Morphological evolution in a time of phenomics DOI Creative Commons
Anjali Goswami, Julien Clavel

Paleobiology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: March 11, 2025

Abstract Organismal morphology was at the core of study biodiversity for millennia before formalization concept evolution. In early to mid-twentieth century, a strong theoretical framework developed understanding both pattern and process morphological evolution, 50 years since founding this journal capture transformational period in quantification analytical tools estimating how diversity changes through time. We are now another inflection point with availability vast amounts high-resolution data sampling extant extinct allowing “omics”-scale analysis. Artificial intelligence is accelerating pace phenomic acquisition even further. This new reality, which ability obtain quickly outpacing analyze it robust, realistic evolutionary models, brings set challenges. Phylogenetic comparative methods have provided insights into processes generating diversity, but reliance on molecular resultant exclusion fossil from most large phylogenetic trees has well-established negative impacts analyses, as we demonstrate examples standard single-rate mode- rate-shift recently described Ornstein-Uhlenbeck climate model. Further development analysis high-dimensional needed, existing can refine our expectations evolution generation under different scenarios, analyses placental skull Cenozoic. Fully transitioning omics era will involve automate extraction meaningful, comparable morphometric images, integrate downstream generate robust models that accurately reflect complexity well-suited data. Combined, these advancements solidify emerging field phenomics appropriately center around deep-time

Language: Английский

Leveraging graphical model techniques to study evolution on phylogenetic networks DOI
Benjamin Teo, Paul Bastide, Cécile Ané

et al.

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2025, Volume and Issue: 380(1919)

Published: Feb. 13, 2025

The evolution of molecular and phenotypic traits is commonly modelled using Markov processes along a phylogeny. This phylogeny can be tree, or network if it includes reticulations, representing events such as hybridization admixture. Computing the likelihood data observed at leaves costly size complexity grows. Efficient algorithms exist for trees, but cannot applied to networks. We show that vast array models trait phylogenetic networks reformulated graphical models, which efficient belief propagation exist. provide brief review on general then focus linear Gaussian continuous traits. how techniques exact approximate (but more scalable) gradient calculations, prove novel results parameter inference some models. highlight possible fruitful interactions between methods. For example, approaches have potential greatly reduce computational costs phylogenies with reticulations.

Language: Английский

Citations

1
Morphological evolution in a time of Phenomics DOI Creative Commons
Anjali Goswami, Julien Clavel

Published: Jan. 10, 2024

Organismal morphology has been at the core of study biodiversity for millennia before formalization concept evolution. In early to mid-twentieth century, a strong theoretical framework was developed understanding both pattern and process morphological evolution on macroevolutionary scale. The past half century transformational period evolutionary morphology, in quantification novel analytical tools estimating how why diversity changes through time, with marked increase studies apparent 1990s. We are now another inflection point evolution, availability vast amounts high-resolution data sampling extant extinct allowing ‘omics’-scale analysis. Artificial intelligence is already increasing pace phenomic collection even further. This new reality, where ability obtain quickly outpacing analyse it robust, realistic models, brings set challenges, we here present analyses demonstrating these challenges discussing solutions. Fully transitioning into “Omics” era will involve development automate extraction meaningful, comparable morphometric from images, integrate fossil large phylogenetic trees downstream analyses, generate robust models that accurately reflect complexity processes well-suited high-dimensional data. Combined, advancements solidify emerging field phenomics appropriately center around analysis unambiguously critical deep-time

Language: Английский

Citations

5
Fast mvSLOUCH: Multivariate Ornstein–Uhlenbeck‐based models of trait evolution on large phylogenies DOI Creative Commons
Krzysztof Bartoszek, John T. Clarke, Jesualdo A. Fuentes-González

et al.

Methods in Ecology and Evolution, Journal Year: 2024, Volume and Issue: 15(9), P. 1507 - 1515

Published: July 8, 2024

Abstract The PCMBase R package is a powerful computational tool that enables efficient calculations of likelihoods for wide range phylogenetic Gaussian models. Taking advantage it, we redesigned the mvSLOUCH . Here, demonstrate how new version can be used to thoroughly examine evolution and adaptation traits in large dataset 1252 vascular plants through use multivariate Ornstein–Uhlenbeck processes. results our analysis ability modelling framework distinguish between various alternative hypotheses regarding functional angiosperms.

Language: Английский

Citations

4
Morphological evolution in a time of phenomics DOI Creative Commons
Anjali Goswami, Julien Clavel

Paleobiology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: March 11, 2025

Abstract Organismal morphology was at the core of study biodiversity for millennia before formalization concept evolution. In early to mid-twentieth century, a strong theoretical framework developed understanding both pattern and process morphological evolution, 50 years since founding this journal capture transformational period in quantification analytical tools estimating how diversity changes through time. We are now another inflection point with availability vast amounts high-resolution data sampling extant extinct allowing “omics”-scale analysis. Artificial intelligence is accelerating pace phenomic acquisition even further. This new reality, which ability obtain quickly outpacing analyze it robust, realistic evolutionary models, brings set challenges. Phylogenetic comparative methods have provided insights into processes generating diversity, but reliance on molecular resultant exclusion fossil from most large phylogenetic trees has well-established negative impacts analyses, as we demonstrate examples standard single-rate mode- rate-shift recently described Ornstein-Uhlenbeck climate model. Further development analysis high-dimensional needed, existing can refine our expectations evolution generation under different scenarios, analyses placental skull Cenozoic. Fully transitioning omics era will involve automate extraction meaningful, comparable morphometric images, integrate downstream generate robust models that accurately reflect complexity well-suited data. Combined, these advancements solidify emerging field phenomics appropriately center around deep-time

Language: Английский

Citations

0