Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data
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” w...
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doaj-4243ec121ba24f768b6eff74d0a956732020-11-25T00:39:13ZengMDPI AGISPRS International Journal of Geo-Information2220-99642015-11-01442496251810.3390/ijgi4042496ijgi4042496Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only DataHugo Costa0Giles M. Foody1Sílvia Jiménez2Luís Silva3School of Geography, University of Nottingham, Nottingham, NG7 2RD, UKSchool of Geography, University of Nottingham, Nottingham, NG7 2RD, UKInBIO—Associate Laboratoy, Research Network in Biodiversity and Evolutionary Biology, Department of Biology, University of the Azores, 9501-801 Ponta Delgada, Azores, PortugalInBIO—Associate Laboratoy, Research Network in Biodiversity and Evolutionary Biology, Department of Biology, University of the Azores, 9501-801 Ponta Delgada, Azores, PortugalSpatial 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.http://www.mdpi.com/2220-9964/4/4/2496species mis-identificationfalse positive errorpresence-onlyMaxEnt |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hugo Costa Giles M. Foody Sílvia Jiménez Luís Silva |
spellingShingle |
Hugo Costa Giles M. Foody Sílvia Jiménez Luís Silva Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data ISPRS International Journal of Geo-Information species mis-identification false positive error presence-only MaxEnt |
author_facet |
Hugo Costa Giles M. Foody Sílvia Jiménez Luís Silva |
author_sort |
Hugo Costa |
title |
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data |
title_short |
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data |
title_full |
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data |
title_fullStr |
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data |
title_full_unstemmed |
Impacts of Species Misidentification on Species Distribution Modeling with Presence-Only Data |
title_sort |
impacts of species misidentification on species distribution modeling with presence-only data |
publisher |
MDPI AG |
series |
ISPRS International Journal of Geo-Information |
issn |
2220-9964 |
publishDate |
2015-11-01 |
description |
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. |
topic |
species mis-identification false positive error presence-only MaxEnt |
url |
http://www.mdpi.com/2220-9964/4/4/2496 |
work_keys_str_mv |
AT hugocosta impactsofspeciesmisidentificationonspeciesdistributionmodelingwithpresenceonlydata AT gilesmfoody impactsofspeciesmisidentificationonspeciesdistributionmodelingwithpresenceonlydata AT silviajimenez impactsofspeciesmisidentificationonspeciesdistributionmodelingwithpresenceonlydata AT luissilva impactsofspeciesmisidentificationonspeciesdistributionmodelingwithpresenceonlydata |
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