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Pseudoabsence generation strategies for species distribution models.
Brice B Hanberry1, Hong S He, Brian J Palik
1Department of Forestry, University of Missouri, Columbia, Missouri, USA. hanberryb@missouri.edu
Plos One
|September 7, 2012
Summary
The strategy for generating pseudoabsences significantly impacts species distribution models. Models using surveyed plots offered the most balanced predictions, highlighting the importance of careful pseudoabsence selection for accurate ecological modeling.
Area of Science:
- Ecology
- Computational Biology
- Conservation Science
Background:
- Species distribution models (SDMs) are crucial for ecological research but depend on several key choices.
- When absence data are unavailable, pseudoabsence generation is a critical step.
- Different pseudoabsence strategies can lead to varied spatial predictions of species occurrence.
Purpose of the Study:
- To evaluate the impact of four distinct pseudoabsence generation strategies on SDMs.
- To determine which pseudoabsence strategy yields the most reliable species distribution predictions.
- To assess the influence of pseudoabsence selection on model outcomes and ecological interpretations.
Main Methods:
- Four pseudoabsence generation methods were tested: random selection, two-step selection with probability thresholds, and selection from surveyed plots.
- Random Forests algorithm was employed with sixteen predictor variables.
- Models were developed for tree species with at least 150 records from Forest Inventory and Analysis surveys in Minnesota.
Main Results:
- The choice of pseudoabsence strategy profoundly influenced the predicted species distribution area.
- All strategies yielded mean AUC values of 0.87 or higher, indicating good model performance.
- Two-step strategies over-predicted presence, while random pseudoabsences under-predicted it.
- Pseudoabsences from surveyed plots resulted in balanced predictions and a strong relationship between species density and predicted occurrence.
Conclusions:
- Pseudoabsence generation is a highly influential factor in SDMs, potentially more so than accuracy metrics alone.
- Evaluating whether a species' predicted range is sufficient yet not excessive is a key assessment criterion.
- Models using pseudoabsences from surveyed plots provide a more ecologically realistic representation of species distributions.
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