Category Archives: Stats Corner

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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“Wait, What Am I Even Saying?” Communicating Statistics To A Wide Audience

If we write about our statistical methods behind our ecology work, and none of our readers understand it, have we really communicated at all?

This month I’m getting meta. It’s been about a year and a half since I started writing the Stats Corner for this blog with the goal of demystifying some of the statistical methods that are used by ecologists every day. At the same time, I’ve been writing a book with Deborah Nolan called “Communicating with Data: The Art of Writing for Data Science.” The book was released this spring, so it seemed like a good time to reflect on writing about statistics accessibly. 

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The How, Why, and When of Transforming Data

We’ve been out in the field, painstakingly collecting each butterfly and measuring its body length and wingspan. Now is the moment of truth. We’re about to make a plot and see if the assumptions we make about the relationship between the two measurements are backed up by a linear regression. Is the relationship between length and wingspan what we’d expect? Will a linear model be appropriate or are we going to have to break out the heavier machinery?

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“Those Things Are Evil”: Prediction Intervals in Mixed Models

Suppose we study salamanders and want to predict body mass based on their body length. We also want to account for different access to food and differing levels of competition at each site we’ve collected our salamanders from. So we fit a linear model with a random effect for site as we only have samples from a subset of sites. (Want a refresher on random effects? We’ve got you covered.)

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Finding Balance on the Bias-Variance Seesaw

Building models is a tricky business. There are lots of decisions involved and competing motivations. Say we are an ecologist studying owl abundance in a park near our school. Our primary goal may be to have a good understanding of what is going on in our data. We don’t want to miss any important relationships between abundance and measurable factors about the landscape. Like if we didn’t include tree cover as an explanatory variable, we might have a model that is underfit since that variable would give us potential information about the availability of spots for owls to nest. 

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It’s All Relative: Measuring Abundance In The Face of Detection Bias

There are many papers out there discussing estimates of abundance and occurrence of a variety of plants and animals. Sometimes you’ll also see references to relative abundance and relative occurrence. What makes researchers go for one estimate over the other? When might you face a similar choice? The goal of this post is to try to shed some light on when you might want to keep things relative.

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The Ecological Fallacy: What Does It Have To Do With Us?

Image Credit: Erik Karits, Pixabay licence, Image Cropped

Every once and awhile the term “ecological fallacy” gets thrown around to critique a particular study. Some Twitter discussion around this pre-print, which compares COVID-19 mortality to vegetable consumption at a country level, got me thinking about the term again. So let’s go through what it is, why it’s a problem, and why sometimes it can’t be avoided.

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