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Neural and behavioral responses to reproductive signals in male chorus frogs.

Code ↔ Paper

5 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 5 matches
  1. [1] § MATERIALS AND METHODS › Behavioral analysis ↔ analysis_combined copy.R, lines 337–383 · score 0.83 · Kenward Roger degrees, pairwise post hoc, lme4, male behavior, freedom, emmeans
  2. [2] § MATERIALS AND METHODS › Regional analysis ↔ analysis_combined copy.R, lines 1–52 · score 0.81 · pairwise comparisons, post hoc, interactive model, better fit, species recognition, neural activity
  3. [3] § MATERIALS AND METHODS › Microscopy and cell counting ↔ analysis_combined copy.R, lines 153–200 · score 0.75 · lateral septum, medial amygdala, dorsal pallium, MeA, positive cells, Dp
  4. [4] § MATERIALS AND METHODS › Evoked neural activity trials ↔ analysis_combined copy.R, lines 153–200 · score 0.68 · lateral septum, medial amygdala, dorsal pallium, MeA, Dp, S6
  5. [5] § MATERIALS AND METHODS › Behavioral analysis ↔ analysis_combined copy.R, lines 385–431 · score 0.60 · body mass, model fit, AICc, SVL, duration, behavioral

Paper

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The authors' code

R · 586 lines · 30 KB · CC-BY-4.0 · 5 matches

  1. # DATA ANALYSIS
  2. # EVOKED NEURAL ACTIVITY IN CHORUS FROGS REVEALS CANDIDATE MECHANISMS OF ENAHANCED SPECIES RECOGNITION
  4. ##### PART 1: FUNCTIONAL SPECIALIZATION OF BRAIN REGIONS
  6. ##### Load packages
  7. library(readxl)
  8. library(MASS)
  9. library(lme4)
  10. library(ggplot2)
  11. library(emmeans)
  13. #### Import data
  14. area_data <- read_excel("R_input_data.xlsx",
  15. sheet = "area_data", col_types = c("text",
  16. "text", "text", "numeric", "numeric"))
  17. ID_info <- read_excel("R_input_data.xlsx",
  18. sheet = "ID_info", col_types = c("text",
  19. "text", "numeric", "numeric"))
  21. ##### Combine data frames into complete dataset
  22. all_data2 <- merge(area_data, ID_info, by.x= "ID", by.y= "ID", all.x=TRUE, all.y=TRUE)
  24. ##### Generate region-specific datasets
  25. IC2 <- all_data2[all_data2$region=="IC",]
  26. Gc2 <- all_data2[all_data2$region=="Gc",]
  27. Str2 <- all_data2[all_data2$region=="Str",]
  28. Mp2 <- all_data2[all_data2$region=="Mp",]
  29. aPOA2 <- all_data2[all_data2$region=="aPOA",]
  30. Ls2 <- all_data2[all_data2$region=="Ls",]
  31. Acc2 <- all_data2[all_data2$region=="Acc",]
  32. Dp2 <- all_data2[all_data2$region=="Dp",]
  33. TP2 <- all_data2[all_data2$region=="TP",]
  34. VH2 <- all_data2[all_data2$region=="VH",]
  35. BST2 <- all_data2[all_data2$region=="BST",]
  36. MeA2 <- all_data2[all_data2$region=="MeA",]
  37. MgV2 <- all_data2[all_data2$region=="MgV",]
  39. ##### Inferior colliculus
  40. IC2_all <- glmer.nb(count~group+call_in_response+offset(log(area))+(1|ID)+(1|sac_time)+(1|sac_day), data=IC2)
  41. ranef(IC2_all)
  42. # IC model with all random effects fails to converge due to near-zero effects of sac day, sac time and individual ID. Will exclude.
  43. IC_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=IC2)
  44. IC_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=IC2)
  45. anova(IC_m1, IC_m2)
  46. # Interactive model is better fit than additive model (p=0.024)
  47. summary(IC_m2)
  48. # figure
  49. ggplot(IC2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  50. # Post hoc analysis of main effects: using additive model for pairwise comparisons since these can be affected by interaction terms
  51. IC_post <- emmeans(IC_m1, "group", data=IC2)
  52. pairs(IC_post, adjust="tukey")
  54. ##### Nucleus accumbens
  55. Acc2_all <- glmer.nb(count~group+call_in_response+(1|ID)+offset(log(area)), data=Acc2_nooutliers)
  56. ranef(Acc2_all)
  57. # Acc model with all random effects fails to converge due to near-zero effects of sac day, sac time and individual ID. Will exclude.
  58. Acc_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=Acc2)
  59. Acc_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=Acc2)
  60. anova(Acc_m1, Acc_m2)
  61. # Interactive model is not a better fit than additive model (p=0.977)
  62. summary(Acc_m1)
  63. # figure
