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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| Format: | Article |
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MDPI
2015
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| Online Access: | https://eprints.nottingham.ac.uk/30980/ |
| _version_ | 1848794102959702016 |
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| author | Costa, Hugo Foody, Giles M. Jiménez, Sílvia Silva, Luís |
| author_facet | Costa, Hugo Foody, Giles M. Jiménez, Sílvia Silva, Luís |
| author_sort | Costa, Hugo |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T19:10:52Z |
| format | Article |
| id | nottingham-30980 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:10:52Z |
| publishDate | 2015 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-309802020-05-04T20:05:55Z https://eprints.nottingham.ac.uk/30980/ Impacts of species misidentification on species distribution modeling with presence-only data Costa, Hugo Foody, Giles M. Jiménez, Sílvia Silva, Luís 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. MDPI 2015-12 Article NonPeerReviewed Costa, Hugo, Foody, Giles M., Jiménez, Sílvia and Silva, Luís (2015) Impacts of species misidentification on species distribution modeling with presence-only data. ISPRS International Journal of Geo-Information, 4 (4). pp. 2496-2518. ISSN 2220-9964 species mis-identification; false positive error; presence-only; MaxEnt 2497 http://www.mdpi.com/2220-9964/4/4/2496 |
| spellingShingle | species mis-identification; false positive error; presence-only; MaxEnt 2497 Costa, Hugo Foody, Giles M. Jiménez, Sílvia Silva, Luís Impacts of species misidentification on species distribution modeling with presence-only data |
| title | 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_short | 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 |
| topic | species mis-identification; false positive error; presence-only; MaxEnt 2497 |
| url | https://eprints.nottingham.ac.uk/30980/ https://eprints.nottingham.ac.uk/30980/ |