Open AccessUsing measurement error models to account for georeferencing error in species distribution models
Author(s) -
VelásquezTibatá Jorge,
Graham Catherine H.,
Munch Stephan B.
Publication year - 2016
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/ecog.01205
Subject(s) - covariate , georeference , statistics , sampling (signal processing) , species distribution , observational error , bayesian probability , distribution (mathematics) , computer science , environmental niche modelling , mathematics , ecology , geography , habitat , physical geography , biology , mathematical analysis , ecological niche , filter (signal processing) , computer vision
Georeferencing error is prevalent in datasets used to model species distributions, inducing uncertainty in covariate values associated with species occurrences that result in biased probability of occurrence estimates. Traditionally, this error has been dealt with at the data‐level by using only records with an acceptable level of error (filtering) or by summarizing covariates at sampling units by using measures of central tendency (averaging). Here we compare those previous approaches to a novel implementation of a Bayesian logistic regression with measurement error (ME), a seldom used method in species distribution modeling. We show that the ME model outperforms data‐level approaches on 1) specialist species and 2) when either sample sizes are small, the georeferencing error is large or when all georeferenced occurrences have a fixed level of error. Thus, for certain types of species and datasets the ME model is an effective method to reduce biases in probability of occurrence estimates and account for the uncertainty generated by georeferencing error. Our approach may be expanded for its use with presence‐only data as well as to include other sources of uncertainty in species distribution models.