  64. ggplot(Acc2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  65. # No significant effects of group or call in response
  67. ##### Griseum centrale
  68. Gc2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_day)+(1|sac_time)+offset(log(area)), data=Gc2)
  69. ranef(Gc2_all)
  70. # Gc model with all random effects fails to converge due to near-zero effects of sac day, sac time, and indiv ID. Will exclude.
  71. Gc2_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=Gc2)
  72. Gc2_m2 <- glm.nb(count~ group*call_in_response+offset(log(area)), data=Gc2)
  73. anova(Gc2_m1, Gc2_m2)
  74. # Interactive model is not better fit than additive model
  75. summary(Gc2_m1)
  76. ggplot(Gc2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  77. # Post hoc analysis of main effects
  78. Gc_post <- emmeans(Gc2_m1, "group", data=Gc2)
  79. summary(Gc_post)
  80. pairs(Gc_post, adjust="tukey")
  81. Gc_post2 <- emmeans(Gc2_m1, "call_in_response",data=Gc2)
  82. pairs(Gc_post2)
  83. # figure
  84. ggplot(Gc2, aes(x=call_in_response, y=count, fill=call_in_response))+geom_boxplot()+scale_fill_manual(values=c("#1A85FF", "#D41159"))+labs(x="Calling in response to stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  86. ##### Striatum
  87. Str2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_day)+(1|sac_time)+offset(log(area)), data=Str2)
  88. ranef(Str2_all)
  89. # Str model with all random effects fails to converge due to near-zero effects of sac day, sac time, and individual ID. Will exclude.
  90. Str2_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=Str2)
  91. Str2_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=Str2)
  92. anova(Str2_m1, Str2_m2)
  93. # Interactive model is not better fit than the additive model (p=0.08)
  94. summary(Str2_m1)
  95. # Post hoc analysis of main effects
  96. Str_post <- emmeans(Str2_m1, "group", data=Str2)
  97. pairs(Str_post, adjust="tukey")
  98. Str_post2 <- emmeans(Str2_m1,"call_in_response",data=Str2)
  99. pairs(Str_post2, adjust="tukey")
  100. # figures
  101. ggplot(Str2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  102. ggplot(Str2, aes(x=call_in_response, y=count, fill=call_in_response))+geom_boxplot()+scale_fill_manual(values=c("#1A85FF", "#D41159"))+labs(x="Calling in response to stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  104. ##### Medial pallium
  105. Mp2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+(1|sac_day)+offset(log(area)), data=Mp2)
  106. ranef(Mp2_all)
  107. # Mp model with all random effects fails to converge due to near-zero effect of sac day. Will exclude.
  108. Mp2_id_time <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+offset(log(area)), data=Mp2)
  109. Mp2_id <- glmer.nb(count~group+call_in_response+(1|ID)+offset(log(area)), data=Mp2)
  110. anova(Mp2_id_time, Mp2_id)
  111. # Model that includes a random effect of sac_time is not a better fit than the model with only a random effect of individual ID (p=0.30)
  112. Mp2_id2 <- glmer.nb(count~group*call_in_response+(1|ID)+offset(log(area)), data=Mp2)
  113. # Interactive model fails to converge to over parameterization
  114. summary(Mp2_id)
  115. # Post hoc analyses of main effects
  116. Mp_post <- emmeans(Mp2_id, "group", data=Mp2)
  117. pairs(Mp_post, adjust="tukey")
  118. # figure
  119. ggplot(Mp2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  122. ##### Preoptic area
  123. poa2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+(1|sac_day)+offset(log(area)), data=aPOA2)
  124. ranef(poa2_all)
  125. # aPOA model with all random effects fails to converge due to near-zero effect of sac day, sac time, and individual ID. Will exclude.
  126. poa2_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=aPOA2)
  127. poa2_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=aPOA2)
  128. anova(poa2_m1, poa2_m2)
  129. # Interactive model is not a better fit than additive model
  130. summary(poa2_m1)
  131. # Post hoc analyses of main effects
  132. poa_post <- emmeans(poa2_m1, "group", data=aPOA2)
  133. pairs(poa_post, adjust="tukey")
  134. # figure
  135. ggplot(aPOA2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  137. ##### BNST
  138. bst2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+(1|sac_day)+offset(log(area)), data=BST2)
