Load & clean data

Sex ratios by density treatment

Both male and female emergence decline with increasing density, but female emergence drops off much more steeply. At density 120 and 240, female emergence is essentially zero as a proportion of starting larvae, while males still manage modest emergence rates.

For emergExp, we have >100% proportion emerged for Density 5 and 10. For Density 5, 8 out of the 40 replicates (20%) had >5 mosquitoes emerge (6 or 7 emerged). For density 10, 2 out of the 20 replicates (10%) had >10 mosquitoes emerge (11 or 12 emerged). HUMAN COUNTING ERROR

{r emergExpF} # #### only females + cumEmer ### # # sEmergence <- sEmergence %>% # # group_by(Jar_ID) %>% # # mutate(allcumEmer = cumsum(Num_Emerge)) %>% # # ungroup() # emerExpF <- emergExp %>% # group_by(Jar_ID) %>% # subset(Sex=="F") %>% #350 # mutate(FcumEmer= cumsum(Num_Emerge)) %>% # # Prop_emerF = (allcumEmer) %>% # ungroup() # #### proportion emerged per day per density/jar#### # emerExpF$Prop_emerF <- emerExpF$FcumEmer/emerExpF$Density #3918 # # add jars where no females emerged from for analysis purposes # sort(unique(emerExpF$Jar_ID), decreasing=F) # length(unique(emerExpF$Jar_ID)) #only 84 jars, so 8 jars w/no F emergence # # no female emergence from Jars: 2, 8, 27, 40 (density 5), 88 (density 120), & 90, 91, 92 (density 240) # missingF <- data.frame( # Jar_ID=c(2,8,27,40,88,90,91,92), # Replicate=c(2,8,27,40,4,2,3,4), # Density=c(rep(5,4), 120, rep(240,3)), # Density_Tx=as.character(c(rep("5",4),"120", rep("240",3))), # Month=c(rep("06",5),"07","06","07"), # Day=c("12", rep("18",2),"19", "21", "01","18","01"), # Exp_Day=c(15,rep(13,2),14,16, 26,13,26), # Time=c("10:18","10:25","11:31","10:22","9:02", "9:40","11:21","9:56"), # Stage=c(rep("pupae",2), rep("none left",2), rep("larvae",4)), # # Males=c(1,3,2,rep(1,3), 0, 1), # Males=0, # Females=0, # Na=0, # Num_Emerge=0, # Event=0, # allcumEmer=c(3,rep(4,2),5, 33,2,7,1), # allProp_emer=c(0.06, rep(0.08, 2), 1, 0.275, 0.00833,0.0292, 0.00417), # Sex="F", # FcumEmer=0, # Prop_emerF=0) # # # emerExpF <- emergExp %>% subset(Sex=="F") %>% # # rbind(emerExpF,missingF) %>% # # mutate(FcumEmer= cumsum(Num_Emerge), # # Prop_emerF= (cumEmer)) %>% # # ungroup() # # # emerExpF <- rbind(emerExpF,missingF) #358 # # length(unique(emerExpF$Jar_ID)) #now have all 92 jars # # save R data # # saveRDS(emerExpF, "~/Desktop/Desktop/2019Oviposition/Data/Clean/emerExpF.RData") #

{r emerExpM} # #### only males + cumEmer #### # emerExpM <- subset(emergExp, Sex=="M") %>% #650 # group_by(Jar_ID) %>% # mutate(cumEmer = cumsum(Event), # Prop_emer = cumEmer/Density) %>% # ungroup() # # add jars where no males emerged from for analysis purposes # sort(unique(emerExpM$Jar_ID), decreasing=F) # ## no NA emergence from Jars: 21, 35, and 39 (all density 5) # missingM <- data.frame( # Jar_ID=c(21,35,39), # Replicate=c(21,35,39), # Density=c(rep(5,3)), # Density_Tx=as.character(c(rep("5",3))), # Month=c("06"), # Day=c("19","22","20"), # Exp_Day=c(14,17,15), # Time=c("9:59","10:04","9:27"), # Stage=c(rep("none left",2), "larvae"), # Males=0, # Females=0, # # Females=c(3,rep(1,2)), # Na=0, # Num_Emerge=0, # Event=0, # allcumEmer=c(3,2,3), # allProp_emer=c(0.6,0.4,0.6), # cumEmer=0, # Prop_emer=0, # Sex="M") # emerExpM <- rbind(emerExpM, missingM) # # length(unique(emerExpM$Jar_ID)) #now have all 92 jars # #save R data # # saveRDS(emerExpM, "~/Desktop/Desktop/2019Oviposition/Data/Clean/emerExpM.RData") #

