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Mapping Species Distributions: Spatial Inference and Prediction

Mapping Species Distributions: Spatial Inference and Prediction

Paperback Ecology, Biodiversity and Conservation

By (author) Janet Franklin

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  • Publisher: CAMBRIDGE UNIVERSITY PRESS
  • Format: Paperback | 336 pages
  • Dimensions: 150mm x 226mm x 20mm | 522g
  • Publication date: 8 February 2010
  • Publication City/Country: Cambridge
  • ISBN 10: 0521700027
  • ISBN 13: 9780521700023
  • Illustrations note: 37 b/w illus. 20 tables
  • Sales rank: 318,179

Product description

Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.

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Author information

Janet Franklin has been a Professor of Biology and Adjunct Professor of Geography at San Diego State University, where she was on the faculty from 1988-2009. In 2009 she joined the faculty of Arizona State University as a Professor in the Schools of Geographical Sciences and Life Sciences. She received the Bachelors' degree on Environmental Biology (1979), the Master of Arts (1983), and the Ph.D. (1988) in Geography, all from the University of California at Santa Barbara. Her research interests include biogeography, landscape ecology, plant ecology, biophysical remote sensing, digital terrain analysis, and geographic information science. She has conducted research on plant community composition, structure, dynamics and spatio-temporal patterns in Mediterranean-climate ecosystems, deserts, tropical dry forests and rain forests. She was the Editor of The Professional Geographer (1997-2000) Board Member of Landscape Ecology (2000-2005), and Associate Editor of Journal of Vegetation Science (1999-2006). She is currently a Board Member of Ecology and Diversity & Distributions. She has published more than 80 refereed book chapters and papers in journals Ecological Applications, Ecological Modelling, Journal of Vegetation Science, Ecology, Diversity & Distributions, Journal of Tropical Ecology and Conservation Biology. She has received research support from NSF, NASA, USGS, Forest Service, California State Parks, National Geographic Society, and others.

Review quote

'This is a very useful book that we commend to anyone interested in species distribution models ... This is probably the best book available on species distribution models.' Oryx

Table of contents

Part I. History and Ecological Basis of Species' Distribution Modeling: 1. Species distribution modeling; 2. Why do we need species' distribution models?; 3. Ecological understanding of species' distributions; Part II. The Data Needed for Modeling Species' Distributions; 4. Data for species' distribution models: the biological data; 5. Data for species' distribution models: the environmental data; Part III. An Overview of the Modeling Methods: 6. Statistical models - modern regression; 7. Machine learning methods; 8. Classification, similarity and other methods for presence-only data; Part IV. Model Evaluation and Implementation: 9. Model evaluation; 10. Implementation of species' distribution models.