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Choice of study area and predictors affect habitat suitability projections, but not the performance of species distribution models of stream biota

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  • Domisch, Sami
  • Kuemmerlen, Mathias
  • Jähnig, Sonja C.
  • Haase, Peter

Abstract

Species distribution models (SDMs) that provide extrapolations of species habitat suitability are increasingly being used in stream ecosystems, however the effects of different modelling techniques on model projections remain unknown. We tested how different study areas and predictors affect SDMs by using consensus projections of a fixed set of 224 stream macroinvertebrate species and five algorithms implemented in BIOMOD/R. Four modelling designs were applied: (1) a landscape as a continuous study area without any discrimination between terrestrial and aquatic realms, (2) a stream network masked a posteriori from the previous design, (3) a stream network as the study area during the model-building stage, and (4) same as (3) but with a hydrologically corrected set of predictors. The true skill statistic (TSS) and accuracy of the consensus projections were not influenced by the different designs (TSS ranged from 0.80 to 1.00, accuracy ranged from 0.70 to 0.96). The projections of design (4) yielded a strong reduction in false positive predictions compared to (1) (on average by 56%), (2) (11%) or (3) (8%). Our results show how SDMs with equally high accuracy may differ widely in habitat suitability projections for benthic macroinvertebrates. As model performance and output are not necessarily congruent, habitat suitability projections of stream biota need to be carefully assessed.

Suggested Citation

  • Domisch, Sami & Kuemmerlen, Mathias & Jähnig, Sonja C. & Haase, Peter, 2013. "Choice of study area and predictors affect habitat suitability projections, but not the performance of species distribution models of stream biota," Ecological Modelling, Elsevier, vol. 257(C), pages 1-10.
    • Handle: RePEc:eee:ecomod:v:257:y:2013:i:c:p:1-10
    DOI: 10.1016/j.ecolmodel.2013.02.019
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    2. Bennetsen, Elina & Gobeyn, Sacha & Goethals, Peter L.M., 2016. "Species distribution models grounded in ecological theory for decision support in river management," Ecological Modelling, Elsevier, vol. 325(C), pages 1-12.
    3. Iturbide, Maialen & Bedia, Joaquín & Herrera, Sixto & del Hierro, Oscar & Pinto, Miriam & Gutiérrez, Jose Manuel, 2015. "A framework for species distribution modelling with improved pseudo-absence generation," Ecological Modelling, Elsevier, vol. 312(C), pages 166-174.
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    5. Markovic, Danijela & Walz, Ariane & Kärcher, Oskar, 2019. "Scale effects on the performance of niche-based models of freshwater fish distributions: Local vs. upstream area influences," Ecological Modelling, Elsevier, vol. 411(C).
    6. Kärcher, Oskar & Frank, Karin & Walz, Ariane & Markovic, Danijela, 2019. "Scale effects on the performance of niche-based models of freshwater fish distributions," Ecological Modelling, Elsevier, vol. 405(C), pages 33-42.
    7. Zhiqiang Chen & Zhibiao Chen, 2018. "Effects of ecological restoration measures on the distribution of Dicranopteris dichotoma at the microscale in the red soil hilly region of China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-17, October.
    8. Kuemmerlen, Mathias & Schmalz, Britta & Guse, Björn & Cai, Qinghua & Fohrer, Nicola & Jähnig, Sonja C., 2014. "Integrating catchment properties in small scale species distribution models of stream macroinvertebrates," Ecological Modelling, Elsevier, vol. 277(C), pages 77-86.
    9. Schuwirth, Nele & Borgwardt, Florian & Domisch, Sami & Friedrichs, Martin & Kattwinkel, Mira & Kneis, David & Kuemmerlen, Mathias & Langhans, Simone D. & Martínez-López, Javier & Vermeiren, Peter, 2019. "How to make ecological models useful for environmental management," Ecological Modelling, Elsevier, vol. 411(C).
    10. Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
    11. Gama, M. & Crespo, D. & Dolbeth, M. & Anastácio, P., 2016. "Predicting global habitat suitability for Corbicula fluminea using species distribution models: The importance of different environmental datasets," Ecological Modelling, Elsevier, vol. 319(C), pages 163-169.
    12. Wentao Yang & Min Deng & Jianbo Tang & Liang Luo, 2023. "Geographically weighted regression with the integration of machine learning for spatial prediction," Journal of Geographical Systems, Springer, vol. 25(2), pages 213-236, April.

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