  139. ranef(bst2_all)
  140. # BST model with all random effects fails to converge due to near-zero effect of sac day, sac time, and individual ID. Will exclude.
  141. bst2_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=BST2)
  142. bst2_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=BST2)
  143. anova(bst2_m1, bst2_m2)
  144. # Interactive model is not a better fit than additive model
  145. summary(bst2_m1)
  146. # Post hoc analyses of main effects
  147. bst_post <- emmeans(bst2_m1, "group", data=BST2)
  148. pairs(bst_post, adjust="tukey")
  149. # figure
  150. ggplot(BST2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  153. ##### Dorsal pallium
  154. dp2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_day)+(1|sac_time)+offset(log(area)), data=Dp2)
  155. ranef(dp2_all)
  156. # Dp model with all random effects fails to converge due to near-zero effect of sac time. Will exclude
  157. dp2_id_day <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_day)+offset(log(area)), data=Dp2)
  158. dp2_id <- glmer.nb(count~group+call_in_response+(1|ID)+offset(log(area)), data=Dp2)
  159. anova(dp2_id, dp2_id_day)
  160. # Model with a random effect of sac day is not significantly better fit than model without
  161. dp2_m2 <- glmer.nb(count~group*call_in_response+(1|ID)+offset(log(area)), data=Dp2)
  162. # Interactive model fails to converge due to over parameterization
  163. summary(dp2_id)
  164. # Post hoc analyses of main effects
  165. dp_post <- emmeans(dp2_id, "group", data=Dp2)
  166. pairs(dp_post, adjust="tukey")
  167. # figure
  168. ggplot(Dp2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  170. ##### Lateral septum
  171. ls2_all <- glmer.nb(count~group+call_in_response+(1|ID)++(1|sac_time)+(1|sac_day)+offset(log(area)), data=Dp2)
  172. ranef(ls2_all)
  173. # Ls model with all random effects fails to converge due to near-zero effect of sac time and sac day. Will exclude
  174. ls2_m1 <- glmer.nb(count~group+call_in_response+(1|ID)+offset(log(area)), data=Ls2)
  175. ls2_m2 <- glmer.nb(count~group*call_in_response+(1|ID)+offset(log(area)), data=Ls2)
  176. # Interactive mdoel fails to converge due to over parameterization
  177. summary(ls2_m1)
  178. # Post hoc analyses of main effects
  179. ls_post <- emmeans(ls2_m1, "group", data=Ls2)
  180. pairs(ls_post, adjust="tukey")
  181. ls_post2 <- emmeans(ls2_m1, "call_in_response", data=Ls2)
  182. pairs(ls_post2, adjust="tukey")
  183. # figure
  184. ggplot(Ls2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  187. ##### Medial amygdala
  188. mea2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+(1|sac_day)+offset(log(area)), data=MeA2)
  189. ranef(mea2_all)
  190. # MeA model with all random effects fails to converge due to near-zero effect of sac time, sac day, and individual ID. Will exclude
  191. mea2_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=MeA2)
  192. mea2_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=MeA2)
  193. anova(mea2_m1, mea2_m2)
  194. # Interactive model is not a better fit than additive model
  195. summary(mea2_m1)
  196. # Post hoc analyses of main effects
  197. mea_post <- emmeans(mea2_m1, "group", data=MeA2)
  198. pairs(mea_post, adjust="tukey")
  199. # figure
  200. ggplot(MeA2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  202. ##### Ventral magnocellular preoptic nucleus
  203. mgv2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_day)+(1|sac_time)+offset(log(area)), data=MgV2)
  204. ranef(mgv2_all)
  205. # MeA model with all random effects fails to converge due to near-zero effect of sac time, sac day, and individual ID. Will exclude
  206. mgv2_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=MgV2)
  207. mgv2_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=MgV2)
  208. anova(mgv2_m1, mgv2_m2)
  209. # Interactive model is not a better fit than additive model
  210. summary(mgv2_m1)
  211. # No main effects
  212. # figure
  213. ggplot(MgV2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  215. ##### Posterior tuberculum
  216. tp2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+(1|sac_day)+offset(log(area)), data=TP2)
