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Persistent Legacy Effects of Marine Heatwaves on Coral Symbioses.

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and Methods › Study Site ↔ Scripts/01_Extracting_site_environmental_data_Fig1.R, lines 266–349 · score 0.72 · degree heating weeks, bleaching alert, SST, DHW, temperature, vertical
  2. [2] § Materials and Methods › Statistical Analyses ↔ Scripts/03_Classifying_symbionts_from_sequence_data_Fig3_FigS3.R, lines 543–595 · score 0.68 · Unifrac dissimilarities, sequence variants, coral colonies, subset, S3, date
  3. [3] § Materials and Methods › Statistical Analyses ↔ Scripts/02_Genus_level_analyses_Fig2_Fig4_FigS4.R, lines 290–350 · score 0.67 · glmmTMB, fit, Beta, leeward, windward, models
  4. [4] § Materials and Methods › Quantifying Acute and Chronic Disturbance Exposure ↔ Scripts/01_Extracting_site_environmental_data_Fig1.R, lines 266–349 · score 0.63 · Degree Heating Week, minimal, SST, daily, DHW, temperature
  5. [5] § Materials and Methods › Statistical Analyses ↔ Scripts/02_Genus_level_analyses_Fig2_Fig4_FigS4.R, lines 188–228 · score 0.60 · collection date, tracked colonies, logistic, regressions, symbiotic, ASV
  6. [6] § Materials and Methods › Clustering and Classifying Symbiodiniaceae Sequence Data ↔ Scripts/03_Classifying_symbionts_from_sequence_data_Fig3_FigS3.R, lines 543–595 · score 0.57 · amplicon sequence variants, classify, unifrac, distances, ASVs, taxa
  7. [7] § Materials and Methods › Study Site ↔ Scripts/02_Genus_level_analyses_Fig2_Fig4_FigS4.R, lines 290–350 · score 0.57 · environmental metrics, island, nitrate, nitrite, leeward, windward
  8. [8] § Results › No Recovery of Pre‐Heatwave Cladocopium Taxa ↔ Scripts/03_Classifying_symbionts_from_sequence_data_Fig3_FigS3.R, lines 684–722 · score 0.57 · C50a, C15h, C116, profiles, ASVs, sequence
  9. [9] § Materials and Methods › Clustering and Classifying Symbiodiniaceae Sequence Data ↔ Scripts/02_Genus_level_analyses_Fig2_Fig4_FigS4.R, lines 1–47 · score 0.54 · symbiont genera, S4, symbiont genus, RRA, background, 90 %

