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ISPRS Int. J. Geo-Inf. 2015, 4(4), 2496-2518; doi:10.3390/ijgi4042496

Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data

School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK
InBIO—Associate Laboratoy, Research Network in Biodiversity and Evolutionary Biology, Department of Biology, University of the Azores, 9501-801 Ponta Delgada, Azores, Portugal
Author to whom correspondence should be addressed.
Academic Editors: Duccio Rocchini and Wolfgang Kainz
Received: 22 September 2015 / Revised: 20 October 2015 / Accepted: 8 November 2015 / Published: 16 November 2015
(This article belongs to the Special Issue Spatial Ecology)
View Full-Text   |   Download PDF [2213 KB, uploaded 19 November 2015]   |  


Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data “contaminated” with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species’ niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling. View Full-Text
Keywords: species mis-identification; false positive error; presence-only; MaxEnt species mis-identification; false positive error; presence-only; MaxEnt

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Costa, H.; Foody, G.M.; Jiménez, S.; Silva, L. Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data. ISPRS Int. J. Geo-Inf. 2015, 4, 2496-2518.

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