A. Townsend Peterson, Jorge Soberón, Richard G. Pearson, Robert P. Anderson, Enrique Martínez-Meyer, Miguel Nakamura, and Miguel Bastos Araújo
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691136868
- eISBN:
- 9781400840670
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691136868.003.0005
- Subject:
- Biology, Ecology
This chapter discusses the process of transforming a species’ primary occurrence data into a synthetic understanding of the geographic and ecological conditions under which the species occurs. The ...
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This chapter discusses the process of transforming a species’ primary occurrence data into a synthetic understanding of the geographic and ecological conditions under which the species occurs. The focus is on correlative models based on occurrence data, since such models can have quite broad applicability. The chapter first considers different types of occurrence data as well as factors that connect the suitability of a site to the existence of a data record documenting the species’ presence or absence at that site. It then examines variations in the geographic and ecological characteristics of species distributions and occurrences, along with sampling bias in geographic and environmental spaces. It also describes the characteristics of absence data before concluding with an assessment of issues of content and availability that affect occurrence data.Less
This chapter discusses the process of transforming a species’ primary occurrence data into a synthetic understanding of the geographic and ecological conditions under which the species occurs. The focus is on correlative models based on occurrence data, since such models can have quite broad applicability. The chapter first considers different types of occurrence data as well as factors that connect the suitability of a site to the existence of a data record documenting the species’ presence or absence at that site. It then examines variations in the geographic and ecological characteristics of species distributions and occurrences, along with sampling bias in geographic and environmental spaces. It also describes the characteristics of absence data before concluding with an assessment of issues of content and availability that affect occurrence data.
A. Townsend Peterson, Jorge Soberón, Richard G. Pearson, Robert P. Anderson, Enrique Martínez-Meyer, Miguel Nakamura, and Miguel Bastos Araújo
- Published in print:
- 2011
- Published Online:
- October 2017
- ISBN:
- 9780691136868
- eISBN:
- 9781400840670
- Item type:
- chapter
- Publisher:
- Princeton University Press
- DOI:
- 10.23943/princeton/9780691136868.003.0009
- Subject:
- Biology, Ecology
This chapter describes a framework for selecting appropriate strategies for evaluating model performance and significance. It begins with a review of key concepts, focusing on how primary occurrence ...
More
This chapter describes a framework for selecting appropriate strategies for evaluating model performance and significance. It begins with a review of key concepts, focusing on how primary occurrence data can be presence-only, presence/background, presence/pseudoabsence, or presence/absence as well as factors that may contribute to apparent commission error. It then considers the availability of two pools of occurrence data: one for model calibration and another for evaluation of model predictions. It also discusses strategies for detecting overfitting or sensitivity to bias in model calibration, with particular emphasis on quantification of performance and tests of significance. Finally, it suggests directions for future research as regards model evaluation, highlighting areas in need of theoretical and/or methodological advances.Less
This chapter describes a framework for selecting appropriate strategies for evaluating model performance and significance. It begins with a review of key concepts, focusing on how primary occurrence data can be presence-only, presence/background, presence/pseudoabsence, or presence/absence as well as factors that may contribute to apparent commission error. It then considers the availability of two pools of occurrence data: one for model calibration and another for evaluation of model predictions. It also discusses strategies for detecting overfitting or sensitivity to bias in model calibration, with particular emphasis on quantification of performance and tests of significance. Finally, it suggests directions for future research as regards model evaluation, highlighting areas in need of theoretical and/or methodological advances.