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Modelling and mapping tick dynamics using volunteered observations.

Irene Garcia-Martí1, Raúl Zurita-Milla2, Arnold J H van Vliet3

  • 1Department of Geo-Information Processing (GIP), Faculty of Geo-Information and Earth Observation (ITC), University of Twente, Enschede, The Netherlands. i.garciamarti@utwente.nl.

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This summary is machine-generated.

Citizen scientists tracked active questing ticks (AQT) to model tick dynamics. Environmental factors like atmospheric water levels were key drivers, enabling daily tick distribution maps for public health insights.

Keywords:
Data analysisEnvironmental modellingRandom forestTick dynamicsVolunteered geographic information (VGI)

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Area of Science:

  • Ecological modeling
  • Geospatial analysis
  • Public health informatics

Background:

  • Tick populations and tick-borne diseases have risen significantly since the mid-1990s.
  • Modeling tick dynamics is complex due to data limitations.
  • This study utilizes volunteered data to model and map tick activity.

Purpose of the Study:

  • To identify key environmental drivers of active questing ticks (AQT) across multiple time scales.
  • To develop daily AQT distribution maps at a national level.
  • To demonstrate the utility of citizen science data in ecological monitoring.

Main Methods:

  • Collected 9 years of AQT data from trained volunteers in the Netherlands.
  • Integrated weather variables, satellite-derived vegetation indices, land cover, and mast years.
  • Employed feature engineering to create 101 predictive features.
  • Utilized a time-aware Random Forest regression model to predict AQT.

Main Results:

  • Developed a model for fitting AQT with improved statistical metrics.
  • Identified atmospheric water variables (evapotranspiration, relative humidity) as more significant predictors than temperature.
  • Generated daily national-level maps of tick dynamics.

Conclusions:

  • This approach enables new applications in environmental research, nature management, and public health.
  • Highlights the value of citizen science in generating geospatial data for monitoring complex environmental phenomena.
  • Provides a framework for understanding and predicting tick dynamics.