Effect of species traits on invasiveness (invasive or naturalized but non-invasive; Models I), and dominance (percent cover in invaded communities; Models II) of alien herbaceous plants.

<p>Reproductive and dispersal species traits (Models a) are classified by whether they are related to seed production (coded 1 before the variable name), or dispersal (coded 2). Other traits affecting invasion success (coded 3) were added to Models b, and variation in traits to Models c (see text for details). Effects are expressed as importance relative to the most important predictor (%), and the overall significance of each model as percent of misclassifications compared to null model with 50% misclassification rate (Models I) and percent of explained variance (Models II); better models have lower misclassification rate in classification trees (Models I) and explain more variance in regression trees (Models II).</p><p>Effect of species traits on invasiveness (invasive or naturalized but non-invasive; Models I), and dominance (percent cover in invaded communities; Models II) of alien herbaceous plants.</p

Optimal classification tree of the probability of a plant species being invasive (yes ■) or naturalized but not invasive (no □) for model including all traits (Model Ib in Table 1).

<p>Each node (polygonal table with splitting variable name) and terminal node (with node number) shows table with columns for invasiveness (Class no or yes) and number (Cases) and percent (%) of cases for each Class. Below the table is the total number of cases (N) and graphical representation of the percentage of no and yes cases in each Class (horizontal bar). For each node, there is a split criterion on its left- and right-hand side, rounded to one decimal point. Vertical depth of each node is proportional to its improvement value that corresponds to the explained variance at the node. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123634#pone.0123634.t001" target="_blank">Table 1</a> for overall misclassification rate of the optimal tree.</p

Description of 29 potential success factors of 173 eradications against invertebrate plant pests, plant pathogens (viruses/viroids, bacteria and fungi) and weeds, used in data mining analyses.

<p>NA  =  information not available.</p>*<p>European Nature Information System habitat classification <a href="http://eunis.eea.europa.eu/habitats.jsp" target="_blank">http://eunis.eea.europa.eu/habitats.jsp</a>.</p>**<p>pathways were defined according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048157#pone.0048157-Hulme2" target="_blank">[44]</a>.</p

Optimal classification tree for factors relating to success and failure of 173 eradication campaigns against invertebrate plant pests, plant pathogens (viruses/viroids, bacteria and fungi) and weeds in a model without any predetermined structure.

<p>Splitting nodes (polygonal tables with splitting variable name) and terminal nodes (with a split criterion above each) show a table with columns for the outcome (success/failure) and % of weighted cases for each outcome, total number of unweighted cases (N), and graphical representation of the percentage of success (grey) and failure (black) weighted cases (horizontal bar). Vertical depth of each node is proportional to its improvement value that corresponds to explained variance at the node. Overall misclassification rate of the optimal tree is 15.8% compared to 50% for the null model, with 16.7% misclassified success and 14.8% failure cases. Sensitivity (true positive rate, defined as the ability of the model to predict that a case is eradicated when it actually is) is 83.3 and specificity (true negative rate, defined as the ability of the model to predict that a case is not eradicated when it is not) 85.2% for learning samples, i.e. the samples not used to build the models for assessment of cross-validation errors, and 77.1 and 69.0%, respectively, for cross-validated samples, i.e. the best estimates that would occur if the models were to be applied to new data, assuming that the new data were drawn from the same distribution as the learning data.</p

Partial dependence plots based on the optimal boosted tree for (a) taxonomic Kingdoms, (b) biogeographic regions, (c) the reaction time between the arrival/detection of the organism and the start of the eradication campaign, (d) the spatial extent of the pest outbreak, (e) the level of biological knowledge and preparedness, and (f) insularity.

<p>The plots show probabilities of success of an eradication campaign for these predictors as net effects, i.e. averaging out the effects of all the other predictors included in the optimal boosted tree. The optimal boosted tree has overall misclassification rate 5.2% with 3.0% misclassified success and 8.0% failure cases. Sensitivity and specificity are respectively 97.0 and 92.0% for learning, and 82.2 and 68.1% for cross-validated samples. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048157#pone-0048157-t001" target="_blank">Table 1</a> for detail description of the predictors and Fig. 1 for detail explanation of misclassification rates, sensitivity and specificity.</p

Optimal classification tree with event-specific factors placed at the top of the tree.

<p>Otherwise as in Fig. 1. Overall misclassification rate of the optimal tree is 18.0% with 15.3% misclassified success and 23.4% failure cases. Sensitivity and specificity are respectively 84.7 and 76.6% for learning, and 66.7 and 64.9% for cross-validated samples. Detail explanation of misclassification rates, sensitivity and specificity is in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0048157#pone-0048157-g001" target="_blank">Fig. 1</a>.</p

Outputs of model ES obtained with 20 presence data located within the 1955 presence area (A) and with 50 presence data located within the 1980 presence area (B).

<p>The model was fitted using 19 input variables in both cases.</p

Significance of effect of training area, size of training dataset, and set of bioclimatic variables on AUC values.

<p>Area 1980 vs. 1955, 6 variables vs. 19 variables, first three principal components (PCA) vs. 19 variables, PCA vs. 6 variables, training dataset size  =  20 vs. 10, big training dataset (more than 50 presence points) vs. small (less than 50).</p><p>***p<0.001 |</p><p>**p<0.01 |</p><p>*p<0.05 |. p<0.1 | NS not significant.</p

Rotated component matrix of Principal Component Analysis.

<p>Varimax rotation method with Kaiser normalization. The components are scaled between 0–1; the closer the values to one, the more variance they explain. Values between 0.7–0.79, 0.8–0.89.</p

Geographical area of the training datasets.

<p>The hatched area represents the WCR distribution before 1955 while the grey area represents the WCR distribution before 1980.</p