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Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery

Species occurrence data provide crucial information for biodiversity studies in the current context of global environmental changes. Such studies often rely on a limited number of occurrence data collected in the field and on pseudo-absences arbitrarily chosen within the study area, which reduces th...

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Autores principales: Hardion, Laurent, Leriche, Agathe, Schwoertzig, Eugénie, Millon, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709242/
https://www.ncbi.nlm.nih.gov/pubmed/26751565
http://dx.doi.org/10.1371/journal.pone.0146899
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author Hardion, Laurent
Leriche, Agathe
Schwoertzig, Eugénie
Millon, Alexandre
author_facet Hardion, Laurent
Leriche, Agathe
Schwoertzig, Eugénie
Millon, Alexandre
author_sort Hardion, Laurent
collection PubMed
description Species occurrence data provide crucial information for biodiversity studies in the current context of global environmental changes. Such studies often rely on a limited number of occurrence data collected in the field and on pseudo-absences arbitrarily chosen within the study area, which reduces the value of these studies. To overcome this issue, we propose an alternative method of prospection using geo-located street view imagery (SVI). Following a standardised protocol of virtual prospection using both vertical (aerial photographs) and horizontal (SVI) perceptions, we have surveyed 1097 randomly selected cells across Spain (0.1x0.1 degree, i.e. 20% of Spain) for the presence of Arundo donax L. (Poaceae). In total we have detected A. donax in 345 cells, thus substantially expanding beyond the now two-centuries-old field-derived record, which described A. donax only 216 cells. Among the field occurrence cells, 81.1% were confirmed by SVI prospection to be consistent with species presence. In addition, we recorded, by SVI prospection, 752 absences, i.e. cells where A. donax was considered absent. We have also compared the outcomes of climatic niche modeling based on SVI data against those based on field data. Using generalized linear models fitted with bioclimatic predictors, we have found SVI data to provide far more compelling results in terms of niche modeling than does field data as classically used in SDM. This original, cost- and time-effective method provides the means to accurately locate highly visible taxa, reinforce absence data, and predict species distribution without long and expensive in situ prospection. At this time, the majority of available SVI data is restricted to human-disturbed environments that have road networks. However, SVI is becoming increasingly available in natural areas, which means the technique has considerable potential to become an important factor in future biodiversity studies.
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spelling pubmed-47092422016-01-15 Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery Hardion, Laurent Leriche, Agathe Schwoertzig, Eugénie Millon, Alexandre PLoS One Research Article Species occurrence data provide crucial information for biodiversity studies in the current context of global environmental changes. Such studies often rely on a limited number of occurrence data collected in the field and on pseudo-absences arbitrarily chosen within the study area, which reduces the value of these studies. To overcome this issue, we propose an alternative method of prospection using geo-located street view imagery (SVI). Following a standardised protocol of virtual prospection using both vertical (aerial photographs) and horizontal (SVI) perceptions, we have surveyed 1097 randomly selected cells across Spain (0.1x0.1 degree, i.e. 20% of Spain) for the presence of Arundo donax L. (Poaceae). In total we have detected A. donax in 345 cells, thus substantially expanding beyond the now two-centuries-old field-derived record, which described A. donax only 216 cells. Among the field occurrence cells, 81.1% were confirmed by SVI prospection to be consistent with species presence. In addition, we recorded, by SVI prospection, 752 absences, i.e. cells where A. donax was considered absent. We have also compared the outcomes of climatic niche modeling based on SVI data against those based on field data. Using generalized linear models fitted with bioclimatic predictors, we have found SVI data to provide far more compelling results in terms of niche modeling than does field data as classically used in SDM. This original, cost- and time-effective method provides the means to accurately locate highly visible taxa, reinforce absence data, and predict species distribution without long and expensive in situ prospection. At this time, the majority of available SVI data is restricted to human-disturbed environments that have road networks. However, SVI is becoming increasingly available in natural areas, which means the technique has considerable potential to become an important factor in future biodiversity studies. Public Library of Science 2016-01-11 /pmc/articles/PMC4709242/ /pubmed/26751565 http://dx.doi.org/10.1371/journal.pone.0146899 Text en © 2016 Hardion et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Hardion, Laurent
Leriche, Agathe
Schwoertzig, Eugénie
Millon, Alexandre
Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery
title Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery
title_full Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery
title_fullStr Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery
title_full_unstemmed Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery
title_short Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery
title_sort species distribution 2.0: an accurate time- and cost-effective method of prospection using street view imagery
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4709242/
https://www.ncbi.nlm.nih.gov/pubmed/26751565
http://dx.doi.org/10.1371/journal.pone.0146899
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