# Need to complete NA df

{r emerExpNA} # #### only males + cumEmer #### # emerExpNA <- subset(emergExp, Sex=="NA") %>% #38 # group_by(Jar_ID) %>% # mutate(cumEmer = cumsum(Event), # Prop_emer = cumEmer/Density) %>% # ungroup() # # add jars where no NAs emerged from for analysis purposes # sort(unique(emerExpNA$Jar_ID), decreasing=F) # ## no NA emergence from Jars: 1-5 (density5), 7-40 (density 5), 41-47 & 51-60 (density 10); 63-68 & 70 (density 20), # ## 72-74, 78, & 80 (density 30), 81 (density 60), 85 (density 120), 90, 92 (density 240) # missingNA <- data.frame( # # 81 85 90 92 # Jar_ID=c(1:5,7:47,51:60,63:68,70,72:74,78,80:81,85,90,92), # Replicate=c(1:5, 7:40, 1:7, 11:20, 3:8,10,2:4,8,10,1,1,2,4), # Density=c(rep(5,39), rep(10,17), rep(20,7),rep(30,5),60,120,rep(240,2)), # Density_Tx=as.character(c(rep("5",39), rep("10",17), rep("20",7), rep("30",5), "60", "120",rep("240",2))), # Month=c(rep("06",72)), # Day=c(rep("20",4), "23","20","23", "20","22","21",rep("19",2),"21", "20",rep("23",58)), # Exp_Day=c(15,rep(20,2), 15, 18,15,18,15,17,16,rep(14,2), 16, 15,rep(20,58)), # Time=c("9:26", "10:18", "9:08", "8:55", "10:43","9:45","10:25", "9:34", "9:22","8:33","9:16","10:34","9:07","10:15", rep("10:40",58)), # Stage=c("none left", "pupae", rep("none left",4), "pupae", rep("none left",65)), # Males=0, # Females=0, # Na=0, # Num_Emerge=0, # Event=0, # allcumEmer=c(4,3,6,5,rep(4,3), rep(6,2),rep(5,3),rep(6,2),rep(5,58)), # allProp_emer=c(0.8,0.6,1.2,1,rep(0.8,3), rep(1.2,2),rep(1,3),rep(1.2,2),1,rep(0.4,57)), # cumEmer=0, # Prop_emer=0, # Sex="NA") # emerExpNA <- rbind(emerExpNA, missingNA)#110 # #save R data # # saveRDS(emerExpNA, "~/Desktop/Desktop/2019Oviposition/Data/Clean/emerExpNA.RData") #

Sex ratio looks like 350:650 (54%); almost 50:50 but could be closer to 35:65 (F:M).

* 350 + 650 = 1000; 1000/2580 (38.7% emerged).

* 350/1290 = 27.13% of F emerged (assuming 50:50).

* 650/1290 = 50.39% of M emerged (assuming 50:50.

```{r survival}

#### try to subset last “row” of allCum aka last emergence

emerExpF\(cumEmer <- as.numeric(emerExpF\)cumEmer)

all_jars <- unique(emerExpF\(Jar_ID) #list of all Jar id in this season # female_list <- list() # for(i in 1:length(all_jars)){ # thisJar_ID <- all_jars[i] # data_subset <- filter(emerExpF, Jar_ID == thisJar_ID) %>% # filter(Prop_emer==max(Prop_emer)) %>% # filter(Exp_Day == min(Exp_Day)) # female_list[[i]] <- data_subset # } # Fsurvival <- bind_rows(female_list) %>% dplyr::select(-Event) # # sort(unique(female_df\)Jar_ID), decreasing=F)

## no female emergence from Jars: 2, 8, 27, 40 (density 5), 88 (density 120), & 90, 91, 92 (density 240)

# Save RData

# saveRDS(Fsurvival, “~/Desktop/Desktop/2019Oviposition/Data/Clean/femaleSurvival.RData”)

## males

emerExpM\(cumEmer <- as.numeric(emerExpM\)cumEmer)

all_jars <- unique(emerExpM\(Jar_ID) #list of all Jar id in this season # male_list <- list() # for(i in 1:length(all_jars)){ # thisJar_ID <- all_jars[i] # data_subset <- filter(emerExpM, Jar_ID == thisJar_ID) %>% # filter(Prop_emer==max(Prop_emer)) %>% # filter(Exp_Day == min(Exp_Day)) # male_list[[i]] <- data_subset # } # # Msurvival <- bind_rows(male_list) %>% dplyr::select(-Event) # # # sort(unique(male_df\)Jar_ID), decreasing=F)

## no Male emergence from Jars: 21, 35, and 39 (all density 5)

# Save RData

# saveRDS(Msurvival, “~/Desktop/Desktop/2019Oviposition/Data/Clean/maleSurvival.RData”)

## both M & F that emerged

allSurvival <- rbind(Fsurvival, Msurvival) %>% dplyr::select(-c(“Month”,“Day”,“Time”))

Save R Data

#saveRDS(allSurvival, “~/Desktop/Desktop/2019Oviposition/Data/Clean/allSurvival.RData”) ```