Species lists

To select the alien aquatic plants used in the atlas, the list in Oficialdegui et al. (2023) was used as a starting point. The species set was then fleshed out employing the technical reports of the LIFE INVASAQUA project (Aquatic Invasive Alien Species of Freshwater and Estuarine Systems: Awareness and Prevention in the Iberian Peninsula) (LIFE17 GIE/ES/000515) (Oliva-Paterna et al. 2020 a, b; 2022).

The alien species selected were divided into two groups: one included introduced and established species (Introduced species) and the other comprised species that are not yet present but that are highly likely to invade (Potential species).

For the species distribution models, global species occurrence data were obtained from GBIF and EASIN. These data were cleaned up by removing erroneous taxonomic occurrences, duplicates, geographic outliers and spatial autocorrelation was reduced to minimize problems with model overfitting (Rodríguez-Merino et al. 2018).

Figure 1. Example of observed species occurrence data

Variable selection

The models' predictor variables were taken from WorldClim (i.e., climatic conditions and altitude) and SEDAC (i.e., anthropogenic effects on the regional environment).

To reduce collinearity within the models, a correlation analysis was performed to select a subset of variables. Variables were chosen based on their biological significance, which meant focusing on climatic variables associated with biological extremes, variables capable of inducing physiological stress in plants, and variables that limit species development and survival (Rodríguez-Merino et al. 2017).

The variables that remained in the model were maximum temperature of warmest month (Bio5), minimum temperature of coldest month (Bio6), precipitation seasonality (coefficient of variation, Bio15), precipitation of driest quarter (Bio17), human footprint and altitude.

Figure 2. Example of predictor variables maps

Model application

The species distribution models were built using the maximum entropy algorithm implemented in MaxEnt software (Phillips et al. 2006, Elith et al. 2011). It is currently considered to be one of the most common and robust methods for modeling potential species distributions. Indeed, it is helpful for modeling the distributions of rare and poorly studied species because it performs well with small sample sizes and needs no more than occurrence data to function (Elith et al. 2011). It has also been used to predict the potential distributions of alien aquatic plants (Rodríguez-Merino et al. 2017, 2018). MaxEnt models options and settings were selected following Rodríguez-Merino et al. (2019a).

Figure 3. Example of species distribution model

Model accuracy

The models were run using information on the species' occurrences in native and non-native ranges (Rodríguez-Merino et al. 2019b). In this process, 80% of the observed species distribution data were utilized for model calibration, and the remaining 20% were employed in model validation. Models were evaluated using the area under the receiver operating characteristic (ROC) curve.

Figure 4. Example of AUC graph

Habitat suitability maps

QGIS software was used to represent the spatial information obtained from the species distribution models. Habitat suitability ranged from low to high for each species.

Figure 5. Example of habitat suitability map (Invasion risk map)

References

Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and distributions, 17(1), 43-57.

Oficialdegui, F. J., Zamora-Marín, J. M., Guareschi, S., Anastácio, P. M., García-Murillo, P., Ribeiro, F., ... & Oliva-Paterna, F. J. (2023). A horizon scan exercise for aquatic invasive alien species in Iberian inland waters. Science of the Total Environment, 869, 161798.

Oliva-Paterna, F. J., Ribeiro, F., Miranda, R., Anastácio, P., García-Murillo, P., Cobo, F., ... & Zamora-Marín, J. M. (2021). LIST OF AQUATIC ALIEN SPECIES OF THE IBERIAN PENINSULA (2020). Updated list of aquatic alien species introduced and established in iberian inland waters.

Oliva-Paterna, F. J., Ribeiro, F., Miranda, R., Anastácio, P., García-Murillo, P., Cobo, F., ... & Zamora-Marín, J. M. (2021). LIST OF POTENTIAL AQUATIC ALIEN SPECIES OF THE IBERIAN PENINSULA (2020). Updated list of the potential aquatic alien species with high risk of invasion in Iberian inland waters.

Oliva-Paterna, F. J., Oficialdegui, F. J., Anastácio, P., García-Murillo, P., Zamora-Marín, J. M., Ribeiro, F., ... & Vieira-Lanero, R. (2022). BLACK LIST AND ALERT LIST OF THE AQUATIC INVASIVE ALIEN SPECIES OF THE IBERIAN PENINSULA–Horizon scanning exercise focused on the high-risk aquatic invasive alien species for the Iberian inland waters.

Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological modelling, 190(3-4), 231-259.

Rodríguez Merino, A., Fernández Zamudio, R., & García Murillo, P. (2017). An invasion risk map for non-native aquatic macrophytes of the Iberian Peninsula. Anales del Jardín Botánico de Madrid, 74 (1), e055.

Rodríguez Merino, A., Fernández Zamudio, R., & García Murillo, P. (2019a). Identifying areas of aquatic plant richness in a Mediterranean hotspot to improve the conservation of freshwater ecosystems. Aquatic Conservation: Marine and Freshwater Ecosystems, 29(4), 589–602.

Rodríguez-Merino, A., Fernández-Zamudio, R., García-Murillo, P., & Muñoz, J. (2019b). Climatic niche shift during Azolla filiculoides invasion and its potential distribution under future scenarios. Plants, 8(10), 424.

Rodriguez-Merino, A., Garcia-Murillo, P., Cirujano, S., & Fernández-Zamudio, R. (2018). Predicting the risk of aquatic plant invasions in Europe: How climatic factors and anthropogenic activity influence potential species distributions. Journal for Nature Conservation, 45, 58-71.