Change to punctuation in abstract2

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