Can citizen science data guide the surveillance of invasive plants? A model-based test with Acacia trees in Portugal

• Type: Article in International Scientific Journal
• Date: 2019

Authors

de Sa, NC (Author) Other; Marchante, H (Author) Other; Marchante, E (Author) Other; Cabral, JA (Author) Other; Joao Honrado (Author) FCUP; Vicente, JR (Author) Other

Journal

Title: Biological Invasions

Vol. 21

Pages: 2127-2141

ISSN: 1387-3547

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 26 Citations

Other information

Authenticus ID: P-00Q-C3P

DOI: 10.1007/s10530-019-01962-6

Abstract (EN): With the rapid expansion of invasive alien plants (IAPs), accurate and timely distribution data is increasingly critical to successful management. However, it is not easy for researchers/technicians to obtain data for all IAPs and territories. In this context, data collected by Citizen Science Platforms can be a useful tool, complementing professional data. We hypothesize that combining IAP data collected by citizens and data collected by researchers can improve the accuracy of species distribution models (SDMs) and optimize surveillance efforts. To test this, we gathered data from a Citizen Science Platform (Invasoras.pt) and from researchers on three invasive Acacia species widespread in Portugal and generated three different datasets: researchers, citizens, and researchers plus citizens. We modelled the potential distribution of the species using an ensemble approach (biomod2 R package) to test the effect of the different datasets on the resulting model accuracy, the selected environmental drivers of species distribution and the predicted spatial distribution. All SDMs obtained very high accuracy, with the highest values being obtained in the models trained with researchers' data. Nevertheless, models trained with citizen data vastly increased the predicted spatial distribution in all cases. The spatial projections of the three models were further compared and ranked to identify the areas of highest surveillance priority for each species, i.e., areas with high agreement between the models but where occurrence data is lacking. These results can be used to guide future surveillance efforts both for citizens and researchers.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15