Tag Archives: statistical ecology

Who Is Simpson And What Does His Paradox Mean For Ecologists?

Edward H. Simpson was a codebreaker at Bletchley Park, the home of Allied code-breakers during the Second World War. While you’d think this would be his claim to fame, perhaps his most lasting contribution is his description of Simpson’s paradox. The paradox describes the phenomena whereby a relationship within a dataset dramatically changes if you look at the data by group or all together. More famous examples of the paradox stem from the medical world or the famous Berkeley admissions example. But what examples can we have in mind in ecological settings to guide us? Let’s consider the dimensions of penguins’ bills compiled from Palmer Station in Antarctica. If we are interested in the relationship between the bill depth and length we might do a preliminary analysis like the following linear regression.

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To Get Great (Statistical) Power, It Takes Great Responsibility

Image Credit: Miss Ophelia, Pixabay licence, Image Cropped

There are a lot of questions in ecological research that ask whether or not something has changed over time, or put more simply, whether two things are different – vegetation levels, climate variables, maybe species diversity.

Suppose we are monitoring nutrient levels in a lake to make sure they stay at levels that are habitable for the fish living there. A change in policy about what is allowed to be dumped into the river by local factories was enacted, and we want to see if there is evidence that the nutrient levels have deteriorated in the year following the change when compared to the year before. 

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Forecasting the Fate of an Ecosystem: The Double-Edged Sword of Predictive Modelling

Image Credit: Amy-Jo, Pixabay licence, Image Cropped

Let’s get the humblebragging out of the way – this week a paper that I wrote was published in the Journal of Applied Ecology. It was a paper that I genuinely enjoyed writing, and it gives a tangible outcome – the forecasting of the establishment of invasive species within a region. The applications are obvious. Knowing where an invasive species is likely to pop up lets us detect it early and take action quickly.

Yet that very tangibility of the outcome has resulted in it being the paper of which I most fear the consequences. So in an exorcism of my general nerves (and as a soft disclaimer), I wanted to talk about why forecasting or predicting anything can be such a complicated undertaking for an ecologist.

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Ecological Modelling, the Coronavirus, and Why They’re Not A Perfect Match

Image Credit: Pharexia, Ratherous, AKS471883, Source Data from  Johns Hopkins University CSSEThe Centers for Disease Control and PreventionNew York TimesCNBC.

As it quickly became clear in late February and early March that COVID-19 was not going away anytime soon, attention turned to trying to figure out when and where the virus would spread. Epidemiologists and virologists have had their work cut out for them, trying to simultaneously reassure and warn people the world over about the dangers, the nature and the potential timeline of the virus.

So it came as somewhat of a surprise to see ecologists try and tip their hat into the ring. Early on in the pandemic, teams of ecologists sprang up, trying to use Species Distribution Models to predict the spread of the virus. And whilst this might sound helpful, many of these studies lacked collaboration with epidemiologists, and their predictions very quickly fell flat. Some studies suggested that areas like Brazil and Central Africa would be largely spared by the virus, which quickly turned out not to be the case. Flaws in the studies were spotted quite quickly by concerned members of both the ecological and epidemiological communities alike, and a few teams got started on responses.

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