Received: 14 December 2018 | Revised: 25 September 2019 | Accepted: 8 October 2019 DOI: 10.1111/geb.13027 RE SE ARCH PAPER Similar factors underlie tree abundance in forests in native and alien ranges Masha T. van der Sande1,2,3,4,5 | Helge Bruelheide2,6 | Wayne Dawson7 | Jürgen Dengler8,9 | Franz Essl10 | Richard Field11 | Sylvia Haider2,6 | 12,13 14,15 16 17 Mark van Kleunen | Holger Kreft | Joern Pagel | Jan Pergl | 2,6 17,18 14 2 Oliver Purschke | Petr Pyšek | Patrick Weigelt | Marten Winter | 19 20 21 22 Fabio Attorre | Isabelle Aubin | Erwin Bergmeier | Milan Chytrý | 23,24 19 25 Matteo Dainese | Michele De Sanctis | Jaime Fagundez | Valentin Golub26 | Greg R. Guerin27 | Alvaro G. Gutiérrez28 | Ute Jandt2,6 | Florian Jansen29 | Borja Jiménez-Alfaro30 | Jens Kattge2,31 | Elizabeth Kearsley32 | Stefan Klotz1,2 | Koen Kramer5,33 | Marco Moretti34 | Ülo Niinemets35,36 | Robert K. Peet37 | 38,39 17 40,41 42 Josep Penuelas | Petr Petřík | Peter B. Reich | Brody Sandel | 43,44 45 46 Marco Schmidt | Maria Sibikova | Cyrille Violle | Timothy J. S. Whitfeld47 | Thomas Wohlgemuth48 | Tiffany M. Knight1,2,6 1 Department of Community Ecology, Helmholtz Centre for Environmental Research–UFZ, Halle (Saale), Germany 2 German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany 3 Department of Biological Sciences, Florida Institute of Technology, Melbourne, Florida 4 Institute for Biodiversity & Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands 5 Forest Ecology and Forest Management Group, Wageningen University & Research, Wageningen, The Netherlands 6 Martin Luther University Halle-Wittenberg, Institute of Biology/Geobotany and Botanical Garden, Halle (Saale), Germany 7 Department of Biosciences, Durham University, Durham, United Kingdom 8 Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 9 Vegetation Ecology, Institute of Environment and Natural Resources (IUNR), Zurich University of Applied Sciences (ZHAW), Switzerland 10 Division of Conservation Biology, Vegetation Ecology and Landscape Ecology, Department of Botany and Biodiversity Research, University of Vienna, Vienna, Austria 11 School of Geography, University of Nottingham, Nottingham, United Kingdom 12 Ecology, Department of Biology, University of Konstanz, Konstanz, Germany 13 Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou, China 14 Biodiversity, Macroecology & Biogeography, University of Goettingen, Göttingen, Germany 15 Centre of Biodiversity and Sustainable Land Use (CBL), University of Goettingen, Göttingen, Germany 16 Landscape & Plant Ecology, University of Hohenheim, Stuttgart, Germany 17 Institute of Botany, Czech Academy of Sciences, Průhonice, Czech Republic 18 Faculty of Science, Department of Ecology, Charles University, Prague, Czech Republic 19 Department of Environmental Biology, University Sapienza of Rome, Rome, Italy 20 Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, Sault Ste Marie, Ontario, Canada 21 Vegetation & Phytodiversity Analysis, University of Göttingen, Göttingen, Germany 22 Department of Botany and Zoology, Masaryk University, Brno, Czech Republic This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2019 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd Global Ecol Biogeogr. 2020;29:281–294.  wileyonlinelibrary.com/journal/geb | 281 282 | VAN DER SANDE et al. 23 Department of Animal Ecology and Tropical Biology, Biocenter, University of Würzburg, Würzburg, Germany 24 Institute for Alpine Environment, EURAC Research, Bolzano, Italy 25 Faculty of Science, Department of Biology, University of A Coruña, Coruña, Spain 26 Institute of Ecology of the Volga River Basin, Russian Academy of Sciences, Tolyatti, Russia 27 Terrestrial Ecosystem Research Network, School of Biological Sciences, The University of Adelaide, Adelaide, South Australia, Australia 28 Departamento de Ciencias Ambientales y Recursos Naturales Renovables, Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Chile 29 Faculty of Agricultural and Environmental Science, University of Rostock, Rostock, Germany 30 Research Unit of Biodiversity (CSIC/UO/PA), University of Oviedo, Mieres, Spain 31 Max Planck Institute for Biogeochemistry, Jena, Germany 32 Computational and Applied Vegetation Ecology (CAVElab), Ghent University, Ghent, Belgium 33 Vegetation, Forest and Landscape Ecology, Wageningen Environmental Research (Alterra), Wageningen University and Research, Wageningen, The Netherlands 34 Swiss Federal Research Institute WSL, Biodiversity and Conservation Biology, Birmensdorf, Switzerland 35 Chair of Crop Science and Plant Biology, Estonian University of Life Sciences, Tartu, Estonia 36 Estonian Academy of Sciences, Tallinn, Estonia 37 Department of Biology, University of North Carolina, Chapel Hill, North Carolina 38 CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain 39 CREAF, Barcelona, Spain 40 Department of Forest Resources, University of Minnesota, St. Paul, Minnesota 41 Hawkesbury Institute for the Environment, Western Sydney University, Penrith South DC, New South Wales, Australia 42 Department of Biology, Santa Clara University, Santa Clara, California 43 Data and Modelling Centre, Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany 44 Scientific