D. Silvestro

Challenges in estimating species age from phylogenetic trees

The paper shows that estimating species age from phylogenetic trees is error-prone due to sampling biases and speciation assumptions, and proposes a probabilistic model to improve accuracy.

Plant diversity darkspots for global collection priorities

The paper identifies global plant diversity darkspots with limited species data and prioritizes regions for collection efforts based on environmental and socio-economic scenarios.

Improved estimation of molecular evolution coupling stochastic simulations and deep learning

This paper combines stochastic simulations with deep learning to estimate molecular evolution parameters, achieving higher accuracy than traditional likelihood-based methods under complex rate variation patterns.

Exploring the Macroevolutionary Signature of Asymmetric Inheritance at Speciation

The paper introduces an Asymmetric Brownian Motion model to study phenotypic inheritance during speciation, finding evidence of character displacement in Darwin finches.

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

This paper investigates the evolution of trophallaxis in ants, showing it emerged in lineages with reduced intra-colonial conflict and contributed to ecological dominance.

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

The paper analyzes the fossil record of turtles to identify extinction events, finding two major ones linked to the K-Pg meteorite and hominins, with the latter affecting terrestrial turtles.

Estimating alpha, beta, and gamma diversity through deep learning

This paper develops a deep learning method to estimate alpha, beta, and gamma plant diversity by predicting species richness from spatial variables, without requiring individual species range mapping.

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

This paper uses a deep learning model to reconstruct the evolution of open habitats in North America, finding they originated 25-20 Ma and expanded rapidly starting 8 Ma.

IUCNN - deep learning approaches to approximate species' extinction risk

IUCNN uses deep learning to predict species' extinction risk from occurrence data, outperforming existing methods on orchid datasets.

Optimising biodiversity protection through artificial intelligence

This paper introduces CAPTAIN, an AI framework for conservation prioritisation that outperforms existing methods and highlights the value of biodiversity monitoring.

Unbiased clade age estimation using a Bayesian Brownian Bridge

The paper defends the Bayesian Brownian Bridge method against criticism, showing its estimates of angiosperm clade ages are valid despite concerns about fossil data scarcity and sampling bias.

Automated conservation assessment of the orchid family using deep learning

This study uses a deep learning model to automate conservation assessments for 13,910 orchid species, identifying 31.2% as Possibly Threatened and highlighting priority conservation areas.

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

The paper develops the FUSE index to identify species that are both functionally unique and endangered, enhancing conservation prioritisation by integrating functional diversity with extinction risk.

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

This study investigates how asymmetrical trait inheritance at speciation affects macroevolutionary analyses, showing that small phenotypic jumps can significantly alter model selection and parameter estimation.

Exploring the impact of political regimes on biodiversity

The paper quantifies how different political regimes affect biodiversity management by linking democracy levels to natural history specimen registrations.

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

The paper introduces sampbias, a method to quantify geographic sampling biases in species occurrence data using a Bayesian approach, demonstrating its application on mammal data from Borneo.

iucn_sim: A new program to simulate future extinctions based on IUCN threat status

The paper introduces a software tool to simulate future species extinctions using IUCN threat status and generation length data, predicting bird extinction rates and biodiversity loss over 100 years.

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

The paper updates PyRate, a Bayesian software for estimating macroevolutionary rates from fossil data, showing significant shifts in marine mammal origination and extinction rates.

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

This study integrates fossil and molecular data to estimate the evolutionary history of New World monkeys, revealing earlier origins and key morphological and geographic changes.

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

This study investigates the origin and diversification of Neotropical orchids in the Andes, finding most lineages originated in the last 15 million years and diversification correlates with Andean orogeny.