INTECOL X - Brisbane

I've been attending the 10^th INTECOL Congress in Brisbane Australia this week. Thanks to the close proximity of the conference to AEDA at UQ, there were some great sessions on adaptive management, modeling, and later this week on expert elicitation of priors. A few quotes:

If you don't know what a differential equation is, then you're not a scientist – Prof Hugh Possingham

All models are beautiful, and some are even useful – Dr. Brendan Wintle

And then there was the definition of AM given by Richard Kingsford and attributed to Norm Myers.

Adaptive Management is like teen sex. Lots of people say they are doing it, only a few actually are, and they are doing it badly.

My own talk ended up in a session on Landscape Ecology – mostly reflections on why some structured decision making workshops work, and some don't. I probably should go back to school and get some proper training before doing any reflecting on social processes, but who's got time to do that? My colleague Rodrigo Bustamante from CSIRO Marine and Atmospheric Science made probably the best point – that the participants have to have "bought into" the workshop idea, and at least the general idea of using the workshop to analyze a decision before coming along. This is where pre-workshop conference calls are essential to discuss what the workshop is meant to achieve.

Useless arithmetic?

Orrin Pilkey and Linda Pilkey-Jarvis have recently made a bit of a splash arguing that mathematical models are useless for environmental management – I haven't read their 2007 book yet, but it received some popular press. Primarily they argue that optimism about models helping environmental managers make better decisions is misplaced. Recently I came across an article in Public Administration Review where they summarize their arguments for policy makers in 10 easy lessons. This quote sums up their paper well:

Quantitative models can condense large amounts of difficult data into simple representations, but they cannot give an accurate answer, predict correct scenario consequences, or accommodate all possible confounding variables, especially human behavior. As such, models offer no guarantee to policy makers that the right actions will be set into policy.

They divide the world of models into quantitative models, exemplified by their favorite whipping horse, the USACE Coastal Engineering Research Center equation for shore erosion, and qualitative models, which

" … eschew precision in favor of more general expectations grounded in experience. They are useful for answering questions such as whether the sea level will rise or fall or whether the fish available for harvest will be large or small (perspective). Such models are not intended to provide accurate answers, but they do provide generalized values: order of magnitude or directional predictions."

What I find somewhat humorous is their assertion that instead of quantitative models we should use qualitative models based on empirical data. All of their examples of model failure are great ones, but in every instance they suggest using trend data and extrapolation as a substitute. This is simply using a simpler statistical model in place of a complex process based one. If trend data is available, then by all means it ought to be used. But what if data (read "experiences") aren't available? Is the alternative to make decisions based on no analysis at all? How is that defensible? And what if the world is changing – like the climate – is experience always going to be a good guide?

While I agree with most of their 10 lessons, I must take issue with one of the lessons. Lesson 4: calibration of models doesn't work either asserts that checking a models ability to predict by comparing model outputs with past events is flawed. While it is true that you can make many models predict a wide range of outcomes just by tweaking the parameters, this isn't something that should be done lightly – and isn't, by modelers with any degree of honesty. There are many ways to adjust the parameters of simulation models against past data using Maximum Likelihood methods, or for more complex models, approaches such as pattern based modeling advocated by Volker Grimm and colleagues. As they suggest, this is no guarantee that the model will continue to predict into the future – but if the model structure is in fact based on an accurate scientific understanding of the processes involved then it better come close. If it doesn't the principle of uniformity on which science relies must come into question. The scientific understanding could be flawed as well, but this could always be true whether you use mathematical models or not. It is also the reason why there shouldn't be only one model (Lesson 8: the only show in town may not be a good one). They also find the prediction of new events (validation) to be questionable, but again, this can be done well, and when independent data are available it is the best way to confirm that the models are useful. Personally I find the failure of a model to predict an event, either past or future, to be an ideal opportunity for learning. Obviously something about reality is different from what we expected, so what is it?

What is particularly intriguing is that they view Adaptive Management as an alternative to quantitative modeling!

I think in the end what they are taking issue with is not quantitative models per se, but rather a monolithic approach to environmental management and policy making. Lesson 7: Models may be used as "fig leaves" for politicians, refuges for scoundrels, and ways for consultants to find the truth according to their clients needs I think gets at this point directly. It isn't models that are the problem, but rather how they get used. So, the solution to avoiding the problems they cite is to use models openly, as the IPCC does. Multiple models, ideally, or at least a range of parameters that express alternative views of reality. Avoid black boxes – follow the "open source" approach to computer programs used to make predictions. And, analyze the data! I think I've said that before.

Pilkey-Jarvis, L and O. Pilkey 2008 Useless Arithmetic: Ten points to ponder when using mathematical models in environmental decision making. Public Administration Review. May, 470-479.