  217. ranef(tp2_all)
  218. # TP model with all random effects fails to converge due to near-zero effect of sac time, sac day, and individual ID. Will exclude
  219. tp2_m1 <- glm.nb(count~group+call_in_response+offset(log(area)), data=TP2)
  220. tp2_m2 <- glm.nb(count~group*call_in_response+offset(log(area)), data=TP2)
  221. anova(tp2_m1, tp2_m2)
  222. # Interactive model is not a better fit than additive model (p=0.08)
  223. summary(tp2_m1)
  224. # No main effects
  225. # figure
  226. ggplot(TP2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  228. ##### Ventral hypothalamus
  229. vh2_all <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+(1|sac_day)+offset(log(area)), data=VH2)
  230. ranef(vh2_all)
  231. # VH model with all random effects fails to converge due to near-zero effect of sac day
  232. vh2_id_time <- glmer.nb(count~group+call_in_response+(1|ID)+(1|sac_time)+offset(log(area)), data=VH2)
  233. vh2_id <- glmer.nb(count~group+call_in_response+(1|ID)+offset(log(area)), data=VH2)
  234. anova(vh2_id_time, vh2_id)
  235. vh2_m2 <- glmer.nb(count~group*call_in_response+(1|ID)+offset(log(area)), data=VH2)
  236. anova(vh2_id, vh2_m2)
  237. # Interactive model is not significantly better fit than additive model
  238. summary(vh2_id)
  239. # No main effects
  240. # figure
  241. ggplot(VH2, aes(x=group, y=count, fill=group))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  244. # Figure of all regions together
  245. ggplot(all_data2, aes(x=group, y=count, fill=group))+ geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Number of pS6-positive cells")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))+facet_wrap(~region)
  248. #### PART 2: FUNCTIONAL CONNECTIVITY AMONG BRAIN REGIONS
  249. #### Load packages
  250. library(readxl)
  251. library(sna)
  252. library(writexl)
  253. library(corrplot)
  255. #### QAP tests to compare similarity among matrices of pS6+ cell counts
  257. # Import data
  258. area_data <- read_excel("R_input_data.xlsx",
  259. sheet = "area_data", col_types = c("text",
  260. "text", "text", "numeric", "numeric"))
  261. ID_info <- read_excel("R_input_data.xlsx",
  262. sheet = "ID_info", col_types = c("text",
  263. "text", "numeric", "numeric"))
  264. # Combine data frames into complete dataset and subset
  265. all_data2 <- merge(area_data, ID_info, by.x= "ID", by.y= "ID", all.x=TRUE, all.y=TRUE)
  266. subset_all_data2 <- all_data2[,c(2,3,4)]
  267. FL <- subset_all_data2[subset_all_data2$group=="FL",]
  268. AL <- subset_all_data2[subset_all_data2$group=="AL",]
  269. Nigrita <- subset_all_data2[subset_all_data2$group=="nigrita",]
  270. Control <- subset_all_data2[subset_all_data2$group=="silence",]
  272. # For each stim group, further refine data
  273. FL_sub <- FL[,c(2,3)]
  274. AL_sub <- AL[,c(2,3)]
  275. Nigrita_sub <- Nigrita[,c(2,3)]
  276. Control_sub <- Control[,c(2,3)]
  278. ## at this point, need to manually transform each subsetted data frame into wide format and reload into R under the same name
  280. # Convert each subsetted data frame into a matrix
  281. FL_matrix <- as.matrix(FL_sub)
  282. AL_matrix <- as.matrix(AL_sub)
  283. Nigrita_matrix <- as.matrix(Nigrita_sub)
  284. Control_matrix <- as.matrix(Control_sub)
  285. # cannot use Control group for further analysis due to zero-inflated data. Standard deviation in many columns approaches zero.
  287. # Generate correlation matrices from each matrix
  288. corFL_matrix <- cor(FL_matrix, method="pearson", use = "complete.obs")
  289. corAL_matrix <- cor(AL_matrix, method="pearson", use = "complete.obs")
  290. corNigrita_matrix <- cor(Nigrita_matrix, method="pearson", use="complete.obs")
  292. # QAP test comparing FL and AL correlation matrices
  293. qap_FL_AL <- qaptest(list(corFL_matrix, corAL_matrix), gcor, g1=1, g2=2, reps=5000)
  294. summary(qap_FL_AL)
  295. # really low correlation. not sig dif. therefore the matrices are different from one another
  296. # null hypothesis is that the correlations are related
  298. # QAP test comparing FL and P. nigrita correlation matrices
  299. qap_FL_N <- qaptest(list(corFL_matrix, corNigrita_matrix), gcor, g1=1, g2=2, reps=5000)
  300. summary(qap_FL_N)
