<scp>RE</scp>voSim: Organism‐level simulation of macro and microevolution

Garwood, Russell J.; Spencer, Alan R. T.; Sutton, Mark D.

doi:10.1111/pala.12420

Cited by 21 publications

(24 citation statements)

References 72 publications

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“…R. Soc. B 288: 20210240 (b) Running simulations Simulations were run using settings that have previously been shown to produce biologically realistic outputs [35]. Environment files were selected using the 'Change environment files' tool.…”

Section: Methods (A) Environment Generationmentioning

confidence: 99%

Evolutionary simulations clarify and reconcile biodiversity-disturbance models

et al. 2021

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There is significant geographic variation in species richness. However, the nature of the underlying relationships, such as that between species richness and environmental stability, remains unclear. The stability-time hypothesis suggests that environmental instability reduces species richness by suppressing speciation and increasing extinction risk. By contrast, the patch-mosaic hypothesis suggests that small-scale environmental instability can increase species richness by providing a steady supply of non-equilibrium environments. Although these hypotheses are often applied to different time scales, their core mechanisms are in conflict. Reconciling these apparently competing hypotheses is key to understanding how environmental conditions shape the distribution of biodiversity. Here, we use REvoSim, an individual-based, eco-evolutionary system, to model the evolution of sessile organisms in environments with varying magnitudes and scales of environmental instability. We demonstrate that when environments have substantial permanent heterogeneity, a high level of localized environmental instability reduces biodiversity, whereas in environments lacking permanent heterogeneity, high levels of localized instability increase biodiversity. By contrast, broad-scale environmental instability, acting on the same time scale, invariably reduces biodiversity. Our results provide a new view of the biodiversity–disturbance relationship that reconciles contrasting hypotheses within a single model and implies constraints on the environmental conditions under which those hypotheses apply. These constraints can inform attempts to conserve adaptive potential in different environments during the current biodiversity crisis.

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Section: Methods (A) Environment Generationmentioning

confidence: 99%

Evolutionary simulations clarify and reconcile biodiversity-disturbance models

et al. 2021

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“…This model represents nonstochastic evolution as it incorporates natural selection. It derives some concepts from the package REvoSim ( Garwood et al 2019 )—for example, the fitness algorithm—but has a focus on the simulation of trees and associated character data. It does not incorporate concepts of space or sexual reproduction.…”

Section: Methodsmentioning

confidence: 99%

“…The environment is formed of five random numbers ( masks ) of size \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$n$\end{document} , where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$n$\end{document} equals the length of the character binary string of each organism. The fitness of every organism in the playing field is calculated following the approach described by Garwood et al (2019) . In brief, this employs an exclusive OR ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\oplus )$\end{document} operation to sum the Hamming distance ( hd ) of the organism to each of the five masks.…”

Section: Methodsmentioning

confidence: 99%

Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

et al. 2020

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Evolutionary inferences require reliable phylogenies. Morphological data have traditionally been analyzed using maximum parsimony, but recent simulation studies have suggested that Bayesian analyses yield more accurate trees. This debate is ongoing, in part, because of ambiguity over modes of morphological evolution and a lack of appropriate models. Here, we investigate phylogenetic methods using two novel simulation models—one in which morphological characters evolve stochastically along lineages and another in which individuals undergo selection. Both models generate character data and lineage splitting simultaneously: the resulting trees are an emergent property, rather than a fixed parameter. Standard consensus methods for Bayesian searches (Mki) yield fewer incorrect nodes and quartets than the standard consensus trees recovered using equal weighting and implied weighting parsimony searches. Distances between the pool of derived trees (most parsimonious or posterior distribution) and the true trees—measured using Robinson-Foulds (RF), subtree prune and regraft (SPR), and tree bisection reconnection (TBR) metrics—demonstrate that this is related to the search strategy and consensus method of each technique. The amount and structure of homoplasy in character data differ between models. Morphological coherence, which has previously not been considered in this context, proves to be a more important factor for phylogenetic accuracy than homoplasy. Selection-based models exhibit relatively lower homoplasy, lower morphological coherence, and higher inaccuracy in inferred trees. Selection is a dominant driver of morphological evolution, but we demonstrate that it has a confounding effect on numerous character properties which are fundamental to phylogenetic inference. We suggest that the current debate should move beyond considerations of parsimony versus Bayesian, toward identifying modes of morphological evolution and using these to build models for probabilistic search methods. [Bayesian; evolution; morphology; parsimony; phylogenetics; selection; simulation.]

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“…In order to provide finer control over the fitness landscape within the simulation, user control of the fitness target (i.e. the count of ones in the genome to environment hamming distance which is considered the peak fitness; see 17,52 ) is now provided. Setting this to zero provides fewer fitness peaks: a count peaks functionality has been added to provide a histogram of possible fitnesses for a given set of masks and simulation settings.…”

Section: Data Generationmentioning

confidence: 99%

Fossils improve phylogenetic analyses of morphological characters

Koch

Garwood

Parry³

2020

Preprint

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Fossils provide our only direct window into evolutionary events in the distant past. Incorporating them into phylogenetic hypotheses of living clades can help elucidate macroevolutionary patterns and processes, such as ancestral states and diversification dynamics. However, the effect fossils have on phylogenetic inference from morphological data remains controversial. Previous studies have highlighted their strong impact on topologies inferred from empirical data, but have not demonstrated that they improve accuracy. The consequences of explicitly incorporating the stratigraphic ages of fossils using tip-dated inference are also unclear. Here we employ a simulation approach to explore how fossil sampling and missing data affect tree reconstruction across a range of inference methods. Our results show that fossil taxa improve phylogenetic analysis of morphological datasets, even when highly fragmentary. Irrespective of inference method, fossils improve the accuracy of phylogenies and increase the number of resolved nodes. They also induce the collapse of ancient and highly uncertain relationships that tend to be incorrectly resolved when sampling only extant taxa. Furthermore, tip-dated analyses which simultaneously infer tree topology and divergence times outperform all other methods of inference, demonstrating that the stratigraphic ages of fossils contain vital phylogenetic information. Fossils help to extract true phylogenetic signals from morphology, an effect that is mediated by both their unique morphology and their temporal information, and their incorporation in total-evidence phylogenetics is necessary to faithfully reconstruct evolutionary history.

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REvoSim: Organism‐level simulation of macro and microevolution

Cited by 21 publications

References 72 publications

Evolutionary simulations clarify and reconcile biodiversity-disturbance models

Evolutionary simulations clarify and reconcile biodiversity-disturbance models

Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations

Fossils improve phylogenetic analyses of morphological characters

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