Current and future modelled distributions for the Corsican Nuthatch.

<p>The distributions are depicted according to the threshold used to transform the probability distribution of the Corsican pine into a binary distribution, used as one of the variables in the modelling of the Corsican Nuthatch. Only points with suitability above either the LPT or the TSS threshold of the Corsican Nuthatch (in brackets) are represented.</p

Localisation of the study area.

<p>Localisation of the study area.</p

Distribution of forests in Corsica.

<p>Light green stands for mixed forests, dark green for coniferous forests and medium green for broad-leaved forests.</p

Representation of the data used in the study.

<p>(a) Corsican Nuthatch data, (b) Corsican pine data, (c) Maritime pine data. For the Corsican pine red circles represents data from coppices (not used in the niche modelling but to determine further LPT).</p

Characteristics of the current and future distributions of the Corsican Nuthatch according to the threshold used to transform the pines distributions into binary distributions used as variables for the modelling.

<p>(TSS: True Skill Statistic, LPT: Lowest Probability Threshold).</p

Current and future distributions modelled for the Corsican pine and the Maritime pine.

<p>Current (a) and future (b) distributions predicted for the Corsican pine according to the considered threshold. Current (c) and future (d) distributions predicted for the Maritime pine according to the considered threshold.</p

Is <i>V</i>. <i>v</i>. <i>nigrithorax</i> niche at equilibrium in its invaded range?

<p>Climatic niche occupied by <i>V</i>. <i>v</i>. <i>nigrithorax</i> in its European invasion range during 2004–2010 (a) and 2011–2015 (b) along the first two axes of the PCA (see (c) for details), showing an evolution during the two periods. Grey shading depicts the occurrence density of the species. The solid and dashed contour lines represent 100% and 50% respectively of the available (background) climate in Europe. (c) Contribution of the climate variables to the first two axes of the PCA (bio1: annual mean temperature, bio4: temperature seasonality, bio5: mean temperature of the warmest month, bio6: mean temperature of the coldest month, bio12: annual precipitation, bio13: precipitation of the wettest month, bio14: precipitation of the driest month, and bio15: precipitation seasonality). (d) Histograms showing the observed niche overlap D (D = 0.45) (bars with a diamond) and simulated niche overlaps (grey bars) on which tests of niche equivalency and niche similarity were calculated from 1000 iterations [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193085#pone.0193085.ref054" target="_blank">54</a>]: niches are similar but not equivalent.</p

SDMs predictions and predictive accuracy.

<p>(a) Climate suitability gradient map, from 0 to 1, predicted by the model (current ensemble consensus) using invasive data from 2004 to 2010 (blue points). Red points represent invasive data recorded after 2010 (2011–2015) that are used to evaluate the model. The dotted circle around the first invasion data (blue triangle) delimits all points that are within 150km of the first invasion data. (b) Climate suitability of all possible points (between 150 and 850 km of the first invasion data) according to their distance to the first invasion (grey points). The full line represents the median climate suitability according to the distance, whereas the dotted lines represent the 10%, 30%, 70% and 90% quantiles (blue and red points as above). Evaluation (red) points above the median have a higher predicted suitability than expected given their distance to the first invasion occurrence. (c) Boxplots representing the range of climate suitability values for all possible points (grey) and invasive data (calibration data in blue and evaluation data in red) depending on their distance to the first invasion data. In all three cases, the predicted suitability of evaluation points is lower than the predicted suitability of calibration points, but is higher than expected given their distance to the first invasion occurrence (all possible points, in grey).</p

Can species distribution models really predict the expansion of invasive species?

<div><p>Predictive studies are of paramount importance for biological invasions, one of the biggest threats for biodiversity. To help and better prioritize management strategies, species distribution models (SDMs) are often used to predict the potential invasive range of introduced species. Yet, SDMs have been regularly criticized, due to several strong limitations, such as violating the equilibrium assumption during the invasion process. Unfortunately, validation studies–with independent data–are too scarce to assess the predictive accuracy of SDMs in invasion biology. Yet, biological invasions allow to test SDMs usefulness, by retrospectively assessing whether they would have accurately predicted the latest ranges of invasion. Here, we assess the predictive accuracy of SDMs in predicting the expansion of invasive species. We used temporal occurrence data for the Asian hornet <i>Vespa velutina nigrithorax</i>, a species native to China that is invading Europe with a very fast rate. Specifically, we compared occurrence data from the last stage of invasion (independent validation points) to the climate suitability distribution predicted from models calibrated with data from the early stage of invasion. Despite the invasive species not being at equilibrium yet, the predicted climate suitability of validation points was high. SDMs can thus adequately predict the spread of <i>V</i>. <i>v</i>. <i>nigrithorax</i>, which appears to be—at least partially–climatically driven. In the case of <i>V</i>. <i>v</i>. <i>nigrithorax</i>, SDMs predictive accuracy was slightly but significantly better when models were calibrated with invasive data only, excluding native data. Although more validation studies for other invasion cases are needed to generalize our results, our findings are an important step towards validating the use of SDMs in invasion biology.</p></div

Comparing SDMs predictive accuracy when trained with or without native data.

<p>Percentiles of validation points (further than 150km from the first invasion record) depending on whether or not native data was accounted for to calibrate the models and on the cut-off year that was used to split the invasive data into calibration and evaluation data. Percentiles are obtained by comparing the predicted climate suitability of a given validation point to the distribution of climate suitability values of all points being at the same distance from the first invasion record than the validation point (i.e., grey points in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193085#pone.0193085.g002" target="_blank">Fig 2B</a>). Percentiles higher than 50^th thus mean that the predicted climate suitability of the validation point is higher than expected given its distance to the first invasion record. For all cut-off years, paired t-test were computed to assess the difference between models with and without native data: a red star indicates significantly higher values (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193085#pone.0193085.s001" target="_blank">S1 Table</a>).</p