  301. # really low correlation. not sig dif. therefore the matrices are different from one another
  303. # QAP test comparing AL and P. nigrita correlation matrices
  304. qap_AL_N <- qaptest(list(corAL_matrix, corNigrita_matrix), gcor, g1=1, g2=2, reps=5000)
  305. summary(qap_AL_N)
  306. # really low correlation. not sig dif. therefore the matrices are different from one another
  308. ####Correlation tests and heatmaps for pairs of regions in each stim group
  309. # Florida
  311. # remove NAs from matrix
  312. FL_matrix2 <- FL_matrix[!rowSums(is.na(FL_matrix)),]
  313. # correlation test between regions
  314. FL_cor <- cor.mtest(FL_matrix2)
  315. # generate heatmap
  316. corrplot(cor(FL_matrix2),method='color',diag = FALSE, type = 'upper',
  318. sig.level = c(0.001, 0.01, 0.05), pch.cex = 0.9, insig = 'label_sig', pch.col = 'black',
  320. order = 'alphabet', title = "", p.mat = FL_cor$p, tl.col="black", tl.cex=0.8,
  321. col=colorRampPalette(c("#785ef0","white","#dc267f"))(100))
  323. # Alabama
  325. # remove NAs from matrix
  326. AL_matrix2 <- AL_matrix[!rowSums(is.na(AL_matrix)),]
  327. # correlation test between regions
  328. AL_cor <- cor.mtest(AL_matrix2)
  329. # generate heatmap
  330. corrplot(cor(AL_matrix2),method='color',diag = FALSE, type = 'upper',
  332. sig.level = c(0.001, 0.01, 0.05), pch.cex = 0.9, insig = 'label_sig', pch.col = 'black',
  334. order = 'alphabet', title = "", p.mat = AL_cor$p, tl.col="black", tl.cex=0.8,
  335. col=colorRampPalette(c("#785ef0","white","#dc267f"))(100))
  337. # P. nigrita
  339. # remove NAs from matrix
  340. Nigrita_matrix2 <- Nigrita_matrix[!rowSums(is.na(Nigrita_matrix)),]
  341. # correlation test between regions
  342. Nigrita_cor <- cor.mtest(Nigrita_matrix2)
  343. # generate heatmap
  344. corrplot(cor(Nigrita_matrix2),method='color',diag = FALSE, type = 'upper',
  346. sig.level = c(0.001, 0.01, 0.05), pch.cex = 0.9, insig = 'label_sig', pch.col = 'black',
  348. order = 'alphabet', title = "", p.mat = Nigrita_cor$p, tl.col="black", tl.cex=0.8,
  349. col=colorRampPalette(c("#785ef0","white","#dc267f"))(100))
  352. #### PART 3: MALE BEHAVIOR ANALYSES
  353. #### Load packages
  354. library(readxl)
  355. library(lmerTest)
  356. library(lme4)
  357. library(emmeans)
  358. library(AICcmodavg)
  359. library(ggplot2)
  361. # Import data
  362. data_behavior <- read_excel("R_input_data.xlsx",
  363. col_types = c("text", "text", "text",
  364. "text", "numeric", "text", "numeric",
  365. "numeric", "numeric", "numeric"))
  366. body_cond <- read_excel("R_input_data.xlsx",
  367. sheet = "body_cond", col_types = c("text",
  368. "numeric", "numeric"))
  369. # Merge behavior and body condition data
  370. data <- merge(data_behavior, body_cond, by.x= "ID", by.y= "ID", all.x=TRUE, all.y=TRUE)
  372. # Replace all NAs in Duration with zeroes
  373. data$Duration[is.na(data$Duration)] <- 0
  374. # Change Behavior and Stimulus columns to factors for input to lmer
  375. data$Behavior <- factor (data$Behavior)
  376. data$Stimulus <- factor (data$Stimulus)
  377. # Model
  378. durationmod <- lmer(Duration ~ Stimulus * Behavior + (1 | frog_ID), data = data)
  379. summary <- summary(durationmod)
  380. # ANOVA with Kenward-Roger degrees of freedom for analysis of fixed effects
  381. anova(durationmod, ddf = "Kenward-Roger")
  382. # Pairwise post hoc analysis of interactive effects
  383. emmeans(durationmod, pairwise ~ Stimulus | Behavior)
  385. #### Test for an effect of body mass and SVL on the duration of behaviors
  387. # Model generation
  388. test1 <-lmer(Duration ~ Stimulus * Behavior * SVL + (1 | frog_ID), data = data, REML=FALSE)
  389. test2 <-lmer(Duration ~ Stimulus * Behavior * mass + (1 | frog_ID), data = data, REML=FALSE)
  390. test3 <- lmer(Duration ~ Stimulus * Behavior + (1 | frog_ID), data = data, REML=FALSE)
  391. # Compare model fits with AICc
  392. Cand.models<-list("SVL"=test1,"mass"=test2, "simple"=test3)
  393. selectionTable <- aictab(cand.set = Cand.models)
  394. selectionTable
  395. # Simple model is the best fit, there is no effect of SVL or body mass on behavior duration
  397. #### Descriptive statistics
  399. # Calling duration
