Summary() returns NaN values when I try to include a variable as linear in a glmm-CodePudding

I am attempting to run a model using glmmTMB. When I include avgt60, it does weird things in the model and I am not really sure why. When I include it as a non poly term, it gives me NaN values. When I include it as a poly() term, it throws the entire model off. When I exclude it, it seems to be fine... I am new to this type of work so any advice is appreciated!

m1 <- glmmTMB(dsi ~ poly(rh60, degree = 2)   poly(wndspd60, degree = 2)   poly(raintt60, degree = 2)   avgt60   (1|year)   (1|site),
              family = "nbinom2", data = weather1)

I get:

Family: nbinom2  ( log )
Formula:          dsi ~ poly(rh60, degree = 2)   poly(wndspd60, degree = 2)   poly(raintt60,      degree = 2)   avgt60   (1 | year)   (1 | site)
Data: weather1

     AIC      BIC   logLik deviance df.resid 
  1647.9   1687.9   -813.0   1625.9      269 

Random effects:

Conditional model:
 Groups Name        Variance  Std.Dev. 
 year   (Intercept) 5.883e-24 2.426e-12
 site   (Intercept) 6.396e-07 7.997e-04
Number of obs: 280, groups:  year, 3; site, 6

Dispersion parameter for nbinom2 family (): 0.232 

Conditional model:
                            Estimate Std. Error z value Pr(>|z|)
(Intercept)                  -7.8560        NaN     NaN      NaN
poly(rh60, degree = 2)1      47.9631        NaN     NaN      NaN
poly(rh60, degree = 2)2      -5.4370        NaN     NaN      NaN
poly(wndspd60, degree = 2)1  61.7092        NaN     NaN      NaN
poly(wndspd60, degree = 2)2 -74.9432        NaN     NaN      NaN
poly(raintt60, degree = 2)1  27.2669        NaN     NaN      NaN
poly(raintt60, degree = 2)2 -72.9072        NaN     NaN      NaN
avgt60                        0.4384        NaN     NaN      NaN

But, without the avgt60 variable...

m1 <- glmmTMB(dsi ~ poly(rh60, degree = 2)   poly(wndspd60, degree = 2)   poly(raintt60, degree = 2)   (1|year)   (1|site),
              family = "nbinom2", data = weather1)


 Family: nbinom2  ( log )
Formula:          dsi ~ poly(rh60, degree = 2)   poly(wndspd60, degree = 2)   poly(raintt60,      degree = 2)   (1 | year)   (1 | site)
Data: weather1

     AIC      BIC   logLik deviance df.resid 
  1648.2   1684.6   -814.1   1628.2      270 

Random effects:

Conditional model:
 Groups Name        Variance  Std.Dev. 
 year   (Intercept) 2.052e-10 1.433e-05
 site   (Intercept) 4.007e-10 2.002e-05
Number of obs: 280, groups:  year, 3; site, 6

Dispersion parameter for nbinom2 family (): 0.23 

Conditional model:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                   1.3677     0.3482   3.928 8.56e-05 ***
poly(rh60, degree = 2)1      23.8058     9.6832   2.458 0.013953 *  
poly(rh60, degree = 2)2      -0.3452     4.2197  -0.082 0.934810    
poly(wndspd60, degree = 2)1  34.4332    10.1328   3.398 0.000678 ***
poly(wndspd60, degree = 2)2 -61.2044     6.5179  -9.390  < 2e-16 ***
poly(raintt60, degree = 2)1  12.0109     6.4949   1.849 0.064417 .  
poly(raintt60, degree = 2)2 -57.2197     6.0502  -9.457  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

If I leave avgt60 in as a poly() term, it throws the entire model off, and nothing is significant. Any thoughts here?

Here's a link to the dataset, with site names redacted:

dsi values for sites 5 and 6 are always zero (only measured in 2021)
dsi values are very high in 2019, only two sites (1 and 3) measured
there is no particular pattern, and certainly not one that's not confounded with site and year.

I would probably try to draw only qualitative conclusions, or very simple quantitative conclusions, from these data ...

library(tidyverse); theme_set(theme_bw())
w3 <- (weather1
    |> as_tibble()
    |> select(-date)
    |> pivot_longer(-c(site, year, dsi), names_to = "var")
    |> mutate(across(c(year,site), factor))
)

theme_set(theme_bw(base_size = 20)    theme(panel.spacing = grid::unit(0, "lines")))
(ggplot(w3)
      aes(x = value, y = dsi, colour = site, shape = year)
      stat_sum(alpha = 0.6)
      stat_summary(fun = mean)
      stat_summary(fun = mean, geom = "line", aes(group = 1), colour = "gray")
      facet_wrap(~var, scale = "free_x")
      scale_y_sqrt()
)

Murtaugh, Paul A. “Simplicity and Complexity in Ecological Data Analysis.” Ecology 88, no. 1 (2007): 56–62.