Farewell, 2010

2010 was a year of many things:

I lost my old paradigm.

I visited Mexico.

I started making pictures again, instead of collecting snapshots.

I finally acquired an Arduino.

And, I came to realize the limits of Adaptive Management. I'm not going to say more about this here, as it is the topic of a couple of articles in preparation. It is clear to me that it isn't the only, best, or often even a good answer to the question of how to translate conservation research into action. David Goulson and colleagues describe the issue very well in a recent forum article in the Journal of Applied Ecology:

For bumblebees, considerable progress has been made in transferring scientific knowledge into practical conservation, but the gulf between evidence and practice remains in some areas, particularly with regard to policy. A major problem in the UK and elsewhere is that no clear mechanism exists for translating scientific evidence into governmental policy. There is little discourse between governmental organizations responsible for conservation and academics carrying out conservation-related research. Decision-making with regard to policies affecting conservation (including agri-environment schemes) is not transparent. Any academic wishing to have an input into conservation policy would be hard put to identify a mechanism by which to do so.

Although they are talking about Bumblebees in the UK, I think the issue is universal. A book that particularly influenced my thinking on this issue is "Embracing watershed politics" by Edella Blomquist and William Schlager. Essentially their message is - politics is everywhere, its the best deal in town, get over it. For making choices about natural resources in the face of differing values and risk tolerances, no matter how much we scientists wish rational thought would prevail, politics is the answer. Goulson was sufficiently frustrated to form an NGO devoted to the conservation of Bumblebees, and engage in the politics directly. I don't want to take that path.

All this has made me question the topic of this blog - too narrow for including what I want to write about, most times. For the moment, I've decided to keep working on it.

Have a happy holiday and a very merry new year!

Trashing the linear model

I have been puzzled by the apparent failure of ecologists to recognize that the "deficit model" of the science policy interface is flawed. Imagine my surprise to find that John Lawton expressed exactly this idea in his 2007 presidential address to the British Ecological Society:

... research can often have noticeably little effect on policy ... There is an extensive social-science literature on why this situation, that is typically ignored by many natural scientists (just as politicians often ignore our evidence!), pertains.

So why, WHY is this message so slow to percolate through the ecological community? I just sat through a half day meeting on how to engage research on Water, Food and Energy at UNL with policy. And - they still don't get it.

Anosognosia

A while ago I came across a reference to a disturbing psychological phenomenon that leaves one unable to recognize one's own incompetence. Now, thanks to Errol Morris, I have a name for it: Anosognosia. According to wikipedia, it is "...a condition in which a person who suffers disability seems unaware of or denies the existence of his or her disability."

Unknown unknowns - I knew they were important.

Decision Support in your stocking

'Tis the season to shop! I just received an email from the National Academies of Sciences with their recommended gift list for scientists and engineers - and #2 was Informing Decisions in a Changing Climate - great gift for myself!
On a closer look, this book has a lot to offer. The chapter on decision support and learning is a great review of a broad interdisciplinary domain, and offers some novel synthetic insights of its own, including Table 3.1 on learning modes. I found the two right hand columns, Adaptive Management and Deliberation with Analysis particularly informative. These two columns are pretty much the same, except for two key, and inextricably linked, differences: the assumed decision maker and the goals. Adaptive management assumes a unitary decision maker who sets goals that persist for the life of the program. In contrast, Deliberation with Analysis assumes a diverse decision maker with goals emerging from collaboration and subject to change.
I found this distinction interesting because the description of AM given in the book is a dead ringer for what I've called the "North American School" before, and more recently my student Jamie McFadden described as the "Experimental Resilience School" in a forthcoming paper. This isn't surprising as Kai Lee was one of the panel members for the report. Deliberation with Analysis sounds much like Adaptive Co-management to me - clearly there are some linkages to follow up on.

Engineered loss of resilience

Resilience is one of those common but slippery concepts that everyone knows what is meant but no one can define properly. One common notion is that engineering a system to resist small disturbances leads to a loss of resilience against larger disturbances. I had the misfortune to personally experience such a loss of resilience yesterday morning.
Our Cuisinart coffeemaker is a thing of beauty - stainless steel, black plastic trim, modern curvy lines. It has a really nice feature that lets you remove the thermal insulated pot while the coffee is brewing, if you are too desperate to wait for the drip cycle to finish. I regularly utilize the feature to gain an extra 30 seconds savoring that rich black magic that starts my day (yes I like coffee). So, the coffeemaker is resilient against short removals of the coffee pot - it continues to function as desired, even when the pot is briefly removed and then replaced.
However, if one is not fully awake, and perhaps a bit rushed, when preparing the morning jolt of java, one might forget to put the pot back into the brewer, and then leave the kitchen for a few moments. This is when that nice feature that protects against small disturbances leads to a greater catastrophe - not only does the coffee maker pour coffee onto the counter, but it does so by backing up and overflowing, carrying coffee grounds into every nook and cranny in the machine, under the toaster, etc. etc.
Thus, engineering a solution to a small disturbance ends up leading to a loss of resilience to greater disturbances.

Permission to model: denied!

Chaeli Judd and Kate Buenau sent along the following criteria for deciding if you are permitted to use a statistical or other modeling method. The answer to all three questions must be yes, preferably with concrete proof.

1. Can you, personally, get a computer to do it?
2. Can you explain the method to a person that doesn’t already know how to do it?
3. Do you understand when not to use it?

Criterion #1 was inspired in part by this quote from a paper by Carl Walters and co-authors:

"As we tell participants in introductions to Adaptive Environmental Assessment and Management (AEAM) workshops, things you can get away with on paper have a nasty way of coming back to haunt you when you try to represent them clearly enough that a computer can reproduce the steps in your reasoning."

I think #3 is really important self-discipline - we should ask ourselves this all the time. Why would we not use _____ for this problem?