  400. # Subset data
  401. calling <- data[data$Behavior== "calling",]
  402. # Subset by stimulus type and calculate mean and sd for each
  403. call_FL <- calling[calling$Stimulus=="FL", "Duration"]
  404. mean(call_FL$Duration)
  405. sd(call_FL$Duration)
  406. call_AL <- calling[calling$Stimulus=="AL", "Duration"]
  407. mean(call_AL$Duration)
  408. sd(call_AL$Duration)
  409. call_nigrita <- calling[calling$Stimulus=="nigrita", "Duration"]
  410. mean(call_nigrita$Duration)
  411. sd(call_nigrita$Duration)
  412. call_silence <- calling[calling$Stimulus=="silence", "Duration"]
  413. mean(call_silence$Duration)
  414. sd(call_silence$Duration)
  416. # Duration in tub
  417. # Subset data
  418. tub <- data[data$Behavior== "in_tub",]
  419. # Subset by stimulus type and calculate mean and sd for each
  420. tub_FL <- tub[tub$Stimulus=="FL", "Duration"]
  421. mean(tub_FL$Duration)
  422. sd(tub_FL$Duration)
  423. tub_AL <- tub[tub$Stimulus=="AL", "Duration"]
  424. mean(tub_AL$Duration)
  425. sd(tub_AL$Duration)
  426. tub_nigrita <- tub[tub$Stimulus=="nigrita", "Duration"]
  427. mean(tub_nigrita$Duration)
  428. sd(tub_nigrita$Duration)
  429. tub_silence <- tub[tub$Stimulus=="silence", "Duration"]
  430. mean(tub_silence$Duration)
  431. sd(tub_silence$Duration)
  433. # Total time in motion
  434. # Subset data
  435. move <- data[data$Behavior== "movement",]
  436. # Subset by stimulus type and calculate mean and sd for each
  437. move_FL <- move[move$Stimulus=="FL", "Duration"]
  438. mean(move_FL$Duration)
  439. sd(move_FL$Duration)
  440. move_AL <- move[move$Stimulus=="AL", "Duration"]
  441. mean(move_AL$Duration)
  442. sd(move_AL$Duration)
  443. move_nigrita <- move[move$Stimulus=="nigrita", "Duration"]
  444. mean(move_nigrita$Duration)
  445. sd(move_nigrita$Duration)
  446. move_silence <- move[move$Stimulus=="silence", "Duration"]
  447. mean(move_silence$Duration)
  448. sd(move_silence$Duration)
  450. # Calling bouts
  452. # Subset by stimulus type and calculate mean and sd for each
  453. callnum_FL <- calling[calling$Stimulus=="FL", "Occurences"]
  454. mean(callnum_FL$Occurences)
  455. sd(callnum_FL$Occurences)
  456. callnum_AL <- calling[calling$Stimulus=="AL", "Occurences"]
  457. mean(callnum_AL$Occurences)
  458. sd(callnum_AL$Occurences)
  459. callnum_nigrita <- calling[calling$Stimulus=="nigrita", "Occurences"]
  460. mean(callnum_nigrita$Occurences)
  461. sd(callnum_nigrita$Occurences)
  462. callnum_silence <- calling[calling$Stimulus=="silence", "Occurences"]
  463. mean(callnum_silence$Occurences)
  464. sd(callnum_silence$Occurences)
  466. # Total time on side of the arena with stimulus speaker
  467. # Subset data
  468. stimside <- data[data$Behavior== "stim_side_total",]
  469. # Subset by stimulus type and calculate mean and sd for each
  470. stim_FL <- stimside[stimside$Stimulus=="FL", "Duration"]
  471. mean(stim_FL$Duration)
  472. sd(stim_FL$Duration)
  473. stim_AL <- stimside[stimside$Stimulus=="AL", "Duration"]
  474. mean(stim_AL$Duration)
  475. sd(stim_AL$Duration)
  476. stim_nigrita <- stimside[stimside$Stimulus=="nigrita", "Duration"]
  477. mean(stim_nigrita$Duration)
  478. sd(stim_nigrita$Duration)
  480. # Total time in pool with stimulus speaker
  481. # Subset data
  482. stimpool <- data[data$Behavior== "stim_pool",]
  483. # Subset by stimulus type and calculate mean and sd for each
  484. stimpool_FL <- stimpool[stimpool$Stimulus=="FL", "Duration"]
  485. mean(stimpool_FL$Duration)
  486. sd(stimpool_FL$Duration)
  487. stimpool_AL <- stimpool[stimpool$Stimulus=="AL", "Duration"]
  488. mean(stimpool_AL$Duration)
  489. sd(stimpool_AL$Duration)
  490. stimpool_nigrita <- stimpool[stimpool$Stimulus=="nigrita", "Duration"]
  491. mean(stimpool_nigrita$Duration)
  492. sd(stimpool_nigrita$Duration)
  494. # Total time on tub edge facing stimulus speaker
  495. # Subset data
  496. stimedge <- data[data$Behavior== "stim_edge",]
  497. # Subset by stimulus type and calculate mean and sd for each
  498. stimedge_FL <- stimedge[stimedge$Stimulus=="FL", "Duration"]
  499. mean(stimedge_FL$Duration)
  500. sd(stimedge_FL$Duration)
  501. stimedge_AL <- stimedge[stimedge$Stimulus=="AL", "Duration"]
