Creating a Statistical Model with a Cherry-picking Process

Steve McIntyre, always gracious in his acknowledgments, mentioned my note in the Australian Institute of Geologists newsletter (AIG News No 83 Mar 2006 pp14) in a post yesterday “The Full Network“.

We’ve discussed on many occasions that you can “get” a HS merely from picking upward-trending series from networks of red noise (David Stockwell had a good note on this phenomenon on his blog a couple of years ago. My first experiments of this type were on the cherry picks in the original Jacoby network.)

This note published way back in May 2006 (citeable but not peer reviewed) was probably the first of my posts that got picked up in other blogs, such as the American Thinker. The graph shows reconstructed temperature anomalies over 2000 years, using completely random numbers with autocorrelation, has a strong resemblance to other published reconstructions, particularly the prominent ‘hockey-stick’ shape, the cooler temperatures around the 1500s and the Medieval Warm Period around the 1000s. This demonstrates that the method from dendroclimatology of choosing proxies based on correlation with the reference period, (aka cherry-picking) will generate plausible climate reconstructions even on random numbers.

This undermines the credibility of reconstructions using this process from proxies, particularly where this source of uncertainty has not been recognized, and confidence intervals have not been expanded to incorporate the additional uncertainty.

Niche Modeling. Chapter Summary

Here is a summary of the chapters in my upcoming book Niche Modeling to be published by CRC Press. Many of the topics have been introduced as posts on the blog. My deepest thanks to everyone who has commented and so helped in the refinement of ideas, and particularly in providing motivation and focus.

Writing a book is a huge task, much of it a slog, and its not over yet. But I hope to get it to the publishers so it will be available at the end of this year. Here is the dustjacket blurb:

Through theory, applications, and examples of inferences, this book shows how to conduct and evaluate ecological niche modeling (ENM) projects in any area of application. It features a series of theoretical and practical exercises in developing and evaluating ecological niche models using a range of software supplied on an accompanying CD. These cover geographic information systems, multivariate modeling, artificial intelligence methods, data handling, and information infrastructure. The author then features applications of predictive modeling methods with reference to valid inference from assumptions. This is a seminal reference for ecologists as well as a superb hands-on text for students.

Part 1: Informatics

Functions: This chapter summarizes major types, operations and relationships encountered in the book and in niche modeling. This and the following two chapters could be treated as a tutorial in the R. For example, the main functions for representing the inverted ‘U’ shape characteristic of a niche — step, Gaussian, quadratic and ramp functions – are illustrated in both graphical from and R code. The chapeter concludes with the ACF and lag plots, in one or two dimensions.

Data: This chapter demonstrates how to manage simple biodiversity databases using R. By using data frames as tables,
it is possible to replicate the basic spreadsheet and relational database operations with R’s powerful indexing functions.
While a database is necessary for large-scale data management, R can eliminate conversion problems as data is moved between systems.

Spatial:
R and image processing operations can perform many of the
elementary spatial operations necessary for niche modeling.
While these do not replace a GIS, it demonstrates that generalization of arithmetic concepts to images can be implemented simple spatial operations efficiently.

Part 2: Modeling

Theory: Set theory helps to identify the basic assumptions
underlying niche modeling, and the relationships and constraints between these
assumptions. The chapter shows the standard definition of the niche as
environmental envelopes is equivalent to a box topology. It is proven that when
extended to infinite dimensions of environmental variables this definition
loses the property of continuity between environmental and geographic spaces.
Using the product topology for niches would retain this property.

Continue reading Niche Modeling. Chapter Summary