Service, Palmengarten der Stadt Frankfurt, Frankfurt am Main, Germany 45 Institute of Botany, Plant Science and Biodiversity Center, Slovak Academy of Sciences, Bratislava, Slovakia 46 Centre d’Ecologie Fonctionnelle et Evolutive (UMR 5175), CNRS, Université Paul Valéry Montpellier, EPHE, Univ Montpellier, Montpellier, France 47 Department of Ecology and Evolutionary Biology, Brown University, Providence, Rhode Island 48 Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland Correspondence Masha T. van der Sande, Department of Abstract Community Ecology, Helmholtz Centre for Aim: Alien plant species can cause severe ecological and economic problems, and Environmental Research - UFZ, Theodor- Lieser-Straße 4, 06120 Halle (Saale), therefore attract a lot of research interest in biogeography and related fields. To iden- Germany. tify potential future invasive species, we need to better understand the mechanisms Email: masha.vandersande@wur.nl underlying the abundances of invasive tree species in their new ranges, and whether Funding information these mechanisms differ between their native and alien ranges. Here, we test two Netherlands Organisation for Scientific Research (NWO), Grant/Award Number: hypotheses: that greater relative abundance is promoted by (a) functional difference 019.171LW.023; German Research from locally co-occurring trees, and (b) higher values than locally co-occurring trees Foundation; German Research Foundation DFG, Grant/Award Number: 264740629; for traits linked to competitive ability. FONDECYT, Grant/Award Number: Location: Global. 11150835; CONICYT-PAI, Grant/Award Number: 82130046; 7th European Time period: Recent. Community Framework Programme, Major taxa studied: Trees. Grant/Award Number: FORECOFUN- SSA PIEF-GA-2010–274798; European Methods: We combined three global plant databases: sPlot vegetation-plot database, Research Council Synergy, Grant/Award TRY plant trait database and Global Naturalized Alien Flora (GloNAF) database. We Number: ERC-SyG-2013-610028; Austrian Science Foundation, Grant/Award Number: used a hierarchical Bayesian linear regression model to assess the factors associated I2086-B16; Czech Science Foundation, with variation in local abundance, and how these relationships vary between native Centre of Excellence PLADIAS, Grant/ Award Number: 14-15414S and RVO and alien ranges and depend on species’ traits. 67985939; EXPRO, Grant/Award Number: Results: In both ranges, species reach highest abundance if they are functionally simi- 19-28807X; VEGA, Grant/Award Number: 0051/15 lar to co-occurring species, yet are taller and have higher seed mass and wood density than co-occurring species. Editor: Brian Enquist VAN DER SANDE et al. | 283 Main conclusions: Our results suggest that light limitation leads to strong environ- mental and biotic filtering, and that it is advantageous to be taller and have denser wood. The striking similarities in abundance between native and alien ranges imply that information from tree species’ native ranges can be used to predict in which habitats introduced species may become dominant. KEYWORDS abundance, dissimilarity, forest, functional traits, global, plant invasion, trees 1 | I NTRO D U C TI O N with particular traits can survive, and/or when particular traits are as- sociated with a competitive advantage and result in the exclusion of Biological invasions by alien species can cause severe environmental species with different traits (Kraft, Adler, et al., 2015). Reflecting this and economic problems (Mack et al., 2000; Petit, Bialozyt, Garnier- dual influence of biotic and abiotic drivers, we henceforth refer to such Géré & Hampe, 2004; Richardson, Hui, Nuñez & Pauchard, 2014; Vilà promotion of trait similarity as “environmental-biotic filtering”. In the et al., 2010). Most alien species, however, are non-invasive in their native range, species could become dominant due to similar mecha- alien range (Pyšek et al., 2017; Williamson & Fitter, 1996), suggest- nisms related to environmental conditions, niche differentiation and/ ing that species-specific properties, such as adaptive traits or phylo- or competitive differences (Chesson, 2000). The question remains, genetic relationships, determine invasion potential. Moreover, many however, how niche differentiation and competitive advantage affect invasive alien species are not considered harmful in their native range species abundance in the native vs. alien ranges, and how they affect (Colautti et al., 2014; Parker et al., 2013), which suggests that they the abundances of invasive in relation to non-invasive alien species. can interact in novel ways with the species in the new conditions of The success of alien tree species in forests is thought to be their alien range. One way to assess a species’ success in its native mainly driven by conditions of the invaded site and factors related and alien ranges is its abundance relative to co-occurring native spe- to their introduction history, such as time since introduction, rea- cies (Van Couwenberghe, Collet, Pierrat, Verheyen & Gégout, 2013). son for introduction and, especially, propagule pressure (Bucharova Surprisingly few studies have compared abundance differences