Paper

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The authors' code

R · 384 lines · 21 KB · no license · 4 matches

  1. library(ggplot2)
  2. library(tibble)
  3. library(tidyr)
  4. library(tidyverse)
  5. library(plyr)
  6. library(dplyr)
  7. library(vegan)
  8. library(glmmTMB)
  9. library(arm)
  10. library(nnet)
  11. library(gamlss)
  13. ######################################################
  14. #Summarising assemblages to symbiont genera, Figure S4
  15. ######################################################
  16. asv_genus_props <- read.csv('Data/Symbiont_ASV_genus_proportions.csv')
  18. #Symbiont genus proportion histogram for Fig S3
  19. asv_genus_props_long<-asv_genus_props %>%
  20. pivot_longer(c(C_prop, D_prop, A_prop),names_to = 'genus', values_to = 'proportion')
  21. asv_genus_props_long$genus<-revalue(asv_genus_props_long$genus, c("C_prop"='Cladocopium',"D_prop"='Durusdinium',"A_prop"='Symbiodinium'))
  23. ggplot(filter(asv_genus_props_long,asv_genus_props_long$proportion>0))+
  24. geom_histogram(position='stack', aes(x=proportion, fill=genus), bins = 50)+
  25. theme_classic(base_size = 13)+
  26. scale_fill_manual(values=c('#6D9B8F','#C1260E','#FFD700'),name='')+
  27. labs(x='Genus proportions >1%',y='n(samples)')+
  28. theme(legend.position = 'inside', legend.position.inside = c(0.5,0.4))+
  29. scale_x_continuous(expand=c(0.01,0.01), n.breaks = 10)+
  30. scale_y_continuous(expand=c(0.01,0.01))+
  31. geom_vline(xintercept = 0.1,linetype='dashed')+
  32. geom_vline(xintercept = 0.9,linetype='dashed')+
  33. annotate("text", x = 0.05, y = 280, label = "Background", size = 3.5,alpha=0.8, angle=90)+
  34. annotate("text", x = 0.51, y = 280, label = "Co-dominant", size = 3.5,alpha=0.8, angle=90)+
  35. annotate("text", x = 0.95, y = 280, label = "Dominant", size = 3.5,alpha=0.8, angle=90)
  37. ggsave('Figures/FigS4_genus_rra.pdf')
  39. ######################################################
  40. #Fig.2: Dominant genera and persistence of Durusdinium
  41. ######################################################
  42. asv_genus_props$map_site_name<-factor(asv_genus_props$map_site_name, levels=c('VL1', 'VL3', 'VL4','L1','M1','M2','M3','M5','H1','VH1','VH2','VH3'))
  43. asv_genus_props$disturbance_cat<-factor(asv_genus_props$disturbance_cat,levels=c('Very Low','Low','Medium','High', 'Very High'))
  44. asv_genus_props$dom_genus<-factor(asv_genus_props$dom_genus, levels=c('C', 'D', 'CD','AD'))
  45. asv_genus_props$exped_class<-paste(asv_genus_props$expedition)
  46. asv_genus_props$exped_class<-revalue(asv_genus_props$exped_class, c('2014'='2014-2015 (i-iv)','2015a'='2014-2015 (i-iv)','2015b'='2014-2015 (i-iv)','2015c'='2014-2015 (i-iv)', '2016a'='2016-2017 (v-vii)', '2016b'='2016-2017 (v-vii)','2017'='2016-2017 (v-vii)', '2018'='2018-2019 (viii-ix)','2019'='2018-2019 (viii-ix)','2023b'='2023 (x)'))
  47. asv_genus_props$exped_class<-factor(asv_genus_props$exped_class, levels=c('2014-2015 (i-iv)', '2016-2017 (v-vii)', '2018-2019 (viii-ix)','2023 (x)'))
  49. # UpSet plot of symbionts hosted by platygyra by expedition
  50. # Sum the frequency of genera present by disturbance and expedition grouping
  51. genus_exp_disturb_df <- asv_genus_props %>%
  52. group_by(exped_class, disturbance_cat, dom_genus) %>%
  53. reframe(n_obs = n())
  54. # Sum samples collected in each expedition grouping
  55. n_samples_by_group <- genus_exp_disturb_df %>%
  56. group_by(exped_class) %>%
  57. dplyr::summarize(sum_n_obs = sum(n_obs))
  58. # Convert sums to percentage of samples in expedition grouping
  59. genus_exp_disturb_df <- genus_exp_disturb_df %>%
  60. left_join(n_samples_by_group) %>%
  61. mutate(prop_n_obs = (n_obs / sum_n_obs) * 100)
