Where is positional uncertainty a problem for species distribution modelling?. Issue 2 (4th September 2013)

• Record Type: Journal Article
• Authors: Naimi, Babak; Hamm, Nicholas A. S.; Groen, Thomas A.; Skidmore, Andrew K.; Toxopeus, Albertus G.
• Is Part Of: Ecography. Volume 37:Issue 2(2014:Feb.)
• Page Start: 191
• Page End: 203
• Publication Date: 2013-09-04
• Subjects: Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/j.1600-0587.2013.00205.x
• Languages: English
• ISSNs: 0906-7590

Abstract

<abstract abstract-type="main" xml:lang="en"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Species data held in museum and herbaria, survey data and opportunistically observed data are a substantial information resource. A key challenge in using these data is the uncertainty about where an observation is located. This is important when the data are used for species distribution modelling (SDM), because the coordinates are used to extract the environmental variables and thus, positional error may lead to inaccurate estimation of the species–environment relationship. The magnitude of this effect is related to the level of spatial autocorrelation in the environmental variables. Using local spatial association can be relevant because it can lead to the identification of the specific occurrence records that cause the largest drop in SDM accuracy. Therefore, in this study, we tested whether the SDM predictions are more affected by positional uncertainty originating from locations that have lower local spatial association in their predictors. We performed this experiment for Spain and the Netherlands, using simulated datasets derived from well known species distribution models (SDMs). We used the K statistic to quantify the local spatial association in the predictors at each species occurrence location. A probabilistic approach using Monte Carlo simulations was employed to introduce the error in the species locations. The results revealed that positional uncertainty in species occurrence data at locations with low local spatial association in predictors reduced the prediction accuracy of the SDMs. We propose that local spatial association is a way to identify the species occurrence records that require treatment for positional uncertainty. We also developed and present a tool in the R environment to target observations that are likely to create error in the output from SDMs as a result of positional uncertainty.</p> </abstract>