Showing posts with label ENM. Show all posts
Showing posts with label ENM. Show all posts

Sunday, February 6, 2011

More Sasquatch Ecological Niche Modeling

An Ecologist from North Carolina, Melissa, breaks down one of our favorite Research Papers. This paper, from the Journal of Biogeography, presents ENMs for Sasquatch. They base their ENMs on putative sightings, auditory detections, and footprint measurements primarily obtained from the Bigfoot Field Researchers Organization (BFRO).

The best thing about this post is she briefly explains, in layman terms, what software was used and how it was used. She also does a great job explaining expected results and conclusions.

North Carolina, Feburary 6, 2011 (Science Storiented) In the science of Sasquatch it's all about distribution. Where is he (or she) and how can I get a photo? The photo I'll leave up to you, and hope you are good at keeping your camera steady to avoid those embarrassingly blurry pictures. The where is he part can be figured out by utilizing user-friendly software, publicly available biodiversity databases, and ecological niche modeling (ENM).

A scientist named Grinnell proposed the ecological niche concept in 1917, so it isn't new. Overall, it's pretty simple. Each species needs a specific set of conditions to survive. The range where these conditions occur is where a species can maintain a population. Since 1917 the concept has been expanded, most notably by Elton in 1927 and MacArthur in 1972, to include a species as part of an ecological community. With this type of model it is possible to characterize the ecological needs of a species, predict and anticipate it's distribution, predict changes in it's distribution with changing land and climate, investigate patterns of speciation and niche divergence, and build scenarios for unknown conditions and behavior. "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." That's how the paper I'm presenting today describes it. What does it mean? Input known, locally collected data and make reasonable predictions of species occurrences given the current modelling technology. That known, locally collected data is becoming more and more available and accessible via museum databases and online data portals.

Sasquatch, or Bigfoot, is currently (pseudo-)classified as a member of a large primate lineage descended from the extinct Asian species (Gigantopithicus blacki), but there is some phylogenetic analysis indicating a possible membership in the ungulate clade. Regular reports have Sasquatch inhabiting the forested lands of western North America, although a type specimen is unavailable. This paper, from the Journal of Biogeography, presents ENMs for Sasquatch. They base their ENMs on putative sightings, auditory detections, and footprint measurements primarily obtained from the Bigfoot Field Researchers Organization (BFRO). Events were assigned geographic coordinates on USGS quad maps and atlases and the ENMs constructed using the maximum entropy niche modelling approach using the software MAXENT. Then environmental layers were constructed for 19 BIOCLIM variables in the WORLDCLIM dataset. The final set of environmental variables included annual mean temperature, mean diurnal range, isothermality, temperature annual range, mean temperature of wettest quarter, mean temperature of driest quarter, precipitation seasonality, precipitation of driest quarter, and precipitation of coldest quarter.

The ENM showed that Sasquatch should be broadly distributed in western North America, with a range comprising such mountain ranges as the Sierra Navadas, the Blue Mountains, the Selkirk Mountains, and the Cascades. The bioclimatic variable that was the best predictor was precipitation in the coldest quarter. And so, it is likely that the distribution will be altered due to global climate change.

Running with that result, the scientists examined the potential ramifications of climate change on remnant Sasquatch populations to predict how the frequency of sightings might change in the future. To do this they projected ENMs generated from the WORLDCLIM data into bioclimatic layers simulated for a doubling of atmospheric CO2. The model predicts that Sasquatch will abandon lower altitudes and lose habitat in coastal regions. But the species will potentially gain habitat in the northern part of the range as well as in several other montane areas. This means that, in the future, you should expect to sight Bigfoot in northern latitudes and at higher elevations.

Another suggestion: Look for American black bears (Ursus americanus) and you may sight Sasquatch. Now, I'm not advocating lurking around bear dens or walking right up on a black bear, but the predicted distribution of Sasquatch is similar to the range of the American black bear. So much so, that it is thought that some Bigfoot sightings were, in fact, misidentified black bears.

Up for a hike in California? Bring your camera.
Here's the paper:
J. D. Lozier, Aniello, P., and Hickerson, M.J. (2009) Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling. Journal of Biogeography: 36(9), 1623-1627. (DOI: 10.1111/j.1365-2699.2009.02152.x)

Science Storiented
Predicting the distribution of Sasquatch in western North America

Global Warming = Bigfoot Migrates North

Saturday, October 31, 2009

Global Warming = Bigfoot Migrates North

Dont take my word for it! There are specialist in a unique scientific discipline called Ecological Niche Modeling (ENM)

Using a database of sightings and footprints for Bigfoot in western North America, the researchers suggest that convincing distributions of an animals range can be generated from questionable data. By comparing the distribution of Bigfoot to that of a black bear, Lozier et al. “suggest that many sightings of this cryptozoid may be cases of mistaken identity.”

