title: Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery.
abstract: 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.
authors.affiliation: Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, 13331 Marseille, France.|Laboratoire Image Ville Environnement (LIVE), Université de Strasbourg, CNRS, 67000 Strasbourg, France.
authors.forename: Laurent
authors.initials: L
authors.lastname: Hardion
authors.affiliation: Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, 13331 Marseille, France.
authors.forename: Agathe
authors.initials: A
authors.lastname: Leriche
authors.affiliation: Laboratoire Image Ville Environnement (LIVE), Université de Strasbourg, CNRS, 67000 Strasbourg, France.
authors.forename: Eugénie
authors.initials: E
authors.lastname: Schwoertzig
authors.affiliation: Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, 13331 Marseille, France.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Millon
mesh_terms: D044822:Biodiversity; D002980:Climate; D003362:Cost-Benefit Analysis; D003625:Data Collection; D004463:Ecology; D004784:Environmental Monitoring / Q000191:economics*; D040362:Geographic Information Systems / Q000191:economics*; D005843:Geography; D016014:Linear Models; D006109:Poaceae; D025341:Principal Component Analysis; D011336:Probability; D013030:Spain