  63. # construct dots of the upset plot
  64. domgenus_dot_df <- data.frame(genera_present = c('C', 'D', 'CD', 'AD')) %>%
  65. mutate(genera_present = factor(genera_present, levels = rev(c('C', 'D', 'CD', 'AD')))) %>%
  66. mutate(`25` = ifelse(grepl('C',genera_present), 'Y', '-'),
  67. `50` = ifelse(grepl('D',genera_present), 'Y', '-'),
  68. `75` = ifelse(grepl('A',genera_present), 'Y', '-')) %>%
  69. pivot_longer(-genera_present, values_to = 'Present', names_to = 'Genus') %>%
  70. filter(Present == 'Y') %>%
  71. mutate(expedition_class = '0_a_UpSet') %>%
  72. mutate(Genus = as.numeric(Genus))
  74. # plots groupings
  75. ggplot() +
  76. geom_line(data = domgenus_dot_df,
  77. aes(x = Genus, y = genera_present, group = genera_present),
  78. size = 3, colour = 'grey40') +
  79. geom_point(data = domgenus_dot_df,
  80. aes(x = Genus, y = genera_present),
  81. shape = 21, size = 5, colour = 'white', fill = 'black')+
  82. xlab('')+ylab('')+
  83. theme_void()+
  84. theme(axis.text.x = element_blank(),axis.text.y=element_blank(), axis.ticks.y = element_blank(),axis.ticks.x = element_blank())
  86. ggsave('Figures/Fig2_clade_groups.pdf', width = 50, height = 50, units = 'mm')
  88. genus_exp_disturb_df$dom_genus<-factor(genus_exp_disturb_df$dom_genus, levels=c('AD','CD','D','C'))
  90. ggplot()+
  91. geom_col(data = genus_exp_disturb_df,
  92. aes(x = prop_n_obs, y = dom_genus, fill = disturbance_cat)) +
  93. facet_grid(. ~ exped_class) +
  94. geom_hline(yintercept = c(0.5,1.5,2.5,3.5,4.5,5.5,6.5),
  95. colour = 'grey78') +
  96. theme_classic(base_size = 13)+
  97. scale_fill_manual(values = c('#01655f','#5db2ab','#c4e9e2','#d7b364','#8e520a'), name='Human disturbance') +
  98. ylab('') +
  99. xlab('Samples (%)') +
  100. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),axis.text.y=element_blank(), axis.ticks.y = element_blank())+
  101. scale_x_continuous(expand=c(0.03,0),limits=c(0,100))+
  102. scale_y_discrete(expand=c(0,0))
  104. ggsave('Figures/Fig2_clade_barplots.pdf', width = 180, height = 80, units = 'mm')
  106. #test the effects of date (2014 to 2023, rescaled) and continuous disturbance (rescaled) on binary dominance of D 10%.
  108. asv_genus_props$collection_date<-as.Date(as.character(asv_genus_props$collection_date),format='%Y%m%d')
  109. asv_genus_props$disturbance_rescaled<-scale(asv_genus_props$cont_disturbance, center = TRUE, scale = TRUE)
  110. asv_genus_props$collection_date_rescaled<-scale(as.numeric(asv_genus_props$collection_date),center=T, scale=T)
  111. asv_genus_props_after<-filter(asv_genus_props, asv_genus_props$before_after=='After')
  112. start_date <- as.Date("2014-08-01")
  113. end_date <- as.Date("2023-09-01")
  114. # Generate the sequence of dates with 1-month intervals using lubridate
  115. date_sequence <- seq.Date(from = start_date, to = end_date, by = "month")
  117. new_data <- expand.grid(
  118. cont_disturbance = seq(from=0, to=7500, by=400),
  119. exped_class = levels(asv_genus_props$exped_class))
  120. logistic_Ddom <- glm(binary_Ddom ~ cont_disturbance * exped_class,
  121. data = asv_genus_props,
  122. family = binomial,
  123. na.action = na.omit)
  124. predictions <- predict(logistic_Ddom, newdata = new_data, type = "response", se.fit = TRUE)
  125. new_data$predicted_Ddom<-predictions$fit
  126. new_data$se<-predictions$se.fit
  127. new_data$upperci <- new_data$predicted_Ddom + 1.96*(new_data$se)
  128. new_data$lowerci <- new_data$predicted_Ddom - 1.96*(new_data$se)
  129. new_data$upperci[new_data$upperci>1]<-1
  130. new_data$lowerci[new_data$lowerci<0]<-0
  132. ggplot() +
  133. theme_classic(base_size = 13)+
