Category Archives: Stats Corner
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.Read more
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?Read more
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.)Read more
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.Read more
I’ve written about fixed, mixed, and random effects in linear models before (and others have too) but I think it’s time to approach the topic with some ecology motivation. What do these different types of effects mean to us in the wild and when might we need to use one over the other? Read on to learn more!Read more
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.Read more
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.
Image Credit: Pharexia, Ratherous, AKS471883, Source Data from Johns Hopkins University CSSE, The Centers for Disease Control and Prevention, New York Times, CNBC.
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.