My “research group” consists of me and anywhere from 3 to 6 graduate students that I advise. I have broad research interests in various ecological and conservation topics and no particular study system or taxon. I also am involved in a wide variety of projects, some of which are collaborations with other faculty at Texas State and elsewhere. As such, my graduate students work on a variety of topics and their projects differ a lot. My “lab” is not organized around any single (large) project where each graduate student does some smaller part of the project as his or her thesis or dissertation. In those labs grad students are primarily expected to participate by completing component parts of the project that are assigned to them. My approach is to assist each graduate student in developing and implementing his or her own project – the development phase usually takes at least one semester and involves much input from me. I also occasionally have funded projects that are essentially ready-to-go for a student – these are stand-alone projects that can usually be completed within 2 – 3 years.
Even though there is a wide variety of projects and topics, my overall research program (including grad student projects) does have a central unifying theme: to acquire ecological knowledge of the factors affecting distribution and abundance of species over multiple spatial scales, and when possible to put that knowledge into action by investigating the implications for conservation of biodiversity and natural areas. The “factors” can be highly varied: habitat, dispersal, physiological tolerances/adaptations, species interactions, climate/weather, historical legacy, land-use practices, and ….. randomness.
With regard to the latter “factor”, I believe in multiple causality and that nature can be very complex. In the context of doing ecological research, this means that I appreciate and value the randomness in data and I don’t think there are any really strong higher-level structuring processes (definitely not from physics) that determine distribution and abundance. Ecologists could learn more from talking with historians and geographers, than physicists. Much of the “randomness” in nature may be due to historical contingency and the simultaneous action of a lot of non-random processes. Above all else, ecology does not need to invoke much more than just natural selection as an organizing process and fundamental explanation. All of this is simply my general “world-view” on nature and ecology – I don’t do research on any of this or even think about it much – I’m not a philosopher.
One consequence of this perspective is that I strongly advocate for very rigorous statistical design and testing. If randomness is pervasive in data (and in nature) then researchers need to be very thorough and exact before claiming to have found pattern in data, and subsequently linking the pattern to an inference. I use null models, data randomization, quantification of Type I and II error rates, mathematical/analytical models and simulation modeling in much of my research. In fact, all of this has become an area of research onto itself. Lastly, this is also a goal that I have in advising graduate students: help them further develop their quantitative/statistical skills and critical thinking ability.