in the & van Kleunen, 2009; Donaldson et al., 2014; Feng & van Kleunen, native versus alien ranges of plant species (Firn et al., 2011; Gallien 2016; Richardson et al., 2014). However, just like other species, alien & Carboni, 2017; Hierro, Maron & Callaway, 2005; van Kleunen, species that are classified as highly invasive only become locally Dawson, Schlaepfer, Jeschke & Fischer, 2010), and the ones that did dominant in some areas and remain rare in others, suggesting that mainly focused on the enemy-release hypothesis (Colautti, Ricciardi, local factors, such as their dissimilarity to the resident community or Grigorovich & MacIsaac, 2004) or on a small set of species (Callaway their competitive trait differences, could strongly determine species & Aschehoug, 2000; Taylor et al., 2016). A better global-scale under- abundance at local scales (Stohlgren, 1999). The lack of research on standing of how patterns associated with mechanisms of coexistence local, plot-scale abundances across broad geographic extents means differ between the native and alien ranges of species would provide in- that this remains an open question. sights into the invasion process that could help with the management Here, we evaluate the patterns of, and possible underlying fac- of incipient invasions and the prevention of new ones. tors associated with, the relative abundance (hereafter referred to as The ability of alien species to establish and become dominant out- “abundance”) of tree species in forest ecosystems in their native and side their native ranges can be driven by several mechanisms, such alien ranges. We focus on 41 tree species occurring in 228,943 plots as favourable environmental conditions (Broennimann et al., 2007), around the world (Figure 1) that have their native and alien ranges on novel properties of the alien species (Carboni et al., 2016) that lead different continents. We address two research questions. First, how do to reduced competition via “niche differentiation” and/or release from species differ in their abundance between the native and alien ranges? enemies, more competitive traits (Lai, Mayfield, Gay-des-combes, We expect that invasive alien species are more (or equally) abundant Spiegelberger & Dwyer, 2015), and/or high propagule pressure due in the alien compared to native range, whereas non-invasive alien spe- to active planting for ornamental or economic purposes (Conedera, cies are less (or equally) abundant in the alien compared to the native Wohlgemuth, Tanadini & Pezzatti, 2018; Lockwood, Cassey & range. Second, how are the local abundances of tree species, in both Blackburn, 2005; Pysek et al., 2015; Taylor et al., 2016). At local spa- their native and alien ranges, related to (multivariate) trait dissimilarity tial scales (e.g., the community level), abundant alien species are often of co-occurring woody species? We address this question in terms of found to be phylogenetically and functionally more dissimilar to the both overall trait dissimilarity (as a measure of niche differentiation), native species than at broader spatial scales (Carboni et al., 2013), and differences in traits specifically linked to competitive ability (see suggesting that niche differentiation increases alien success at local next paragraph). Furthermore, we examine the effects of human in- scales. However, high trait similarity among alien and native species fluence and climatic conditions on local abundance. We expected a can also occur if environmental conditions are such that only species priori that niche differentiation, competitive differences, and wet and 284 | VAN DER SANDE et al. D  /DWLWXGH  í í    /RQJLWXGH E  /DWLWXGH  í í    /RQJLWXGH F I G U R E 1 Plot locations in the native range (a) and alien range (b) across all 41 species. Points are transparent to show differences in plot densities [Colour figure can be viewed at wileyonlinelibrary.com] warm climates would all enhance species abundance in both the na- human-modified areas, which could increase propagule pressure in for- tive and the alien ranges. Human influence may favour alien species ests (i.e., spatial variation in how often species may arrive in the region), through transport, use and planting of these species in the surrounding and through disturbances (e.g., gap openings) in forests. VAN DER SANDE et al. | 285 We test two main hypotheses. First (hypothesis 1), in both the native the GloNAF database (van Kleunen et al., 2019). We focused on neo- and the alien ranges, local tree species abundance increases with high phytes, defined as species that were introduced after the year 1492 trait dissimilarity compared with co-occurring woody species (Carboni in the respective regions. The GloNAF database contains lists of alien et al., 2016). The importance of trait dissimilarity for abundance of spe- species for non-overlapping regions (countries or regions within coun- cies, however, may shift between the native and alien ranges, depend- tries) across the world. We could thus match all plots from sPlot with a ing on environmental conditions. For example, if climatic conditions are region of GloNAF, and label the alien species in the plots. Species that more favourable in