The <i>N</i> = 25 countries included in the analyses with environmental variables and species richness.

<p>Environmental variables: area (in 10³ km², islands excluded), averages of net primary productivity (<i>NPP</i>; in kg C per m² per year) and annual mean temperature (<i>T</i>; in °C), annual rainfall (<i>R</i>; in mm) and minimal potential evapotranspiration (<i>PET_min</i>; in mm), and the spatial heterogeneity in primary productivity (<i>NPP_range</i>), annual mean temperature (<i>T_range</i>), annual rainfall (<i>R_range</i>), minimal potential evapotranspiration (<i>PET_minrange</i>). Species richness for spiders (<i>S_a</i>), beetles (<i>S_c</i>), bugs (<i>S_h</i>), ants (<i>S_f</i>), herbivores (<i>S_her</i>) and carnivores (<i>S_car</i>).</p

Map of Europe showing the studied environmental variables.

<p>(A) <i>NPP</i> = net primary productivity, (B) <i>T</i> = annual mean temperature, (C) <i>R</i> = annual rainfall, and (D) <i>PET_min</i> = potential evapotranspiration of the coldest month, plus (E) the <i>N</i> = 16 study locations with their species richness <i>S_locations</i> (all groups combined; average between natural and disturbed habitat) and (F) the <i>N</i> = 25 analysed countries with their species richness <i>S_countries</i> (all groups combined).</p

Interactive effects of <i>T</i> on species richness of (A) ants and (B) spiders in <i>N</i> = 16 near-natural (open circles) and <i>N</i> = 16 intensive agricultural (filled triangles) habitats.

<p>Residuals are from models of species richness corrected for the number of individuals captured in the respective habitat <i>N</i> and, in ants, for <i>R</i> (“Best” model in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045875#pone-0045875-t005" target="_blank">Table 5</a>).</p

Models of species richness in <i>N</i> = 25 European countries.

<p>Abbreviations: PDR = productivity-diversity relationships, AE = ambient energy, IGM = interim general model, SEH = spatial environmental heterogeneity, Herbi = herbivores, Carni = carnivores. “NA” indicates cells that were excluded from the respective models. Empty cells were considered, but the respective explanatory variable did not remain in the model according to the second order Akaike Information Criterion (<i>AICc</i>). <i>I</i>-lm denotes Moran’s <i>I</i> correlation coefficient for the residuals of a linear regression model not corrected for spatial autocorrelation. <i>I</i>-sar denotes the respective Moran’s <i>I</i> for spatial autoregressive error models. <i>AICc</i> and <i>r</i>² values are from the spatial regression models, whereby <i>r</i>² is Nagelkerke’s pseudo <i>r</i>² based on maximum likelihood. Values in the columns of environmental variables are model coefficients, with significance levels denoted by asterisks:</p>*<p><i>p</i><0.05, ** <i>p</i><0.01, *** <i>p</i><0.001.</p>a<p>Cook’s distance was >0.5 for Portugal. When excluded, <i>T</i> no longer remained in the model.</p>b<p>Cook’s distance was >0.5 for Portugal. When excluded, the slope for <i>T</i> became slightly steeper (434.37*).</p>c<p>Cook’s distance was >0.5 for Portugal. When excluded, the slope for <i>T</i> became slightly flatter (153.17*).</p>d<p>Cook’s distance was >0.5 for Portugal. When excluded, none of the <i>IGM</i> variables remained in the model.</p

Pearson’s correlation coefficients between explanatory variables among the <i>N</i> = 25 countries.

<p>For abbreviations see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045875#pone-0045875-t001" target="_blank">Table 1</a>. Area was log₁₀−transformed prior to the analysis.</p

The <i>N</i> = 16 sampling locations with geographic coordinates, environmental variables, species richness and individual numbers.

<p>Environmental variables: primary productivity (<i>NPP</i>; in kg C per m² per year), annual mean temperature (<i>T</i>; in °C), annual rainfall (<i>R</i>; in mm), minimal potential evapotranspiration (<i>PET_min</i>; in mm). Species richness (<i>S</i>) and individual numbers (<i>N</i>) per habitat with subscripts as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045875#pone-0045875-t001" target="_blank">Table 1</a>.</p

Pearson’s correlation coefficients between explanatory variables among the <i>N</i> = 16 locations.

<p>For abbreviations see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045875#pone-0045875-t002" target="_blank">Table 2</a>.</p

Impact scheme of the Global Invasive Species Database, implemented by the IUCN Species Survival Commission (SSC) Invasive Species Specialist Group.

<p>The GISD stores detailed information on more than 800 invasive alien species, including on the impacts they cause. The GISD has recently been redesigned, and all information has been re-classified in order to improve the searching functionalities of the database. The schema developed for the revised GISD has allowed all species stored in the database to be coded in respect of the direct mechanisms by which their impacts occur (e.g., predation), and by the outcomes of those impact mechanisms on the environment or on human activities. For example, the grass <i>Imperata cylindrica</i> (Poales: Poaceae) almost doubles litter biomass in invaded locations, which increases potential fuel for fires (impact mechanism coded as flammability, and impact outcome as modification of fire regime). The plant <i>Schinus terebinthifolius</i> (Sapindales: Anacardiaceae) is a bio-fouling agent, forming dense thickets in gullies and river bottoms, with the ultimate effect of changing the hydrology of river streams of invaded freshwater bodies (mechanism coded as bio-fouling, and impact outcome described as modification of hydrology). The insect <i>Adelges piceae</i> (Hemiptera: Adelgidae) releases a toxin causing stress to trees, which eventually die. The impact outcome of <i>A. piceae</i> is described in GISD as damage to forestry, with its mechanism of impact coded as poisoning/toxicity, but it can also be coded as having an environmental impact on plant/animal health, as it has been here. In the table, mechanisms and outcomes are reported in two separate columns, and the three examples of the connections between mechanisms and outcomes are shown. Impact outcomes in the GISD database can be environmental or socio-economic, but our categorisation scheme of species in terms of the magnitudes of their impacts (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001850#pbio-1001850-g002" target="_blank">Figure 2</a>; <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001850#pbio-1001850-t001" target="_blank">Table 1</a>) concerns only the former.</p

The different categories in the alien species impact scheme, and the relationship between them.

<p>Descriptions of the categories are provided in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001850#pbio-1001850-box002" target="_blank">Box 2</a>. The CG category is not represented in this diagram as CG taxa may be found in any category.</p

Impact criteria for assigning alien species to different categories in the classification scheme (Box 2).

<p>These categories are for species that have been evaluated, have alien populations (i.e., are known to have been introduced outside their native range), and for which there is adequate data to allow classification (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001850#pbio-1001850-g002" target="_blank">Figure 2</a>). Classification follows the general principle outlined in the first row. However, we specifically outlined the different mechanisms through which an alien species can cause impacts in order to help assessors to look at the different aspects and to identify potential research gaps. Numbers next to different impact classes reference the numbering of impacts in the classification of impact mechanisms in the GISD (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001850#pbio-1001850-g001" target="_blank">Figure 1</a>).</p