Spatially autocorrelated sampling falsely
inﬂates measures of accuracy for
presence-only niche models
Samuel D. Veloz*
Department of Environmental Science and
Policy, University of California, One Shields
Avenue, Davis, CA, USA
*Correspondence: Samuel D. Veloz, Department
of Environmental Science and Policy, University
of California, Davis, One Shields Avenue, Davis,
CA 95616, USA.
Aim Environmental niche models that utilize presence-only data have been
increasingly employed to model species distributions and test ecological and
evolutionary predictions. The ideal method for evaluating the accuracy of a niche
model is to train a model with one dataset and then test model predictions against
an independent dataset. However, a truly independent dataset is often not
available, and instead random subsets of the total data are used for ‘training’ and
‘testing’ purposes. The goal of this study was to determine how spatially
autocorrelated sampling affects measures of niche model accuracy when using
subsets of a larger dataset for accuracy evaluation.
Location The distribution of Centaurea maculosa (spotted knapweed;
Asteraceae) was modelled in six states in the western United States: California,
Oregon, Washington, Idaho, Wyoming and Montana.
Methods Two types of niche modelling algorithms – the genetic algorithm for
rule-set prediction (GARP) and maximum entropy modelling (as implemented
with Maxent) – were used to model the potential distribution of C. maculosa
across the region. The effect of spatially autocorrelated sampling was examined by
applying a spatial ﬁlter to the presence-only data (to reduce autocorrelation) and
then comparing predictions made using the spatial ﬁlter with those using a
random subset of the data, equal in sample size to the ﬁltered data.
Results The accuracy of predictions from both algorithms was sensitive to the
spatial autocorrelation of sampling effort in the occurrence data. Spatial ﬁltering
led to lower values of the area under the receiver operating characteristic curve
plot but higher similarity statistic (I) values when compared with predictions
from models built with random subsets of the total data, meaning that spatial
autocorrelation of sampling effort between training and test data led to inﬂated
measures of accuracy.
Main conclusions The ﬁndings indicate that care should be taken when
interpreting the results from presence-only niche models when training and test data
have been randomly partitioned but occurrence data were non-randomly sampled
(in a spatially autocorrelated manner). The higher accuracies obtained without the
spatial ﬁlter are a result of spatial autocorrelation of sampling effort between training
and test data inﬂating measures of prediction accuracy. If independently surveyed data
for testing predictions are unavailable, then it may be necessary to explicitly account
for the spatial autocorrelation of sampling effort between randomly partitioned
training and test subsets when evaluating niche model predictions.
Accuracy assessment, Centaurea maculosa, environmental niche model, GARP,
invasive species, Maxent, similarity statistic I, spatial autocorrelation, spatially
autocorrelated sampling, western United States.
Journal of Biogeography (J. Biogeogr.) (2009) 36, 2290–2299
www.blackwellpublishing.com/jbi ª 2009 Blackwell Publishing Ltd