Image Credit: WomEOS, CC BY-SA 2.0, Image Cropped
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!
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.
Image Credit: Bureau of Land Management, CC BY 2.0, Image Cropped
A common goal of ecologists is to understand the population abundance of a particular species. We might be looking for the California condor as part of assessing how well the recovery project is going. This requires some field work, going out to a variety of sites and counting animals that we see. How do we choose which sites to go to? Even in the era of camera traps, we still need to know where to put our extra set of eyes. It would be a shame to have a particular camera not get any action due to an unlucky placement. We don’t have infinite time and money after all!
Image Credit: Grand Velas Riviera Maya, CC BY-SA 2.0, Image Cropped
In ecological studies, the quality of the data we use is often a concern. For example, individual animals may be cryptic and hard to detect. Certain sites that we should really be sampling might be hard to reach, so we end up sampling more accessible, less relevant ones. Or it could even be something as simple as recording a raven when we’re really seeing a crow (check our #CrowOrNo if you have problems with that last one). Modeling approaches aim to mitigate the effect on our results of these shortcomings in the data collection.
However, even if we had perfect data, when we decide how to model that data, we have to make choices that may not match the reality of the scenario we are trying to understand. Model mis-specification is a generic term for when our model doesn’t match the processes which have generated the data we are trying to understand. It can lead to biased estimates of covariates and incorrect uncertainty quantification.