From 0643ba47028cd6b29325c28b9a393c8e322625af Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 15 Aug 2024 18:04:01 +0100 Subject: [PATCH 01/16] Switch out Butterfield 2011 for Jones et al 1994 for issue #66 --- JOSS/paper.bib | 4 ++-- JOSS/paper.md | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/JOSS/paper.bib b/JOSS/paper.bib index 87ae3c2..c092847 100644 --- a/JOSS/paper.bib +++ b/JOSS/paper.bib @@ -1,12 +1,12 @@ @article{Barido-Sottani_Saupe_Smiley_Soul_Wright_Warnock_2020, title={Seven rules for simulations in paleobiology}, volume={46}, ISSN={0094-8373, 1938-5331}, DOI={10.1017/pab.2020.30}, abstractNote={Simulations are playing an increasingly important role in paleobiology. When designing a simulation study, many decisions have to be made and common challenges will be encountered along the way. Here, we outline seven rules for executing a good simulation study. We cover topics including the choice of study question, the empirical data used as a basis for the study, statistical and methodological concerns, how to validate the study, and how to ensure it can be reproduced and extended by others. We hope that these rules and the accompanying examples will guide paleobiologists when using simulation tools to address fundamental questions about the evolution of life.}, number={4}, journal={Paleobiology}, publisher={Cambridge University Press}, author={Barido-Sottani, Joëlle and Saupe, Erin E. and Smiley, Tara M. and Soul, Laura C. and Wright, April M. and Warnock, Rachel C. M.}, year={2020}, month=nov, pages={435–444}, language={en} } - @article{Butterfield_2011, title={Animals and the invention of the Phanerozoic Earth system.}, volume={26}, DOI={10.1016/j.tree.2010.11.012}, abstractNote={Animals do not just occupy the modern biosphere, they permeate its structure and define how it works. Their unique combination of organ-grade multicellularity, motility and heterotrophic habit makes them powerful geobiological agents, imposing myriad feedbacks on nutrient cycling, productivity and environment. Most significantly, animals have “engineered” the biosphere over evolutionary time, forcing the diversification of, for example, phytoplankton, land plants, trophic structure, large body size, bioturbation, biomineralization and indeed the evolutionary process itself. This review surveys how animals contribute to the modern world and provides a basis for reconstructing ancient ecosystems. Earlier, less animal-influenced biospheres worked quite differently from the one currently occupied, with the Ediacaran-Cambrian radiation of organ-grade animals marking a fundamental shift in macroecological and macroevolutionary expression.}, number={2}, journal={Trends in ecology & evolution}, author={Butterfield, Nicholas J}, year={2011}, month=feb, pages={81–7} } @article{Condamine_Rolland_Morlon_2013, title={Macroevolutionary perspectives to environmental change}, volume={16}, ISSN={1461-0248}, DOI={10.1111/ele.12062}, abstractNote={Ecology Letters is a broad-scope ecology journal considering all taxa, in any biome and geographic area, and spanning community, microbial & evolutionary ecology.}, journal={Ecology Letters}, publisher={John Wiley & Sons, Ltd}, author={Condamine, Fabien L. and Rolland, Jonathan and Morlon, Hélène}, year={2013}, month=jan, pages={72–85}, language={en} } @article{Dolson_Ofria_2021, title={Digital Evolution for Ecology Research: A Review}, volume={9}, ISSN={2296-701X}, DOI={10.3389/fevo.2021.750779}, url={https://www.frontiersin.org/articles/10.3389/fevo.2021.750779}, abstractNote={In digital evolution, populations of computational organisms evolve via the same principles that govern natural selection in nature. These platforms have been used to great effect as a controlled system in which to conduct evolutionary experiments and develop novel evolutionary theory. In addition to their complex evolutionary dynamics, many digital evolution systems also produce rich ecological communities. As a result, digital evolution is also a powerful tool for research on eco-evolutionary dynamics. Here, we review the research to date in which digital evolution platforms have been used to address eco-evolutionary (and in some cases purely ecological) questions. This work has spanned a wide range of topics, including competition, facilitation, parasitism, predation, and macroecological scaling laws. We argue for the value of further ecological research in digital evolution systems and present some particularly promising directions for further research.}, journal={Frontiers in Ecology and Evolution}, author={Dolson, Emily and Ofria, Charles}, year={2021} } @article{Furness_Garwood_Sutton_2023, title={REvoSim v3: A fast evolutionary simulation tool with ecological processes}, volume={8}, rights={Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (CC-BY-NC-ND)}, ISSN={2475-9066}, DOI={10.21105/joss.05284}, abstractNote={Furness et al., (2023). REvoSim v3: A fast evolutionary simulation tool with ecological processes. Journal of Open Source Software, 8(89), 5284, https://doi.org/10.21105/joss.05284}, number={89}, journal={Journal of Open Source Software}, author={Furness, Euan N. and Garwood, Russell J. and Sutton, Mark D.}, year={2023}, month=sep, pages={5284}, language={en} } @article{Garwood_Spencer_Sutton_2019, title={REvoSim: Organism-level simulation of macro and microevolution}, volume={62}, rights={© The Palaeontological Association}, ISSN={1475-4983}, DOI={10.1111/pala.12420}, abstractNote={Macroevolutionary processes dictate the generation and loss of biodiversity. Understanding them is a key challenge when interrogating the earth–life system in deep time. Model-based approaches can reveal important macroevolutionary patterns and generate hypotheses on the underlying processes. Here we present and document a novel model called REvoSim (Rapid Evolutionary Simulator) coupled with a software implementation of this model. The latter is available here as both source code (C++/Qt, GNU General Public License) and as distributables for a variety of operating systems. REvoSim is an individual-based model with a strong focus on computational efficiency. It can simulate populations of 105–107 digital organisms over geological timescales on a typical desktop computer, and incorporates spatial and temporal environmental variation, recombinant reproduction, mutation and dispersal. Whilst microevolutionary processes drive the model, macroevolutionary phenomena such as speciation and extinction emerge. We present results and analysis of the model focusing on validation, and note a number potential applications. REvoSim can serve as a multipurpose platform for studying both macro and microevolution, and bridges this divide. It will be continually developed by the authors to expand its capabilities and hence its utility.}, number={3}, journal={Palaeontology}, author={Garwood, Russell J. and Spencer, Alan R. T. and Sutton, Mark D.}, year={2019}, pages={339–355}, language={en} } @article{Guillerme_2024, title={treats: A modular R package for simulating trees and traits}, volume={15}, rights={© 2024 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.}, ISSN={2041-210X}, DOI={10.1111/2041-210X.14306}, abstractNote={Simulating biological realistic data is an important step to understand and investigate biodiversity. Simulated data can be used to generate null, base line or neutral models. These can be used either in comparison to observed data to estimate the mechanisms that generated the data. Or they can be used to explore, understand and develop theoretical advances by proposing toy models. In evolutionary biology, simulations often involve the need of an evolutionary process where descent with modification is at the core of how the simulated data are generated. These evolutionary processes can then be nearly infinitely modified to include complex processes that affect the simulations such as traits co-evolution, competition mechanisms or mass extinction events. Here I present the treats package, a modular R package for trees and traits simulations. This package is based on a simple birth death algorithm from which all steps can easily be modified by users. treats also provides a tidy interface through the treats object, allowing users to easily run reproducible simulations. It also comes with an extend manual regularly updated following users’ questions or suggestions.