I am an ex-physicist who has reviewed* several PhD memoirs of friends who were biologists. There were "experimentalists" and "theoreticians" in that cohort.
By far what they were lacking most was knowledge of statistical methods. Some were so bad that I mentally could not cope to go through the whole thesis. The defenses were very successful.
So learn statistical methods from a purely practical angle if you want to be ethical in your research. There are so many butchered statistical results that yours will probably slip through so no need to be overly stressed. Note that the ones who will read you are likely to have the same understanding of statistics as you.
This said practical statistical methods are really worth learning so that you know if you are going in the right direction before someone else does and then a lot of unrecoverable work (or time) is lost.
The silver medal goes, ex aequo, to calculus and differential equations.
Again, we are talking practical stuff here, not theory.
With calculus, you will be able to understand how various functions behave and what to look for to understand their behaviour. Expected level is high-school.
Differential equations are, I think, how the world is described (but they do not want us to know). Sooner or later you will find a "change of something is a function of the something", or twice this. I do not think you should learn how to solve them (this is tricky to say the least, in physics we used to have a year-long course just on differential equations). You should really try, however, to find someone knowledgeable so that you can get their opinion about feasibility/countability/etc.
Story time: my wife ended up with a stiff equation when working on enzymes and attempted to resolve it with (I forgot the name of the program they were using in the 90's at her biology department). It was a disaster, mostly because no one in the dream of them could detect a stiff equation (they did not know about this beast) and it would have taken them eons to compute them the way the intended. She turned to me (tadam!) and I did the computation for her and guess what - she found out that the "standard behaviour" that was in use at the time had an issue because the equation was not solved correctly. Then came the Nobel prize and everything (well, it would have come if we did not switch to different lives after that)
The bronze (and special prize) goes to learning to code with Python. Look up Jupyter, spend 2 afternoons learning Python, then a week to learn Pandas and you will have an incredibly powerful tool in your toolbox. You can become the hero of the team with this.
* and have ben traumatized by