glmm: model representation problem with standardized variables

[Leprechault]

Hi Everyone!!

I standardized my input variables (ds.scale) before glmm adjustments but in the final plot, I have a problem with the real-world scale of my variables and the predicted values by model (m_6). In my plot:

I´d like the original scale of my temp and storage variables represented in my better model (m_6). What is the correct approach for this? Do not standardise my input variables, despite I lot of warmings? Some data transformation at the end? I make:

#Packages
library(lme4)
library(ggplot2)
library(ggeffects)
library(tidyverse)
library(bbmle)
library(broom)

#Open my dataset
myds<-read.csv("https://raw.githubusercontent.com/Leprechault/trash/main/ds.desenvol.csv")
str(myds)
# 'data.frame': 400 obs. of  4 variables:
#  $ temp       : num  0 0 0 0 0 0 0 0 0 0 ...
#  $ storage    : int  5 5 5 5 5 5 5 5 5 5 ...
#  $ rep        : chr  "r1" "r2" "r3" "r4" ...
#  $ development: int  0 23 22 27 24 25 24 22 0 22 ...

# Storage (days) is temporally correlated with temperature then mixed model
ds.scale<- myds %>%
  mutate(across(c(temp, storage), ~ drop(scale(.))))

# Models creation Poisson/Negative binomial
m_1 <- glmer(development ~ temp + storage +
               (1 | storage ), data = ds.scale,
                 family = "poisson")
m_2 <- glmer(development ~ poly(temp,2) + storage +
               (1 | storage ), data = ds.scale,
                 family = "poisson")  
m_3 <- glmer(development ~ poly(temp,2) + poly(storage,2) +
               (1 | storage ), data = ds.scale,
                 family = "poisson")  
m_4 <- glmer.nb(development ~ temp + storage +
               (1 | storage ), data = ds.scale)
m_5 <- glmer.nb(development ~ poly(temp,2) + storage +
               (1 | storage ), data = ds.scale)  
m_6 <- glmer.nb(development ~ poly(temp,2) + poly(storage,2) +
               (1 | storage ), data = ds.scale)  
modList <- tibble::lst(m_1,m_2,m_3,m_4,m_5,m_6)
bbmle::AICtab(modList)

#     dAIC df
# m_6  0.0 7
# m_3  1.0 6
# m_5  3.3 6
# m_2  5.0 5
# m_4 17.9 5
# m_1 21.0 4

# Plot the results for my better model (m_6)
mydf <- ggpredict(m_6, terms = c("temp [all]", "storage[all]"))

# For temp
ggplot(mydf, aes(x, predicted)) +
  geom_point(data=myds, aes(temp, development), alpha = 0.5) +
  geom_line() +
  labs(x = "temp", y = "development")

# For storage
ggplot(mydf, aes(x, predicted)) +
  geom_point(data=myds, aes(storage, development), alpha = 0.5) +
  geom_line() +
  labs(x = "storage", y = "development")
# -------------------------------------------------------------------------------------------  

Please, any help with it?