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Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling

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  • Merckx, Bea
  • Steyaert, Maaike
  • Vanreusel, Ann
  • Vincx, Magda
  • Vanaverbeke, Jan

Abstract

Nowadays, species are driven to extinction at a high rate. To reduce this rate it is important to delineate suitable habitats for these species in such a way that these areas can be suggested as conservation areas. The use of habitat suitability models (HSMs) can be of great importance for the delineation of such areas. In this study MaxEnt, a presence-only modelling technique, is used to develop HSMs for 223 nematode species of the Southern Bight of the North Sea. However, it is essential that these models are beyond discussion and they should be checked for potential errors. In this study we focused on two categories (1) errors which can be attributed to the database such as preferential sampling and spatial autocorrelation and (2) errors induced by the modelling technique such as overfitting, In order to quantify these adverse effects thousands of nulls models were created. The effect of preferential sampling (i.e. some areas where visited more frequenty than others) was investigated by comparing model outcomes based from null models sampling the actual sampling stations and null models sampling the entire mapping area (Raes and ter Steege, 2007). Overfitting is exposed by a fivefold cross-validation and the influence of spatial autocorrelation is assessed by separating test and training sets in space. Our results clearly show that all these effects are present: preferential sampling has a strong effect on the selection of non-random species models. Crossvalidation seems to have less influence on the model selection and spatial autocorrelation is also strongly present. It is clear from this study that predefined thresholds are not readily applicable to all datasets and additional tests are needed in model selection.

Suggested Citation

  • Merckx, Bea & Steyaert, Maaike & Vanreusel, Ann & Vincx, Magda & Vanaverbeke, Jan, 2011. "Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling," Ecological Modelling, Elsevier, vol. 222(3), pages 588-597.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:3:p:588-597
    DOI: 10.1016/j.ecolmodel.2010.11.016
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    References listed on IDEAS

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    1. Merckx, Bea & Goethals, Peter & Steyaert, Maaike & Vanreusel, Ann & Vincx, Magda & Vanaverbeke, Jan, 2009. "Predictability of marine nematode biodiversity," Ecological Modelling, Elsevier, vol. 220(11), pages 1449-1458.
    2. Suárez-Seoane, Susana & García de la Morena, Eladio L. & Morales Prieto, Manuel B. & Osborne, Patrick E. & de Juana, Eduardo, 2008. "Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution," Ecological Modelling, Elsevier, vol. 219(1), pages 17-29.
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    Citations

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    Cited by:

    1. Wiltshire, Kathryn H & Tanner, Jason E, 2020. "Comparing maximum entropy modelling methods to inform aquaculture site selection for novel seaweed species," Ecological Modelling, Elsevier, vol. 429(C).
    2. Aubry, Philippe & Francesiaz, Charlotte & Guillemain, Matthieu, 2024. "On the impact of preferential sampling on ecological status and trend assessment," Ecological Modelling, Elsevier, vol. 492(C).
    3. 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.
    4. Mahya Norallahi & Hesam Seyed Kaboli, 2021. "Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 119-137, March.
    5. Wimhurst, Joshua J. & Greene, J. Scott & Koch, Jennifer, 2023. "Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model," Applied Energy, Elsevier, vol. 352(C).

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