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Fit-for-Purpose: Species Distribution Model Performance Depends on Evaluation Criteria – Dutch Hoverflies as a Case Study

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Listed:
  • Jesús Aguirre-Gutiérrez
  • Luísa G Carvalheiro
  • Chiara Polce
  • E Emiel van Loon
  • Niels Raes
  • Menno Reemer
  • Jacobus C Biesmeijer

Abstract

Understanding species distributions and the factors limiting them is an important topic in ecology and conservation, including in nature reserve selection and predicting climate change impacts. While Species Distribution Models (SDM) are the main tool used for these purposes, choosing the best SDM algorithm is not straightforward as these are plentiful and can be applied in many different ways. SDM are used mainly to gain insight in 1) overall species distributions, 2) their past-present-future probability of occurrence and/or 3) to understand their ecological niche limits (also referred to as ecological niche modelling). The fact that these three aims may require different models and outputs is, however, rarely considered and has not been evaluated consistently. Here we use data from a systematically sampled set of species occurrences to specifically test the performance of Species Distribution Models across several commonly used algorithms. Species range in distribution patterns from rare to common and from local to widespread. We compare overall model fit (representing species distribution), the accuracy of the predictions at multiple spatial scales, and the consistency in selection of environmental correlations all across multiple modelling runs. As expected, the choice of modelling algorithm determines model outcome. However, model quality depends not only on the algorithm, but also on the measure of model fit used and the scale at which it is used. Although model fit was higher for the consensus approach and Maxent, Maxent and GAM models were more consistent in estimating local occurrence, while RF and GBM showed higher consistency in environmental variables selection. Model outcomes diverged more for narrowly distributed species than for widespread species. We suggest that matching study aims with modelling approach is essential in Species Distribution Models, and provide suggestions how to do this for different modelling aims and species’ data characteristics (i.e. sample size, spatial distribution).

Suggested Citation

  • Jesús Aguirre-Gutiérrez & Luísa G Carvalheiro & Chiara Polce & E Emiel van Loon & Niels Raes & Menno Reemer & Jacobus C Biesmeijer, 2013. "Fit-for-Purpose: Species Distribution Model Performance Depends on Evaluation Criteria – Dutch Hoverflies as a Case Study," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-11, May.
    • Handle: RePEc:plo:pone00:0063708
    DOI: 10.1371/journal.pone.0063708
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    2. Bell, David M. & Schlaepfer, Daniel R., 2016. "On the dangers of model complexity without ecological justification in species distribution modeling," Ecological Modelling, Elsevier, vol. 330(C), pages 50-59.
    3. Tereza Cristina Giannini & Wilian França Costa & Guaraci Duran Cordeiro & Vera Lucia Imperatriz-Fonseca & Antonio Mauro Saraiva & Jacobus Biesmeijer & Lucas Alejandro Garibaldi, 2017. "Projected climate change threatens pollinators and crop production in Brazil," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-13, August.
    4. Perennes, Marie & Diekötter, Tim & Groß, Jens & Burkhard, Benjamin, 2021. "A hierarchical framework for mapping pollination ecosystem service potential at the local scale," Ecological Modelling, Elsevier, vol. 444(C).
    5. Barker, Justin R. & MacIsaac, Hugh J., 2022. "Species distribution models: Administrative boundary centroid occurrences require careful interpretation," Ecological Modelling, Elsevier, vol. 472(C).
    6. Leandro, Camila & Jay-Robert, Pierre & Mériguet, Bruno & Houard, Xavier & Renner, Ian W., 2020. "Is my sdm good enough? insights from a citizen science dataset in a point process modeling framework," Ecological Modelling, Elsevier, vol. 438(C).
    7. Halvorsen, Rune & Mazzoni, Sabrina & Dirksen, John Wirkola & Næsset, Erik & Gobakken, Terje & Ohlson, Mikael, 2016. "How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt?," Ecological Modelling, Elsevier, vol. 328(C), pages 108-118.
    8. Grimmett, Liam & Whitsed, Rachel & Horta, Ana, 2020. "Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics," Ecological Modelling, Elsevier, vol. 431(C).
    9. Santiago José Elías Velazco & Franklin Galvão & Fabricio Villalobos & Paulo De Marco Júnior, 2017. "Using worldwide edaphic data to model plant species niches: An assessment at a continental extent," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-24, October.

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