by Janet Franklin (with contributions by Jennifer A. Miller)
A title from the ever popular Ecology, Biodiversity & Conservation series from Cambridge University Press, Mapping Species Distribution presents the theory and practice of species distribution modeling in an accessible way. Numerous figures and short sub-sections aid understanding and make it an easy book to dip into.
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.
Part I: History and ecological basis of species distribution and 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 (Janet Franklin and Jennifer A. Miller)
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