}, number={4}, journal={Methods in Ecology and Evolution}, author={Guillerme, Thomas}, year={2024}, pages={647–656}, language={en} } + @article{Jones_Lawton_Shachak_1994, title={Organisms as Ecosystem Engineers}, volume={69}, ISSN={0030-1299}, DOI={10.2307/3545850}, abstractNote={Ecosystem engineers are organisms that directly or indirectly modulate the availability of resources to other species, by causing physical state changes in biotic or abiotic materials. In so doing they modify, maintain and create habitats. Autogenic engineers (e.g. corals, or trees) change the environment via their own physical structures (i.e. their living and dead tissues). Allogenic engineers (e.g. woodpeckers, beavers) change the environment by transforming living or non-living materials from one physical state to another, via mechanical or other means. The direct provision of resources to other species, in the form of living or dead tissues is not engineering. Organisms act as engineers when they modulate the supply of a resource or resources other than themselves. We recognise and define five types of engineering and provide examples. Humans are allogenic engineers par excellence, and also mimic the behaviour of autogenic engineers, for example by constructing glasshouses. We explore related concepts including the notions of extended phenotypes and keystone species. Some (but not all) products of ecosystem engineering are extended phenotypes. Many (perhaps most) impacts of keystone species include not only trophic effects, but also engineers and engineering. Engineers differ in their impacts. The biggest effects are attributable to species with large per capita impacts, living at high densities, over large areas for a long time, giving rise to structures that persist for millennia and that modulate many resource flows (e.g. mima mounds created by fossorial rodents). The ephemeral nests constructed by small, passerine birds lie at the opposite end of this continuum. We provide a tentative research agenda for an exploration of the phenomenon of organisms as ecosystem engineers, and suggest that all habitats on earth support, and are influenced by, ecosystem engineers.}, number={3}, journal={Oikos}, publisher={[Nordic Society Oikos, Wiley]}, author={Jones, Clive G. and Lawton, John H. and Shachak, Moshe}, year={1994}, pages={373–386} } @article{Keating_Sansom_Sutton_Knight_Garwood_2020, title={Morphological Phylogenetics Evaluated Using Novel Evolutionary Simulations}, volume={69}, rights={All rights reserved}, ISSN={1063-5157}, DOI={10.1093/sysbio/syaa012}, abstractNote={Abstract. Evolutionary inferences require reliable phylogenies. Morphological data have traditionally been analyzed using maximum parsimony, but recent simulat}, number={5}, journal={Systematic Biology}, publisher={Oxford Academic}, author={Keating, Joseph N. and Sansom, Robert S. and Sutton, Mark D. and Knight, Christopher G. and Garwood, Russell J.}, year={2020}, month=sep, pages={897–912}, language={en} } @article{Mongiardino_Koch_Garwood_Parry_2021, title={Fossils improve phylogenetic analyses of morphological characters}, volume={288}, rights={All rights reserved}, DOI={10.1098/rspb.2021.0044}, abstractNote={Fossils provide our only direct window into evolutionary events in the distant past. Incorporating them into phylogenetic hypotheses of living clades can help time-calibrate divergences, as well as elucidate macroevolutionary dynamics. However, the effect fossils have on phylogenetic reconstruction from morphology remains controversial. The consequences of explicitly incorporating the stratigraphic ages of fossils using tip-dated inference are also unclear. Here, we use simulations to evaluate the performance of inference methods across different levels of fossil sampling and missing data. 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 under the fossilized birth–death process outperform undated 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 distinctive morphology and their temporal information, and their incorporation in total-evidence phylogenetics is necessary to faithfully reconstruct evolutionary history.}, number={1950}, journal={Proceedings of the Royal Society B: Biological Sciences}, publisher={Royal Society}, author={Mongiardino Koch, Nicolás and Garwood, Russell J. and Parry, Luke A.}, year={2021}, month=may, pages={20210044} } @article{Mongiardino_Koch_Garwood_Parry_2023, title={Inaccurate fossil placement does not compromise tip-dated divergence times}, volume={66}, rights={© 2023 The Authors. Palaeontology published by John Wiley & Sons Ltd on behalf of The Palaeontological Association.}, ISSN={1475-4983}, DOI={10.1111/pala.12680}, abstractNote={Time-scaled phylogenies underpin the interrogation of evolutionary processes across deep timescales, as well as attempts to link these to Earth’s history. By inferring the placement of fossils and using their ages as temporal constraints, tip dating under the fossilized birth–death (FBD) process provides a coherent prior on divergence times. At the same time, it also links topological and temporal accuracy, as incorrectly placed fossil terminals should misinform divergence times. This could pose serious issues for obtaining accurate node ages, yet the interaction between topological and temporal error has not been thoroughly explored. We simulate phylogenies and associated morphological datasets using methodologies that incorporate evolution under selection, and are benchmarked against empirical datasets. We find that datasets of 300 characters and realistic levels of missing data generally succeed in inferring the correct placement of fossils on a constrained extant backbone topology, and that true node ages are usually contained within Bayesian posterior distributions. While increased fossil sampling improves the accuracy of inferred ages, topological and temporal errors do not seem to be linked: analyses in which fossils resolve less accurately do not exhibit elevated errors in node age estimates. At the same time, inferred divergence times are biased, probably due to a mismatch between the FBD prior and the shape of our simulated trees. While these results are encouraging, suggesting that even fossils with uncertain affinities can provide useful temporal information, they also emphasize that palaeontological information cannot overturn discrepancies between model priors and the true diversification history.}, number={6}, journal={Palaeontology}, author={Mongiardino Koch, Nicolás and Garwood, Russell J. and Parry, Luke A.}, year={2023}, pages={e12680}, language={en} } - @article{Puttick_O_Reilly_Pisani_Donoghue_2019, title={Probabilistic methods outperform parsimony in the phylogenetic analysis of data simulated without a probabilistic model}, volume={62}, ISSN={00310239}, DOI={10.1111/pala.12388}, abstractNote={Abstract: To understand patterns and processes of the diversification of life, we require an accurate understanding of taxon interrelationships. Recent studies have suggested that analyses of morphological character data using the Bayesian and maximum likelihood Mk model provide phylogenies of higher accuracy compared to parsimony methods. This has proved controversial, particularly studies simulating morphology-data under Markov models that assume shared branch lengths for characters, as it is claimed this leads to bias favouring the Bayesian or maximum likelihood Mk model over parsimony models which do not explicitly make this assumption. We avoid these potential issues by employing a simulation protocol in which character states are randomly assigned to tips, but datasets are constrained to an empirically realistic distribution of homoplasy as measured by the consistency index. Datasets were analysed with equal weights and implied weights parsimony, and the maximum likelihood and Bayesian Mk model. We find that consistent (low homoplasy) datasets render method choice largely irrelevant, as all methods perform well with high consistency (low homoplasy) datasets, but the largest discrepancies in accuracy occur with low consistency datasets (high homoplasy). In such cases, the Bayesian Mk model is significantly more accurate than alternative models and implied weights parsimony never significantly outperforms the Bayesian Mk model. When poorly supported branches are collapsed, the Bayesian Mk model recovers trees with higher resolution compared to other methods. As it is not possible to assess homoplasy independently of a tree estimate, the Bayesian Mk model emerges as the most reliable approach for categorical morphological analyses.}, number={1}, journal={Palaeontology}, author={Puttick, Mark N. and O’Reilly, Joseph E. and Pisani, Davide and Donoghue, Philip C. J.}, editor={Rahman, Imran}, year={2019}, pages={1–17}, language={en} } + @article{Puttick_O_Reilly_Pisani_Donoghue_2019, title={Probabilistic methods outperform parsimony in the phylogenetic analysis of data simulated without a probabilistic model}, volume={62}, ISSN={00310239}, DOI={10.1111/pala.12388}, abstractNote={Abstract: To understand patterns and processes of the diversification of life, we require an accurate understanding of taxon interrelationships. Recent studies have suggested that analyses of morphological character data using the Bayesian and maximum likelihood Mk model provide phylogenies of higher accuracy compared to parsimony methods. This has proved controversial, particularly studies simulating morphology-data under Markov models that assume shared branch lengths for characters, as it is claimed this leads to bias favouring the Bayesian or maximum likelihood Mk model over parsimony models which do not explicitly make this assumption. We avoid these potential issues by employing a simulation protocol in which character states are randomly assigned to tips, but datasets are constrained to an empirically realistic distribution of homoplasy as measured by the consistency index. Datasets were analysed with equal weights and implied weights parsimony, and the maximum likelihood and Bayesian Mk model. We find that consistent (low homoplasy) datasets render method choice largely irrelevant, as all methods perform well with high consistency (low homoplasy) datasets, but the largest discrepancies in accuracy occur with low consistency datasets (high homoplasy). In such cases, the Bayesian Mk model is significantly more accurate than alternative models and implied weights parsimony never significantly outperforms the Bayesian Mk model. When poorly supported branches are collapsed, the Bayesian Mk model recovers trees with higher resolution compared to other methods. As it is not possible to assess homoplasy independently of a tree estimate, the Bayesian Mk model emerges as the most reliable approach for categorical morphological analyses.}, number={1}, journal={Palaeontology}, author={Puttick, Mark N. and O'Reilly, Joseph E. and Pisani, Davide and Donoghue, Philip C. J.}, editor={Rahman, Imran}, year={2019}, pages={1–17}, language={en} } @article{Wright_Hillis_2014, title={Bayesian Analysis Using a Simple Likelihood Model Outperforms Parsimony for Estimation of Phylogeny from Discrete Morphological Data}, volume={9}, ISSN={1932-6203}, DOI={10.1371/journal.pone.0109210}, number={10}, journal={PLoS ONE}, author={Wright, April M. and Hillis, David M.}, editor={Poon, Art F. Y.}, year={2014}, month=oct, pages={e109210}, language={en} } diff --git a/JOSS/paper.md b/JOSS/paper.md index d547581..c35b8a3 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -93,7 +93,7 @@ TREvoSim v3.0.0 includes a suite of new features that allow the investigation of ### Ecosystem engineering -A new ecosystem engineering system allows the impact of organism-environment feedback to be investigated [@Butterfield_2011]. When the ecosystem engineering option is enabled (it is disabled in default settings), a species is assigned ecosystem engineering status halfway through a run, and passes this status to descendents. When this occurs, the genome of that organism is either used to overwrite an environmental mask, or added to the environment as an additional mask. Overwriting a mask reduces the hamming distance between engineers and masks, and -- assuming a low fitness target -- thus directly improves their fitness relative to non-engineers. In contrast, adding a mask changes the fitness landscape changes the nature of the fitness landscape for all organisms, but with a weaker direct benefit to ecosystem engineers. Ecosystem engineering can occur just once (‘one-shot’ ecosystem engineering) or can be repeated after the first application (‘persistent’). When a mask is added in the first application, it is overwritten in subsequent applications when ecosystem engineers are persistent. A new facility to log the ecosystem-engineering status of individuals is provided. +A new ecosystem engineering system allows the impact of organism-environment feedback to be investigated [@Jones_Lawton_Shachak_1994]. When the ecosystem engineering option is enabled (it is disabled in default settings), a species is assigned ecosystem engineering status halfway through a run, and passes this status to descendents. When this occurs, the genome of that organism is either used to overwrite an environmental mask, or added to the environment as an additional mask. Overwriting a mask reduces the hamming distance between engineers and masks, and -- assuming a low fitness target -- thus directly improves their fitness relative to non-engineers. In contrast, adding a mask changes the fitness landscape changes the nature of the fitness landscape for all organisms, but with a weaker direct benefit to ecosystem engineers. Ecosystem engineering can occur just once (‘one-shot’ ecosystem engineering) or can be repeated after the first application (‘persistent’). When a mask is added in the first application, it is overwritten in subsequent applications when ecosystem engineers are persistent. A new facility to log the ecosystem-engineering status of individuals is provided. ### Expanding playing field From 6431d1a7dfc7d424e8793040a1f013bc031e55b1 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 15 Aug 2024 18:13:54 +0100 Subject: [PATCH 02/16] Clarify the real world implications of expanding playing field for issue #66 --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index c35b8a3..69160e6 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -97,7 +97,7 @@ A new ecosystem engineering system allows the impact of organism-environment fee ### Expanding playing field -This new option alters competition such that it occurs between species rather than between individuals (i.e. it removes intra-species competition from the simulation). When enabled (disabled by default), this is achieved by allowing only one individual from each species to be present in the playing field at any time. As such, the playing field grows to accommodate new species, which appear following the standard speciation rules. On duplication of an individual, juveniles overwrite the previous member of their species in the playing