Abstract
AbstractAimInvasive alien species (IAS) can cause profound impacts on ecosystem function and diversity, human health, well‐being and livelihoods. Climate change is an important driver of biological invasions, so it is critical to develop models and climate‐driven scenarios of IAS range shifts to establish preventive measures. In this study, we analyse how projected changes in the frequency and magnitude of climate extreme events could affect the spread of the six most widely distributed invasive vertebrate species in the Iberian Peninsula.LocationIberian Peninsula.TaxaRed avadavat (Amandava amandava), common waxbill (Estrilda astrild), monk parakeet (Myiopsitta monachus), rose‐ringed parakeet (Psittacula krameri), American mink (Neovison vison) and pond slider (Trachemys scripta).MethodsWe followed best‐practice standards for species distribution models (SDMs) regarding handling of the response and predictor variables, model building and evaluation using metrics that assess different facets of model performance. We used an ensemble approach with four modelling methods of varying complexity, including both regression‐based and tree‐based machine‐learning algorithms. We analysed five regional models for current (1971–2000) and future climate (2021–2050). We used principal components analysis to assess consensus among model outputs and positively weighed predictions from well‐performing models.ResultsSelected models showed high consensus and good predictive capacity on block cross‐validation areas. Generalized Linear Models and Generalized Additive Models scored highest in reliability (calibration), but Bayesian Additive Regression Trees provided the best balance between calibration and discrimination capacity. Forecasts include visible changes in environmental favourability, with losses generally outweighing the gains, but with some areas becoming more favourable for several species.Main conclusionsIncreased frequency and/or intensity of climate extreme events associated with ongoing climate change are projected to reduce overall invasion risk for the species examined although increases in favourability should be expected locally.
Highlights
Invasive alien species are among the five direct drivers of environmental change with the largest relative impacts on biodiversity and ecosystem services (Brondizio et al, 2019; Early et al, 2016)
We used three metrics that focus on different perspectives of model performance: (a) the Area Under the receiver operating characteristic Curve (AUC), which measures overall discrimination performance, that is the ability of the model to distinguish presence from absence localities by assigning them higher predicted values; (b) the True Skill Statistic (TSS), which measures classification performance, that is the proportion of correctly classified presences and absences (Allouche et al, 2006), using training prevalence as the threshold value for classifying predicted presences and predicted absences; and (c) Miller’s calibration slope, which assesses model reliability, that is the overall deviation of predicted probabilities from observed occurrence frequencies (Miller et al, 1991; Pearce & Ferrier, 2000)
Understanding how invasive species might change their potential distributional area in the future is critical for the implementation of adequate surveillance, control or eradication measures into local and regional management plans
Summary
Invasive alien species are among the five direct drivers of environmental change with the largest relative impacts on biodiversity and ecosystem services (Brondizio et al, 2019; Early et al, 2016). To forecast potential favourable areas for invasion, we built ensembles of species distribution models (SDMs) (Araújo & New, 2007) using, as predictor variables, extreme climate events metrics projected onto the future based on different climate change scenarios (see section 2.2 in Methods for details). SDMs characterize species’ current potential distributions, that is the climate space where these invaders have already been found, and project changes in the distribution of climate space according to future climate change scenarios (Peterson et al, 2011) This process enables early detection of areas with high risk of invasion, allowing a rapid response where it is most necessary and effective. Given the projected changes in climate extremes for the Mediterranean region, we expect a reduction in the favourable area of invasive species exhibiting low tolerance to extreme warm temperatures and that require a markedly wet season to survive and grow
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