  502. mean(stimedge_AL$Duration)
  503. sd(stimedge_AL$Duration)
  504. stimedge_nigrita <- stimedge[stimedge$Stimulus=="nigrita", "Duration"]
  505. mean(stimedge_nigrita$Duration)
  506. sd(stimedge_nigrita$Duration)
  508. # Total time on side of the arena opposite stimulus speaker
  509. # Subset data
  510. opposide <- data[data$Behavior== "oppo_side_total",]
  511. # Subset by stimulus type and calculate mean and sd for each
  512. oppo_FL <- opposide[opposide$Stimulus=="FL", "Duration"]
  513. mean(oppo_FL$Duration)
  514. sd(oppo_FL$Duration)
  515. oppo_AL <- opposide[opposide$Stimulus=="AL", "Duration"]
  516. mean(oppo_AL$Duration)
  517. sd(oppo_AL$Duration)
  518. oppo_nigrita <- opposide[opposide$Stimulus=="nigrita", "Duration"]
  519. mean(oppo_nigrita$Duration)
  520. sd(oppo_nigrita$Duration)
  522. # Total time in pool opposite stimulus speaker
  523. # Subset data
  524. oppopool <- data[data$Behavior== "oppo_pool",]
  525. # Subset by stimulus type and calculate mean and sd for each
  526. oppopool_FL <- oppopool[oppopool$Stimulus=="FL", "Duration"]
  527. mean(oppopool_FL$Duration)
  528. sd(oppopool_FL$Duration)
  529. oppopool_AL <- oppopool[oppopool$Stimulus=="AL", "Duration"]
  530. mean(oppopool_AL$Duration)
  531. sd(oppopool_AL$Duration)
  532. oppopool_nigrita <- oppopool[oppopool$Stimulus=="nigrita", "Duration"]
  533. mean(oppopool_nigrita$Duration)
  534. sd(oppopool_nigrita$Duration)
  536. # Total time on edge of tub opposite stimulus speaker
  537. # Subset data
  538. oppoedge <- data[data$Behavior== "oppo_edge",]
  539. # Subset by stimulus type and calculate mean and sd for each
  540. oppoedge_FL <- oppoedge[oppoedge$Stimulus=="FL", "Duration"]
  541. mean(oppoedge_FL$Duration)
  542. sd(oppoedge_FL$Duration)
  543. oppoedge_AL <- oppoedge[oppoedge$Stimulus=="AL", "Duration"]
  544. mean(oppoedge_AL$Duration)
  545. sd(oppoedge_AL$Duration)
  546. oppoedge_nigrita <- oppoedge[oppoedge$Stimulus=="nigrita", "Duration"]
  547. mean(oppoedge_nigrita$Duration)
  548. sd(oppoedge_FL$Duration)
  550. #### Figure generation
  552. # import wide data set
  553. wide_data <- read_excel("R_input_data.xlsx",
  554. + sheet = "data_wide", col_types = c("numeric",
  555. "text", "text", "text", "text", "numeric",
  556. "numeric", "numeric", "numeric",
  557. "numeric", "numeric", "numeric",
  558. "numeric", "numeric", "numeric",
  559. "numeric", "numeric", "numeric",
  560. "numeric", "numeric", "numeric",
  561. "numeric", "numeric", "numeric",
  562. "numeric", "numeric", "numeric",
  563. "numeric", "numeric", "numeric",
  564. "numeric", "numeric", "numeric",
  565. "numeric", "numeric", "numeric",
  566. "numeric"))
  569. # Create dataset without Silence stimulus group for some graphs
  570. data_nosilence <- wide_data[!(wide_data$Stimulus %in% "silence"),]
  572. # calling duration
  573. ggplot(wide_data, aes(x=Stimulus, y=calling_duration, fill=Stimulus))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Calling duration (s)")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  575. # movement duration
  576. ggplot(wide_data, aes(x=Stimulus, y=movement_duration, fill=Stimulus))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Movement duration (s)")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  578. # in tub duration
  579. ggplot(wide_data, aes(x=Stimulus, y=in_tub_duration, fill=Stimulus))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Duration in central tub (s)")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  581. # stimulus side duration
  582. ggplot(data_nosilence, aes(x=Stimulus, y=stim_side_total_duration, fill=Stimulus))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Time on stimulus side of arena (s)")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))
  584. # opposite side duration
  585. ggplot(data_nosilence, aes(x=Stimulus, y=oppo_side_total_duration, fill=Stimulus))+geom_boxplot()+scale_fill_manual(values=c("#648FFF", "#DC267F", "#FE6100", "#FFB000"))+labs(x="Stimulus", y="Time on side of arena opposite the stimulus (s)")+theme_classic()+theme(legend.position="none")+theme(axis.title=element_text(size=15))+theme(axis.text=element_text(size=12))

