Probably Python and R for statistical analysis, which is common nowadays in most empirical sciences.

lm(turtle_gender ~ temp, data = data_frame)

Bayesian analysis of complex intelligent systems via friston's free energy principle and active inference? Or machine learning?

Personally love the stuff circling Michael Levin at tufts university. I could also imagine there's a lot of unique model building in different biological/ecological niches.

Computational biology and ecology are a huge part of those fields. I work at a research lab (in computer science) and one of our sister labs is dedicated to environmental stuff and has mostly biologists and ecologists employed at it; a large part of the research they do involves supercomputing somehow, so we tend to partner with them a lot. As an example, modeling population growth or decline due to a change in the population's environment is one such use of computing and statistics in biology and ecology.

Science researchers and students often spend a lot of their time doing statistical analysis, including using programming for that.

Humans are animals, and humans invented programming. Therefore, programming is applied biology.