Modelling freshwater species distributions can be challenging, as biases in the case of species that are obscure or difficult to observe 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 environmental standard limits on a more objective basis.