field. Otherwise, the playing field operates as normal, i.e. it is ordered by fitness and duplication of an individual selected using a geometric distribution links fitness to fecundity. +This new option alters competition such that it occurs between species rather than between individuals. When enabled (disabled by default), this is achieved by allowing only one individual from each species to be present in the playing field at any time. As such, the playing field grows to accommodate new species, which appear following the standard speciation rules. On duplication of an individual, juveniles overwrite the previous member of their species in the playing field. Otherwise, the playing field operates as normal, i.e. it is ordered by fitness and duplication of an individual selected using a geometric distribution links fitness to fecundity. Enabling the expanding playing field removes intra-species competition from the simulation: this can be used to investigate the impact that intra- v.s. inter-species competition has on evolutionary processes during a run, and on the phylogenetic outcomes of a simulation. ### Match peaks From b58878f4108bc4e4941abfc5b910c848fe5a8bfa Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 15 Aug 2024 19:17:54 +0200 Subject: [PATCH 03/16] Clarify the real world implications of match fitness peaks for issue #66 --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 69160e6..8a87474 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -101,7 +101,7 @@ This new option alters competition such that it occurs between species rather th ### Match peaks -When there are multiple environments for any given playing field, by default masks are seeded with random numbers, and may thus have different peak fitness values. This new option instead seeds each playing field with environments that have the same peak fitness. This is achieved by doing a site-wise randomisation of the sequence of zeros and ones across masks, between environments. For example, with three masks in a given environment, the first site may be 1,0,0 for masks 1,2 and 3 respectively -- when this option is enabled, the pattern from site one may be moved to site seven between the first and second environment, and this is repeated for all sites. This operation ensures that the best achievable fitness at the start of a simulation will remain the same, but will be achieved by a different genome across environments. As the simulation progresses and mutations to the masks occur, matching peaks are no longer guaranteed. Additionally, the simulation uses a heuristic algorithm to generate an initial seed organism that has the same fitness in each environment (in >99% of cases) when this option is selected. In general, this option allows finer control of the fitness landscape. +When there are multiple environments for any given playing field, by default masks are seeded with random numbers, and may thus have different peak fitness values. This new option instead seeds each playing field with environments that have the same peak fitness. This is achieved by doing a site-wise randomisation of the sequence of zeros and ones across masks, between environments. For example, with three masks in a given environment, the first site may be 1,0,0 for masks 1,2 and 3 respectively -- when this option is enabled, the pattern from site one may be moved to site seven between the first and second environment, and this is repeated for all sites. This operation ensures that the best achievable fitness at the start of a simulation will remain the same, but will be achieved by a different genome across environments. As the simulation progresses and mutations to the masks occur, matching peaks are no longer guaranteed. Additionally, the simulation uses a heuristic algorithm to generate an initial seed organism that has the same fitness in each environment (in >99% of cases) when this option is selected. In general, this option allows finer control of the fitness landscape, and its impact on evolution to be investigated. ### No selection From d5f52766d0ea5e78e199ed09376d7c2dd667811d Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 15 Aug 2024 19:21:52 +0200 Subject: [PATCH 04/16] Clarify the real world implications of no selection for issue #66 --- JOSS/paper.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 8a87474..29f4337 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -77,7 +77,7 @@ bibliography: paper.bib # Summary Simulations provide valuable insights into the patterns and processes of evolution, and the performance of analytical methods used to investigate empirical data -[@Wright_Hillis_2014; @Barido-Sottani_Saupe_Smiley_Soul_Wright_Warnock_2020; @Dolson_Ofria_2021]. Here we present TREvoSim v3.0.0: an agent- or individual-based model written in C++, in which digital organisms evolve, creating phylogenetic character data and trees. Trees inferred from empirical data always carry uncertainty, but TREvoSim can create a known tree alongside associated character data, allowing -- for example -- assessment of phylogenetic inference methods [@Keating_Sansom_Sutton_Knight_Garwood_2020; @Mongiardino_Koch_Garwood_Parry_2021; @Mongiardino_Koch_Garwood_Parry_2023]. The v3.0.0 release adds a range of features to allow greater flexibility in simulating phylogenetic trees and character data (more logging options, finer control over character character and simulation parameters), and to facilitate the study of broader evolutionary topics (e.g. ecosystem engineering, adaptive landscapes, selection). +[@Wright_Hillis_2014; @Barido-Sottani_Saupe_Smiley_Soul_Wright_Warnock_2020; @Dolson_Ofria_2021]. Here we present TREvoSim v3.0.0: an agent- or individual-based model written in C++, in which digital organisms evolve, creating phylogenetic character data and trees. Trees inferred from empirical data always carry uncertainty, but TREvoSim can create a known tree alongside associated character data, allowing -- for example -- assessment of phylogenetic inference methods [@Keating_Sansom_Sutton_Knight_Garwood_2020; @Mongiardino_Koch_Garwood_Parry_2021; @Mongiardino_Koch_Garwood_Parry_2023]. The v3.0.0 release adds a range of features to allow greater flexibility in simulating phylogenetic trees and character data (more logging options, finer control over character character and simulation parameters), and to facilitate the study of broader evolutionary topics (e.g. ecosystem engineering, adaptive landscapes, selection). # Background @@ -105,11 +105,11 @@ When there are multiple environments for any given playing field, by default mas ### No selection -Another new addition is a no selection mode — when this is enabled, organisms for replication are chosen from the playing field at random, rather than using fitness to determine replication probability. When this option is enabled, the simulation functions under drift. +Another new addition is a no selection mode — when this is enabled, organisms for replication are chosen from the playing field at random, rather than using fitness to determine replication probability. When this option is enabled, the simulation functions under drift, allowing study of e.g. neutral vs selective regimes. ### Playing field mixing -By default TREvoSim playing fields are independent data structures, and organisms in one playing field do not compete with those in others during a simulation. This new option allows configurable mixing of organisms between playing fields, which can be asymmetrical if desired. Playing field mixing can facilitate the study of, for example, the dynamics of invasive species or biotic interchanges. +By default TREvoSim playing fields are independent data structures, and organisms in one playing field do not compete with those in others during a simulation. This new option allows configurable mixing of