Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling

Abstract

The availability of user‐friendly software and publicly available biodiversity databases has led to a rapid increase in the use of ecological niche modelling to predict species distributions. A potential source of error in publicly available data that may affect the accuracy of ecological niche models (ENMs), and one that is difficult to correct for, is incorrect (or incomplete) taxonomy. Here we remind researchers of the need for careful evaluation of database records prior to use in modelling, especially when the presence of cryptic species is suspected or many records are based on indirect evidence. To draw attention to this potential problem, we construct ENMs for the North American Sasquatch (i.e. Bigfoot). Specifically, we use a large database of georeferenced putative sightings and footprints for Sasquatch in western North America, demonstrating how convincing environmentally predicted distributions of a taxon’s potential range can be generated from questionable site‐occurrence data. We compare the distribution of Bigfoot with an ENM for the black bear, Ursus americanus, and suggest that many sightings of this cryptozoid may be cases of mistaken identity.

Insights into North Pacific right whale Eubalaena japonica habitat from historic whaling records

“…The removal of these data improved model performance and generated more interpretable predictions. Understanding such biases in opportunistically collected presence data is essential for accurate interpretation of habitat models (Lozier et al 2009). While such experiments are by no means definitive, they help challenge the assumptions we hold about our data and our study system.…”

Forecasting Extinctions: Uncertainties and Limitations

“…The danger of using eyewitness accounts as scientific data is clearly illustrated by the many ‗sightings' of the mythical North American Bigfoot or Sasquatch. Lozier and his colleagues recently used the numerous claims of sightings or footprints create a bioclimatic envelope model (BEM) of the present-day distribution of Bigfoot [11]. Interestingly, when they ran a model for black bear calibrated from the same region from which the Bigfoot sightings were recorded, the two models for the contemporary distribution of the two species were almost identical.…”

Ecological niche modelling requires real presence data and appropriate study regions: a comment on Medone et al. (2015)

“…Low-quality presence data preclude reliable modelling of species' niches [3][4][5]. Two errors are widely recognized, namely incorrect taxonomic identifications leading to the use of presence data not belonging to the focal species [3,5] and assignment of incorrect latitude or longitude during the georeferencing process [4].…”

“…These errors flaw the model calibration process by assuming environmental conditions not matching those of sites in which the focal species is or can be present. Common steps to assure high-quality data for ENM analyses include: (1) examine museum specimens to both confirm the taxonomic identification of the focal species and obtain associated locality data [3,6], (2) georeference with as many sources as necessary (e.g. maps, GIS software, gazetteers, field notes, interviews with specimen collectors; [7]), (3) exclude data with high georeferencing uncertainties [4]; and (4) account analytically for uncertainties generated in the georeferencing process [8][9][10].…”