylogenies. This approach poses problems because phylogenetic trees contain direct information about species identity only at the tips and not along the branches. Here, we show that incomplete taxon sampling, extinction, and different assumptions about speciation modes can significantly alter the relationship between true species age and phylogenetic branch lengths, leading to high error rates. We found that these biases can lead to erroneous interpretations of eco-evolutionary patterns derived from the comparison between phylogenetic age and other traits, such as extinction risk. InnovationFor bifurcating speciation, which is the default assumption in most analyses, we propose a probabilistic approach to improve the estimation of species ages, based on the properties of a birth-death process. We show that our model can reduce the error by one order of magnitude under cases of high extinction and high percentage of unsampled extant species. Main conclusionOur results call for caution in interpreting the relationship between phylogenetic ages and eco-evolutionary traits, as this can lead to biased and erroneous conclusions. We show that, under the assumption of bifurcate, it is possible to obtain better approximations of species age by combining information from branch lengths with the expectations of a birth-death process.

evolutionary biology↗

Plant diversity darkspots for global collection priorities

O_LIMore than 15% of all vascular plant species may remain scientifically undescribed, and many of the >340,000 described species have no or few geographic records documenting their distribution. Identifying and understanding taxonomic and geographic knowledge shortfalls is key to prioritising future collection and conservation efforts. C_LIO_LIUsing extensive data for 343,523 vascular plant species and time-to-event analyses, we conducted multiple tests related to plant taxonomic and geographic data shortfalls, and identified 32 global diversity darkspots (regions predicted to lack most information about their species diversity and distribution). We defined priority regions for future collection according to several socio-economic and environmental scenarios. C_LIO_LIMost plant diversity darkspots are found within biodiversity hotspots, except New Guinea. We identify New Guinea, the Philippines, Borneo, Myanmar, India, Turkey, Colombia, Ecuador, and Peru, as global collection priorities under all environmental and socio-economic conditions considered. C_LIO_LIOur study provides a framework to accelerate plant species documentation for the implementation of conservation actions. As digitisation of the worlds herbaria progresses, collection and conservation priorities may soon be identifiable at finer scales. C_LI

plant biology↗

Improved estimation of molecular evolution coupling stochastic simulations and deep learning

Models have always been central to inferring molecular evolution and to reconstructing phylogenetic trees. Their use typically involves the development of a mechanistic framework reflecting our understanding of the underlying biological processes, such as nucleotide substitutions, and the estimation of model parameters by maximum likelihood or Bayesian inference. However, deriving and optimizing the likelihood of the data is not always possible under complex evolutionary scenarios or tractable for large datasets, often leading to unrealistic simplifying assumptions in the fitted models. To overcome this issue, we couple stochastic simulations of genome evolution with a new supervised deep learning model to infer key parameters of molecular evolution. Our model is designed to directly analyze multiple sequence alignments and estimate per-site evolutionary rates and divergence, without requiring a known phylogenetic tree. The accuracy of our predictions matches that of likelihood-based phylogenetic inference, when rate heterogeneity follows a simple gamma distribution, but it strongly exceeds it under more complex patterns of rate variation, such as codon models. Our approach is highly scalable and can be efficiently applied to genomic data, as we show on a dataset of 26 million nucleotides from the clownfish clade. Our simulations also show that the per-site rates obtained by deep learning increase the likelihood of the true tree and could therefore lead to more accurate phylogenetic inference. We propose that future advancements in phylogenetic analysis will benefit from a semi-supervised learning approach that combines deep-learning estimation of substitution rates, which allows for more flexible models of rate variation, and probabilistic inference of the phylogenetic tree, which guarantees interpretability and a rigorous assessments of statistical support.

