I originally started writing this post two years ago, back when Donald Trump had just been elected as President of the USA. I didn’t finished writing it then because I assumed that the topic was just a passing fad, something none of us actually took seriously. Fast-forward to today, the US government has been shut down for more than three weeks as Trump tries to hold the country hostage over his border wall with Mexico. I suppose now is a as good a time to finally publish this post, even though a paper was published on the topic in BioScience last year.
As one of the most absurd campaign promises in recent history, Donald Trump’s commitment to building a wall between the United State and Mexico has attracted many critics. Many scoffed at his claims that such a structure will keep out the make-believe mob of bad hombres chomping at the bit to sell drugs to innocent Americans. Others giggled at Trump’s conviction that those very same bad hombres would pay for his trademark erection.
Evolution is creeping into several different aspects of ecology. The latest buzz is all about integrating ecology and evolution. Perhaps you’ve heard of the latest research trends in eco-evolutionary dynamics or community phylogenetics?
Please don’t misunderstand me, I am not implying that evolution is not important in explaining patterns in nature, nor am I suggesting that we should disregard evolutionary explanations for these patterns. Instead, I believe that in order to gain a deeper understanding of ecology, we should perhaps partially blind our views using “evolution blinkers”. In fact, I’d even be so bold as to claim that unless we blind ourselves to evolution, we will never be able to fully grasp the true nature of ecological processes. Unifying ecology and evolution might actual limit our ability to build ecology as a science.
No matter at which scale you look at it, nature is remarkable.
Like many others, I was taught ecology in a very hierarchical way: individual organisms are part of a wider populations of species, collections of species form communities and communities come together to make up ecosystems. Similarly, single trees are nested within forests, which aggregate to form biomes. I’m sure you can come up with many comparable examples.
The trouble with such neat spatial hierarchies is that they lure us into believing that if patterns appear similar at several different spatial scales, then the processes leading to these patterns should also be similar. It’s so easy to assume that nature is like a set of Russian Dolls: each daughter exactly the same as its mother, only slightly smaller. But this is not necessarily the case.
One of the most fruitful sub-fields in ecology is using climate variables to predict species’ geographic distributions. For the uninitiated, species distribution modelling assumes that species are limited in their distributions to suitable climate zones. By studying the environmental conditions where species are known to occur, you can infer the total geographic distribution by calculating the suitability of unsampled regions based on the environmental. Furthermore, using the same principle, species distribution modelling can forecast the effect of future climate change of the distribution of life on earth.
The trouble lies in the spatially-structured world we live in. Species distributions, especially at large spatial scales, are spatially-autocorrelated due to constrained dispersal. Similarly, climate variables are also spatially structured because the meteorological processes at proximal regions are generally more similar than those at distant sites.
When trying to link species distributions to climate conditions, the challenge lies is separating spatial and environmental correlations in species distributions. Specifically, we should identify three patterns in the geographical species distributions.
We must first identify ‘true’ correlations with the environment, which are independent of spatial patterns (E|S).
Next, we must identify the environmental-associations that also have a strong spatial structure (E∩S). This is known as exogenous spatial autocorrelation because it is due to autocorrelation is the underlying variables.
Finally, we need to identify spatial patterns that are completely independent of environmental conditions (S|E). This is called endogenous spatial autocorrelation because it supposedly stems from spatial processes, such as dispersal.