Additional file 1 of An agent-based model clarifies the importance of functional and developmental integration in shaping brain evolution
Creators: name: Avin, Shahar
name: Currie, Adrian
name: Montgomery, Stephen H.
Publisher: name: figshare
Year: 2021
Subjects: subject: Biophysics
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Neuroscience
subject: Evolutionary Biology
subject: Ecology
subject: Developmental Biology
subject: 111714 Mental Health
subject: FOS: Health sciences
subject: FOS: Health sciences
subject: 110309 Infectious Diseases

description: Additional file 1: Figures S1-S23. Figure S1. A simplified, pictorial depiction of the model. Pages 4–5. Figure S2. Evolution of ‘mosaicism’ under alternative conditions, full comparisons, run with 5% mutation size and 100 generations (sister to Fig. 1). Page 6. Figure S3. Evolution of ‘mosaicism’ under alternative conditions, full comparisons, run with 50% mutation size and 100 generations (extension of Fig. 1). Page 7. Figure S4. Evolution of ‘mosaicism’ under alternative conditions, full comparisons, run with 50% mutation size and 10 generations (extension of Fig. 1). Page 8. Figure S5. Generation number at convergence during simulations of competition between evolving populations with different D values under alternative conditions (companion to Fig. 2). Page 9. Figure S6. Competition between evolving populations with different D values under alternative conditions, full comparisons, run with 5% mutation size and 100 generations (sister to Fig. 2). Page 10. Figure S7. Competition between evolving populations with different D values under alternative conditions, full comparisons, run with 50% mutation size and 100 generations (extension of Figs. 2 and 3). Page 11. Figure S8. Competition between evolving populations with different D values under alternative conditions, full comparisons, run with 50% mutation size and 10 generations (extension of Figs. 2 and 3). Page 12. Figure S9. Competition between evolving populations with different D values under tailored environmental conditions, run with 5% mutation size, showing the average size of each brain component and the frequency of competing D values (extension of Figs. 2 and 3). Page 13. Figure S10. Competition between evolving populations with different D values under tailored environmental conditions, full comparisons, run with 5% mutation size and 100 generations (extension of Figs. 2 and 3). Page 14. Figure S11. Competition between evolving populations with different D values under tailored environmental conditions, full comparisons, run with 50% mutation size and 100 generations (extension of Figs. 2 and 3). Page 15. Figure S12. Competition between evolving populations with different D values under tailored environmental conditions, full comparisons, run with 50% mutation size and 10 generations (extension of Figs. 2 and 3). Page 16. Figure S13. Conditions at convergence of simulations of competition between evolving populations with different D values in a randomly varying environment, under different life history conditions (companion to Fig. 2). Page 17. Figure S14. Competition between evolving populations with different D values in a varying environment, under different life history conditions, full comparisons, run with 5% mutation size and 100 generations (sister to Fig. 4). Page 18. Figure S15. Competition between evolving populations with different D values in a varying environment, under different life history conditions, full comparisons, run with 50% mutation size and 100 generations (extension of Fig. 4). Page 19. Figure S16. Competition between evolving populations with different D values in a varying environment, under different life history conditions, full comparisons, run with 50% mutation size and 10 generations (extension of Fig. 4). Page 20. Figure S17. Selected, representative, individual simulations showing fluctuations in population frequencies over 100 generations for a 5% mutation size (A-D) or a 50% mutation size (E-H) (extension of Fig. 4). Page 21. Figure S18. Subtle effects of the size of environmental fluctuations on competition between evolving populations with different D values in a varying environment (extension of Figure S16). Page 22. Figure S19. Relationship between population frequencies between partially mosaic brains (D = 0.5) and fully mosaic (D = 0), or concerted brains (D = 1) from simulations in varying environmental conditions and a 5% mutation size (A) or 50% mutation size (B) (extension of Fig. 4). Page 23. Figure S20. Evolution of ‘mosaicism’ under alternative conditions with upper and lower bounds on brain size, full comparisons, run with 5% mutation size and 100 generations (extension of Fig. 6). Page 24. Figure S21. Competition between evolving populations with different D values under alternative conditions with upper and lower bounds with upper and lower bounds on brain size, full comparisons, run with 5% mutation size and 100 generations (extension of Fig. 6). Page 25. Figure S22. Selected, representative, individual simulations showing fluctuations in population frequencies over 50 generations for a 5% mutation size, with upper and lower bounds with upper and lower bounds on brain size (extension of Fig. 6). Page 26. Figure S23. Conditions at convergence of simulations of competition between evolving populations with different D values in a randomly varying environment, with upper and lower bounds on brain size, under different life history conditions (extension of Fig. 6). Page 27.