the alien range, then this could increase the im- were not labelled as “alien” were labelled as “native”. portance of being functionally dissimilar to the co-occurring species. Second (hypothesis 2), local abundance depends on competitive trait differences between focal and co-occurring species: in this hypothe- 2.2 | Plot selection sis, abundance is predicted to increase as values for traits related to competitive ability increase relative to co-occurring species. Alien tree We selected plots that had geographic coordinates because these species that have higher growth and reproduction rates than their na- could be linked to a GloNAF region, and that were classified as “forest” tive competitors, and clonal ability (i.e., higher values for “competitive (i.e., with a minimum tree layer cover of 25%). It could be that some traits”), yet that have the capacity to invade dark forest understories, of the trees in the plots were planted. Unfortunately, we did not have have been shown to be successful (Martin, Canham & Marks, 2009). detailed information about the land use history of the plots. To reduce We focus on four traits that capture a large part of the functional the likelihood of including plantation forests, we excluded plots with differences among species with respect to competition for light and fewer than three woody species. We also excluded plots that were other resources, that are positioned along the r–K continuum and that smaller than 100 m2 because these often contain too few woody in- are associated with tolerance to drought and shade (adult height, H; dividuals for community-level analyses. The average plot size was 362 specific leaf area, SLA; seed mass, SM; wood density, WD; Westoby, m2, with a maximum of 40,000 m2 (4 ha). If species had plots in their 1998). All four traits have been reported to be good predictors of alien range on the same continent as plots in their native range (e.g., species' establishment, performance and abundance (Rejmanek & some plots for Acer negundo and Acer pseudoplatanus), then these alien Richardson, 1996; Theoharides & Dukes, 2007; van Kleunen, Weber plots were excluded to ensure abiotic and biotic differences between & Fischer, 2010), represent different tissues and ecological strategies the native and alien ranges. This resulted in native and alien ranges (Westoby, 1998; Aubin et al., 2016), have high original coverage for that were on different continents for all species. In total across the our species (i.e., before gap-filling), and are not strongly correlated species, we used 228,943 plots, of which 213,359 were in the native (r < .4 in our data). H captures the trade-off between a species’ capac- ranges and 15,584 in the alien ranges (Figure 1). ity to benefit from high light levels and early reproduction (Westoby, 1998). SLA captures the trade-off between light capture efficiency and strong, long-lived leaves (Poorter, Niinemets, Poorter, Wright & Villar, 2.3 | Focal tree species selection 2009). SM increases recruitment in the understorey but usually at the cost of reduced seed number and, hence, colonization ability from In total, we included 41 species from 24 families (Supporting seed rain (Bruun & Ten Brink, 2008; Rejmanek & Richardson, 1996; Information Appendix S1). We included species that are part of the Thomson, Moles, Auld & Kingsford, 2011). WD is associated with high global tree list of Botanic Gardens Conservation International (http:// tolerance to shade and drought, and slow growth rates (Ameztegui et www.bgci.org/global_tree_search.php), which also includes palms and al., 2017; Markesteijn, Poorter, Bongers, Paz & Sack, 2011). For all four some other tree-like species. Our 41 “tree species” included 1 palm traits, higher values are taken to mean greater competitive advantage (Syagrus romanzoffiana), 1 non-woody species (Carica papaya), 7 gym- in these forest ecosystems. We address our questions and hypothe- nosperm trees and 32 angiosperm trees. The focal tree species that ses by combining a global vegetation-plot database (sPlot; Bruelheide we included occurred in at least three plots in their native and at least et al., 2019), a global plant trait database (TRY; Kattge et al., 2011), three in their alien range, and had their native and alien ranges on dif- and global information on naturalized alien plant species [Global ferent continents. A total of 15 of these species are native to Eurasia, Naturalized Alien Flora (GloNAF); van Kleunen et al., 2015]. 24 to the Americas, 1 to Africa and 1 to Oceania. More alien tree spe- cies exist, but these were not found in sufficient numbers of plots and/ or have their native and alien ranges on the same continent. The 41 2 | M E TH O DS alien species were classified as “invasive” or “non-invasive” based on information from Rejmánek and Richardson (2013). 