analysis_combined copy.R, under CC-BY-4.0 · at the source

Overview

Authors: Carlie B. Ochoa1, Ashley M. Loeven1, Debra Ann Fadool1,2,3, Emily Moriarty Lemmon1,2
ORCID iDs: Carlie B. Ochoa, Ashley M. Loeven, Debra Ann Fadool, Emily Moriarty Lemmon
  1. Department of Biological Science, Florida State University, 319 Stadium Drive, Tallahassee, FL 32306, USA
  2. Program in Neuroscience, Florida State University, Tallahassee, FL 32306, USA
  3. Institute of Molecular Biophysics, Florida State University, Tallahassee, FL 32306, USA
Institutions: Florida State University (United States)
Journal: The Journal of experimental biology, volume 229, issue 8, article jeb251686
Dates: received 1 October 2025; accepted 8 March 2026; published online 20 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1242/jeb.251686 · PMID 41837375 · PMCID PMC13143210 · OpenAlex W7137050230
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: other (organism), cognitive (subfield)
Methods: Statistics, Connectivity, fMRI & imaging
Keywords: Species recognition, Social decision-making network, pS6, Auditory midbrain, Acoustic signaling
MeSH: Anura*, Sexual Behavior, Animal*, Vocalization, Animal*, Animals, Brain, Female, Male (* major topic)
Topic: Amphibian and Reptile Biology (Global and Planetary Change, Environmental Science), according to OpenAlex
Funding: NSF (NSF) (2121058); National Institute on Deafness and Other Communication Disorders (T32DC000044); Florida State University
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

Species recognition and courtship behaviors are powerful drivers of speciation. Here, we investigated the neural and behavioral signatures of species recognition in Upland chorus frogs (Pseudacris feriarum). Populations of this species that are sympatric with congeners (e.g. Pseudacris nigrita) have evolved divergent male mating calls and enhanced acoustic discrimination by females owing to costly interspecific hybridization. Herein, we examined evoked neural activity and behaviors in male P. feriarum in response to sympatric, allopatric or heterospecific calls, or silence, via phospho-S6 ribosomal protein immunofluorescence. The sympatric call evoked activity in several brain regions that regulate spatial navigation and social decision making, indicating that this call type may be an important trigger for navigating to and within a complex chorus environment. Moreover, each stimulus resulted in a unique pattern of coactivation among brain regions. Despite these neural changes, there were no differences in behavioral response to each stimulus. Our results suggest that signal input and behavioral output are coded independently in the brains of male chorus frogs. Together, these findings represent a first step towards understanding the neural basis of conspecific recognition in a system in which this trait contributes to ongoing diversification.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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figshare 31904626

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 3 files, 1 script
Software Heritage: not checked
Found in: the end of the paper
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
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1 file
At the source: doi.org/10.6084/m9.figshare.31904626

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 5 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 7 MeSH terms, 3 funders, 68 references.

Cite

This paper

Ochoa, C. B., Loeven, A. M., Fadool, D. A., & Lemmon, E. M. (2026). Neural and behavioral responses to reproductive signals in male chorus frogs. The Journal of experimental biology, 229(8), jeb251686. https://doi.org/10.1242/jeb.251686

BibTeX

@article{ochoa2026neural,
author = {Ochoa, Carlie B. and Loeven, Ashley M. and Fadool, Debra Ann and Lemmon, Emily Moriarty},
title = {{Neural and behavioral responses to reproductive signals in male chorus frogs}},
journal = {The Journal of experimental biology},
year = {2026},
month = apr,
volume = {229},
number = {8},
pages = {jeb251686},
publisher = {The Company of Biologists},
issn = {0022-0949},
doi = {10.1242/jeb.251686},
url = {https://doi.org/10.1242/jeb.251686},
pmid = {41837375},
pmcid = {PMC13143210}
}

RIS

TY - JOUR
AU - Ochoa, Carlie B.
AU - Loeven, Ashley M.
AU - Fadool, Debra Ann
AU - Lemmon, Emily Moriarty
TI - Neural and behavioral responses to reproductive signals in male chorus frogs
T2 - The Journal of experimental biology
J2 - J Exp Biol
PY - 2026
DA - 2026/04/20
VL - 229
IS - 8
SP - jeb251686
SN - 0022-0949
PB - The Company of Biologists
DO - 10.1242/jeb.251686
UR - https://doi.org/10.1242/jeb.251686
LA - en
ER -

CSL-JSON

{
"id": "10.1242/jeb.251686",
"type": "article-journal",
"title": "Neural and behavioral responses to reproductive signals in male chorus frogs",
"container-title": "The Journal of experimental biology",
"author": [
{
"family": "Ochoa",
"given": "Carlie B."
},
{
"family": "Loeven",
"given": "Ashley M."
},
{
"family": "Fadool",
"given": "Debra Ann"
},
{
"family": "Lemmon",
"given": "Emily Moriarty"
}
],
"container-title-short": "J Exp Biol",
"volume": "229",
"issue": "8",
"page": "jeb251686",
"DOI": "10.1242/jeb.251686",
"PMID": "41837375",
"PMCID": "PMC13143210",
"ISSN": "0022-0949",
"publisher": "The Company of Biologists",
"URL": "https://doi.org/10.1242/jeb.251686",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
20
]
]
}
}

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Journal: Nature communications
In common: emmeans, lmerTest, lme4, 1 other tool, 1 reference
[10] doi:10.7554/elife.103566 [code]
Effort produces after-effects costly for others but valued for self.
Journal: eLife
In common: emmeans, lmerTest, lme4, 1 other tool, 1 reference

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