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Future distribution of wild boar in a highly anthropogenic landscape: Models combining hunting bag and citizen science data

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  • Rutten, Anneleen
  • Casaer, Jim
  • Swinnen, Kristijn R.R.
  • Herremans, Marc
  • Leirs, Herwig

Abstract

Wild boar is one of the most widespread mammals of the world and in many regions wild boar populations continue to expand. Especially in highly anthropogenic landscapes, increasing numbers of wild boar lead to a rising number of contacts with human activities causing human-wildlife impacts. In the heavily fragmented landscape of Flanders (northern Belgium) where the wild boar re-appeared in 2006 after more than half a century of absence, it is crucial to get a better understanding of the probable further distribution of wild boar in order to assess potential impacts in the near future. Wild boar occurrences have been collected by two citizen science programs: through an online observation platform and based on the reported locations of wild boar shot by hunters. This allowed us to construct a MaxEnt habitat suitability model. We constructed a new approach to define background manipulation to correct for sampling bias due to uneven sampling effort or due to areas in which hunting is not allowed based on the construction of bias files using this information. Model outcomes based on this new approach for background manipulation were compared with the known method of spatial thinning. All model outcomes were found comparable reflecting the utility of our new approach when limited data are available and spatial thinning would result in insufficient data for modelling. Our MaxEnt models show that coniferous forest, deciduous forest, maize, scrub and other low cover play a key role in increasing the habitat suitability for wild boar. Built up areas and the extent of habitat diversity only had a minor influence on habitat suitability reflecting wild boars’ behavioural flexibility to adapt to human-dominated landscapes. Unoccupied suitable habitat is mainly found in the centre of Flanders, although highly scattered. Habitat suitability in the West of Flanders was limited.

Suggested Citation

  • Rutten, Anneleen & Casaer, Jim & Swinnen, Kristijn R.R. & Herremans, Marc & Leirs, Herwig, 2019. "Future distribution of wild boar in a highly anthropogenic landscape: Models combining hunting bag and citizen science data," Ecological Modelling, Elsevier, vol. 411(C).
    • Handle: RePEc:eee:ecomod:v:411:y:2019:i:c:s0304380019303126
    DOI: 10.1016/j.ecolmodel.2019.108804
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    References listed on IDEAS

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    1. Boria, Robert A. & Olson, Link E. & Goodman, Steven M. & Anderson, Robert P., 2014. "Spatial filtering to reduce sampling bias can improve the performance of ecological niche models," Ecological Modelling, Elsevier, vol. 275(C), pages 73-77.
    2. Sillero, Neftalí, 2011. "What does ecological modelling model? A proposed classification of ecological niche models based on their underlying methods," Ecological Modelling, Elsevier, vol. 222(8), pages 1343-1346.
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    Cited by:

    1. Van Eupen, Camille & Maes, Dirk & Herremans, Marc & Swinnen, Kristijn R.R. & Somers, Ben & Luca, Stijn, 2021. "The impact of data quality filtering of opportunistic citizen science data on species distribution model performance," Ecological Modelling, Elsevier, vol. 444(C).
    2. Van Eupen, Camille & Maes, Dirk & Herremans, Marc & Swinnen, Kristijn R.R. & Somers, Ben & Luca, Stijn, 2022. "Species profiles support recommendations for quality filtering of opportunistic citizen science data," Ecological Modelling, Elsevier, vol. 467(C).
    3. Karami, Peyman & Tavakoli, Sajad, 2022. "Identification and analysis of areas prone to conflict with wild boar (Sus scrofa) in the vineyards of Malayer County, western Iran," Ecological Modelling, Elsevier, vol. 471(C).

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