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Species distribution models grounded in ecological theory for decision support in river management

Author

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  • Bennetsen, Elina
  • Gobeyn, Sacha
  • Goethals, Peter L.M.

Abstract

Species distribution modelling has gained importance since the introduction of the Water Framework Directive. Several efforts have been made for the development of decision support tools to aid river basin managers. However, there is a mismatch between the available ecological models and stakeholder needs. For example, models can be so complex that they can only be applied on a limited set of species, or models can be so qualitative that they fail to deliver insight in the underlying processes behind changing ecological quality. Yet, much is known already about ecology and ecography, in general and for specific species. To valorize this available knowledge, we have developed species distribution models for macroinvertebrates in Flanders grounded in ecological theories. We introduce a conceptual approach based on niche and landscape filter theories. To apply the concept on many different macroinvertebrate species, the model development uses both data from Flanders, expert knowledge and data from other similar river systems. Implementing these niche and migration models results in a moderate predictive accuracy (average Kappa of 0.19). For sensitive species that are essential for an ecological quality status the approach results in a higher accuracy. Despite the moderate predictive accuracy, the resulting models have a good applicability. The models concur well with ecological knowledge on species preferences. Furthermore, throughout the model development process stakeholders and end users have been involved to discuss model structure and its related assumptions. This ensures that the developed model is credible and acceptable. This model approach has shown to be a way forward for ecological decision support in river management in Flanders, but inclusion of additional knowledge on migration behaviour and species interactions could help to improve the predictive accuracy the models in the future.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:325:y:2016:i:c:p:1-12
    DOI: 10.1016/j.ecolmodel.2015.12.016
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    References listed on IDEAS

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    1. C. J. Vörösmarty & P. B. McIntyre & M. O. Gessner & D. Dudgeon & A. Prusevich & P. Green & S. Glidden & S. E. Bunn & C. A. Sullivan & C. Reidy Liermann & P. M. Davies, 2010. "Global threats to human water security and river biodiversity," Nature, Nature, vol. 467(7315), pages 555-561, September.
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    3. 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.
    4. Austin, Mike, 2007. "Species distribution models and ecological theory: A critical assessment and some possible new approaches," Ecological Modelling, Elsevier, vol. 200(1), pages 1-19.
    5. Bernd Klauer & Michael Rode & Johannes Schiller & Uwe Franko & Melanie Mewes, 2012. "Decision Support for the Selection of Measures according to the Requirements of the EU Water Framework Directive," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(3), pages 775-798, February.
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    Cited by:

    1. Shin-Ruoh Juang & Szu-Hung Chen & Chen-Fa Wu, 2017. "An Expert-Based Assessment Model for Evaluating Habitat Suitability of Pond-Breeding Amphibians," Sustainability, MDPI, vol. 9(2), pages 1-20, February.
    2. Li, Mengyi & Deng, Mingjiang & Ling, Hongbo & Xu, Jia, 2024. "Development of a new rotational irrigation model in an arid basin based on ecological zoning and sluice regulation," Agricultural Water Management, Elsevier, vol. 296(C).
    3. Vermeiren, Peter & Reichert, Peter & Schuwirth, Nele, 2020. "Integrating uncertain prior knowledge regarding ecological preferences into multi-species distribution models: Effects of model complexity on predictive performance," Ecological Modelling, Elsevier, vol. 420(C).
    4. Gobeyn, Sacha & Mouton, Ans M. & Cord, Anna F. & Kaim, Andrea & Volk, Martin & Goethals, Peter L.M., 2019. "Evolutionary algorithms for species distribution modelling: A review in the context of machine learning," Ecological Modelling, Elsevier, vol. 392(C), pages 179-195.
    5. Yan, Dong & Chen, Lin & Sun, Huaiwei & Liao, Weihong & Chen, Haorui & Wei, Guanghui & Zhang, Wenxin & Tuo, Ye, 2022. "Allocation of ecological water rights considering ecological networks in arid watersheds: A framework and case study of Tarim River basin," Agricultural Water Management, Elsevier, vol. 267(C).

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