Open access publications in the SLU publication database

Abstract

Invasive alien plants are considered a major driver of global biodiversity loss. Therefore, there is a huge demand of spatial and temporal data on their distribution for investigating possible drivers of species invasions and for predictions of future distributions. We use Google Street View imagery (GSV) as a new source of spatial and temporal data. GSV provides millions of panoramic views along road networks worldwide allowing for the identification of many plant species, including invasive ones. Thus, GSV has a great potential to support ecological research in documenting species distribution, but reliable validation of its precision and accuracy is lacking. Here, we describe and evaluate an approach using GSV to visually track the spread of invasive alien plants, the North American goldenrods (Solidago canadensis and S. gigantea) occurring abundantly along road network in Poland (Central Europe). We determined presence/absence of the species along 160 randomly selected transects of a length of 500 m by visual inspection of GSV images and compared it with field surveys at the same transects. We show that the occurrence of goldenrods in GSV is a reliable predictor of their occurrence in the wild. Sampling parameters, like road width, season when GSV pictures were taken and number of months elapsed since taking the GSV pictures, did not change the correlation between outputs of the two methods (GSV and field sampling). Furthermore, both the occurrence of goldenrods observed in the field and their occurrence in GSV have similar relations to habitat characteristics investigated (the same direction of relationship and similar effect size). We suggest Google Street View images may be an additional tool to be used in the detection and tracking of the spread of invasive alien plants along roadsides. The approach may be useful in assessing temporal changes in roadside vegetation and managing problematic plant species across large spatial scales and may contribute to the further development of more efficient sampling methods in ecological studies.