Recent Comments
- Nick Stokes @ Radiative Equilibrium (Miskolczi Part 4)
- Chris Morris @ Drought Exceptional Circumstances Report MIA
- Jan Pompe @ Radiative Equilibrium (Miskolczi Part 4)
- stas peterson @ Greenhouse Heat Engine
- Neal J. King @ Radiative Equilibrium (Miskolczi Part 4)
- Jan Pompe @ Radiative Equilibrium (Miskolczi Part 4)
- Neal J. King @ Radiative Equilibrium (Miskolczi Part 4)
- Neal J. King @ Radiative Equilibrium (Miskolczi Part 4)
- Jan Pompe @ Radiative Equilibrium (Miskolczi Part 4)
- Neal J. King @ Radiative Equilibrium (Miskolczi Part 4)
Abouts
Blogroll
Literature
Subscribe
First Time At Niche Modeling?
This is a blog on the power of numeracy. My first book — Niche Modeling — is now in print.The first six chapters are tutorial topics in R programming and theoretical topics in niche modeling: functions, data, spatial, topology, environmental data collections, and examples. The last six chapters are about using niche modeling to detect errors: bias, autocorrelation, non-linearity, long term persistence, circularity and fraud - useful information for any biological modeler.
June 30, 2006
About Niche Modeling
The following is a new version of the About Page. Gradually getting this website organized the way I want it.
I have always been fascinated with prediction.
As an undergraduate I made stock predictors on the first PCs and lost money in 1987.
Studied maths, statistics and started a PhD in ecological prediction.
Developed betting systems and lost money.
Studied algorithms for predicting species distributions and developed GARP which other people used for cool things like finding new species of chameleon in Madagascar.
Developed automated trading systems for FOREX in 2002 and lost money.
So I know a few things about prediction, and more about how not to do prediction. In addition, in this blog I hope to pass on a few, and help people to predict better. Like predicting the risk to poultry from Bird Flu using GIS spatial analysis. Or monitoring the health of different types of hydrocoral polyps on reefs. The possibilities are endless. (more…)
June 28, 2006
Phillips et al. Maxent
The paper by S.J. Phillips, R.P. Anderson, and R.E. Schapire — A maximum entropy approach to species distribution modeling — introduces to niche modelers for the first time the Maximum Entropy approach well known in machine learning. They also provide the Maxent software for predicting species distribution using Maxent, and evaluate against a well know method called DesktopGARP in predicting the distribution of two Neotropical mammals, a sloth Bradypus variegatus and a rodent Microryzomys minutus.
The Maxent principle is to estimate the probability distribution, such as the spatial distribution of a species, that is most spread out subject to constraints such as the known observations of the species. Maxent uses entropy as the means to generalize specific observations of presence of a species, and does not require or even incorporate absence points within the theoretical framework. Presence-only points are observations of the presence of a species. For a variety of reasons, absence of a species is not usually recorded. (more…)
June 27, 2006
Rings of Noise on Hockey Stick Graph
Finally, one journalist has the message right: Duane Freese in his article — “Hockey Stick Shortened?” — at TechCentralStation reports on the National Academy of Sciences report “Surface Temperature Reconstructions for the Last 2,000 Years“. Repetition of the consensus view of strong evidence of recent global warming is not newsworthy. Increase in the uncertainty of the Millennial temperature record is. He says:
The most gratifying thing about the National Academy of Science panel report last week into the science behind Michael Mann’s past temperature reconstructions - the iconic “hockey stick” isn’t what the mainstream media have been reporting — the panel’s declaration that the last 25 years of the 20th Century were the warmest in 400 years.
The hockey stick, in short, is 600 years shorter than it was before and the uncertainties for previous centuries are larger than Mann gave credence. And when the uncertainty of the paleoclimatogical record increases with time, the uncertainty about human contribution is likewise increased. Why? For a reason noted on page 103 of the report: climate model simulations for future climates are tuned to the paleoclimatogical proxy evidence of past climate change.