  134. geom_ribbon(data=new_data, aes(ymin=lowerci, ymax=upperci,x=cont_disturbance),alpha=0.2) +
  135. geom_line(data=new_data,aes(y=predicted_Ddom, x=cont_disturbance))+
  136. geom_jitter(data=asv_genus_props, aes(y=binary_Ddom, x=cont_disturbance), colour='darkgrey',width = 300, height =0, alpha=0.5, shape=1)+
  137. ylab("Durusdinium over 10%")+
  138. xlab("Local Disturbance")+
  139. facet_grid(. ~ exped_class) +
  140. theme(axis.text.x = element_text(angle=90), strip.text = element_blank())+
  141. scale_y_continuous(breaks = c(0.00, 0.5, 1.00), expand = c(0.02,0.06))
  143. ggsave('Figures/Fig2_clade_logres.pdf', width = 150, height = 80, units = 'mm')
  145. #effect of continuous disturbance on binary Ddom
  146. logistic_Ddom201415 <- glm(binary_Ddom ~ disturbance_rescaled,
  147. data = filter(asv_genus_props, asv_genus_props$exped_class=='2014-2015 (i-iv)'),
  148. family = binomial,
  149. na.action = na.omit)
  150. summary(logistic_Ddom201415) # z=5.946, p=2.74e-09 ***
  152. logistic_Ddom201617 <- glm(binary_Ddom ~ disturbance_rescaled,
  153. data = filter(asv_genus_props, asv_genus_props$exped_class=='2016-2017 (v-vii)'),
  154. family = binomial,
  155. na.action = na.omit)
  156. summary(logistic_Ddom201617) # z=0.005, p=0.996
  158. logistic_Ddom201819 <- glm(binary_Ddom ~ disturbance_rescaled,
  159. data = filter(asv_genus_props, asv_genus_props$exped_class=='2018-2019 (viii-ix)'),
  160. family = binomial,
  161. na.action = na.omit)
  162. summary(logistic_Ddom201819) # z=0.005, p=0.996
  164. logistic_Ddom2023 <- glm(binary_Ddom ~ disturbance_rescaled,
  165. data = filter(asv_genus_props, asv_genus_props$exped_class=='2023 (x)'),
  166. family = binomial,
  167. na.action = na.omit)
  168. summary(logistic_Ddom2023) # z=0.42, p=0.675
  170. logistic_Ddomafterexpeds <- glm(binary_Ddom ~ rescale(collection_date),
  171. data = filter(asv_genus_props, asv_genus_props$exped_class!='2014-2015 (i-iv)'),
  172. family = binomial,
  173. na.action = na.omit)
  174. summary(logistic_Ddomafterexpeds) # z=1.251, p=0.211
  176. nobeforelowdist<-filter(asv_genus_props, asv_genus_props$exped_class!='2014-2015 (i-iv)'|asv_genus_props$cont_disturbance>2000)
  177. logistic_Ddombefhighafterexpeds <- glm(binary_Ddom ~ rescale(collection_date) + rescale(cont_disturbance),
  178. data = nobeforelowdist,
  179. family = binomial,
  180. na.action = na.omit)
  181. summary(logistic_Ddombefhighafterexpeds) # collection date: z=1.702, p=0.0888, disturbance: z=-0.189, p=0.8501
  183. sample_size_tab<-asv_genus_props %>%
  184. group_by(exped_class)%>%
  185. dplyr::summarise(ncols=length(unique(coral_tag)),nsamps=length(unique(sample_name)))
  186. sample_size_tab
  188. #Tracked colonies D persistence
  189. ad_tracked<-read.csv('Data/AD_tracked_colony_transitions.csv')
  190. ad_tracked$aft.collection_date<-as.Date(as.character(ad_tracked$aft.collection_date),format='%Y%m%d')
  191. logistic_Ddom_tracked<- glm(retained_ddom_binary ~ rescale(aft.collection_date) + rescale(cont_disturbance),
  192. data=ad_tracked,
  193. family=binomial,
  194. na.action=na.omit)
  195. logistic_Ddom_tracked
  196. #model will not converge because all colonies retained their Durusdinium (variance =0).
  198. ##############################################
  199. #Fig.4a, Fig.4b: Flux between symbiotic states
  200. ##############################################
  202. ad_tracked$strict_timespan<-as.factor(ad_tracked$strict_timespan)
  203. start_date <- as.Date("2016-11-01")
  204. end_date <- as.Date("2023-09-01")
  205. # Generate the sequence of dates with 1-month intervals using lubridate
  206. date_sequence <- seq.Date(from = start_date, to = end_date, by = "month")