organisms between playing fields, which can be asymmetrical if desired. Playing field mixing can facilitate the study of, for example, the dynamics of invasive species or biotic interchanges. ### Stochastic layer @@ -123,7 +123,7 @@ This implements a limited period of increased rates of environmental change that ### Character limits -New options allow finer control of TREvoSim functions that employ genome characters. Characters in TREvoSim are used in several portions of the algorithm -- they form the basis of calculating fitness of organisms, and are also employed in the identification of species. In previous versions of TREvoSim, all characters were used for both functions, through a user-defined total character number. From v3, a separate limit on the character count used for species selection and/or the fitness calculation can be applied (in the default settings, all are the same). When either -- or both -- differ from the total character number, only a subset of characters (those from zero to the limit) are included in the defined operations, and others can evolve independent of these processes (i.e., in the absence of selective forces, akin to more neutral drift-like processes). +New options allow finer control of TREvoSim functions that employ genome characters. Characters in TREvoSim are used in several portions of the algorithm -- they form the basis of calculating fitness of organisms, and are also employed in the identification of species. In previous versions of TREvoSim, all characters were used for both functions, through a user-defined total character number. From v3, a separate limit on the character count used for species selection and/or the fitness calculation can be applied (in the default settings, all are the same). When either -- or both -- differ from the total character number, only a subset of characters (those from zero to the limit) are included in the defined operations, and others can evolve independent of these processes (i.e., in the absence of selective forces, akin to more neutral drift-like processes). ### Default simulation parameters From 7c7a749bf23aabec5a0e3823ff3996fe2d4349f5 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 15 Aug 2024 19:24:14 +0200 Subject: [PATCH 05/16] Clarify the real world implications of perturbations for issue #66 --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 29f4337..37783d2 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -117,7 +117,7 @@ Provides a layer of abstraction between an organism’s genome and the bits used ### Perturbations -This implements a limited period of increased rates of environmental change that occurs halfway through a run (when half the requested species have evolved, or at half the requested iteration number). This is intended for study of scenarios where evolutionary dynamics are driven by variations in the rate of environmental change[@Condamine_Rolland_Morlon_2013]. There is an option to also increase mixing between playing fields during a perturbation. +This implements a limited period of increased rates of environmental change that occurs halfway through a run (when half the requested species have evolved, or at half the requested iteration number). This is intended for study of scenarios where evolutionary dynamics are driven by variations in the rate of environmental change[@Condamine_Rolland_Morlon_2013]. There is an option to also increase mixing between playing fields during a perturbation. This system can provide insights into the impact of disturbance and rapid environmental change, for example, on evolution in a non-spatially explicit setting. ## Software modifications From eb9b2c7860f610d7e8a6e67c40710a54a0a011f4 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 15 Aug 2024 19:27:02 +0200 Subject: [PATCH 06/16] Update test information to reflect v3 release version --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 37783d2..74ba2b5 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -139,7 +139,7 @@ Simulation-termination can now be configured to occur after either a user-define ### Codebase Tests -TREvoSim v3.0.0 introduces a test suite covering all aspects of the simulation mechanics. These are included in the build pipeline, which will fail if any tests return a failure. +TREvoSim v3.0.0 introduces a test suite covering all aspects of the simulation mechanics. These can be accessed by the user, through the graphical user interface, and by developers through an integrated development environment such as Qt creator, as outlined in the documentation. # Statement of need From d1e297d0c51841da4d08a2a509821a79e605aa84 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Fri, 16 Aug 2024 16:31:00 +0100 Subject: [PATCH 07/16] Update small detail on tests in JOSS documentation --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index debe773..5cb3918 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -139,7 +139,7 @@ Simulation-termination can now be configured to occur after either a user-define ### Codebase Tests -TREvoSim v3.0.0 introduces a test suite covering all aspects of the simulation mechanics. These can be accessed by the user, through the graphical user interface, and by developers through an integrated development environment such as Qt creator, as outlined in the documentation. +TREvoSim v3.0.0 introduces a test suite covering all aspects of the simulation mechanics. These can be accessed by the user, through the graphical user interface, by developers through an integrated development environment such as Qt creator, and by both through running the test binary, as outlined in the documentation. # Statement of need From 0bc0107e849ccc81d05beadd58677bdd0ddce57c Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Sun, 18 Aug 2024 11:16:48 +0200 Subject: [PATCH 08/16] Change to punctuation in abstract2 --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 5cb3918..12b6436 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -77,7 +77,7 @@ bibliography: paper.bib # Summary Simulations provide valuable insights into the patterns and processes of evolution, and the performance of analytical methods used to investigate empirical data -[@Wright_Hillis_2014; @Barido-Sottani_Saupe_Smiley_Soul_Wright_Warnock_2020; @Dolson_Ofria_2021]. Here we present TREvoSim v3.0.0: an agent- or individual-based model written in C++, in which digital organisms evolve, creating phylogenetic character data and trees. Trees inferred from empirical data always carry uncertainty, but TREvoSim can create a known tree alongside associated character data, allowing -- for example -- assessment of phylogenetic inference methods [@Keating_Sansom_Sutton_Knight_Garwood_2020; @Mongiardino_Koch_Garwood_Parry_2021; @Mongiardino_Koch_Garwood_Parry_2023]. The v3.0.0 release adds a range of features to allow greater flexibility in simulating phylogenetic trees and character data (more logging options, finer control over character character and simulation parameters), and to facilitate the study of broader evolutionary topics (e.g. ecosystem engineering, adaptive landscapes, selection). +[@Wright_Hillis_2014; @Barido-Sottani_Saupe_Smiley_Soul_Wright_Warnock_2020; @Dolson_Ofria_2021]. Here we present TREvoSim v3.0.0: an agent-/individual-based model written in C++, in which digital organisms evolve, creating phylogenetic character data and trees. Trees inferred from empirical data always carry uncertainty, but TREvoSim can create a known tree alongside associated character data, allowing -- for example -- assessment of phylogenetic inference methods [@Keating_Sansom_Sutton_Knight_Garwood_2020; @Mongiardino_Koch_Garwood_Parry_2021; @Mongiardino_Koch_Garwood_Parry_2023]. The v3.0.0 release adds a range of features to allow greater flexibility in simulating phylogenetic trees and character data (more logging options, finer control over character character and simulation parameters), and to facilitate the study of broader evolutionary topics (e.g. ecosystem engineering, adaptive landscapes, selection). # Background From 41e801f6866f1b52173342056b12bf60bcd55f7d Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Sun, 18 Aug 2024 11:43:08 +0200 Subject: [PATCH 09/16] Minor improvements to wording throughout the paper, nothing of significance --- JOSS/paper.