evolutionary biology↗

Benchmarking imputation methods for discrete biological data

Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, to quantify species extinction risks, or to evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance due to incomplete sampling limited data and resource availabilities. Several imputation methods exist to predict missing values and have been successfully evaluated and used to fill the gaps in datasets of quantitative traits. Here we explore the performance of different imputation methods on discrete biological traits i.e. qualitative or categorical traits such as diet or habitat. We develop a bioinformatics pipeline to impute trait data combining phylogenetic, machine learning, and deep learning methods while integrating a simulation framework to evaluate their performance on synthetic datasets. Using this pipeline we run a wide range of simulations under different missing rates, mechanisms, and biases and different evolutionary models. Our results indicate that a new ensemble approach, where we combined the imputation results of a selection of imputation methods provides the most robust and accurate prediction of missing discrete traits. We apply our pipeline to an incomplete trait dataset of 1015 elasmobranch species (including sharks and rays) and found a high imputation accuracy of the predictions based on an expert-based assessment of the missing traits. Our bioinformatic pipeline, implemented in an open-source R package, facilitates the application and comparison of multiple imputation methods to make robust predictions of missing trait values in biological datasets.

bioinformatics↗

Exploring the Macroevolutionary Signature of Asymmetric Inheritance at Speciation

AO_SCPLOWBSTRACTC_SCPLOWPopular comparative phylogenetic models such as Brownian Motion, Ornstein-Ulhenbeck, and their extensions, assume that, at speciation, a trait value is inherited identically by the two descendant species. This assumption contrasts with models of speciation at the micro-evolutionary scale where phenotypic distributions of the descendants are sub-samples of the ancestral distribution. Various described mechanisms of speciation can lead to a displacement of the ancestral phenotypic mean among descendants and an asymmetric inheritance of the ancestral phenotypic variance. In contrast, even macro-evolutionary models that account for intraspecific variance assume symmetrically conserved inheritance of the ancestral phenotypic distribution at speciation. Here we develop an Asymmetric Brownian Motion model (ABM) that relaxes the hypothesis of symmetric and conserved inheritance of the ancestral distribution at the time of speciation. The ABM jointly models the evolution of both intra- and inter-specific phenotypic variation. It also allows the mode of phenotypic inheritance at speciation to be inferred, ranging from a symmetric and conserved inheritance, where descendants inherit the ancestral distribution, to an asymmetric and displaced inheritance, where descendants inherit divergent phenotypic means and variances. To demonstrate this model, we analyze the evolution of beak morphology in Darwin finches, finding evidence of character displacement at speciation. The ABM model helps to bridge micro- and macro-evolutionary models of trait evolution by providing a more robust framework for testing the effects of ecological speciation, character displacement, and niche partitioning on trait evolution at the macro-evolutionary scale.

evolutionary biology↗

Ecological change and conflict reduction led to the evolution of a transformative social behavior in ants

Behavioral innovations can be ecologically transformative for lineages that perform them and for their associated communities. Many ecologically dominant, superorganismal, and speciose ant lineages use a mouth-to-mouth fluid exchange behavior - trophallaxis - to share both exogenously sourced and endogenously produced materials across their colonies, while lineages that are less abundant, less cooperative and less speciose tend not to perform this behavior. How and why this behavior evolved and fixed in only some ant lineages remains unclear and whether this trait enables ants ecological dominance is not yet understood. Here we show that trophallaxis evolved in two major events ~110 Ma in lineages that today encompass 36% of ants, and in numerous smaller and more recent events. We found that trophallaxis evolved early only in ant lineages that had reduced intra-colonial conflict by losing workers ability to reproduce. Our causal models indicate that this signature behavior of superorganismal ants required social cooperation and ecological opportunism, and likely contributed to the large colony sizes and speciation patterns of the ants that use it and dominate our landscapes today. We hypothesize that the early evolution of trophallaxis was brought about by a major shift in terrestrial ecosystems through the origin and diversification of flowering plants and the consequent opportunistic inclusion of nectar and sap-sucker honeydew in the ant diet.

evolutionary biology↗