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Fuzzy set theory for predicting the potential distribution and cost-effective monitoring of invasive species

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  • Costa, Hugo
  • Ponte, Nuno B.
  • Azevedo, Eduardo B.
  • Gil, Artur

Abstract

The presence of invasive species has been predicted using species distribution models (SDMs) and presence-only data to assist environmental management. However, SDMs include substantial uncertainty and the lack of absence data hampers the use of probabilistic predictions. A non-statistical theoretical basis able to deal with uncertainty on which to model invasive species distributions with presence-only data is thus needed. Fuzzy set theory satisfies these two requirements but has been little used. This paper proposes a fuzzy modelling approach for predicting invasive species potential distributions using presence-only data and SDMs to support the design of cost-effective monitoring schemes. The invasion of Gunnera tinctoria (Molina) Mirbel (Giant rhubarb) in the island of São Miguel (the Azores, Portugal) is used as case study. The latter involved the prediction of the potential distribution of the invader using MaxEnt and the selection of priority areas for monitoring the spread of the invader in case of scarcity of resources. In addition, MaxEnt was used within a traditional (non-fuzzy) approach, unable to quantity and report on uncertainty. The results of the fuzzy and non-fuzzy approaches are compared and their differences discussed, thus highlighting the potential benefits of using fuzzy set theory for species distribution modelling and management.

Suggested Citation

  • Costa, Hugo & Ponte, Nuno B. & Azevedo, Eduardo B. & Gil, Artur, 2015. "Fuzzy set theory for predicting the potential distribution and cost-effective monitoring of invasive species," Ecological Modelling, Elsevier, vol. 316(C), pages 122-132.
    • Handle: RePEc:eee:ecomod:v:316:y:2015:i:c:p:122-132
    DOI: 10.1016/j.ecolmodel.2015.07.034
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    References listed on IDEAS

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    1. Fennell, Mark & Murphy, James E. & Armstrong, Cristina & Gallagher, Tommy & Osborne, Bruce, 2012. "Plant Spread Simulator: A model for simulating large-scale directed dispersal processes across heterogeneous environments," Ecological Modelling, Elsevier, vol. 230(C), pages 1-10.
    2. Brito de Azevedo, Eduardo & Santos Pereira, Luis & Itier, Bernard, 1999. "Modelling the local climate in island environments: water balance applications," Agricultural Water Management, Elsevier, vol. 40(2-3), pages 393-403, May.
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    Cited by:

    1. Früh, Linus & Kampen, Helge & Kerkow, Antje & Schaub, Günter A. & Walther, Doreen & Wieland, Ralf, 2018. "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations," Ecological Modelling, Elsevier, vol. 388(C), pages 136-144.
    2. Moreira, Miguel & Fonseca, Catarina & Vergílio, Marta & Calado, Helena & Gil, Artur, 2018. "Spatial assessment of habitat conservation status in a Macaronesian island based on the InVEST model: a case study of Pico Island (Azores, Portugal)," Land Use Policy, Elsevier, vol. 78(C), pages 637-649.
    3. Tang, P.Z. & Liu, J.Z. & Lu, H.W. & Wang, Z. & He, L., 2017. "Information-based Network Environ Analysis for Ecological Risk Assessment of heavy metals in soils," Ecological Modelling, Elsevier, vol. 344(C), pages 17-28.
    4. Liu, Fang & McShea, William J. & Li, Diqiang, 2017. "Correlating habitat suitability with landscape connectivity: A case study of Sichuan golden monkey in China," Ecological Modelling, Elsevier, vol. 353(C), pages 37-46.

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