Plot of survival curves according to hypothesis

exploration/GR3D_Rdescription/GR3Dfunction.R

@@ -191,7 +191,7 @@ spawnerSurvivalPostReproductionTempRef <- function(Triver,Tref, coeffa, coeffb){
 }
 
 #Dome-shape curve with temperature effect 
-spawnerSurvivalPostReproductionWithBellCurve <- function(Triver, Tmin, Topt, Tmax, coeffa, coeffb){
+spawnerSurvivalPostReproductionWithBellCurve <- function(Triver, Tmin, Topt, Tmax, coeffb){
   
   #P1: 
   #SpRiverPostSpawn = probSurvAfterRepro/(probSurvAfterRepro + (1 - probSurvAfterRepro)*exp(-coeffLogit*(TemperatureEffect(Triver, Tmin, Topt, Tmax))))

exploration/NEA_calibration_offline/1-Calibration_Thermal_Curves.Rmd

@@ -102,12 +102,15 @@ quantile_spring = nea_presence_temp%>%
   summarise(Tmin = min(spring_river_temperature),
             Q1 = quantile(spring_river_temperature, 0.01),
             Q5 = quantile(spring_river_temperature, 0.05),
+            Med = median(spring_river_temperature),
+            Mean = mean(spring_river_temperature),
             Q95 = quantile(spring_river_temperature, 0.95),
             Q99 = quantile(spring_river_temperature, 0.99),
             Tmax = max(spring_river_temperature),.groups = 'drop')  
   #flextable() %>% 
   #autofit()
 
+#----------------------------------------------------------------------
 #see how many times and which watershed had survival below each quantile 
 
 nea_presence_temp %>% 
@@ -131,14 +134,13 @@ nea_presence_temp %>%
             freq_out = ((n_occurence_out/n_tot)*100),.groups ='drop') %>% 
   distinct()
   #summarize(n = n_distinct(basin_name))
-
-
 #-----------------  
 nea_presence_temp %>% 
   group_by(obs_1900_1950) %>% 
-  filter(spring_river_temperature > quantile(spring_river_temperature, 0.95)) %>% 
-  group_by(year) %>% 
-  summarize(n = n_distinct(basin_name))
+  filter(spring_river_temperature > quantile(spring_river_temperature, 0.99)) %>% 
+  group_by(basin_name) %>% 
+  summarize(n = n_distinct(year)) %>% 
+  inner_join(nea_riverBasinFeatures %>% select(basin_name, lat_outlet), by = 'basin_name')
   #arrange(desc(n))
 
 #-------------------
@@ -153,6 +155,25 @@ nea_presence_temp %>%
   summarize(n = n())
   #arrange(desc(n))
 
+#----------------------
+nea_presence_temp %>% 
+  filter(basin_name == 'St. Johns') %>% 
+  ggplot()+
+  geom_histogram(aes(x = spring_river_temperature > quantile_spring$Q99), position = 'identity',binwidth = 0.2, stat ='count')
+
+#-------------------------------------------------------------
+#Do the same for spring and summer temperature combined 
+#--------------------------------------------------------------
+quantile_ss = nea_presence_temp%>% 
+  group_by(obs_1900_1950) %>% 
+  summarise(Tmin = min(c(spring_river_temperature, summer_river_temperature)),
+            Q1 = quantile(c(spring_river_temperature,summer_river_temperature), 0.01),
+            Q5 = quantile(c(spring_river_temperature,summer_river_temperature), 0.05),
+            Med = median(c(spring_river_temperature,summer_river_temperature)),
+            Mean = mean(c(spring_river_temperature,summer_river_temperature)),
+            Q95 = quantile(c(spring_river_temperature,summer_river_temperature), 0.95),
+            Q99 = quantile(c(spring_river_temperature,summer_river_temperature), 0.99),
+            Tmax = max(c(spring_river_temperature,summer_river_temperature)),.groups = 'drop')  
 
 
 ```
@@ -447,7 +468,7 @@ ToptSurv = TminSurv + lambda *(TmaxSurv - TminSurv)
 
 
 ggp_A = ggplot() +
-  geom_line(aes(temperature, temperatureEffect (temperature, res_0A$par['TminSurv'], 
+  geom_line(aes(temperature, temperatureEffect(temperature, res_0A$par['TminSurv'], 
                                                 Topt = ToptSurv,
                                                 res_0A$par['TmaxSurv'])))+
    geom_vline(xintercept = c(8,26))
@@ -468,7 +489,7 @@ ToptSurv = TminSurv + lambda *(TmaxSurv - TminSurv)
 
 
 ggp_quant = ggplot() +
-  geom_line(aes(temperature, temperatureEffect (temperature, res_0A_quantile$par['TminSurv'], 
+  geom_line(aes(temperature, temperatureEffect(temperature, res_0A_quantile$par['TminSurv'], 
                                                 Topt = ToptSurv,
                                                 res_0A_quantile$par['TmaxSurv'])))+
    geom_vline(xintercept = c(8,26))
@@ -628,6 +649,8 @@ quantile_sum = nea_presence_temp%>%
   summarise(Tmin = min(summer_river_temperature),
             Q1 = quantile(summer_river_temperature, 0.01),
             Q5 = quantile(summer_river_temperature, 0.05),
+            Med = median(summer_river_temperature),
+            Mean = mean(summer_river_temperature),
             Q95 = quantile(summer_river_temperature, 0.95),
             Q99 = quantile(summer_river_temperature, 0.99),
             Tmax = max(summer_river_temperature),.groups = 'drop')