Biology subjects

Silvestro, D.

Publications and source records attributed to Silvestro, D..

34 records · Page 2Linked to original sources

bioRxiv2022

Two major extinction events in the evolutionary history of turtles: one caused by a meteorite, the other by hominins

We live in a time of highly accelerated extinction, which has the potential to mirror past mass extinction events. However, the rarity of these events and the restructuring of diversity that they cause complicate direct comparisons between the current extinction crisis and earlier mass extinctions. Among animals, turtles (Testudinata) are one of few groups which both have a sufficient fossil record and a sufficiently stable ecological importance to enable meaningful comparisons between the end Cretaceous mass extinction and the ongoing extinction event. In this paper we analyze the fossil record of turtles and recover three significant peaks in extinction rate. Two of these are in the Cretaceous, the second of these took place at the Cretaceous-Paleogene transition (K-Pg), reflecting the overall patterns previously reported for many other taxa. The third major extinction event started in the Pliocene and continues until now. This peak only affected terrestrial turtles and started much earlier in Eurasia and Africa lineages than elsewhere. This suggests that it may be linked to co-occurring hominins rather than having been caused by global climate change.

evolutionary biology↗

bioRxiv2022

Estimating alpha, beta, and gamma diversity through deep learning

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.

evolutionary biology↗

bioRxiv2021

The evolution of open habitats in North America revealed by deep learning models

Open vegetation today constitutes one of the most extensive biomes on earth, including temperate grasslands and tropical savannas. Yet these biomes originated relatively recently in earth history, likely replacing forested habitats as recently as the second half of the Cenozoic, although the timing of their origination and the dynamics of their expansion remain uncertain. Here, we present a new hypothesis of paleovegetation change in North America, showing that open habitats originated between 25 and 20 Ma in the center of the continent, and expanded rapidly starting 8 Ma to eventually become the most prominent vegetation type today. To obtain space-time predictions of paleovegetation, we developed a new Bayesian deep learning model that utilizes available information from fossil evidence, geologic models, and paleoclimate proxies. We compiled a large dataset of paleovegetation reconstructions from the peer-reviewed literature, which we used in combination with current vegetation data to train the model. The model learns to predict vegetation based on the learned associations between the vegetation at a given site and multiple biotic and abiotic predictors: fossil mammal occurrences, plant macrofossils, estimates of temperature and precipitation, latitude, and the effects of spatial and temporal autocorrelation. Our results provide a new, spatially detailed reconstruction of habitat evolution in North America and our deep learning model paves the way for a new quantitative approach to estimating paleovegetation changes.

evolutionary biology↗

bioRxiv2021

IUCNN - deep learning approaches to approximate species' extinction risk

AimThe global Red List (RL) from the International Union for the Conservation of Nature is the most comprehensive global quantification of extinction risk, and widely used in applied conservation as well as in biogeographic and ecological research. Yet, due to the time- consuming assessment process, the RL is biased taxonomically and geographically, which limits its application on large scales, in particular for understudied areas such as the tropics, or understudied taxa, such as most plants and invertebrates. Here we present IUCNN, an R- package implementing deep learning models to predict species RL status from publicly available geographic occurrence records (and other traits if available). InnovationWe implement a user-friendly workflow to train and validate neural network models, and subsequently use them to predict species RL status. IUCNN contains functions to address specific issues related to the RL framework, including a regression-based approach to account for the ordinal nature of RL categories and class imbalance in the training data, a Bayesian approach for improved uncertainty quantification, and a target accuracy threshold approach that limits predictions to only those species whose RL status can be predicted with high confidence. Most analyses can be run with few lines of code, without prior knowledge of neural network models. We demonstrate the use of IUCNN on an empirical dataset of [~]14,000 orchid species, for which IUCNN models can predict extinction risk within minutes, while outperforming comparable methods. Main conclusionsIUCNN harnesses innovative methodology to estimate the RL status of large numbers of species. By providing estimates of the number and identity of threatened species in custom geographic or taxonomic datasets, IUCNN enables large-scale analyses on the extinction risk of species so far not well represented on the official RL.

ecology↗

bioRxiv2021

Optimising biodiversity protection through artificial intelligence

Over a million species face extinction, carrying with them untold options for food, medicine, fibre, shelter, ecological resilience, aesthetic and cultural values. There is therefore an urgent need to design conservation policies that maximise the protection of biodiversity and its contributions to people, within the constraints of limited budgets. Here we present a novel framework for spatial conservation prioritisation that combines simulation models, reinforcement learning and ground validation to identify optimal policies. Our methodology, CAPTAIN (Conservation Area Prioritisation Through Artificial Intelligence Networks), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a fixed budget, our model protects substantially more species from extinction than the random or naively targeted protection of areas. CAPTAIN also outperforms the most widely used software for spatial conservation prioritisation (Marxan) in 97% of cases and reduces species loss by an average of 40% under simulations, besides yielding prioritisation maps at substantially higher spatial resolution using empirical data. We find that regular biodiversity monitoring, even if simple and with a degree of inaccuracy - characteristic of citizen science surveys - substantially improves biodiversity outcomes. Given the complexity of people-nature interactions and wealth of associated data, artificial intelligence holds great promise for improving the conservation of biological and ecosystem values in a rapidly changing and resource-limited world.

