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Has Occam’s razor shredded ecology to bits?

December 4, 2012

At its most basic level, ecology attempts to explain the richness and abundances of species over space and time.

I feel that ecology has been notoriously poor at achieving this goal in most situations. It is not uncommon for fisheries to collapse, for pest control measures to be unsuccessful and for species conservation efforts or re-introductions to fail. Although there are often political and/or economic factors contributing to these failures of applied ecology, poor and/or contradictory ecological predictions frequently also play a role.

Compared to other scientific disciplines, ecology has disappointingly few predictive theories that are both practically useful and broadly applicable. Instead, ecology is highly fragmented and our explanations for the richness and abundances of species over space and time tend to be both taxonomically and geographically specific. The common excuse for this situation is that nature is complex. Few dispute this. However, why should the complexity of nature be an adequate excuse for favouring simple, system-specific explanations of species richness and/or abundance patterns? In practice, this leads to uncertainty when attempting to apply ecological principles to new, previously uninvestigated systems.

Why aren’t we looking for a grand, universal model for predicting species richness or species abundances over space or time? It can’t be possibly be for lack of data. I do suspect that any initial attempts at a universal model for predicting species richness or abundance over space or time would be complex and would also likely have both low predictive capacity and low universality, but I would argue that this is also true for our current system-specific models.

In taking a reductionist view of ecology, we have parsed it out into a multitude of sub-disciplines and found that different things are important predictors of species richness or abundance in different sub-disciplines, and we’ve stopped there. It seems that we’ve lost sight of “understanding the whole” and instead are sprawling out into examining more and more parts from different perspectives. I believe that favouring small-scale simplicity in this way is strongly impeding the unified, integrated progression of the discipline as a whole.

Developing universal models for predicting species richness or abundances over space or time (expressed in terms of information we have the ability to measure) would be a very daunting task. I do, however, think it is sorely needed in order for the highly fragmented field of ecology to provide us with a more complete understanding of our natural world.

“It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.” – Albert Einstein, in “On the Method of Theoretical Physics,” the Herbert Spencer Lecture, Oxford, June 10, 1933.

Or, more simply:

“Everything should be as simple as it can be but not simpler.” – Robert Sessions, in “How a ‘Difficult’ Composer Gets That Way,” The New York Times,  January 8, 1950.

Please feel free to chime in with any ideas of potential starting points for universal ecological models, or with any measurable values that might make important parameters in such models.

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2 Comments
  1. The logistic growth function might be the best starting point for a universal model for predicting species abundance over time. Progress could be made by attempting to provide a complete description of the factors that affect each component of the function (carrying capacity, current population size and intrinsic rate of increase) and substituting formulas for estimating each component (using measurable information) in place of the components themselves.

  2. Jeff Houlahan permalink

    This is, as you know, Joe, a topic that is near and dear to my heart. Over at Dynamic Ecology Jeremy Fox has suggested that the use of modern time series analysis and model fitting has resulted in big steps forward in our understanding of population cycles. I don’t know this literature well enough to dispute this point but maybe (related to your suggestion) that’s a place to start…how well do those models predict cycles?
    Another potentially fruitful avenue could be turning species-area relationships into a tool I could use for a new set of patches where I know little or nothing about the number of species in the area. Best, Jeff Houlahan.

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