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JOSS/paper.md

@@ -82,7 +82,7 @@ Simulations provide valuable insights into the patterns and processes of evoluti
 # 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.
 
-![Figure 1 - A simplified overview of TREvoSim. Green text represents a user-defined variable and the given value is default. Top left are data structures, and the algorithm employed in simulations is shown top right. Bottom shows how a tree is built: by default genomes for each species are recorded on their extinction. A full description of the algorithm is available in Keating et al. (2020) . ](./Figure_01.png)
+![Figure 1 - A simplified overview of TREvoSim. Green text represents a user-defined variable and the given value is default. Top left are data structures, and the algorithm employed in simulations is shown top right. Bottom shows how a tree is built: by default genomes for each species are recorded on their extinction. A full description of the algorithm is available in [@Keating_Sansom_Sutton_Knight_Garwood_2020]. ](./Figure_01.png)
 
 # New features
 
@@ -138,7 +138,7 @@ TREvoSim v3.0.0 introduces a test suite covering all aspects of the simulation m
 
 # Statement of need
 
-Typically, phylogenetic simulations are conducted using deterministic or stochastic approaches [e.g. @Puttick_O_Reilly_Pisani_Donoghue_2019; Guillerme_Puttick_Marcy_Weisbecker_2020], such as birth-death models or randomly generated data. TREvoSim complements these by using a selection-driven, agent-based approach: the data generated are different in a number of ways to those created using a stochastic model (Keating et al. 2020). The data generated by the software is likely to violate the assumptions of many common models used in the process of phylogenetic inference, incorporating a level of model misspecification resembling that expected from empirical datasets. Default settings have also been validated to reflect a number of features of empirical data matrices and trees. Given that (true) phylogenetic trees and character data are an emergent property of the simulation, the software is particularly well suited to simulation studies that can be analysed through phylogenetic trees and character data matrices. These include, for example: the impact of missing data on phylogenetic inference; the impact of rates of environmental change on character evolution; and the nature of evolution under different fitness landscapes. 
+Typically, phylogenetic simulations are conducted using deterministic or stochastic approaches [e.g. @Puttick_O_Reilly_Pisani_Donoghue_2019; Guillerme_Puttick_Marcy_Weisbecker_2020], such as birth-death models or randomly generated data. TREvoSim complements these by using a selection-driven, agent-based approach: the data generated are different in a number of ways to those created using a stochastic model [@Keating_Sansom_Sutton_Knight_Garwood_2020]. The data generated by the software is likely to violate the assumptions of many common models used in the process of phylogenetic inference, incorporating a level of model misspecification resembling that expected from empirical datasets. Default settings have also been validated to reflect a number of features of empirical data matrices and trees. Given that (true) phylogenetic trees and character data are an emergent property of the simulation, the software is particularly well suited to simulation studies that can be analysed through phylogenetic trees and character data matrices. These include, for example: the impact of missing data on phylogenetic inference; the impact of rates of environmental change on character evolution; and the nature of evolution under different fitness landscapes. 
 
 # Current associated projects