The upcoming Ketchum project uses samples gathered at random (a process that could never be duplicated). This new mathematical model would require samples gathered from the high density areas you will see on the maps. Restricting such activity to these areas would make strides towards scientific credibility instead of hackneyed samples being sent in by known hoaxers. Evidence needs to be measurable.
For example, the following would be a common claim by many Bigfoot researchers. However, is it evidence? What would be evidence for the existence of a North American Ape?
"I have been researching Bigfoot in Washington and have 3 hours of video and castings of 2 different foot prints in the snow. I now have a chunk of hair I found on a broken tree branch. I need to find out what kind of animal it came from. It is about 3 inches long and like dry fishing line and there is some hair that is like a under coat or winter coat. You my think I'm crazy but that's ok because I know what I have and some day the world will know too."Scientist need more then just that kind of evidence so J. D. Lozier, P. Anilio, and M. J. Hickerson submitted a paper with their results on Bear and Sasquatch sightings ( below you will see density for both animals)
|Bigfoot density of sightings|
|Bear density of sightings|
This all looks very similar. The authors did a correlative study and explain how niche modeliing works.
"Ecological niche models (ENMs) and species distribution models have become increasingly popular tools for predicting the geographic ranges of species and have been important for conservation (Kremen et al., 2007), for predicting changes in distribution from past or future climatic events (Hijmans & Graham, 2006), and for investigating patterns of speciation and niche divergence (Wiens & Graham, 2005; Carstens & Richards, 2007; Warren et al., 2008). The basic premise of the ENM approach is to predict the occurrence of species on a landscape from georeferenced site locality data and sets of spatially explicit environmental data layers that are assumed to correlate with the species’ range. In many cases, models are based on researchers’ own collection data and on detailed knowledge of the taxa being studied, making predictions reasonable depictions of species occurrences given the current modelling technology. However, the increasing availability of locality data in online literature, museum databases and online data portals [e.g. GBIF (http://data.gbif.org/)] is providing unprecedented access to biodiversity data and allowing researchers to greatly expand the deployment of species distribution models and/or ENMs. While the value of publicly available sample locality data is not questioned, the consequent introduction of errors in the accuracy of specimen identity and georeferencing could be problematic for developing ENMs from public data sources (Graham et al., 2004; Soberón & Peterson, 2004). Although georeferencing inaccuracies can be identified in databases from qualitative or quantitative accuracy thresholds (e.g. http://manisnet.org/GeorefGuide.html), poor taxonomy and/or misidentification may be less detectable. This issue may be particularly problematic, for example, with cryptic species or subspecies that are morphologically similar but may have very distinct ecological requirements and geographic distributions, or for those data sources that contain indirect observations rather than references only to physical specimens."
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. Journal of Biogeography
The fact that a model can be fit to any random assemblage of locality points is, of course, not surprising, but in cases where the distribution of these points appears to be organized, records may be less carefully scrutinized for errors. For instance, the Sasquatch sightings shown in Fig. 1 occur in areas where we might expect to observe such an organism based on preconceived notions, but we may be seriously biasing our inference of the distribution if many (or all) of the records represent misidentified black bear sightings. Although beyond the scope of this piece, it would be interesting for future theoretical studies to explore the effects of including misidentified specimens on ENMs, either using simulations or empirical data from closely related species.The conclusion is that people are seeing Black Bears and Brown Bears. Interesting. I guess people will say the Sasquatch share the same environment and that explains why they have been proven to overlap.