Next Article in Journal
Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China
Next Article in Special Issue
Mapping Historical Data: Recovering a Forgotten Floristic and Vegetation Database for Biodiversity Monitoring
Previous Article in Journal
Towards a Standard Plant Species Spectral Library Protocol for Vegetation Mapping: A Case Study in the Shrubland of Doñana National Park
Article Menu

Export Article

Open AccessArticle
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

1
School of Geography, University of Nottingham, Nottingham, NG7 2RD, UK
2
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]   |  

Abstract

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
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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top