Freshwater management using multi-objective optimization of species distribution models

Study proposes novel approach for training freshwater species distribution models to limit over- and underestimation of habitat suitability

Data resources used via GBIF : 65,000 species occurrences
Cloeon dipterum
Pond olive (Cloeon dipterum) observed near Els Poblets, Valencia, Spain by Katja Schulz. Photo via iNaturalist (CC BY 4.0)

Modelling freshwater species distributions can be challenging as biases due to obscure or difficult to observe species may lead to underestimating the true habitat suitability. In this paper, authors present a solution to this problem involving optimizing models based on multiple objectives rather than just one.

Using data from the GBIF-mediated Limnodata Neerlandica dataset the authors evaluated the performance of their multi-objective optimization (MOO) approach—called the non-dominated sorting genetic algorithm II—against regular single objective models of 11 pollution-sensitive macroinvertebrate species (including Cloeon dipterum shown above).

Their results showed that the MOO approach is two-to-four times more efficient at identifying large range distributions, while only requiring four per cent longer runtimes per training. To support decision-making, the authors propose a closer collaboration between model developers and freshwater managers to set environmetal standard limits on a more objective basis.

Original article

Gobeyn S and Goethals PLM (2019) Multi-objective optimisation of species distribution models for river management. Water Research. Elsevier BV 163: 114863. Available at: