Improving Species Distribution Modelling of freshwater invasive species for management applications

Abstract

Freshwater ecosystems rank among the most endangered ecosystems in the world and are under increasing threat from aquatic invasive species (AIS). Understanding the range expansion of AIS is key for mitigating their impacts. Most approaches rely on Species Distribution Models (SDMs) to predict the expansion of AIS, using mainly environmental variables, yet ignore the role of human activities in favouring the introduction and range expansion of AIS. In this study, we use five SDM algorithms (independently and in ensemble) and two accuracy measures (TSS, AUC), combined with a null modelling approach, to assess the predictive performance of the models and to quantify which predictors (environmental and anthropogenic from the native and introduced regions) best explain the distribution of nine freshwater invasive species (including fish, arthropods, molluscs, amphibians and reptiles) in a large island (Great Britain), and which species characteristics affect model performance. Our results show that the distribution of invasive species is difficult to predict by SDMs, even in cases when TSS and AUC model accuracy values are high. Our study strongly advocates the use of null models for testing SDMs performance and the inclusion of information from the native area and a variety of both human-related and environmental predictors for a more accurate modelling of the range expansion of AIS. Otherwise, models that only include climatic variables, or rely only on standard accuracy measures or a single algorithm, might result in mismanagement of AIS.

Peer Review #1 of "Experimental evidence of chemical attraction in the mutualistic zebra mussel-killer shrimp system (v0.1)"

“…In Great Britain, the killer shrimp was first detected in 2010 (Rewicz et al 2014) and is only present in 8 locations, making it difficult to predict its future dispersal (Rodríguez-Rey et al 2019). The zebra mussel, in contrast, was first detected in 1824 and is now established in 376 locations in England and Wales (Aldridge et al 2004;Rodríguez-Rey et al 2019), potentially increasing the number of favourable locations for killer shrimp many fold. Yet, current invasive species prioritisation lists (Boets et al 2014;Carboneras et al 2018) and risk assessment guidelines (Roy et al 2018) tend to view invasive species in isolation, making no allowance for invasion facilitation.…”

Experimental evidence of chemical attraction in the mutualistic zebra mussel-killer shrimp system

“…This is of particular concern when the species is a recent invader since there is typically insufficient information to predict areas at risk or to guide management (Morales, Fernández & Baca-González, 2017). In Great Britain, the killer shrimp was first detected in 2010 (Rewicz et al, 2014) and is only present in 8 locations, making it difficult to predict its future dispersal (Rodríguez-Rey et al, 2019). The zebra mussel, in contrast, was first detected in 1824 and is now established in 376 locations in England and Wales (Aldridge, Elliott & Moggridge, 2004; Rodríguez-Rey et al, 2019), potentially increasing the number of favourable locations for killer shrimp many fold.…”

“…In Great Britain, the killer shrimp was first detected in 2010 (Rewicz et al, 2014) and is only present in 8 locations, making it difficult to predict its future dispersal (Rodríguez-Rey et al, 2019). The zebra mussel, in contrast, was first detected in 1824 and is now established in 376 locations in England and Wales (Aldridge, Elliott & Moggridge, 2004; Rodríguez-Rey et al, 2019), potentially increasing the number of favourable locations for killer shrimp many fold. Yet, current invasive species prioritisation lists (Boets et al, 2014; Carboneras et al, 2018) and risk assessment guidelines (Roy et al, 2018) tend to view invasive species in isolation, making no allowance for invasion facilitation.…”

Forecasting the spread associated with climate change in Eastern Europe of the invasive Asiatic flea beetle, Luperomorpha xanthodera (Coleoptera: Chrysomelidae)

“…Further, all predictors were projected into an Equal Area Projection (WGS84 -Cylindrical Equal Area), to correct for any latitudinal bias (Brown, 2014); spatial jack-knife was set at 5 replicates per parameter and 3 spatial groups were created. Models were calibrated for both primary and secondary range localities of this species, a technique used to improve performance of predictions and reduce uncertainties (Broennimann & Guisan, 2008) when modelling the distribution of invasive species (Cerasoli et al, 2019;Iannella et al, 2019a;Perret et al, 2019;Rodríguez-Rey et al, 2019).…”