Abstract
Mapping species spatial distribution using spatial inference and prediction requires a lot of data. Occurrence data are generally not easily available from the literature and are very time-consuming to collect in the field. For that reason, we designed a survey to explore to which extent large-scale databases such as Google maps and Google street view could be used to derive valid occurrence data. We worked with the Pine Processionary Moth (PPM) Thaumetopoea pityocampa because the larvae of that moth build silk nests that are easily visible. The presence of the species at one location can therefore be inferred from visual records derived from the panoramic views available from Google street view. We designed a standardized procedure allowing evaluating the presence of the PPM on a sampling grid covering the landscape under study. The outputs were compared to field data. We investigated two landscapes using grids of different extent and mesh size. Data derived from Google street view were highly similar to field data in the large-scale analysis based on a square grid with a mesh of 16 km (96% of matching records). Using a 2 km mesh size led to a strong divergence between field and Google-derived data (46% of matching records). We conclude that Google database might provide useful occurrence data for mapping the distribution of species which presence can be visually evaluated such as the PPM. However, the accuracy of the output strongly depends on the spatial scales considered and on the sampling grid used. Other factors such as the coverage of Google street view network with regards to sampling grid size and the spatial distribution of host trees with regards to road network may also be determinant.
Highlights
Globalisation and one of its most publicized consequences, the species redistribution, has received a considerable attention during the last decades [1,2,3]
Large sampling grid The field data collected for the large scale survey showed that the Pine Processionary Moth (PPM) was present all over the study area: there were 178 cells classified as ‘‘presenc’’ and 5 as ‘‘absence’’
The number of false negatives was 13 and there was no case of false positive (Table 1)
Summary
Globalisation and one of its most publicized consequences, the species redistribution, has received a considerable attention during the last decades [1,2,3]. A lot of research efforts has been directed towards unravelling ecological processes implied in the spread of species [2,4]. GISs have proven precious to monitor species spread [5]. The consequences of data scarcity are dramatic for example in the field of species distribution modelling [13,14] where collecting occurrence data is not always easy, time consuming, and is often non-environmentally friendly (because of the gas emission of vehicles used for the survey). As a result, updating or completing existing data sets is difficult albeit it is the very first step of ecological analysis and modelling
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