  207. ad_tracked$disturbance_cat<-factor(ad_tracked$disturbance_cat, levels=c('Very Low','Low','Medium','High','Very High'))
  208. ad_tracked$X2_timespans<-as.factor(ad_tracked$X2_timespans)
  209. ad_tracked$timespan<-as.factor(ad_tracked$timespan)
  210. ad_tracked$transition_AD<-factor(ad_tracked$transition_AD, levels=c('D_D','AD_AD','AD_D','D_AD'))
  211. ad_tracked$rescaled_A_change_asv <- (ad_tracked$A_change_asv + 1) / 2 #rescale between 0 and 1 for binomial & beta regressions
  213. #effect of continuous disturbance
  214. library(betareg)
  216. AD1619<-filter(ad_tracked, ad_tracked$X2_timespans!='2019-23')
  217. beta_change1619 <- betareg(rescaled_A_change_asv ~ rescale(cont_disturbance),
  218. data =AD1619)
  219. summary(beta_change1619)
  220. AD1923<-filter(ad_tracked, ad_tracked$timespan=='Jul19-Sep23')
  221. beta_change1923 <- betareg(rescaled_A_change_asv ~ rescale(cont_disturbance),
  222. data = AD1923)
  223. summary(beta_change1923)
  225. p_values_1619 <- summary(beta_change1619)$coefficients$mean[, "Pr(>|z|)"]
  226. p_values_1923 <- summary(beta_change1923)$coefficients$mean[, "Pr(>|z|)"]
  227. p_values_combined <- c(p_values_1619, p_values_1923)
  228. p.adjust(p_values_combined, method = "bonferroni") #1619: z=−2.636,p Bonferroni= 0.0336, 1923: z=2.70, p Bonferroni=0.0276
  230. #predict models for plotting
  231. new_AD1619 <- data.frame(cont_disturbance = seq(min(AD1619$cont_disturbance),
  232. max(AD1619$cont_disturbance), length.out = 184))
  233. # Compute model matrix (auto-applies rescale)
  234. new_AD1619$model_matrix <- model.matrix(~ rescale(cont_disturbance), data = new_AD1619)
  235. # Extract coefficients and variance-covariance matrix
  236. coefs_1619 <- coef(beta_change1619)[1:2]
  237. vcov_1619 <- vcov(beta_change1619)[1:2, 1:2]
  238. # Compute predicted values
  239. new_AD1619$predicted <- plogis(new_AD1619$model_matrix %*% coefs_1619) # Logit transformation
  240. # Compute standard errors
  241. se_AD1619 <- sqrt(diag(new_AD1619$model_matrix %*% vcov_1619 %*% t(new_AD1619$model_matrix)))
  242. # Compute shading range (mean ± 1 SE)
  243. new_AD1619$lower <- plogis(new_AD1619$model_matrix %*% coefs_1619 - se_AD1619)
  244. new_AD1619$upper <- plogis(new_AD1619$model_matrix %*% coefs_1619 + se_AD1619)
  245. # Ensure shading stays in (0,1) range of beta regression
  246. new_AD1619$lower <- pmax(0, new_AD1619$lower)
  247. new_AD1619$upper <- pmin(1, new_AD1619$upper)
  248. new_AD1619$X2_timespans<-c('2016-17, 2017-18 or 2018-19')
  250. new_AD1923 <- data.frame(cont_disturbance = seq(min(AD1923$cont_disturbance),
  251. max(AD1923$cont_disturbance), length.out = 59))
  252. # Compute model matrix (auto-applies rescale)
  253. new_AD1923$model_matrix <- model.matrix(~ rescale(cont_disturbance), data = new_AD1923)
  254. # Extract coefficients and variance-covariance matrix
  255. coefs_1923 <- coef(beta_change1923)[1:2]
  256. vcov_1923 <- vcov(beta_change1923)[1:2, 1:2]
  257. # Compute predicted values
  258. new_AD1923$predicted <- plogis(new_AD1923$model_matrix %*% coefs_1923) # Logit transformation
  259. # Compute standard errors
  260. se_AD1923 <- sqrt(diag(new_AD1923$model_matrix %*% vcov_1923 %*% t(new_AD1923$model_matrix)))
  261. # Compute shading range (mean ± 1 SE)
  262. new_AD1923$lower <- plogis(new_AD1923$model_matrix %*% coefs_1923 - se_AD1923)
  263. new_AD1923$upper <- plogis(new_AD1923$model_matrix %*% coefs_1923 + se_AD1923)
  264. # Ensure shading stays in (0,1) range of beta regression
  265. new_AD1923$lower <- pmax(0, new_AD1923$lower)
  266. new_AD1923$upper <- pmin(1, new_AD1923$upper)
  267. new_AD1923$X2_timespans<-c('2019-23')
  268. AD_betareg<-rbind(new_AD1619,new_AD1923)
  270. ggplot() +
