Abstract
Predicting the distribution of endangered species from habitat data
is frequently perceived to be a useful technique. Models that predict
the presence or absence of a species are normally judged by the number
of prediction errors. These may be of two types: false positives
and false negatives. Many of the prediction errors can be traced
to ecological processes such as unsaturated habitat and species interactions.
Consequently, if prediction errors are not placed in an ecological
context the results of the model may be misleading. The simplest,
and most widely used, measure of prediction accuracy is the number
of correctly classified cases. There are other measures of prediction
success that may be more appropriate. Strategies for assessing the
causes and costs of these errors are discussed. A range of techniques
for measuring error in presence/absence models, including some that
are seldom used by ecologists (e.g. ROC plots and cost matrices),
are described. A new approach to estimating prediction error, which
is based on the spatial characteristics of the errors, is proposed.
Thirteen recommendations are made to enable the objective selection
of an error assessment technique for ecological presence/absence
models.
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