If you are interested in some aspect of vegetation ecology described in the lab research interest, feel free to contact me to discuss the topic of your potential work in my lab. You may come for discussion even if your idea is far from my interest or offer – we may just sit and discuss.
The following is a list of topics I would be happy to offer and supervise. It's far from exhaustive - I am open to other suggestions if I found them interesting.
I have a set of research topics related to our current cloud and wind research project. Contact me for more details if you are interested, we can discuss.
I expect that student will learn several important things: how to ask interesting ecological questions, how to collect vegetation data in the field, how to analyze them in appropriate way, how to come up with an ecologically sound interpretations of observed results and how to communicate these results with others (in oral and written way). I am open to supervise both more field-oriented or more methodologically-oriented studies and any combination in between. However, I am not interested in purely numerical studies without an interesting ecological context (i.e. just playing numbers), and I am not able to supervise taxonomically oriented studies (since I am not taxonomist).
You can expect the open and friendly atmosphere in the lab, and opportunity for (hopefully) inspiring discussions related to different topics of vegetation ecology and doing science in general. I can help you to learn theoretical and practical background needed by vegetation ecologist, including field survey skills and data analysis. Please, expect that all communication (oral and written) will be in English. I understand that you may have difficulties in using English if it is not your mother language (it’s not mine neither), and I will be happy to help you with improving your English skills as long as you will be willing to work on yourself.
I believe that to make research successful and enjoyable at the same time, the following things are overly important: a curiosity in discovering new things, good field and lab training, creating opportunities for fruitful discussion, open data policy, and transparent analytical procedure. Good ecologist should be able to combine field and lab approaches - it is important to touch nature in the field and collect good field data, but it is also important to process, analyze and interpret data correctly, and know how to present them.
Considering numerical analysis, my belief is in methodological simplicity – although many fancy modern (and often complex) statistical methods exist, if the choice is to be done between more advanced method and simple or traditional one, both leading to results with similar interpretation, my choice would be to go for the simpler one, since more people will actually understand it. Also, transparent description of analysis and honest interpretation of their outcomes is a must. Although I often work with numbers, I think about ecology at the first place - all those numerical games are here to help us to understand and interpret what's going on in nature, not the other way around.