Interpreting CLMM results using the ordinal package in R - and how to plot it

Question

I'm still a bit of a newbie when it comes to R and running statistical analyses, and this is the first time I've worked with CLMM so I'm a bit confused.

Using the data site2, the question I wanted to answer was whether or not there was a correlation between the habitat NEW_CAT that moose were killed in and the average temperature TEMPMOY for the day of their death.

CAT_HABITAT  TEMPMOY ZONE ANNEE  DBC   NEW_CAT
1       Autre  7.70000   26  2019 SABJ PCT_AUTRE
2       Autre  6.00000   28  2017 SABB PCT_AUTRE
3        Resi  9.50000   28  1990 SABB  PCT_RESI
4        Resi  7.50000    1  1999 SABJ  PCT_RESI
5       Autre 15.30000    4  2001  ERT PCT_AUTRE
6        Regu 10.82105    1  1988 SABJ  PCT_RESI 

The habitats were divided into three categories, Humi, Resi, and Autre, all pertaining to the type of habitat within a 1.78km of where the moose were killed. These categories were all assigned values adding up to 1. Using this, I sorted the three habitats into NEW_CAT, selecting for the highest percentage out of the three.

My formula looks like this:

moose.clmm2 <- clmm(NEW_CAT ~ TEMPMOY + (1|ZONE) + (1|DBC) + (1|ANNEE), data = site2, link = "probit", threshold = "flexible")

taking into account the random effects for the hunting zone they were killed in ZONE, specific bioclimatic environment DBC and year ANNEE.

After running my analysis, I received the following results:

#formula: NEW_CAT ~ TEMPMOY + (1 | ZONE) + (1 | DBC) + (1 | ANNEE)
#data:    site2

#link   threshold nobs   logLik     AIC        niter     max.grad cond.H 
#probit flexible  709279 -514510.18 1029032.37 353(3004) 6.97e-01 2.7e+06

#Random effects:
#  Groups Name        Variance Std.Dev.
#ANNEE  (Intercept) 0.1644   0.4054  
#ZONE   (Intercept) 0.2822   0.5312  
#DBC    (Intercept) 1.4265   1.1943  
#Number of groups:  ANNEE 42,  ZONE 25,  DBC 8 

#Coefficients:
#  Estimate Std. Error z value Pr(>|z|)    
#TEMPMOY 0.0090163  0.0004112   21.93   <2e-16 ***
#  ---
#  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

#Threshold coefficients:
#  Estimate Std. Error z value
#PCT_AUTRE|PCT_HUMI   1.2031     0.4771   2.521
#PCT_HUMI|PCT_RESI    1.7169     0.4771   3.598
#(1492 observations deleted due to missingness)

I'm guessing the results are significant, considering TEMPMOY is described with ***? In order to understand these results a bit better, I would like to plot them in R. Which plot or package should I look into? How should I set it up?

Thank you for your time.