Abstract
1. Species distribution modelling is used increasingly in both applied
and theoretical research to predict how species are distributed and
to understand attributes of species' environmental requirements.
In species distribution modelling, various statistical methods are
used that combine species occurrence data with environmental spatial
data layers to predict the suitability of any site for that species.
While the number of data sharing initiatives involving species' occurrences
in the scientific community has increased dramatically over the past
few years, various data quality and methodological concerns related
to using these data for species distribution modelling have not been
addressed adequately. 2. We evaluated how uncertainty in georeferences
and associated locational error in occurrences influence species
distribution modelling using two treatments: (1) a control treatment
where models were calibrated with original, accurate data and (2)
an error treatment where data were first degraded spatially to simulate
locational error. To incorporate error into the coordinates, we moved
each coordinate with a random number drawn from the normal distribution
with a mean of zero and a standard deviation of 5 km. We evaluated
the influence of error on the performance of 10 commonly used distributional
modelling techniques applied to 40 species in four distinct geographical
regions. 3. Locational error in occurrences reduced model performance
in three of these regions; relatively accurate predictions of species
distributions were possible for most species, even with degraded
occurrences. Two species distribution modelling techniques, boosted
regression trees and maximum entropy, were the best performing models
in the face of locational errors. The results obtained with boosted
regression trees were only slightly degraded by errors in location,
and the results obtained with the maximum entropy approach were not
affected by such errors. 4. Synthesis and applications. To use the
vast array of occurrence data that exists currently for research
and management relating to the geographical ranges of species, modellers
need to know the influence of locational error on model quality and
whether some modelling techniques are particularly robust to error.
We show that certain modelling techniques are particularly robust
to a moderate level of locational error and that useful predictions
of species distributions can be made even when occurrence data include
some error.
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