In the process of applying to graduate schools and scholarships, I’ve written and rewritten my “research statement”. Which I don’t mind too much, because I’m actually pretty excited about the research I’d like to be doing – this is the benefit of avoiding grad school for long enough to decide what I’d actually want to do there.

The trick in writing a “statement” of research or purpose or what have you is boiling down the details to fit the inevitably ridiculous space restrictions without loosing the flavour of the thing altogether. Too general and it sounds like a list of shopworn platitudes (“landscape ecology is useful”, “remote sensing is improving”), too long and well, it doesn’t fit in the form. Working in a slightly esoteric field doesn’t help, I can probably assume my audience knows GIS and landscape ecology, but not necessarily exactly what remote sensing is or what it can do, nor complexity theory or how it relates to ecology. So if I go too heavily into describing what these fields are, I don’t have room to more than gloss over how they can interrelate.

I haven’t really solved this problem. But here, for what it’s worth, is what I think might be my best yet short-short summary of my research interests. I wrote it in an email to former collaborator who has agreed to supply a reference for me.

I think landscape ecology is strategically significant because it most directly informs policy questions, which generally require landscape inferences. One of the more powerful tools available for accelerating ecology’s grasp of landscape-level dynamics is complexity theory. Complexity theory (emergence, non-linearity, etc), originating in statistical physics, describes and explore how systems of large spatial extent can be the non-linear product of many smaller-scale interactions. Scale and thresholds are thus (as always) crucial issues, and ones which complexity may provide significant traction on. Methodologically, there is an underdeveloped synergy between remote sensing and spatial modelling: remote sensing is usually used for mapping/monitoring, but applied carefully could yield significant data for answering ecological questions, and it’s a natural fit for providing empirical data for landscape modelling. Remotely sensed data is spatially continous, so it can be readily imported into data models, and it has the potential to span multiple ecological scales in a single dataset, which is key for complexity research. Therefore, it’s my plan to gain a background which I currently lack in complex ecological theory while pushing integration of remote sensing into ecological theory development.

That’s my statement and I’m sticking to it.