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 12b6436..80cfcc0 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -81,7 +81,7 @@ Simulations provide valuable insights into the patterns and processes of evoluti # Background -A range of digital platforms to study evolution and ecology exist, with varied approaches and levels of abstraction [@Dolson_Ofria_2021]. TREvoSim is sister-package to the spatially explicit eco-evolutionary simulation REvoSim [@Garwood_Spencer_Sutton_2019; @Furness_Garwood_Sutton_2023]. v1.0.0 was developed to investigate the accuracy and precision of phylogenetic inference methods [@Keating_Sansom_Sutton_Knight_Garwood_2020]. After further development, TREvoSim v2.0.0 was used to investigate the impact fossils have on phylogenetic inference and evolutionary timescales [@Mongiardino_Koch_Garwood_Parry_2021; @Mongiardino_Koch_Garwood_Parry_2023]. In brief, TREvoSim is a non spatially-explicit model in which organisms — which consist of a genome of binary characters — compete within a structure called the playing-field to echo natural selection (Figure 1). Their chance of replication is dictated by a fitness algorithm that assesses organismal fit against a series of random numbers (masks, constituting an environment). On replication, organisms have a chance of mutation, and descendents overwrite a current member of the playing field. The simulation has a lineage-based species concept, and at the end of a simulation can output trees and characters (species genomes), as well as logging the simulation state as the model runs. +A range of digital platforms to study evolution and ecology exist, with varied approaches and levels of abstraction [@Dolson_Ofria_2021]. TREvoSim is a sister-package to the spatially explicit eco-evolutionary simulation REvoSim [@Garwood_Spencer_Sutton_2019; @Furness_Garwood_Sutton_2023]. v1.0.0 was developed to investigate the accuracy and precision of phylogenetic inference methods [@Keating_Sansom_Sutton_Knight_Garwood_2020]. After further development, TREvoSim v2.0.0 was used to investigate the impact fossils have on phylogenetic inference and evolutionary timescales [@Mongiardino_Koch_Garwood_Parry_2021; @Mongiardino_Koch_Garwood_Parry_2023]. In brief, TREvoSim is a non spatially-explicit model in which organisms -- which consist of a genome of binary characters -- compete within a structure called the playing-field to echo natural selection (Figure 1). Their chance of replication is dictated by a fitness algorithm that assesses organismal fit against a series of random numbers (masks, constituting an environment). On replication, organisms have a chance of mutation, and descendents overwrite a current member of the playing field. The simulation has a lineage-based species concept, and at the end of a simulation the software can output trees and characters (species genomes), as well as logging the simulation state as the model runs. ![Figure 1 - A simplified overview of TREvoSim. Green text represents a user-defined variable and the given value is default. The figure is split into data structures, the fitness calculation, the algorithm, and the tree. In the tree, character change is represented by change on the Y axis, time on the X. Any lineage is likely to comprise multiple individuals: modal genomes are shown in solid lines, and non-modal via dashed lines. By default, genomes for each species are recorded on their extinction. A full description of the algorithm is available in the [TREvoSim documentation](https://trevosim.readthedocs.io/en/latest/).](./Figure_01.png) @@ -93,7 +93,7 @@ TREvoSim v3.0.0 includes a suite of new features that allow the investigation of ### Ecosystem engineering -A new ecosystem engineering system allows the impact of organism-environment feedback to be investigated [@Jones_Lawton_Shachak_1994]. When the ecosystem engineering option is enabled (it is disabled in default settings), a species is assigned ecosystem engineering status halfway through a run, and passes this status to descendents. When this occurs, the genome of that organism is either used to overwrite an environmental mask, or added to the environment as an additional mask. Overwriting a mask reduces the hamming distance between engineers and masks, and -- assuming a low fitness target -- thus directly improves their fitness relative to non-engineers. In contrast, adding a mask changes the fitness landscape changes the nature of the fitness landscape for all organisms, but with a weaker direct benefit to ecosystem engineers. Ecosystem engineering can occur just once (‘one-shot’ ecosystem engineering) or can be repeated after the first application (‘persistent’). When a mask is added in the first application, it is overwritten in subsequent applications when ecosystem engineers are persistent. A new facility to log the ecosystem-engineering status of individuals is provided. +A new ecosystem engineering system allows the impact of organism-environment feedback to be investigated [@Jones_Lawton_Shachak_1994]. When the ecosystem engineering option is enabled (it is disabled in default settings), a species is assigned ecosystem engineering status halfway through a run, and passes this status to descendents. When this occurs, the genome of that organism is either used to overwrite an environmental mask, or added to the environment as an additional mask. Overwriting a mask reduces the Hamming distance between engineers and masks, and -- assuming a low fitness target -- thus directly improves their fitness relative to non-engineers. In contrast, adding a mask changes the nature of the fitness landscape for all organisms, but with a weaker direct benefit to ecosystem engineers. Ecosystem engineering can occur just once (‘one-shot’ ecosystem engineering) or can be repeated after the first application (‘persistent’). When a mask is added in the first application, it is overwritten in subsequent applications when ecosystem engineers are persistent. A new facility to log the ecosystem-engineering status of individuals is provided. ### Expanding playing field @@ -101,11 +101,11 @@ This new option alters competition such that it occurs between species rather th ### Match peaks -When there are multiple environments for any given playing field, by default masks are seeded with random numbers, and may thus have different peak fitness values. This new option instead seeds each playing field with environments that have the same peak fitness. This is achieved by doing a site-wise randomisation of the sequence of zeros and ones across masks, between environments. For example, with three masks in a given environment, the first site may be 1,0,0 for masks 1,2 and 3 respectively -- when this option is enabled, the pattern from site one may be moved to site seven between the first and second environment, and this is repeated for all sites. This operation ensures that the best achievable fitness at the start of a simulation will remain the same, but will be achieved by a different genome across environments. As the simulation progresses and mutations to the masks occur, matching peaks are no longer guaranteed. Additionally, the simulation uses a heuristic algorithm to generate an initial seed organism that has the same fitness in each environment (in >99% of cases) when this option is selected. In general, this option allows finer control