Niche Modeling: Predictions from Statistical Distributions

Gross, Louis J. (Series edited by)/ Lenhart, Suzanne (Series edited by)/ Maini, Philip K. (Series edited by)/ Etheridge, Alison M. (Series edited by)/ Voit, Eberhard O. (Series edited by)/ Ranganathan, Shoba (Series edited by)/ Safer, Hershel (Series edited by)/ Stockwell, David (Author)

ISBN 10: 1584884940 / ISBN 13: 9781584884941

Published by Chapman & Hall, 2006

About this Item

1st edition. 201 pages. 9.25x6.25x0.50 inches. In Stock.

Seller Inventory # x-1584884940

Bibliographic Details

• Title: Niche Modeling: Predictions from Statistical Distributions
• Publisher: Chapman & Hall
• Publication Date: 2006
• Language: English
• ISBN 10: 1584884940
• ISBN 13: 9781584884941
• Binding: Hardcover
• Condition: Brand New
• Weight: 0.57 kilograms

About this title

Synopsis

Using theory, applications, and examples of inferences, Niche Modeling: Predictions from Statistical Distributions demonstrates how to conduct and evaluate niche modeling projects in any area of application. It features a series of theoretical and practical exercises for developing and evaluating niche models using the R statistics language. The author discusses applications of predictive modeling methods with reference to valid inferences from assumptions. He elucidates varied and simplified examples with rigor and completeness. Topics include geographic information systems, multivariate modeling, artificial intelligence methods, data handling, and information infrastructure. Above all, successful niche modeling requires a deep understanding of the process of creating and using probability. Off-the-shelf statistical packages are tailored exactly to applications but can hide problematic complexities. Recipe book implementations fail to educate users in the details, assumptions, and pitfalls of analysis, but may be able to adapt to the specific needs of each study. Examining the sources of errors such as autocorrelation, bias, long term persistence, nonlinearity, circularity, and fraud, this seminal reference provides an understanding of the limitations and potential pitfalls of prediction, emphasizing the importance of avoiding errors.

About the Author

Stockwell, David