If you said you saw a unicorn, a flying monkey, or the Easter Bunny, you’d probably get a few hearty laughs. And no, they wouldn’t be laughing with you, they would be laughing at you. But what about the North American Sasquatch? Bigfoot sightings are very real, or at least the sightees believe so. There are whole organizations dedicated to meticulously recording and analyzing every Bigfoot sighting, footprint, sound, and smell (yes, many claim the Sasquatch smell is an overpowering, pungent stench reminiscent of dirty diapers).
A sizable amount of publically available data exists that organizes and quantifies Bigfoot sightings. The Bigfoot Field Researcher’s Organization is perhaps the largest of these organizations. The BFRO has its very own “comprehensive database of credible sightings and related reports maintained by an all-volunteer network of bigfoot/sasquatch researchers, archivists, and investigators in the United States and Canada.” To some, Sasquatch sightings are a science.
For those of you who aren’t out prowling the North American forests in search of a larger-than-life monster, here’s a little background information. The Sasquatch is a 10-foot-tall, 500-pound, hairy, giant ape-like animal that allegedly lives in North America, evading human eyes except for a few lucky individuals who have taken photos and found larger-than-life footprints; thus the name “Bigfoot.” Bigfoot enthusiasts claim the species is a descendent of the primate Gigantopithicus blacki, a huge ape that died out 100,000 years ago. There is much contention about whether or not such a creature exists, with arguments on both sides from conspiracy theorists to renowned scientists.
In 2009, a group of scientists led by Jeff Lozier, now at the University of Alabama, decided to put Bigfoot sighting data to the test. But testing Sasquatch sighting data that wasn’t their main goal—they aimed to test the validity of ENM’s: Ecological Niche Models. ENM’s are computer-generated models based on sets of data that predict the distribution of a species’ potential range in an environment. ENM’s often turn out erroneous because of incomplete or incorrect taxonomy. And often, unreliable data can produce very believable and “solid” distribution models.
The researchers set out to make an ENM for the North American Sasquatch using data from the Bigfoot Field Researchers Organization. The database included sightings, footprint locations, and sounds allegedly from Bigfoot in western North America, from 1944 to 2005. The researchers also produced an ENM for the American black bear, Ursus americanus, in order to compare this to the Sasquatch distribution. Many Sasquatch doubters believe that black bear sightings are mistaken for Bigfoot views. But an important difference existed between the two data sets: the black bear data was based on physical specimens, whereas the Sasquatch data was based on indirect observations.
Lozier and colleagues weren’t entirely surprised by their results. The Sasquatch ENM predicted a broad distribution in North America; the black bear ENM was incredibly similar. Bigfoot was found in the same places as black bears, with the same frequency under varied climate conditions.
What does this mean for future scientific studies? Lozier’s research has more than a tinge of irony. It was poking fun at the prevalence of Ecological Niche Models in science and their supposed infallibility. But the study, though humorous, wasn’t disputing the power of ENM’s or telling scientists to stop using them. On the contrary, these models are a very powerful way to look at species’ data and predict what effect changes in habit and climate could have on species diversity.
But the Sasquatch study does come with a warning: check your data. Be cognizant of indirect observations that aren’t based on an actual physical specimen. Misidentification of a species can amplify results in a model and produce results that tell a false story. Modern technology and forms of communication make it incredibly easy to get a huge amount of publically available data quite easily and from many different sources. Problems arise particularly when species look alike and live in the same environments, but in reality have very different geographic distributions.
“The point of this paper has been to point out how very sensible-looking, well-preforming ENMs can be constructed from questionable observation data.” The paper goes on to read “…as museums begin to develop databases of collections, all effort should be made to ensure taxonomic accuracy prior to releasing information to the public and to update these databases as new research accumulates for difficult species groups.” Take note, researchers everywhere: bad data leads to bad results and widespread myth belief.
The study authors were careful to include everyone who contributed to the study in their Acknowledgements. They end with expressing their gratitude to the individual who perhaps contributed the most to inspiring this study—“whoever invented April Fool’s Day.”
Find the complete study in the Journal of Biogeography.