2.1 | sPlot and GloNAF databases We used the sPlot database, which contains vegetation plots across 2.4 | Species abundance the globe in which composition and abundance of plant communities were recorded (www.idiv.de/splot​; Bruelheide et al., 2019). To iden- The “relative abundance” of a focal species per plot was calculated as tify alien species in the plots, we matched the sPlot database with the species’ share of the woody vegetation cover, based on canopy 286 | VAN DER SANDE et al. coverage. For all plots in sPlot, this relative cover of each species was 2.6.2 | Competitive trait differences calculated as the ratio between individual species' cover and total cover of all woody species. Across all plots, we had a total of 3,240 To measure species’ competitive differences, we used the differ- woody species. We excluded all non-woody species from the calcu- ences between ln-transformed trait values of the focal tree species lation of relative coverage, so that we could test clear hypotheses and the abundance-weighted mean traits of the co-occurring spe- among woody species in forest ecosystems. cies. This was done separately for the four traits: SLA, H, SM and WD. Here, a positive value for any of the trait differences (ΔSLA, ΔH, ΔSM or ΔWD) in a plot indicates that the focal species has, on 2.5 | Functional traits average, a higher respective trait value (i.e., more competitive with respect to that trait) than the co-occurring woody species, whereas We focused on specific leaf area (SLA; cm2/g), adult tree height (H; m), negative values indicate that the focal species has, on average, lower oven-dried seed mass (SM; mg) and wood density (WD; g/cm3). We trait values (less competitive) than its neighbours. obtained trait information for all species in the study plots from the For all indices, we used the abundance-weighted mean dis- global trait database TRY (Kattge et al., 2011), by constructing a tax- tance instead of the minimal distance (i.e., the distance to the onomic backbone as described in Bruelheide et al. (2019). In sPlot, species in the plot that has most similar trait values), because abun- trait values for species without data in TRY were covered by gap-filling dance-weighted indices are best at disentangling environmental using a Bayesian hierarchical probabilistic factorization (for details see filtering from competition effects (Gallien, Carboni & Münkemüller, Schrodt et al., 2015), which gave similar results to non-gap-filled data 2014). If there were alien species in the plots in the native range, or (Bruelheide et al., 2019). All traits are based on species-level data; alien species other than the focal tree species in the alien range, then hence, we could not include intraspecific trait variation. these species were excluded. We kept all remaining angiosperm and gymnosperm woody species in the plots for the calculation of the above-mentioned indices, as their trait values fall along similar eco- 2.6 | Dissimilarity and competitive trait differences logical dimensions and are indicators of similar ecological functions (Díaz et al., 2015; Niklas & Spatz, 2010). 2.6.1 | Dissimilarity As a measure of niche separation, we calculated multivariate trait 2.7 | Human influence and climatic conditions dissimilarity (using SLA, H, SM and WD) between a focal tree spe- cies and co-occurring woody species, for all plots in the native and We used the human influence index (HII; Gallardo, Zieritz & Aldridge, alien ranges, using the multivariate trait dissimilarity index (Gower, 2015) as a proxy for human influence. The data were obtained from the 1971). High values of dissimilarity in these traits indicate weak en- Socioeconomic Data and Applications Center (SEDAC) via http://sedac. vironmental–biotic filtering. All trait values were log-transformed ciesin.colum​bia.edu/data/set/wilda​reas-v2-human​-influ​ence-index​- before calculation of dissimilarity and competitive trait differences, geogr​aphic​/, and had a 1-km2 resolution. The proxy is based on nine to avoid strong effects of extreme values. The multivariate trait dis- variables related to human population pressure (human population similarity index is based on the absolute difference in trait values density), human land use and infrastructure (built-up areas, night-time between the focal tree species and the abundance-weighted mean lights, land use/land cover), and human access (coastlines, roads, rail- of all co-occurring woody species. We refer to this measure through- roads, navigable rivers). Human influence may especially favour alien out the manuscript as “multivariate trait dissimilarity”. Because all species through transport, use and planting of these species in the sur- of the traits are continuous, the multivariate trait dissimilarity index rounding anthropogenic areas, which could increase propagule pres- is based on range-normalized Manhattan distances converted sure in forests (i.e., spatial variation in how often species may arrive in into similarity [which is correlated (r > .95) with dissimilarity based the region, directly or indirectly due to human activity). Furthermore, on Euclidean distances]. We calculated the multivariate trait dis- human influence could have a positive effect on alien species through similarity index using the daisy function of the cluster package in R disturbance or nutrient deposition, which may increase the establish- (Maechler, Rousseeuw, Struyf, Hubert & Hornik, 2017). We used ment success and growth of alien species in the surrounding region multivariate trait dissimilarity instead of dissimilarity in