Advances and challenges in modelling the impacts of invasive alien species on aquatic ecosystems
Corrales, X.; Katsanevakis, S.; Coll, M.; Heymans, J.J.; Piroddi, C.; Ofir, E.; Gal, G. (2020). Advances and challenges in modelling the impacts of invasive alien species on aquatic ecosystems. Biological Invasions 22(3): 907-934. https://dx.doi.org/10.1007/s10530-019-02160-0
In: Biological Invasions. Springer: London. ISSN 1387-3547; e-ISSN 1573-1464, meer
Invasive alien species (IAS) have become an important driver of biodiversity change and exert severe pressure on natural ecosystems. The development of modelling approaches to assess and predict their distributions and impacts, and evaluate management options has increased substantially. We reviewed these modelling approaches, applied in aquatic ecosystems, using a systematic review approach in line with the preferred reporting items for systematic reviews and meta-analyses. According to our results, multispecies/ecosystem models dominated the applications, with dynamic and non-spatial models being the most prevalent. Most of the models included an additional stressor, mainly fisheries, climate change or nutrient loading. The impacts on biota focused on predation, but also on competition and ecosystem functioning, while the impacts on ecosystem services focused on food provision and water purification. At species/population level, most of the studies reported negative impacts; while at multispecies/ecosystem level, negative and both negative and positive impacts were similarly represented. We reflect on the ability of current models to assess different impacts of IAS populations and highlight the need to advance their capabilities to predict future impacts. Further development of models that allow capturing the arrival, establishment and spread of IAS and assess their impacts in an integrated way is still needed. Spatial-temporal modelling techniques bridging with novel analytical capabilities (such as environmental DNA to investigate the presence of IAS and metabarcoding and machine learning to predict future trophic behavior and distributions) may be the key for future achievement.
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