Zen

I recently wrote about the value of giving up attachments to ideas, which is a key part of Buddhism, for Adaptive Management. My colleague Scott Field added another nugget of Zen wisdom relevant to Adaptive Management:

"Seeking the Buddha is much like riding an ox in search of the ox itself."

This from a book on Zen Buddhism. Just replace "Buddha" with Adaptive Management. Works for me.

The Principle Challenge

I came across a really cool article on automating phenology measurements - using remote sensing data and photographs to track development of vegetation at many different scales. This is potentially a really useful way to monitor stuff - quantitative measurements of vegetation that can be reliably and automatically generated over time. However, my favorite part of the entire article was this quote:

For land management, the principle challenge relates to prediction. Managers need to know how today's management decisions will impact tomorrow's ecosystem processes.

Ra! Ra! Sis Boom Bah! Yes! And guess what, that means using models. All the fancy remote sensing in the world is no good unless you can use that data to meet the principle challenge. The trouble is, managers are often reluctant to recognize that models can be helpful. In my recent experience, if models are "known" to have flaws (see quote by George Box), or produce a range of predictions because of statistical error in parameter estimates or inherent variation (demographic stochasticity), then they are labeled useless. Better to use gut instinct to make decisions.

I do believe models are useful even when they are not (and they never will be) perfect.


Jeffery T Morisette et al. (2009) Tracking the rhythm of the seasons in the face of global change:
phenological research in the 21st century. Frontiers in Ecology and the Environment 7:253-260

Zen Buddhism and Adaptive Management

I've been entertaining a visitor this week, Dr. Scott Field, currently a lecturer at the Naval Postgraduate School in Monterey. 5 years ago we worked together on a project analysing fox monitoring data from Eyre Peninusula in South Australia. This week Scott's been conducting the 2nd Quinquennial fox monitoring analysis - and he has results! Nice ones. Moral of the story - analyze your data. It helps. Really.

Scott's recent work has focused on conflict resolution in International Relations, and he had a few extremely interesting comments about Adaptive Management. One thing that causes problems is people rejecting analyses and predictions that could help them with decision making. The difficulty arises if they are not able to understand the underlying methods used to derive those analyses, then they have no way to discuss them. This leads people to react emotionally, rather than critically. Thus, it is critically important to conduct analyses in groups and provide lots of opportunities for feedback and interaction. This still isn't going to solve the problem when you are dealing with sophisticated analyses of complex data. But recognizing that issue will help me to not respond in kind.

The broader notion that we've come up with is that Buddhism has alot to offer the practice of adaptive management - hey, stay with me for a bit. One of Buddhism's "Four Noble Truths" is that Attachment leads to suffering. Attachment can be to things, but also to ideas. So the clear connection to AM is that when someone is attached to an idea about how the world works, then it is hard for them to expose that idea to data and analysis - the risk is that they might have to give up their idea, leading to suffering. Thus finding a good solution to an environmental problem involves giving up attachments to ideas. The trouble is there is no way to force someone else to give up their attachments.

There are other components of Buddhism that have lessons for AM, but I'm just learning about them so won't write any more just now.

Blending math and biology - nothing new there!

One of the things we're pretty proud of here at UNL is our developing collaboration between Biology and Mathematics - we've done joint REU projects, and the crown jewel is the NSF funded Research for Undergraduates in Theoretical Ecology project. We want to do more. Then I saw this quote from R.A. Fisher on the jacket of Alan Hasting's textbook on Population Biology:

I can imagine no more beneficial chance in scientific education than that which would allow [biology and mathematics] to appreciate something of the imaginative grandeur of the realms of thought explored by the other.

Wow. So he was already recognizing this gap in 1930! We've got some catching up to do.

Wrapping around vs. setting up

The 2003 Biological opinion on the Missouri River Mainstem operations required that habitat creation and other "reasonable and prudent alternatives" to avoid jeopardy be conducted using Adaptive Management, because there were significant disagreements about the effectiveness of the RPA elements. However, because of the urgent nature of the problem, the RPA elements, especially habitat creation activities, were started immediately without working out the details of how adaptive management would be used. This is in stark contrast to other adaptive management processes, such as on the Platte River and in the Everglades, where planning and organizing the governance of the adaptive management plans took years - or even decades. This makes the Missouri River an interesting case study in how important those governance bits are.

As a result, the teams that I am working with are trying to take an existing batch of actions, including experiments at various scales, different monitoring programs, and restoration actions, and wrap an AM framework around them. This is exciting on one hand, because stuff is really getting done - including experimental habitat restoration complete with monitoring. On the other hand, it is incredibly difficult, because now there are alot of people with vested interests in projects and programs who are reluctant to embrace change. This is understandable, because a changed program management structure may not include "your" program, or at a minimum may require you to interact with people that you didn't interact with before. Thus a huge part of getting AM off the ground on the Missouri River involves managing people's expectations - and just plain communicating with them. Often.

So far, I'd have to say that I have a much greater appreciation for the governance piece than I did before!