The algorithms take information about sightings or recorded incidences of a species, find commonalities among those sightings against maps of other ecological data (i.e rainfall, forest type, presence of other species, etc.), and produce a geographic distribution for the target species.

The paper, “Predicting the distribution of Sasquatch in western North America: anything goes with ecological niche modelling,” constructs ecological niche models (ENMs) for the elusive Bigfoot. By using a large database of georeferenced sightings and footprints for Sasquatch in western North America, Lozier and his colleagues aim to demonstrate how convincing environmentally predicted distributions of a taxon’s potential range can be generated from questionable site-occurrence data. Lozier et al. do not take an explicit stance on the existence of Bigfoot, but rather make use of publicly available data sets with questionable records to illustrate the danger of using incomplete data to make statistical correlations.

Read a full article from NATURE below.

Bigfoot study highlights habitat modelling flaws
Accurate prediction of climate change's effects is as elusive as the fabled apeman.

John Whitfield

Climate change, it turns out, is going to be a mixed blessing for the sasquatch. The legendary American apeman will lose some of its existing habitat in the coastal and lowland regions of the northwestern United States, but gain a lot of new land in the Rocky Mountains and Canada.

So say biologist Jeff Lozier of the University of Illinois at Urbana-Champaign and his colleagues, in an analysis just published in theJournal of Biogeography1. But they're not really worried about bigfoot. Instead, they're trying to warn their colleagues that ecological models are only as good as the data that go into them.

Lozier's team subjected bigfoot to a technique called ecological niche modelling. This involves taking records of where a species has been found, and then, by combining these with environmental data, deducing where it ought to live or has lived in the past, present or future.
Such models are among the main tools in efforts to predict and plan for the biological effects of climate change. And because their predictions can be displayed as intuitive and dramatic maps, they have a psychological power beyond most scientific graphics.

Mistaken identity

But researchers' enthusiasm for such analyses risks outpacing their understanding of them, says Lozier. "The method is really new, and it's not fully worked out. I think some people have been seduced by the pretty output."

“We were trying to do the same thing for the yeti.”
One problem is misidentification. It's hard to judge whether someone really saw what they thought they saw where they saw it, particularly in less well-studied groups such as insects — or American apemen. Mistake one species for another, for example, and your model will mislead.

Such errors can be hard to spot, because even if all the data are all highly dubious, a model based on them can still give a plausible-looking result, as Lozier and his colleagues found when they analysed sightings recorded by the Bigfoot Field Researchers Organization.
The reported sightings imply that the wooded and mountainous areas of California, Oregon and Washington teem with sasquatch at present. Warm the climate, and, like many other species, it will probably move north and uphill.

"It's a perfect commentary on the potential problems of this approach," says Lozier. "Plus, it's a sasquatch paper."

No crystal ball

Unlikely as it sounds, Lozier's paper scooped work by another group. "We were trying to do the same thing for the yeti," says ecologist Carsten Rahbek of the University of Copenhagen. Like Lozier, he wanted to show that models could turn dubious data into plausible-looking predictions.
A few years ago, only a few labs had the expertise to do ecological niche models. But now they are accessible to just about everyone, thanks to online data sources and user-friendly modelling software.

Much of the resulting work is "very naive", says Rahbek. "I'm editor-in-chief of a journal (Ecography) that gets a lot of these studies, and we reject nine out of ten."

Misidentification isn't even the biggest problem with these models, says ecologist Joaquin Hortal of Imperial College London. More important is bias: if researchers only collect along roads, for example, then models will suggest that the species lives only along roads. "Biodiversity data [are] usually environmentally and spatially biased," he says.

Even if accurate data go in, a model's predictions of where species will go, and which are most at risk of extinction, will be imprecise and uncertain. "We in the modelling community need to be a bit more humble about how precise our predictions are, and acknowledge the errors of estimates, which are huge, more than we do," says Rahbek. "It's just damn hard to predict the future."
So if you need to be cautious about ecological niche models' inputs and you can't be certain about their outputs, are they any use at all? Yes, says Rahbek, because their predictions show consistent trends, such as European wildlife moving north and east as the climate warms. If the data were all random noise, then the predictions would be, too. 
  • References

    1. Lozier, J. D., Aniello, P. & Hickerson, M. J. J. Biogeogr. published online. doi:10.1111/j.1365-2699.2009.02152.x (2009).

Paper on Ecological Niche Modeling of Bigfoot

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