“Ecosystem Gestalt,” Hawaiian Ways of Knowing and the Power of Modeling
I am currently at the Ecological Society of America’s 96th Annual Meeting, held in Austin, Texas. The conference has been enlightening so far: workshops started this weekend, and great talks abound for the upcoming hours and days. This morning, the plenary talk was presented by Stephen W. Pacala, ESA MacArthur Lecturer and Director of the Princeton Environmental Institute. He called upon ecologists to “remember the social contract” of stewardship (the theme of this year’s meeting is Planetary Stewardship) and to apply themselves effectively to the intersection of modeling and management. He provided a few examples of cases where models have been directly and successfully applied to management, and used the connections between these studies to demonstrate why they were so successful, and how we, as ecologists, might make our research useful to us and to our planet.
One of the most interesting themes of these great studies that Dr. Pacala brought up was the value of knowing your system. This, he explained, is primarily a time investment: spend years and years in the ecosystem you study, get to know it through experience, and use this experience to gain an “almost subconscious” understanding of your system. If other people are working in the same system, a kind of “system Gestalt” occurs, at which point intuitive understanding reveals the important questions, which you can then ask and answer with powerful methodological tools.
When models by separate but equally knowledgeable scientists converge on the same solution (i.e., two people write a model to predict something and both models come to the same conclusion), policy decisions are made. Examples given in the talk were SIR models used to control foot-and-mouth disease in the UK, Ray Hilborn‘s work on the Beverton-Holt stock-recruitment curve for the Chinook salmon fishery in the Pacific, and models for the recovery of European spruce forests after invasions by bark beetles. Scientists that worked on all these studies had excellent knowledge of their systems.
Herein lies another intersection between Hawaiian knowledge systems and Western science. Despite the focus of Western science on objective evaluation and the observable world, researchers doing some of the greatest, most game-changing, most useful studies spent years learning the system before they created models for it and made very high-impact predictions that were then verified by real events. Holistic knowledge of an ecosystem, or even a whole watershed, is plentiful in Hawaii, a place whose culture incorporates place, person, and family into one element. A view that I have heard many times is that Hawaiian knowledge focuses on a whole, while Western science seeks to pick things apart. Methodologically, this may be true: in research, we seek to tease apart different processes to quantify their effects, and the method of research does a very good job of describing observable phenomena. Hawaiian ways of knowing are primarily experiential, and build holistic knowledge from personal and ancestral experience. However, research and traditional knowledge systems are not necessarily opposed to one another. As emphasized by Dr. Pacala, knowledge of the system as a whole is integral to using science analytically.
Conversely, models have a predictive power that can strengthen experiential knowledge. A rapidly changing planet and new management issues for our most valuable ecosystems may be disastrous for both humans and the systems on which they depend. When fisheries crash, or diseases spread, the right models can converge on a very precise and effective solution to the problem: using the SIR model, researchers in the UK were about to determine exactly the number of cows and sheep they needed to euthanize (and where) in order to reduce the growth rate of foot and mouth disease enough to control it.
If you have the kind of profound knowledge of your system, the kind that is passed down from Hawaiian kupuna to younger generations and learned through years of collective experience, learning the math you need to make a predictive model is relatively easy. Pacala also argued in his talk this morning that it is also fast: it “only takes about two months for math to percolate into the brain,” when learning your system could take a lifetime. The power of experience is strong, and the power of prediction that can come from modeling is also strong– but only together can they create the kind of ground-breaking studies that make real, long-lasting (and life-saving) policy decisions.