single traits (and hence, facilitate spread to our forest plots) and/or in the plots. because these are more likely to capture niche differences (Kraft, As a measure of climatic conditions that the species experience, Godoy, et al., 2015), to keep the model simpler and avoid collinear- and to evaluate the climatic conditions associated with high species ity among predictor variables, and to incorporate possible trade-offs abundance, we used the standardized precipitation and evapotranspi- among traits. High values of trait dissimilarity indicate that the focal ration index (SPEI), which is a climatic drought index based on precipi- tree species is novel in terms of the measured traits compared to its tation and temperature, at a spatial resolution of 0.5° (Vicente-Serrano, co-occurring species, whereas low values of trait dissimilarity indi- Beguería & López-Moreno, 2010). Positive values indicate humid con- cate that the focal species is similar in these traits to its co-occurring ditions and negative values indicate dry conditions. We calculated the species. SPEI based on a temporal scale of 12 months, which means that the VAN DER SANDE et al. | 287 index of a specific month is based on that month and the 11 preced-  ing months. SPEI at this time-scale is mainly related to variations in groundwater storage (Vicente-Serrano et al., 2010). Average monthly SPEI values were calculated based on average monthly rainfall and av- erage monthly potential evapotranspiration. Potential evapotranspira-  tion was derived from average monthly temperature and latitude, using the “Thornthwaite” function from the “SPEI” package in R (Begueria & 5HODWLYHDEXQGDQFH Serrano, 2015). Monthly rainfall and temperature were obtained from CHELSA (climatologies at high resolution for the earth’s land surface areas), see further details in Bruelheide et al., 2019. Besides the origi- nal SPEI values, we also calculated the dissimilarity between local SPEI  and species’ optimal SPEI. Optimal SPEI was calculated as the average SPEI at which the species occurred in its native range. This index indi- cates how close a species’ occurrence is to the centre of that species’ climatic niche, although this may not always be a good measure of Hí “optimal” conditions (Dallas et al., 2017). 2.8 | Statistical analyses Hí To test for differences in abundance between the native and alien QDWLYHUDQJH DOLHQUDQJH ranges across species, we used a linear mixed-effects model includ- F I G U R E 2 Differences in relative abundance between native ing species and plot as random intercepts, using the lmer function of (grey) and alien (red) ranges across all species. The mean relative the lme4 package (Bates, Maechler, Bolker & Walker, 2015; Figure 2). abundance in the native range (mean = 0.21, median = 0.06) was We formulated a two-level hierarchical linear regression model significantly higher than the relative abundance in the alien range (using Bayesian inference) to test for effects of multivariate trait dis- (mean = 0.14, median = 0.04; χ2 = 36.373, p < .001). The line in similarity, competitive trait differences [in specific leaf area (ΔSLA), the box represents the median, and the upper and lower edges of the box represent the 95% confidence interval. For species- plant height (ΔH), seed mass (ΔSM) and wood density (ΔWD)], specific differences in relative abundance between ranges, see human influence index (HII) and the standardized precipitation Supporting Information Appendix S7 [Colour figure can be viewed and evapotranspiration index (SPEI) on relative abundance, and to at wileyonlinelibrary.com] test how these effects differ between native and alien ranges and depend on species' traits. The first level of the model describes the species-specific responses of relative abundance to the above- in the interactions between range (native versus alien) and other pre- mentioned predictor variables, range (native versus alien range), and dictors because these interaction effects on abundance (i.e., slope the two-way interactions of each predictor variable with range. We differences between native and alien ranges) were generally small included a random effect of plot identity and incorporated interspecific (Figure 3). Relative abundance, the response variable, was ln-trans- variation in the intercept as well as in the slope parameters for all formed to meet assumptions of normality of the model residuals, and main effects. Additionally, we included woody species richness in the all variables were scaled prior to analysis by subtracting the mean and plot as a predictor of abundance because high richness (which is cor- dividing by the standard deviation (Schielzeth, 2010). The variance related with large plot size) decreases the average relative abundance inflation factors of the variables included in the model were all <4, of species and can therefore influence the effects of dissimilarity and indicating no problem of collinearity (Zuur, Ieno & Elphick, 2010). competitive differences. We only included species richness to cor- To analyse the hierarchical linear regression model, we applied rect for this effect on relative abundance, and hence did not include Bayesian inference because this can