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 â€˜Uâ€™ shape characteristic of a niche — step, Gaussian, quadratic and ramp functions â€“ 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 Râ€™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.

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