  271. theme_test(base_size = 13)+
  272. geom_line(data=AD_betareg, aes(y=predicted, x=cont_disturbance),colour='black', size=0.3)+
  273. geom_ribbon(data=AD_betareg, aes(ymax=upper, ymin=lower, x=cont_disturbance),
  274. fill='grey',alpha=0.4)+
  275. geom_jitter(data=ad_tracked, aes(y=rescaled_A_change_asv, x=cont_disturbance),
  276. colour='grey60', width = 300, height =0, size=2)+
  277. ylab("Change in Symbiodinium")+
  278. xlab("Human Disturbance")+
  279. facet_grid(. ~ X2_timespans) +
  280. theme(axis.text.x = element_text(angle=90), legend.position = 'none')+
  281. scale_y_continuous(breaks=c(0.25,0.5,0.75), labels=c('-0.5','0.0','+0.5'))
  283. ggsave('Figures/A_by_disturbance.pdf', units = 'mm', width=105, height=90)
  285. ad_tracked$coral_order<-as.factor(ad_tracked$coral_order)
  286. ad_tracked$coral_order<-factor(ad_tracked$coral_order, levels=rev(levels(ad_tracked$coral_order)))
  287. ad_tracked$timespan<-factor(ad_tracked$timespan, levels=c('Mar16-Nov16','Nov16-Jul17','Jul17-Jul18','Jul18-Jul19','Jul19-Sep23'))
  290. ######################################################
  291. #Fig.4c: Environmental metric effects on Symbiodinium
  292. ######################################################
  293. site_metadata<-read.csv('Data/Platygyra_site_metadata.csv')
  294. ad_tracked<-dplyr::left_join(ad_tracked,site_metadata,by='map_site_name', relationship = 'many-to-one')
  295. ad_tracked$coral_tag<-as.factor(ad_tracked$coral_tag)
  296. ad_tracked$map_site_name<-as.factor(ad_tracked$map_site_name)
  297. ad_tracked$cont_disturbance_rescaled<-rescale(ad_tracked$cont_disturbance)
  298. ad_tracked$site_meanafter_nitratenitrite_rescaled<-rescale(ad_tracked$site_meanafter_nitratenitrite)
  299. ad_tracked$site_meanafter_vis_rescaled<-rescale(ad_tracked$site_meanafter_vis)
  300. ad_tracked$meanafter_chla_rescaled<-rescale(ad_tracked$meanafter_chla)
  301. ad_tracked$disturbance_cat<-factor(ad_tracked$disturbance_cat, levels=c('Very Low','Low','Medium','High','Very High'))
  302. ad_tracked$island_side<-factor(ad_tracked$island_side, levels=c('leeward', 'windward'))
  305. ad_trackedmod1<-gamlss(A_change_asv~ X2_timespans *site_meanafter_nitratenitrite_rescaled+ X2_timespans*site_meanafter_vis_rescaled+ X2_timespans*meanafter_chla_rescaled +random(coral_tag)+ random(map_site_name)+random(disturbance_cat), control = gamlss.control(trace = TRUE, lambda = "auto"), data= ad_tracked)
  306. summary(ad_trackedmod1)
  308. ad_trackedmod2<-glmmTMB(A_change_asv~ X2_timespans*site_meanafter_nitratenitrite_rescaled*island_side+ X2_timespans*site_meanafter_vis_rescaled*island_side+ X2_timespans*meanafter_chla_rescaled*island_side +(1|disturbance_cat)+(1|coral_tag)+(1|map_site_name), data= ad_tracked)
  309. summary(ad_trackedmod2)
  311. ad_trackedmod3<-glmmTMB(A_change_asv~ X2_timespans*site_meanafter_nitratenitrite_rescaled+ X2_timespans*site_meanafter_vis_rescaled+ X2_timespans*meanafter_chla_rescaled+ X2_timespans*island_side +(1|disturbance_cat), data= ad_tracked)
  312. summary(ad_trackedmod3)
  314. ad_trackedmod4<-glmmTMB(A_change_asv~ X2_timespans*site_meanafter_nitratenitrite_rescaled+ X2_timespans*site_meanafter_vis_rescaled+ X2_timespans*meanafter_chla_rescaled + (1|island_side)+(1|disturbance_cat), data= ad_tracked)
  315. summary(ad_trackedmod4)
  317. #ad_trackedmod2 is superior to other models in terms of AIC and variance explained
  318. #building on this, using a beta distribution improves model fit compared to a gausssina distribution
  319. ad_betareg<-glmmTMB(rescaled_A_change_asv~ X2_timespans*site_meanafter_nitratenitrite_rescaled*island_side+ X2_timespans*site_meanafter_vis_rescaled*island_side+ X2_timespans*meanafter_chla_rescaled*island_side +(1|disturbance_cat)+(1|coral_tag)+(1|map_site_name),family = beta_family(), data= ad_tracked)
