Have you ever see the number of people who drowned by falling into a swimming pool–films starring Nicholas Cage model? You might also know it as “linear regression.” Have you ever seen a species distribution model? By calling it thus we make the same limiting semantic complexification as in the first case.
This post is not about the debate over whether we should be calling it species distribution modeling or ecological niche modeling (notice the participle form of each term–adding an ing refers to the act of modeling). I’m talking about calling them species distribution models.
To put it bluntly, the underlying mathematics of a SDM doesn’t care whether it’s depicting a species or anything else. In fact there are numerous examples of applications of species-less “SDMs”:
- Snakebite prevalence (Yañez-Arenas et al. 2016).
- Presence of Pygmies in Africa (Olivero et al. 2016)
- Safe sites for rescue helicopters to land in Yosemite National Park (Doherty et al. 2013)
I even once met a person who uses Maxent to locate opportune places for underwater archaeology!
The fundamental commonality that allows all of these phenomena to be modeled by “SDM” algorithms is the nature of the response data–either unary (i.e., just presences) or binary (presences and absences). Ergo, if you can describe a pattern with unary or binary data, you can also likely apply an “SDM”.
So feel free to refer to “my species distribution model” to reference your particular model of a species’ distribution. But don’t let the moniker box you into thinking they’re just for species!