Add a tiny bit more context for issue #58

JOSS/paper.bib

@@ -1,5 +1,7 @@
  @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{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{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} }

JOSS/paper.md

@@ -77,12 +77,11 @@ 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]. This is particularly true for building phylogenies (evolutionary trees) where the true relationships between groups are unknowable in empirical settings and using empirical data.  Simulations can create a known phylogenetic tree alongside associated character data, and therefore play a powerful role in assessing the adequacy of approaches for inferring trees from character data. Here we present TREvoSim v3.0.0: an individual-based model written in C++, with a focus on creating phylogenetic character data and trees. Previous versions have been used to study 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 new features that allow the study of evolutionary processes, in addition to phylogenetic methods.  
+[@Wright_Hillis_2014; @Barido-Sottani_Saupe_Smiley_Soul_Wright_Warnock_2020; @Dolson_Ofria_2021]. This is particularly true for building phylogenies (evolutionary trees) where the true relationships between groups are unknowable in empirical settings and using empirical data. Simulations can create a known phylogenetic tree alongside associated character data, and therefore play a powerful role in assessing the adequacy of approaches for inferring trees from character data. Here we present TREvoSim v3.0.0: an individual-based model written in C++, with a focus on creating phylogenetic character data and trees. Previous versions have been used to study 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 new features that allow the study of evolutionary processes, in addition to phylogenetic methods.  
 
 # Background 
 
-
-TREvoSim 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 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.
 
 ![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)