Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- Part I History and ecological basis of species distribution modeling
- Part II The data needed for modeling species distributions
- 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
- References
- Index
8 - Classification, similarity and other methods for presence-only data
Published online by Cambridge University Press: 05 August 2012
- Frontmatter
- Contents
- Preface
- Acknowledgments
- Part I History and ecological basis of species distribution modeling
- Part II The data needed for modeling species distributions
- 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
- References
- Index
Summary
Introduction
Many of modeling methods described in Chapters 6 and 7 require observations of species presence and absence, preferably a lot of them (in order to characterize complex response functions), well distributed in space and along environmental gradients. These data are required to estimate model parameters or to derive decision rules for supervised classification. If presence and absence data are available, the modeling approaches that are designed for binary response variables, discussed in the previous chapters, generally give more accurate predictions than models based on presence-only data (e.g., Brotons et al., 2004), but not always (Hirzel et al., 2001). But what if only observations of species presence (but not absence) are available, or what if there are no georeferenced observations at all, but simply some expert knowledge on species habitat requirements? The methods described in this chapter can be, and have been, applied to SDM in these situations. It is actually a very common predicament, to have species presence data, or no species location data at all, and the reasons for that are reviewed here.
If a species has been recorded as being present in a location, we can be fairly certain that it occurs there (except for taxonomic misidentifications). We then make the assumption the occurrence of an organism indicating habitat use, occupancy or suitability for the purpose of modeling.
- Type
- Chapter
- Information
- Mapping Species DistributionsSpatial Inference and Prediction, pp. 180 - 206Publisher: Cambridge University PressPrint publication year: 2010
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