  320. summary(ad_betareg)
  321. #random effects explain very little variance
  323. # Output model coefficients
  325. ad_betareg.coef <- data.frame(confint(ad_betareg, full = TRUE))
  326. names(ad_betareg.coef)[names(ad_betareg.coef) == "X2.5.."] <- "LowerCI"
  327. names(ad_betareg.coef)[names(ad_betareg.coef) == "X97.5.."] <- "UpperCI"
  329. ad_betareg.coef <- ad_betareg.coef[c(3:8,10,12), ]
  330. rownames(ad_betareg.coef)[1] <- "1619_nitratenitrite"
  331. rownames(ad_betareg.coef)[2] <- "1619_exposure"
  332. rownames(ad_betareg.coef)[3] <- "1619_vis"
  333. rownames(ad_betareg.coef)[4] <- "1619_chla"
  334. rownames(ad_betareg.coef)[5] <- "1923_nitratenitrite"
  335. rownames(ad_betareg.coef)[6] <- "1923_exposure"
  336. rownames(ad_betareg.coef)[7] <- "1923_vis"
  337. rownames(ad_betareg.coef)[8] <- "1923_chla"
  339. ad_betareg.coef$variable <- c('nitratenitrite','exposure','vis','chla','nitratenitrite','exposure','vis','chla')
  340. ad_betareg.coef$variable <- factor(ad_betareg.coef$variable, levels=c('vis','nitratenitrite','chla','exposure'))
  341. ad_betareg.coef$Time<-c('2016-17, 2017-18 or 2018-19','2016-17, 2017-18 or 2018-19','2016-17, 2017-18 or 2018-19','2016-17, 2017-18 or 2018-19','2019-23','2019-23','2019-23','2019-23')
  344. ggplot(ad_betareg.coef, aes(x = variable, y = Estimate)) +
  345. geom_hline(yintercept = 0, color = gray(1/2), lty = 2)+
  346. geom_pointrange(aes(x = variable, y = Estimate, ymin = LowerCI, ymax = UpperCI, color=variable,fill=variable),position = position_dodge(width = 1/2), shape = 21, fatten = 2, size = 1)+
  347. facet_wrap(~Time)+ scale_color_manual(values=c('black','grey50','grey50','grey50'))+scale_fill_manual(values=c('black','grey50','grey50','grey50'))+theme_classic()+
  348. theme(panel.grid.major.y = element_line(),axis.text.x = element_text(angle=45,hjust = 1), strip.background = element_rect(fill='grey85',linewidth = 0.5),panel.border=element_rect(fill=NA,colour='black'),
  349. axis.line = element_line(linewidth = 0.2) ,legend.position = 'none')+labs(x='',y='Change in proportion A in re-samples')+
  350. scale_x_discrete(labels=c('Visibility','Nitrate & nitrite','Chlorophyll-a','Exposure'))
  353. #####################################################################
  354. # Plots RRA Symbiodinium by expedition for tracked colonies
  355. #####################################################################
  357. # Lists tracked colonies
  358. ad_tracked<-read.csv('Data/AD_tracked_colony_transitions.csv')
  359. length(unique(ad_tracked$coral_tag))
  361. library(tidyverse)
  363. # organizes tracked colonies for plotting as a heatmap by colony and expedition
  364. repeat_samples <- asv_genus_props %>%
  365. filter(coral_tag %in% ad_tracked$coral_tag) %>%
  366. mutate(disturbance_cat = factor(disturbance_cat, levels = c("Very High", "High", "Medium", "Low", "Very Low"))) %>%
  367. arrange(disturbance_cat) %>%
  368. mutate(plot_code = paste(disturbance_cat, coral_tag)) %>%
  369. mutate(plot_code = factor(plot_code, levels = unique(plot_code)))
  371. # Plots heatmap
  372. ggplot() +
  373. geom_tile(data = repeat_samples,
  374. aes(expedition, plot_code, fill = A_prop)) +
  375. theme_test(base_size = 8) +
  376. scale_fill_gradientn(colours = c("#C1260E", "#FFD700"), limits = c(0.01, max(repeat_samples$A_prop)), na.value = "grey90") +
  377. geom_vline(xintercept = 4.5) +
  378. theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
  380. ggsave('Figures/A_dynamism.pdf', units = 'in', width=3, height=7)
  382. #####################################################################

