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Ecological Niches and Geographic Distributions (MPB-49)$
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A. Townsend Peterson, Jorge Soberón, Richard G. Pearson, Robert P. Anderson, Enrique Martínez-Meyer, Miguel Nakamura, and Miguel B. Araújo

Print publication date: 2011

Print ISBN-13: 9780691136868

Published to Princeton Scholarship Online: October 2017

DOI: 10.23943/princeton/9780691136868.001.0001

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PRINTED FROM PRINCETON SCHOLARSHIP ONLINE (www.princeton.universitypressscholarship.com). (c) Copyright Princeton University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in HSO for personal use (for details see www.princeton.universitypressscholarship.com/page/privacy-policy).date: 14 November 2018

Evaluating Model Performance and Significance

Evaluating Model Performance and Significance

Chapter:
(p.150) Chapter Nine Evaluating Model Performance and Significance
Source:
Ecological Niches and Geographic Distributions (MPB-49)
Author(s):

A. Townsend Peterson

Jorge Soberón

Richard G. Pearson

Robert P. Anderson

Enrique Martínez-Meyer

Miguel Nakamura

Miguel Bastos Araújo

Publisher:
Princeton University Press
DOI:10.23943/princeton/9780691136868.003.0009

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.

Keywords:   model performance, primary occurrence data, commission error, model calibration, model prediction, overfitting, sensitivity, model significance, model evaluation

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