Three way k-fold cross-validation of resource selection functions

A resource selection function (RSF) yields a prediction that is proportional to the probability of use of a resource unit by an organism. Because many apparently adequate models fail in new areas or time periods we developed a method for model selection and evaluation based on the model’s ability to...

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Bibliographic Details
Published in:Ecological modelling 2008-04, Vol.212 (3), p.244-255
Main Authors: Wiens, Trevor S., Dale, Brenda C., Boyce, Mark S., Kershaw, G. Peter
Format: Article
Language:English
Subjects:
Animal, plant and microbial ecology
Aves
Baird’s Sparrow
Biological and medical sciences
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Habitat selection
Horned Lark
k-Fold cross-validation
Methods and techniques (sampling, tagging, trapping, modelling...)
Model selection
Prediction
Resource selection function
Spatial variation
Sprague’s Pipit
Temporal variation
Validation
Vertebrates: general zoology, morphology, phylogeny, systematics, cytogenetics, geographical distribution
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Summary:A resource selection function (RSF) yields a prediction that is proportional to the probability of use of a resource unit by an organism. Because many apparently adequate models fail in new areas or time periods we developed a method for model selection and evaluation based on the model’s ability to predict generally, spatially, and temporally. This work is an extension of previous work using k-fold cross-validation to evaluate models developed using presence-only study designs. A RSF model’s utility is its ability to predict, so this method is applicable to any RSF model regardless of study design. The use and application of our proposed 3-way evaluation using the RSF Plot Index (RPI) statistic is illustrated using survey data of grassland birds, Landsat imagery, soil data, and a Digital Elevation Model from the Canadian Forces Base Suffield in southeastern Alberta. The sensitivity of the RPI statistic to the number and placement of bins is addressed and a method is presented to ameliorate this problem. The 3-way method provides the means to not only select the model with the best predictive power, but to understand the limitations of all models under consideration. Test results of best models using an independent field season are presented.
ISSN:0304-3800
1872-7026
DOI:10.1016/j.ecolmodel.2007.10.005