ecology↗

bioRxiv2021

Unbiased clade age estimation using a Bayesian Brownian Bridge

In a recent paper1 we presented a new model, the Bayesian Brownian Bridge (BBB), to infer clade age based on fossil evidence and modern diversity. We benchmarked the method with extensive simulations, including a wide range of diversification histories and sampling heterogeneities that go well beyond the necessarily simplistic model assumptions. Applying BBB to 198 angiosperm families, we found that their fossil record is compatible with clade origins earlier than most contemporary palaeobotanical interpretations. In particular, we estimated with high probability that crown-angiosperms originated before the Cretaceous (> 145 Ma). Budd and colleagues2 critique our study, arguing that the BBB model is biased towards older estimates when fossil data are scarce or absent, that our underlying fossil dataset is unsound, that our clade age estimates are therefore biased by early diverging lineages that are underrepresented in the fossil record, and that pooling of fossil data for analysis at higher taxonomic ranks overcomes these biases. Here, we explore their points and perform new simulations to show that their critique has no merit.

paleontology↗

bioRxiv2020

Automated conservation assessment of the orchid family using deep learning

IUCN Red List assessments are essential for prioritizing conservation needs but are resource-intensive and therefore only available for a fraction of global species richness. Tropical plant species are particularly under-represented on the IUCN Red List. Automated conservation assessments based on digitally available geographic occurrence records can be a rapid alternative, but it is unclear how reliable these assessments are. Here, we present automated conservation assessments for 13,910 species of the diverse and globally distributed Orchid family (Orchidaceae), based on a novel method using a deep neural network (IUC-NN), most of which (13,049) were previously unassessed by the IUCN Red List. We identified 4,342 (31.2 % of the evaluated orchid species) as Possibly Threatened with extinction (equivalent to the IUCN categories CR, EN, or VU) and point to Madagascar, East Africa, south-east Asia, and several oceanic islands as priority areas for orchid conservation. Furthermore, the Orchid family provides a model, to test the sensitivity of automated assessment methods to issues with data availability, data quality and geographic sampling bias. IUC-NN identified threat-ened species with an accuracy of 84.3%, with significantly lower geographic evaluation bias compared to the IUCN Red List, and was robust against low data availability and geographic errors in the input data. Overall, our results demonstrate that automated assessments have an important role to play in achieving goals of identifying the species that are at greatest risk of extinction.

ecology↗

bioRxiv2020

Functionally unique, specialised, and endangered (FUSE) species: towards integrated metrics for the conservation prioritisation toolbox

Identifying species with disproportionate contributions to biodiversity can lead to effective conservation prioritisation. Despite well-established methods for identifying endangered species adding inordinately to evolutionary diversity, in this context functional diversity has been overlooked. Here, we compare different metrics designed to identify threatened species that contribute strongly to functional diversity. We use the diverse and threatened global marine megafauna as a case study. We found that functional contributions of species are not fully captured in a single metric. Although we found a very strong correlation between functional specialisation and distinctiveness, functional uniqueness was only moderately correlated with the other two metrics and identified a different set of top-10 species. These functional contributions were then integrated and combined with extinction risk to identify species that are both important contributors to functional diversity and endangered. For instance, the top-10 Functionally Unique Specialized and Endangered (FUSE) species contains three critically endangered, five endangered and two vulnerable species which - despite comprising only 3% of species - are among the top 10% most functionally unique and hold 15% of the global functional richness. The FUSE index was remarkably robust to different mathematical formulations. Combining one or more facets of a species contribution to functional diversity with endangerment, such as with the FUSE index, adds to the toolbox for conservation prioritisation. Nevertheless, we discuss how these new tools must be handled with care alongside other metrics and information.

ecology↗

bioRxiv2020

Exploring the impact of political regimes on biodiversity

National governments are the main actors responsible for mapping and protecting their biodiversity, but countries differ in their capacity, willingness, and effectiveness to do so. We quantify the global biodiversity managed by different regime types and developed a tool to explore the links between level of democracy and other key socio-economic variables with the number of natural history specimens registered within country boundaries. Using this tool, distinct and previously unknown patterns emerge around the world, that urge for increased collaboration between the natural and social sciences to further explore these patterns and their underlying processes.

