One of the great challenges in trying to manage both planetary and local ecological issues is that nature is really darn complicated: everything, as John Muir remakred long ago, is tied to everything else, and the nature of those connections is both complex and changing. If we want to make good decisions about how we work with natural systems, we need to become better able to understand that complexity.
Luckily, it seems we are on the brink of just such understanding:
Creative approaches at the interface of ecology, statistics, mathematics, informatics, and computational science are essential for improving our understanding of complex ecological systems. For example, new information technologies, including powerful computers, spatially embedded sensor networks, and Semantic Web tools, are emerging as potentially revolutionary tools for studying ecological phenomena. These technologies can play an important role in developing and testing detailed models that describe real-world systems at multiple scales. Key challenges include choosing the appropriate level of model complexity necessary for understanding biological patterns across space and time, and applying this understanding to solve problems in conservation biology and resource management. Meeting these challenges requires novel statistical and mathematical techniques for distinguishing among alternative ecological theories and hypotheses. Examples from a wide array of research areas in population biology and community ecology highlight the importance of fostering synergistic ties across disciplines for current and future research and application.
This paper's somewhat slow going for a relative lay-person like myself, but full of interesting insights into subjects like modeling ecological complexity, biodiversity extrapolation techniques, spatiotemporal landscape connectivity analysis and ecoinformatics. The take-away is clear, though -- we're still a ways off from being able to really know nature through technology, but the gap is closing fast.
This paper is in the most recent issue of the journal Bioscience: Complexity in Ecology and Conservation: Mathematical, Statistical, and Computational Challenges
Jessica L Green, Alan Hastings, Peter Arzberger, Francisco J Ayala, et al. Bioscience. Washington: Jun 2005. Vol. 55, Iss. 6; p. 501
And very interesting it is too. It's interesting to see how this kind of stuff is driven by trends (there are a few papers in the references section using the early 1990s buzzword "fractal") which seem to be coalescing into something a little more solid now.
The real challenge with this complexity business is making complexity digestable by people with limited motivation and/or capacity to do so.
An excellent post - thanks, Alex! "Systems Dynamics" is an extremely useful modeling technique for research like this - it has a mature methodology with well-developed software. It's been used more for the analysis of complex social and economic systems, but it's mathematically robust and completely suited to this work.
I was struck by the paper's emphasis on modeling at multiple scales. Our relationship with, and management of, nature's complexity will need to be at multiple scales too. That hints at a "fractal" approach to sustainability, as "Anon" pointed out. By "fractal" I mean the same or similar underlying rules carried out at multiple scales.
I believe a powerful tool for codifying intelligent design and management at multiple scales is a "Pattern Language" of sustainability. There have been some attempts at this - see the groundbreaking work by Christopher Alexander and colleagues, as well as the work of the Portland, OR, "Ecotrust" - but there's a long, long way to go. I wish that the "Bright Green Wiki" you helped start could have a parallel effort: an open-source, web-based effort to write a "Sustainability Pattern Language."