of the fitness landscape, and its impact on evolution to be investigated. +When there are multiple environments for any given playing field, by default masks are seeded with random numbers, and may thus have different peak fitness values. This new option (disabled by default) instead seeds each playing field with environments that have the same peak fitness. This is achieved by doing a site-wise randomisation of the sequence of zeros and ones across masks, between environments. For example, with three masks in a given environment, the first site may be 1,0,0 for masks 1,2 and 3 respectively -- when this option is enabled, the pattern from site one may be moved to site seven between the first and second environment, and this is repeated for all sites. This operation ensures that the best achievable fitness at the start of a simulation will remain the same, but will be achieved by a different genome across environments. In v3.0.0, as the simulation progresses and mutations to the masks occur, matching peaks are no longer guaranteed (it is possible this will change in future releases). Additionally, the simulation uses a heuristic algorithm to generate an initial seed organism that has the same fitness in each environment (in >99% of cases) when this option is selected. In general, this option allows finer control of the fitness landscape, and its impact on evolution to be investigated. ### No selection -Another new addition is a no selection mode — when this is enabled, organisms for replication are chosen from the playing field at random, rather than using fitness to determine replication probability. When this option is enabled, the simulation functions under drift, allowing study of e.g. neutral vs selective regimes. +Another new addition is a no selection mode -- when this is enabled (disabled by default), organisms for replication are chosen from the playing field at random, rather than using fitness to determine replication probability. When this option is enabled, the simulation functions under drift, allowing study of e.g. neutral vs selective regimes. ### Playing field mixing @@ -113,11 +113,11 @@ By default TREvoSim playing fields are independent data structures, and organism ### Stochastic layer -Provides a layer of abstraction between an organism’s genome and the bits used for the fitness calculation. It achieves this using many-to-one mapping (i.e. using a sequence of multiple bits to define the value of a single bit), the map being defined by the user. For example, the sequence of bits 0110 in a genome might map to a 1 bit in the genome calculation, whilst are 1100 may map to 0. As such, any individual bit does not necessarily have an impact on the fitness of an organism as a whole, allowing e.g. neutral mutations and less strongly adaptationist dynamics. +Provides a layer of abstraction between an organism’s genome and the bits used for the fitness calculation (this option is disabled by default). It achieves this using many-to-one mapping (i.e. using a sequence of multiple bits to define the value of a single bit), the map being defined by the user. For example, the sequence of bits 0110 in a genome might map to a 1 bit in the genome calculation, whilst are 1100 may map to 0. As such, any individual bit does not necessarily have an impact on the fitness of an organism as a whole, allowing e.g. neutral mutations and less strongly adaptationist dynamics. ### Perturbations -This implements a limited period of increased rates of environmental change that occurs halfway through a run (when half the requested species have evolved, or at half the requested iteration number). This is intended for study of scenarios where evolutionary dynamics are driven by variations in the rate of environmental change[@Condamine_Rolland_Morlon_2013]. There is an option to also increase mixing between playing fields during a perturbation. This system can provide insights into the impact of disturbance and rapid environmental change, for example, on evolution in a non-spatially explicit setting. +When enabled (disabled by default), this implements a limited period of increased rates of environmental change that occurs halfway through a run (when half the requested species have evolved, or at half the requested iteration number). This is intended for study of scenarios where evolutionary dynamics are driven by variations in the rate of environmental change[@Condamine_Rolland_Morlon_2013]. There is an option to also increase mixing between playing fields during a perturbation. This system can provide insights into the impact of disturbance and rapid environmental change, for example, on evolution in a non-spatially explicit setting. ## Software modifications @@ -127,7 +127,7 @@ New options allow finer control of TREvoSim functions that employ genome charact ### Default simulation parameters -New default values for simulation parameters are introduced with this release. Outputs created using these default variables have been compared against twelve empirical, total evidence analyses [following the approach of, and using empirical data sourced from @Mongiardino_Koch_Garwood_Parry_2021] for three measures of tree shape and homoplasy. The empirical data, analysis script, and resulting graphs have been placed within the source code repository, and the results are presented in the documentation. +New default values for simulation parameters are introduced with this release. Outputs created using these default variables have been compared against twelve empirical, total evidence analyses [following the approach of, and using empirical data sourced from, @Mongiardino_Koch_Garwood_Parry_2021] for three measures of tree shape and homoplasy. The empirical data, analysis script, and resulting graphs have been placed within the source code repository, and the results are presented in the documentation. ### Running log @@ -139,7 +139,7 @@ Simulation-termination can now be configured to occur after either a user-define ### Codebase Tests -TREvoSim v3.0.0 introduces a test suite covering all aspects of the simulation mechanics. These can be accessed by the user, through the graphical user interface, by developers through an integrated development environment such as Qt creator, and by both through running the test binary, as outlined in the documentation. +TREvoSim v3.0.0 introduces a test suite covering all aspects of the simulation mechanics. These can be accessed by the user, through the graphical user interface, by developers through an integrated development environment such as Qt Creator, and by both through running the test binary, as outlined in the documentation. # Statement of need From bd817dff607c7f6b863756bf2dd73183375bbaf2 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Sun, 18 Aug 2024 14:55:09 +0200 Subject: [PATCH 10/16] Add reviewer acknowledgements to JOSS paper #60 --- JOSS/paper.