ecology↗

bioRxiv2020

sampbias, a method for quantifying geographic sampling biases in species distribution data

Geo-referenced species occurrences from public databases have become essential to biodiversity research and conservation. However, geographical biases are widely recognized as a factor limiting the usefulness of such data for understanding species diversity and distribution. In particular, differences in sampling intensity across a landscape due to differences in human accessibility are ubiquitous but may differ in strength among taxonomic groups and datasets. Although several factors have been described to influence human access (such as presence of roads, rivers, airports and cities), quantifying their specific and combined effects on recorded occurrence data remains challenging. Here we present sampbias, an algorithm and software for quantifying the effect of accessibility biases in species occurrence datasets. Sampbias uses a Bayesian approach to estimate how sampling rates vary as a function of proximity to one or multiple bias factors. The results are comparable among bias factors and datasets. We demonstrate the use of sampbias on a dataset of mammal occurrences from the island of Borneo, showing a high biasing effect of cities and a moderate effect of roads and airports. Sampbias is implemented as a well-documented, open-access and user-friendly R package that we hope will become a standard tool for anyone working with species occurrences in ecology, evolution, conservation and related fields.

ecology↗

bioRxiv2019

iucn_sim - Improved predictions of future extinctions using IUCN status assessments

The ongoing environmental crisis poses an urgent need to forecast the who, where, and when of future species extinctions, as such information is crucial for targeting conservation efforts. Commonly, such forecasts are made based on conservation status assessments produced by the International Union for Conservation of Nature (IUCN). However, when researchers apply these IUCN conservation status data for predicting future extinctions, important information is often omitted, which can impact the accuracy of these predictions. Here we present a new approach and a software for simulating future extinctions based on IUCN conservation status information, which incorporates generation length information of individual species when modeling extinction risks. Additionally, we explicitly model future changes in conservation status for each species, based on status transition rates that we estimate from the IUCN assessment history of the last decades. Finally, we apply a Markov chain Monte Carlo algorithm to estimate extinction rates for each species, based on the simulated future extinctions. These estimates inherently incorporate the chances of conservation status changes and the generation length for each given species and are specific to the simulated time frame. We demonstrate the utility of our approach by estimating extinction rates for all bird species. Our average extinction risk estimate for the next 100 years across all birds is 6.98 x 10-4 extinctions per species-year, and we predict an expected biodiversity loss of between 669 to 738 bird species within that time frame. Further, the rate estimates between species sharing the same IUCN status show larger variation than the rates estimated with alternative approaches, which reflects expected differences in extinction risk among taxa of the same conservation status. Our method demonstrates the utility of applying species-specific information to the estimation of extinction rates, rather than assuming equal extinction risks for species assigned to the same conservation status.

evolutionary biology↗

bioRxiv2019

raxmlGUI 2.0 beta: a graphical interface and toolkit for phylogenetic analyses using RAxML

RaxmlGUI is a graphical user interface to RAxML, one of the most popular and widely used software for phylogenetic inference using maximum likelihood. Here we present raxmlGUI 2.0, a complete rewrite of the GUI which seamlessly integrates RAxML binaries for all major operating systems with an intuitive graphical front-end to set up and run phylogenetic analyses. Our program offers automated pipelines for analyses that require multiple successive calls of RAxML and built-in functions to concatenate alignment files while automatically specifying the appropriate partition settings. In addition to RAxML 8.x, raxmlGUI 2.0 also supports the new RAxML Next Generation. RaxmlGUI facilitates phylogenetic analyses by coupling an intuitive interface with the unmatched performance of RAxML.

bioinformatics↗

bioRxiv2019

On the effect of asymmetrical trait inheritance on models of trait evolution

Current phylogenetic comparative methods modeling quantitative trait evolution generally assume that, during speciation, phenotypes are inherited identically between the two daughter species. This, however, neglects the fact that species consist of a set of individuals, each bearing its own trait value. Indeed, because descendent populations after speciation are samples of a parent population, we can expect their mean phenotypes to randomly differ from one another potentially generating a "jump" of mean phenotypes due to asymmetrical trait inheritance at cladogenesis. Here, we aim to clarify the effect of asymmetrical trait inheritance at speciation on macroevolutionary analyses, focusing on model testing and parameter estimation using some of the most common models of quantitative trait evolution. We developed an individual-based simulation framework in which the evolution of species phenotypes is determined by trait changes at the individual level accumulating across generations and cladogenesis occurs then by separation of subsets of the individuals into new lineages. Through simulations, we assess the magnitude of phenotypic jumps at cladogenesis under different modes of trait inheritance at speciation. We show that even small jumps can strongly alter both the results of model selection and parameter estimations, potentially affecting the biological interpretation of the estimated mode of evolution of a trait. Our results call for caution when interpreting analyses of trait evolution, while highlighting the importance of testing a wide range of alternative models. In the light of our findings, we propose that future methodological advances in comparative methods should more explicitly model the intra-specific variability around species mean phenotypes and how it is inherited at speciation.

