What are the basic skills required to pursue future studies in theoretical ecology?
Theoretical ecology
From wikipedia

Theoretical ecology is the scientific discipline devoted to the study of ecological systems using theoretical methods such as simple conceptual models, mathematical models, computational simulations, and advanced data analysis. Effective models improve understanding of the natural world by revealing how the dynamics of species populations are often based on fundamental biological conditions and processes. Further, the field aims to unify a diverse range of empirical observations by assuming that common, mechanistic processes generate observable phenomena across species and ecological environments. Based on biologically realistic assumptions, theoretical ecologists are able to uncover novel, non-intuitive insights about natural processes. Theoretical results are often verified by empirical and observational studies, revealing the power of theoretical methods in both predicting and understanding the noisy, diverse biological world.
The field is broad and includes foundations in applied mathematics, computer science, biology, statistical physics, genetics, chemistry, evolution, and conservation biology. Theoretical ecology aims to explain a diverse range of phenomena in the life sciences, such as population growth and dynamics, fisheries, competition, evolutionary theory, epidemiology, animal behavior and group dynamics, food webs, ecosystems, spatial ecology, and the effects of climate change.
Theoretical ecology has further benefited from the advent of fast computing power, allowing the analysis and visualization of large-scale computational simulations of ecological phenomena. Importantly, these modern tools provide quantitative predictions about the effects of human induced environmental change on a diverse variety of ecological phenomena, such as: species invasions, climate change, the effect of fishing and hunting on food network stability, and the global carbon cycle.

Computational Biology
from wikipedia

Computational biology involves the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems.1 The field is broadly defined and includes foundations in computer science, applied mathematics, animation, statistics, biochemistry, chemistry, biophysics, molecular biology, genetics, genomics, ecology, evolution, anatomy, neuroscience, and visualization.2
Computational biology is different from biological computation, which is a subfield of computer science and computer engineering using bioengineering and biology to build computers, but is similar to bioinformatics, which is an interdisciplinary science using computers to store and process biological data.

Field Ecology
Ecology can be studied empirically through data analysis, through lab experiments or through field observations and experimentations. Field ecology refers to this last point that is, it refers to empirical ecology in the field.
Conclusion
Potential Misunderstanding
In your post, you seem to suggest that field ecology is a subfield of theoretical ecology. It is not! Either you work as a theoretician or as an empiricist (and then eventually as a field ecologist). Of course many people do both theory and empiric work.
Note that whether you are theoretical or an empiricist working in ecology, you will need to have a good understanding of the theory behind the science of ecology.
Computational biology vs theoretical ecology
In opposition to empirical ecology and field ecology, computational biology and theoretical ecology do not have a hierarchical relationship. If you work in theoretical ecology you can make analytical modelling or numerical (computer) modelling. If you work in computational biology you could analyze data in genetics, you could model cell processes, you could simulate interacting populations in ecology and many other things.
What should you chose?
What skills do you need?

Do you like data analysis (statistics and R and python coding)? You will have to analyze your data whatever you chose!

Do you like numerical simulations (lots of coding, incl. languages like C++)? If no, then maybe avoid computational ecology!

Do you like analytical modelling (lots of math)? If no, then maybe avoid making analytical models in ecology!

Do you like messy, complicated, tiring field work? if not, then maybe avoid working as a field ecologist.


What would you imagine to work on?

What question would you like an answer to? Think about it and try to understand how you would address the question you are trying to answer.

My advice

Do not be afraid of any field, any technics, any programming language, etc... You are good enough and have enough time to learn what you do not know.

Do whatever you like most!