Modelling distribution and abundance with presence‐only data

Modelling distribution and abundance with presence‐only data Summary 1 Presence‐only data, for which there is no information on locations where the species is absent, are common in both animal and plant studies. In many situations, these may be the only data available on a species. We need effective ways to use these data to explore species distribution or species use of habitat. 2 Many analytical approaches have been used to model presence‐only data, some inappropriately. We provide a synthesis and critique of statistical methods currently in use to both estimate and evaluate these models, and discuss the critical importance of study design in models where only presence can be identified 3 Profile or envelope methods exist to characterize environmental covariates that describe the locations where organisms are found. Predictions from profile approaches are generally coarse, but may be useful when species records, environmental predictors and biological understanding are scarce. 4 Alternatively, one can build models to contrast environmental attributes associated with known locations with a sample of random landscape locations, termed either ‘pseudo‐absences’ or ‘available’. Great care needs to be taken when selecting random landscape locations, because the way in which they are selected determines the modelling techniques that can be applied. 5 Regression‐based models can provide predictions of the relative likelihood of occurrence, and in some situations predictions of the probability of occurrence. The logistic model is frequently applied, but can rarely be used directly to estimate these models; instead, case–control or logistic discrimination should be used depending on the sample design. 6 Cross‐validation can be used to evaluate model performance and to assess how effectively the model reflects a quantity proportional to the probability of occurrence. However, more research is needed to develop a single measure or statistic that summarizes model performance for presence‐only data. 7 Synthesis and applications. A number of statistical procedures are available to explore patterns in presence‐only data; the choice among them depends on the quality of the presence‐only data. Presence‐only records can provide insight into the vulnerability, historical distribution and conservation status of species. Models developed using these data can inform management. Our caveat is that researchers must be mindful of study design and the biases inherent in presence data, and be cautious in the interpretation of model predictions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Applied Ecology Wiley

Modelling distribution and abundance with presence‐only data

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Publisher
Wiley
Copyright
Copyright © 2006 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0021-8901
eISSN
1365-2664
D.O.I.
10.1111/j.1365-2664.2005.01112.x
Publisher site
See Article on Publisher Site

Abstract

Summary 1 Presence‐only data, for which there is no information on locations where the species is absent, are common in both animal and plant studies. In many situations, these may be the only data available on a species. We need effective ways to use these data to explore species distribution or species use of habitat. 2 Many analytical approaches have been used to model presence‐only data, some inappropriately. We provide a synthesis and critique of statistical methods currently in use to both estimate and evaluate these models, and discuss the critical importance of study design in models where only presence can be identified 3 Profile or envelope methods exist to characterize environmental covariates that describe the locations where organisms are found. Predictions from profile approaches are generally coarse, but may be useful when species records, environmental predictors and biological understanding are scarce. 4 Alternatively, one can build models to contrast environmental attributes associated with known locations with a sample of random landscape locations, termed either ‘pseudo‐absences’ or ‘available’. Great care needs to be taken when selecting random landscape locations, because the way in which they are selected determines the modelling techniques that can be applied. 5 Regression‐based models can provide predictions of the relative likelihood of occurrence, and in some situations predictions of the probability of occurrence. The logistic model is frequently applied, but can rarely be used directly to estimate these models; instead, case–control or logistic discrimination should be used depending on the sample design. 6 Cross‐validation can be used to evaluate model performance and to assess how effectively the model reflects a quantity proportional to the probability of occurrence. However, more research is needed to develop a single measure or statistic that summarizes model performance for presence‐only data. 7 Synthesis and applications. A number of statistical procedures are available to explore patterns in presence‐only data; the choice among them depends on the quality of the presence‐only data. Presence‐only records can provide insight into the vulnerability, historical distribution and conservation status of species. Models developed using these data can inform management. Our caveat is that researchers must be mindful of study design and the biases inherent in presence data, and be cautious in the interpretation of model predictions.

Journal

Journal of Applied EcologyWiley

Published: Jun 1, 2006

References

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