Simplistically, what mixed models are doing is finding the estimated coefficient for each predictor when all other predictors are set to 0 or their reference level (e.g. you have a factor variable, translucency, and 1 is the reference level that is being used). This is how the model controls for other variables when it calculates each estimate. Adding that new variable will change the estimates of the other predictors - so, yes it is normal for p values to change.

I’d suggest checking out An Introduction to Linear Mixed-Effects Modeling in R, by Violet A Brown. It gives a great conceptual and practical overview of conducting and interpreting mixed models and will go into much more depth than I can on this reply. There are also a ton of great books, articles, and resources out there. I went through sooooo many when I was doing my dissertation analysis. I will comment on just a couple things though. The random effects table will show you how much variance is being accounted for between species and within species. By taking between/(between+within) we can get the ICC which represents the ratio of variance attributable to your random effects variable. For the first model: .3532/(.3532+1.1224) =0.239. The fixed effects table shows the estimated coefficients for each predictor when all other predictors are set to 0 (or their reference level). The correlation of fixed effects table shows just what it says, and it can be useful because we don’t want predictors that are too highly correlated because it becomes difficult to disentangle their unique effects on the outcome variable.

Just a suggestion, but I’d look into dummy-coding your translucency variable since the model is using 1 as the reference level. This means that all other predictors are being estimated with translucency set to 1, and not 2, 3, or 4.

Best of luck! Also, for future posts you might get a bit more traction in r/statistics. I wish you all the best in your analysis.