evolutionary biology↗

bioRxiv2018

Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework

The estimation of origination and extinction rates and their temporal variation is central to understanding diversity patterns and the evolutionary history of clades. The fossil record provides the most direct evidence of extinction and biodiversity changes through time and has long been used to infer the dynamics of diversity changes in deep time. The software PyRate implements a Bayesian framework to analyze fossil occurrence data to estimate the rates of preservation, origination and extinction while incorporating several sources of uncertainty. This fully probabilistic approach allows us to explicitly assess the statistical support of alternative macroevolutionary hypotheses and to infer credible intervals around parameter estimates. Here, we present a major update of the software, which implements substantial methodological advancements, including more complex and realistic models of preservation, a reversible jump Markov chain Monte Carlo algorithm to estimate origination and extinction rates and their temporal variation, and a substantial boost in performance. We demonstrate the new functionalities through extensive simulations and with the analysis of a large dataset of Cenozoic marine mammals. We identify several significant shifts in origination and extinction rates of marine mammals, underlying a late Miocene diversity peak and a subsequent 50% diversity decline towards the present. Our analyses indicate that explicit statistical model testing, which is often neglected in fossil-based macroevolutionary analyses, is crucial to obtain accurate and robust results. PyRate provides a flexible, statistically sound analytical framework, which we think can serve as a useful toolkit for many future studies in paleobiology.

paleontology↗

bioRxiv2017

Evolutionary history of New World monkeys revealed by molecular and fossil data

New World monkeys (parvorder Platyrrhini) are one of the most diverse groups of primates, occupying today a wide range of ecosystems in the American tropics and exhibiting large variations in ecology, morphology, and behavior. Although the relationships among the almost 200 living species are relatively well understood, we lack robust estimates of the timing of origin, the ancestral morphology, and the evolution of the distribution of the clade. Here we integrate paleontological and molecular evidence to investigate the evolutionary dynamics of extinct and extant platyrrhines. We develop an analytical framework to infer ancestral states, the evolution of body mass, and changes in latitudinal ranges through time. Our results show that extant platyrrhines originated some 5-10 million years earlier than previously assumed, likely dating back to the Middle Eocene ([~] 43 million years ago, Ma). The estimated ancestral platyrrhine was strikingly small - weighing [~] 0.4 kg, as compared to the largest modern species over 10 kg - matching the size of their presumed Eocene North African ancestors. Small-sized callitrichines (marmosets and tamarins) retained a small body mass throughout their evolutionary history, thus challenging the hypothesis of phyletic dwarfism as an explanation to their adaptive traits. In contrast, a rapid change in body mass range took place as the three families diverged between the Late Oligocene and the Early Miocene. That period also marks a peak in diversity of fossil platyrrhines and is associated with their widest latitudinal range, expanding as far to the South as Patagonia. This geographic expansion is temporally coincident with a significant increase in platyrrhine population size inferred from genomic data, and with warm and humid climatic conditions linked to the Miocene Climatic Optimum and the lower elevation of the Andes. These results unveil the early evolution of an iconic group of monkeys and showcase the advantages of integrating fossil and molecular data for estimating evolutionary rates and trends.

evolutionary biology↗

bioRxiv2017

Recent origin of Neotropical orchids in the world’s richest plant biodiversity hotspot

* The Andean mountains of South America are the most species-rich biodiversity hotspot worldwide with about 15% of the worlds plant species, in only 1% of the worlds land surface. Orchids are a key element of the Andean flora, and one of the most prominent components of the Neotropical epiphyte diversity, yet very little is known about their origin and diversification.\n\n* We address this knowledge gap by inferring the biogeographical history and evolutionary dynamics of the two largest Neotropical orchid groups (Cymbidieae and Pleurothallidinae), using two unparalleled, densely-sampled orchid phylogenies (including 400+ newly generated DNA sequences), comparative phylogenetic methods, geological and biological datasets.\n\n* We find that the majority of Andean orchid lineages only originated in the last 15 million years. Most Andean lineages are derived from lowland Amazonian ancestors, with additional contributions from Central America and the Antilles. Species diversification is correlated with Andean orogeny, and multiple migrations and re-colonizations across the Andes indicate that mountains do not constrain orchid dispersal over long timescales.\n\n* Our study sheds new light on the timing and geography of a major Neotropical radiation, and suggests that mountain uplift promotes species diversification across all elevational zones.

evolutionary biology↗