Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes

Mitchell, Peter J, Monk, Jacquomo and Laurenson, Laurie 2016, Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes, Methods in ecology and evolution, In Press, pp. 1-10, doi: 10.1111/2041-210X.12645.

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Title Sensitivity of fine-scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes
Author(s) Mitchell, Peter J
Monk, JacquomoORCID iD for Monk, Jacquomo
Laurenson, LaurieORCID iD for Laurenson, Laurie
Journal name Methods in ecology and evolution
Season In Press
Start page 1
End page 10
Total pages 10
Publisher Wiley
Place of publication Chichester, Eng.
Publication date 2016-09-01
ISSN 2041-210X
Keyword(s) georeferencing error
habitat suitability
model performance
occurence data accuracy
spatial error
Summary To generate realistic predictions, species distribution models require the accurate coregistration of occurrence data with environmental variables. There is a common assumption that species occurrence data are accurately georeferenced; however, this is often not the case. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models. This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data. Distribution models were constructed for kelp (Ecklonia radiata), across a large study site (680 km2) off the coast of southeastern Australia. Generalized additive models were used to predict distributions based on fine-resolution (2·5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video. The effects of different levels of locational uncertainty in combination with sample size were evaluated by comparing model performance and predicted distributions. While locational uncertainty was observed to influence some measures of model performance, in general this was small and varied based on the accuracy metric used. However, simulated locational uncertainty caused changes in variable importance and predicted distributions at fine scales, potentially influencing model interpretation. This was most evident with small sample sizes. Results suggested that seemingly high-performing, fine-scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrated the need to consider predictions across geographic space rather than performance alone. The findings are important for conservation managers as they highlight the inherent variation in predictions between equally performing distribution models, and the subsequent restrictions on ecological interpretations.
Language eng
DOI 10.1111/2041-210X.12645
Field of Research 060207 Population Ecology
060399 Evolutionary Biology not elsewhere classified
Socio Economic Objective 970105 Expanding Knowledge in the Environmental Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Wiley
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