Holistic Management practitioners have long known that reductionism makes for poor land management.  When one relies too heavily on a search for single causes of a problem, or has a myopic perspective on farm/ranch priorities, this can lead to financial ruin.  They also know that the ecosystems which they steward are complex; these complex systems respond to management decisions in novel and unanticipated ways, leading to constant surprises for land managers with decades of experience in the field.

This appreciation has been a source of tension with the research community.  Indeed, ecological research is fraught with challenges, many of them socio-economic in nature (like the difficulty in securing long-term funding), and others methodological or philosophical.

Ecological research, perhaps more so than any other field of biological inquiry, relies heavily on statistical correlation to ferret-out the complex and hidden relationships within natural systems.  The controlled conditions of the laboratory are of limited use in a field where natural relationships are the primary focus of study.

A recent article in Wired magazine challenges readers with the provocative title “Trials and Errors: Why Science Is Failing Us”.  While this article deals mostly with the difficulties pharmaceutical researchers are now encountering in the design of new medicines, as I read it I could not help but be reminded of the similar difficulties ecological researchers face when attempting to understand the intricacies of managed natural systems.  The following passage from the article elucidates the nature of these challenges:

The good news is that, in the centuries since Hume, scientists have… continued to discover new cause-and-effect relationships at a blistering pace. This success is largely a tribute to the power of statistical correlation, which has allowed researchers to pirouette around the problem of causation. Though scientists constantly remind themselves that mere correlation is not causation, if a correlation is clear and consistent, then they typically assume a cause has been found—that there really is some invisible association between the measurements.

Researchers have developed an impressive system for testing these correlations. For the most part, they rely on an abstract measure known as statistical significance… This test defines a “significant” result as any data point that would be produced by chance less than 5 percent of the time. While a significant result is no guarantee of truth, it’s widely seen as an important indicator of good data, a clue that the correlation is not a coincidence.

But here’s the bad news: The reliance on correlations has entered an age of diminishing returns. At least two major factors contribute to this trend. First, all of the easy causes have been found, which means that scientists are now forced to search for ever-subtler correlations, mining that mountain of facts for the tiniest of associations. Is that a new cause? Or just a statistical mistake? The line is getting finer; science is getting harder. Second—and this is the biggy—searching for correlations is a terrible way of dealing with the primary subject of much modern research: those complex networks at the center of life. While correlations help us track the relationship between independent measurements, such as the link between smoking and cancer, they are much less effective at making sense of systems in which the variables cannot be isolated. Such situations require that we understand every interaction before we can reliably understand any of them. Given the byzantine nature of biology, this can often be a daunting hurdle, requiring that researchers map not only the complete cholesterol pathway but also the ways in which it is plugged into other pathways.

Correlation is not casuality, and the search for correlations is now in a spiral of diminishing returns as complexity threatens to overwhelm our quaint and contrived mathematical models.  But what does this all mean for land managers?  After all, most folks are busy with the day to day realities of cattle prices, conception rates, harvesting schedules, and farm repairs.  Should land managers give up on science altogether and rely solely on observation?

Not so fast.  Let’s remember, science is a set of tools, and statistical correlation is a sub-set of tools within the broader framework.  While reducing natural relationships to a set of statistical correlations may seem a poor substitute for perfect understanding, it remains one of the most powerful tools we have to enhance our comprehension of the natural world.  Research is not a substitute for observation in the field, nor can we ever expect to have perfect knowledge of the systems which we manage.  We are imperfect beings with limited knowledge in a complex world.  A little humility is never a bad thing.

Thus the emphasis on holistic thinking.  The best we can hope for is adapt our management to new thinking and new information as we receive it.  Understanding the limitations of research gives us a useful lens through which we can view and analyze the results of scientific studies.  We must recognize that our decisions may have unintended consequences, and we must be vigilant to this possibility.  Our use of monitoring and feedback loops builds this recognition into the management process.  And although statistical correlation is a tool with limitations, it is still a powerful one that requires substantial data gathering to be effective, so work with scientists to gather as much data as you can where and when possible.  And remember that skepticism is always a healthy and worthwhile position to take when evaluating the results of any study, most particularly and especially when that study confirms your own human biases and beliefs.