accommodate the complex hier- an interaction between species richness and range. In the second archical structure of our model and data. Parameter estimation was level of the model, to better understand what drives the differences done using the jags software (Plummer, 2013) and the rjags package in these regression slopes among species, each regression slope was in R (Plummer, Stukalov, et al., 2016). We set uninformative priors itself described by a normal linear regression sub-model that uses for all parameters; for all slopes of the fixed effects and predictors species' traits (SLA, H, SM and WD) as predictors. In other words, of the random slopes we set priors with mean = 0 and variance = species-level trait values were used as predictors of the interspecific 103, and for the standard deviation parameters (associated with the variation in regression parameters (intercept and slopes). This hierar- slopes of the fixed effects and predictors of the random slopes) we chical model structure ensures that uncertainty in regression slopes set priors with a uniform distribution between 0 and 100. Samples of is not neglected when estimating how they depend on species' traits the parameter posterior distribution were obtained from 3 indepen- (Houslay & Wilson, 2017). We did not describe interspecific variation dent Markov chains using Markov chain Monte Carlo (MCMC), each 288 | VAN DER SANDE et al. 1DWLYH 0XOWLYDULDWH GLVVLPLODULW\ $OLHQ 1DWLYH ¨6/$ $OLHQ 1DWLYH ¨+HLJKW $OLHQ 1DWLYH ¨60 $OLHQ 1DWLYH ¨:' $OLHQ 1DWLYH +,, $OLHQ 1DWLYH 63(, $OLHQ í í     0HGLDQVWDQGDUGL]HGHVWLPDWH F I G U R E 3 Results of the effects of trait dissimilarity, competitive trait differences and environmental conditions on local species abundance in their native (black) and alien (red) ranges. Range (native versus alien) refers to the status of the focal species in the plot. Median estimates of standardized effect sizes with 95% credible intervals for the across-species mean effect of predictor variables on relative abundance are shown. ∆SLA = competitive differences in specific leaf area; ∆Height = competitive differences in adult height; ∆SM = competitive differences in seed mass; ∆WD = competitive differences in wood density; HII = human influence index; SPEI = standardized precipitation and evapotranspiration index; SPR = species richness. Negative effects of multivariate trait dissimilarity indicate that focal species that are functionally similar to co-occurring species reach highest abundance. Positive and negative values for competitive trait differences (i.e., ∆SLA, ∆Height, ∆SM and ∆WD) indicate that focal species with respectively higher and lower trait values than the average co-occurring species obtain highest abundances. For numerical values of parameter estimates, see Supporting Information Appendix S14 [Colour figure can be viewed at wileyonlinelibrary.com] running for 10,000 iterations after a burn-in phase of 1,000 itera- 3 | R E S U LT S tions. We checked for convergence of the model visually by plotting the posterior sample distribution and trace plot of each parameter We first assessed abundance differences between native and alien (Supporting Information Appendix S2), and by evaluating Gelman ranges. We found that the relative abundance across the investi- and Rubin’s univariate and multivariate potential scale reduction gated tree species (compared to all woody species in the plot, and factors (Gelman & Rubin, 1992), using the gelman.diag function of ranging between 0 and 1) was slightly but significantly higher in the coda package in R (Plummer, Best, Cowles, & Vines, 2006). The the native compared to the alien range (Figure 2), while the abun- univariate and multivariate potential scale reduction factors were dance distribution was similar between the native and alien ranges all ≤1.01, indicating that the MCMC sampler converged. We found (Supporting Information Appendix S6). Results differed strongly no significant phylogenetic signal in our model output (Supporting among the species; of the 24 species classified as invasive aliens Information Appendix S3). A mathematical model description can be (Rejmánek & Richardson, 2013), only 6 had significantly higher found in Supporting Information Appendix S4, and the R script in abundance in the alien than in the native range, and 3 had signifi- Supporting Information Appendix S5. All analyses were performed cantly higher abundance in the native range. Of the 17 species clas- in R version 3.4.3 (R Core Team, 2017). sified as non-invasive, only 4 showed significant differences: 2 had VAN DER SANDE et al. | 289 higher and 2 had lower abundance in the alien range (Supporting (hypothesis 1). In contrast, we found a negative relationship between Information Appendix S7). multivariate trait dissimilarity and abundance in both the native and We then assessed which factors may determine the local-scale alien ranges (Figure 3), suggesting an over-riding influence of environ- abundance in the native and the alien ranges using a hierarchal lin- mental–biotic filtering. Furthermore, we expected that species with ear model. The model accounted for 31% of the variation in relative higher values for competitive traits