02_Genus_level_analyses_Fig2_Fig4_FigS4.R at commit e9232cb, no license · at the source

Overview

Authors: D Buzzoni1, A Van Nynatten1, R Cunning2, J K Baum1
ORCID iDs: D Buzzoni, R Cunning, J K Baum
  1. Department of Biology, University of Victoria, Victoria, British Columbia, Canada
  2. Conservation Research Department, John G. Shedd Aquarium, Chicago, Illinois, USA
Institutions: University of Victoria (Canada); Shedd Aquarium (United States)
Journal: Global change biology, volume 32, issue 3, article e70818
Dates: received 8 August 2025; accepted 20 February 2026; published online 24 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1111/gcb.70818 · PMID 41877353 · PMCID PMC13013739 · OpenAlex W7140181797
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Connectivity, Statistics, Machine learning
Keywords: climate change, coral bleaching, human disturbance, multiple stressors, recovery trajectory, secondary succession, Symbiodiniaceae, symbiont shuffling
MeSH: Anthozoa*, Climate Change*, Dinoflagellida*, Hot Temperature*, Symbiosis*, Animals, Coral Reefs (* major topic)
Topic: Coral and Marine Ecosystems Studies (Ecology, Environmental Science), according to OpenAlex
Funding: Pew Charitable Trusts; Rufford Foundation; The Pew Charitable Trusts, Pew Fellowship in Marine Conservation; Government of Canada's New Frontiers in Research Fund (NFRF), BIOSCAN (NFRFT-2020-00073); Natural Sciences and Engineering Research Council of Canada; Rufford Maurice Laing Foundation; National Geographic Society (NGS‐146R‐18, NGS‐63112R‐19); The Leverhulme Trust, Study Abroad Studentship (SAS-2021-047); Canada Foundation for Innovation (CFI) Leaders Opportunity Fund; David and Lucile Packard Foundation; British Columbia Knowledge Development Fund; National Geographic Society, Committee for Research and Exploration (NGS-146R-18, NGS-63112R-19); National Science Foundation (NSF) RAPID (OCE-1446402); Leverhulme Trust (SAS‐2021‐047); University of Victoria
Citations: cited by 1 paper (Europe PMC); 104 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

baumlab/Buzzoni_et_al_2026_Platygyra

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: e9232cb13fbd79e61b6cee6fbdae3aa0fd7b6330, 26 February 2026
Languages: R (4)
Size: 46 files, 4 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), ggplot2 (3 files), patchwork (2 files), glmmTMB (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
5 files
At the source: github.com/baumlab/Buzzoni_et_al_2026_Platygyra

Zenodo 18792037

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), ggplot2 (3 files), patchwork (2 files), glmmTMB (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.18792037

nitschkematthew/Symbiodatabaceae

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State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 2320cbdda104c451f837ab62869941574c2d2071, 13 July 2020
Languages: R (5)
Size: 20 files, 5 scripts
Software Heritage: not archived
Found in: the references
Holds: README, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (4 files), reshape2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/nitschkematthew/Symbiodatabaceae

The paper's code and data availability statement is in the Data section.

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  • 3 repositories 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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Code and data availability statement

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Read it in the paper: doi.org/10.1111/gcb.70818.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 8 keywords, 7 MeSH terms, 15 funders, 93 references.

Cite

This paper

Buzzoni, D., Van Nynatten, A., Cunning, R., & Baum, J. K. (2026). Persistent Legacy Effects of Marine Heatwaves on Coral Symbioses. Global change biology, 32(3), e70818. https://doi.org/10.1111/gcb.70818

BibTeX

@article{buzzoni2026persistent,
author = {Buzzoni, D and Van Nynatten, A and Cunning, R and Baum, J K},
title = {{Persistent Legacy Effects of Marine Heatwaves on Coral Symbioses}},
journal = {Global change biology},
year = {2026},
month = mar,
volume = {32},
number = {3},
pages = {e70818},
publisher = {Wiley},
issn = {1354-1013},
doi = {10.1111/gcb.70818},
url = {https://doi.org/10.1111/gcb.70818},
pmid = {41877353},
pmcid = {PMC13013739}
}

RIS

TY - JOUR
AU - Buzzoni, D
AU - Van Nynatten, A
AU - Cunning, R
AU - Baum, J K
TI - Persistent Legacy Effects of Marine Heatwaves on Coral Symbioses
T2 - Global change biology
J2 - Glob Chang Biol
PY - 2026
DA - 2026/03/01
VL - 32
IS - 3
SP - e70818
SN - 1354-1013
PB - Wiley
DO - 10.1111/gcb.70818
UR - https://doi.org/10.1111/gcb.70818
LA - en
ER -

CSL-JSON

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"title": "Persistent Legacy Effects of Marine Heatwaves on Coral Symbioses",
"container-title": "Global change biology",
"author": [
{
"family": "Buzzoni",
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"given": "J K"
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"container-title-short": "Glob Chang Biol",
"volume": "32",
"issue": "3",
"page": "e70818",
"DOI": "10.1111/gcb.70818",
"PMID": "41877353",
"PMCID": "PMC13013739",
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"language": "en",
"issued": {
"date-parts": [
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2026,
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1
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}
}

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