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 80cfcc0..988ca31 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -167,6 +167,7 @@ TREvoSim v3.0.0 source code and binaries are freely available from [Zenodo](http # Acknowledgements -RJG and RSS were supported by the NERC award NE/T000813/1 and development began during the BBSRC grant BB/N015827/1 awarded to RSS and RJG. RJG is supported by the Alexander von Humboldt Foundation; RJG and LAP by Leverhulme Trust Research Project Grant (RPG-2023-234); FSD by NERC fellowship NE/W00786X/1; and LAP by NERC fellowship NE/W007878/1. +RJG and RSS were supported by the NERC award NE/T000813/1 and development began during the BBSRC grant BB/N015827/1 awarded to RSS and RJG. RJG is supported by the Alexander von Humboldt Foundation; RJG and LAP by Leverhulme Trust Research Project Grant (RPG-2023-234); FSD by NERC fellowship NE/W00786X/1; and LAP by NERC fellowship NE/W007878/1. We are grateful to the reviewers of this contribution, Martin R. Smith and Alison T. Cribb, for suggestions that significantly improved the quality of this publication and the TREvoSim documentation, and to Martin R. Smith for additional auditing of our code. + # References From f4edf47caca5c8fd1a95fe45d1c38cbf55040ca7 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Mon, 19 Aug 2024 18:37:18 +0200 Subject: [PATCH 11/16] Minor addition to funding details --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 988ca31..5456119 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -167,7 +167,7 @@ TREvoSim v3.0.0 source code and binaries are freely available from [Zenodo](http # Acknowledgements -RJG and RSS were supported by the NERC award NE/T000813/1 and development began during the BBSRC grant BB/N015827/1 awarded to RSS and RJG. RJG is supported by the Alexander von Humboldt Foundation; RJG and LAP by Leverhulme Trust Research Project Grant (RPG-2023-234); FSD by NERC fellowship NE/W00786X/1; and LAP by NERC fellowship NE/W007878/1. We are grateful to the reviewers of this contribution, Martin R. Smith and Alison T. Cribb, for suggestions that significantly improved the quality of this publication and the TREvoSim documentation, and to Martin R. Smith for additional auditing of our code. +RJG and RSS were supported by the NERC award NE/T000813/1 and development began during the BBSRC grant BB/N015827/1 awarded to RSS and RJG. RJG is supported by the Alexander von Humboldt Foundation; RJG, LAP, and RSS by Leverhulme Trust Research Project Grant (RPG-2023-234); FSD by NERC fellowship NE/W00786X/1; and LAP by NERC fellowship NE/W007878/1. We are grateful to the reviewers of this contribution, Martin R. Smith and Alison T. Cribb, for suggestions that significantly improved the quality of this publication and the TREvoSim documentation, and to Martin R. Smith for additional auditing of our code. # References From 272aa17defa8d556f414573c7c6dbf930f37de2f Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 22 Aug 2024 10:24:45 +0200 Subject: [PATCH 12/16] Update JOSS paper to point to 3.0.0 release --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 5456119..fc23fb2 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -163,7 +163,7 @@ RJG developed and coded TREvoSim, with support on testing and releasing from ART # Availability -TREvoSim v3.0.0 source code and binaries are freely available from [Zenodo](https://doi.org/10.5281/zenodo.10866261) and [GitHub](https://github.com/palaeoware/trevosim). Newer releases of the TREvoSim software will be available on GitHub. Full documentation is available from [ReadTheDocs](https://trevosim.readthedocs.io/en/latest/). +TREvoSim v3.0.0 source code and binaries are freely available from [Zenodo](https://doi.org/10.5281/zenodo.13358420) and [GitHub](https://github.com/palaeoware/trevosim/releases/tag/v3.0.0). Newer releases of the TREvoSim software will be available on GitHub. Full documentation is available from [ReadTheDocs](https://trevosim.readthedocs.io/en/latest/). # Acknowledgements From e03fa748cc1312ecefc57e453de212b98790349f Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Thu, 22 Aug 2024 17:13:57 +0200 Subject: [PATCH 13/16] Update affiliation of TV to correct university title --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index 5456119..94b1307 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -68,7 +68,7 @@ affiliations: index: 8 - name: Department of Earth Sciences, University of Oxford, Oxford, OX1 3AN, UK index: 9 - - name: Erasmus Mundus Joint Master Degree PANGEA, Lille University, France + - name: Erasmus Mundus Joint Master Degree PANGEA, Université de Lille, France index: 10 date: 24 March 2024 bibliography: paper.bib From 740f710a750f5052c0ae0bebbe257730d0f4d764 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Tue, 10 Sep 2024 10:21:09 +0100 Subject: [PATCH 14/16] Correct vs. on line 100 --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index fc23fb2..bd151c5 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -97,7 +97,7 @@ A new ecosystem engineering system allows the impact of organism-environment fee ### Expanding playing field -This new option alters competition such that it occurs between species rather than between individuals. When enabled (disabled by default), this is achieved by allowing only one individual from each species to be present in the playing field at any time. As such, the playing field grows to accommodate new species, which appear following the standard speciation rules. On duplication of an individual, juveniles overwrite the previous member of their species in the playing field. Otherwise, the playing field operates as normal, i.e. it is ordered by fitness and duplication of an individual selected using a geometric distribution links fitness to fecundity. Enabling the expanding playing field removes intra-species competition from the simulation: this can be used to investigate the impact that intra- v.s. inter-species competition has on evolutionary processes during a run, and on the phylogenetic outcomes of a simulation. +This new option alters competition such that it occurs between species rather than between individuals. When enabled (disabled by default), this is achieved by allowing only one individual from each species to be present in the playing field at any time. As such, the playing field grows to accommodate new species, which appear following the standard speciation rules. On duplication of an individual, juveniles overwrite the previous member of their species in the playing field. Otherwise, the playing field operates as normal, i.e. it is ordered by fitness and duplication of an individual selected using a geometric distribution links fitness to fecundity. Enabling the expanding playing field removes intra-species competition from the simulation: this can be used to investigate the impact that intra- vs. inter-species competition has on evolutionary processes during a run, and on the phylogenetic outcomes of a simulation. ### Match peaks From e4078881900a32ab329078d787c691b87f6338b4 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Tue, 10 Sep 2024 10:28:54 +0100 Subject: [PATCH 15/16] Fix another vs. --- JOSS/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/JOSS/paper.md b/JOSS/paper.md index ca42266..cdf23d6 100644 --- a/JOSS/paper.md +++ b/JOSS/paper.md @@ -105,7 +105,7 @@ When there are multiple environments for any given playing field, by default mas ### No selection -Another new addition is a no selection mode -- when this is enabled (disabled by default), organisms for replication are chosen from the playing field at random, rather than using fitness to determine replication probability. When this option is enabled, the simulation functions under drift, allowing study of e.g. neutral vs selective regimes. +Another new addition is a no selection mode -- when this is enabled (disabled by default), organisms for replication are chosen from the playing field at random, rather than using fitness to determine replication probability. When this option is enabled, the simulation functions under drift, allowing study of e.g. neutral vs. selective regimes. ### Playing field mixing From 2fb12573277ff7d7c506eb9f514ead57bb40d1b7 Mon Sep 17 00:00:00 2001 From: RussellGarwood Date: Tue, 10 Sep 2024 10:34:43 +0100 Subject: [PATCH 16/16] Update draft PDF yaml --- .github/workflows/draft-pdf.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 7cf09b7..f2fc243 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -14,7 +14,7 @@ jobs: # This should be the path to the paper within your repo. paper-path: ./JOSS/paper.md - name: Upload - uses: actions/upload-artifact@v1 + uses: actions/upload-artifact@v3 with: name: paper # This is the output path where Pandoc will write the compiled