Biological Invasions

, Volume 21, Issue 6, pp 2127–2141 | Cite as

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

  • Nuno César de SáEmail author
  • Hélia Marchante
  • Elizabete Marchante
  • João Alexandre Cabral
  • João Pradinho Honrado
  • Joana Raquel Vicente
Original Paper


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 ( 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.


Acacia species Ensemble modelling Invasive alien plants SDM Citizen science 



N. César de Sá was supported through the Project INVADER-IV (PTDC/AAGREC/4896/2014). E. Marchante and H Marchante were supported by European funds FEDER and Portugal 2020/POCI and national funds FCT through project INVADER-IV (PTDC/AAGREC/4896/2014). E. Marchante was also supported by Projecto ReNATURE—Valorization of the Natural Endogenous Resources of the Centro Region (Centro 2020, Centro-01-0145-FEDER-000007). J.R. Vicente was supported by European funds POPH/FSE and national funds FCT trough the Post-Doc Grant SFRH/BPD/84044/2012. J.A. Cabral and J.R. Vicente were supported as CITAB researchers by European Investment Funds by FEDER/COMPETE/POCI—Operacional Competitiveness and Internacionalization Programme, under Project POCI-01-0145-FEDER-006958 and National Funds by FCT—Portuguese Foundation for Science and Technology, under the project UID/AGR/04033/2013. The authors thank Alex Brandson for English revision of the final version of the manuscript.

Supplementary material

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Supplementary material 1 (DOCX 4532 kb)
10530_2019_1962_MOESM2_ESM.docx (15 kb)
Supplementary material 2 (DOCX 14 kb)


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Functional Ecology, Department of Life SciencesUniversity of CoimbraCoimbraPortugal
  2. 2.Instituto Politécnico de CoimbraESAC, CFECoimbraPortugal
  3. 3.Institute of Environmental Sciences CMLLeiden UniversityLeidenThe Netherlands
  4. 4.Laboratory of Applied Ecology, CITAB – Centre for the Research and Technology of Agro-Environment and Biological SciencesUniversity of Trás-os-Montes e Alto DouroVila RealPortugal
  5. 5.Research Network in Biodiversity and Evolutionary Biology, Research Centre in Biodiversity and Genetic Resources (InBIO-CIBIO)Universidade do PortoVairãoPortugal
  6. 6.Faculdade de CiênciasUniversidade do PortoPortoPortugal

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