than co-occurring trees would be abundance. We found a negative effect of multivariate trait dissimi- more abundant (hypothesis 2). Indeed, we found that species that are larity in both ranges, meaning that species tend to be most abun- taller and have denser wood than their neighbours tended to be more dant when they are functionally similar to their co-occurring species abundant in both their native and alien ranges, and species with larger (Figure 3; Supporting Information Appendix S8a). This effect was espe- seeds were more abundant in the native range (Figure 3, Supporting cially strong for non-invasive alien species, and for invasive species in Information Appendix S8b). Hence, high multivariate similarity, but the native range (Supporting Information Appendix S9). We found pos- competitive differences in specific traits, lead to highest abundance. itive effects of ∆WD and ∆H in both ranges, and of ∆SM in the native range, indicating that species with higher wood density and adult height than co-occurring species had highest abundance in both the native 4.1 | Environmental–biotic filtering drives and alien ranges, and species with higher seed mass than co-occurring abundance in native and alien ranges species had higher abundance in the native range (Figure 3, Supporting Information Appendix S8b). Abundance was negatively associated with Our finding that tree species with low trait dissimilarity to co-occurring species richness (Figure 3), positively associated with humidity (SPEI) trees are relatively more abundant is consistent with a recent global in the alien range (only), and not associated with the HII. Results were study that showed weak effects of trait dissimilarity on tree growth similar when using a more balanced design with similar numbers of (Kunstler et al., 2016). However, our results sharply contrast with hy- plots in the native and alien ranges (Supporting Information Appendix pothesis 1, and with results of many studies in the invasion literature S10). SPEI similarity to species' optimal conditions was not associated on different vegetation types, which find that local-scale species abun- with abundance (Supporting Information Appendix S11). dance is mainly determined by biotic interactions and niche differentia- Species-specific SLA values increased the effect of ∆SLA on abun- tion (Carboni et al., 2013). Trees in forest ecosystems may experience dance, indicating that species with high SLA experienced a stronger ef- strong environmental–biotic filtering at local spatial scales (e.g., plots fect of ∆SLA on their abundance than species with low SLA (Figure 4). < 1 ha) because the dense vegetation causes strong light limitation, Similarly, species-specific WD increased the effect of ∆WD on abun- which could explain our observed negative effect of trait dissimilarity dance. These results were robust to excluding gymnosperms, papaya on abundance (Montesinos-Navarro et al., 2018). Thus, our results con- (Carica papaya) and a palm species (Syagrus romanzoffiana) (Supporting tradict the idea that functionally dissimilar alien species would experi- Information Appendix S12), but effects on abundance of gymnosperms ence reduced competition with native species, and would become most themselves were weak (Supporting Information Appendix S13). Thus, dominant (Rundel, Dickie & Richardson, 2014). Functional dissimilarity the stronger effects of ∆SLA and ∆WD for species with high SLA and may be important in more open ecosystems such as grasslands (Carboni WD mainly applied to angiosperm trees. et al., 2016), but our results, and the results of Kunstler et al. (2016), suggest that this is not the case for tree species in forest ecosystems. 4 | D I S CU S S I O N 4.2 | Higher wood density, height and seed mass We evaluated differences in local abundance of tree species be- than co-occurring trees increase abundance in tween their native and alien ranges, and the factors accounting for native and alien ranges abundance in both ranges. We expected that alien species, and in particular invasive alien species, would be more abundant in the We expected, and found, that species that grow taller and have denser alien compared to the native range. However, we found that species wood and larger seeds compared to their neighbours reach highest tend to be slightly less abundant in their alien range (Figure 2). These abundance (Figure 3, Supporting Information Appendix S8b). Species results indicate that, even though species are classified as invasive, with taller adult stature than co-occurring species (i.e., positive ∆H) their local-scale abundance varies strongly. A lower abundance in can access higher light conditions that allow fast growth, and contrib- the alien range has also been found for herbaceous species (Firn et ute disproportionally to woody abundance in the plot (Slik et al., 2013), al., 2011), and could be explained by various factors, such as local- which may enhance their abundance levels. WD is a generally impor- scale competition by resident tree species, less favourable environ- tant trait for trees in forests, as high WD is related to shade toler- mental conditions in the alien range, and their time since arrival. It ance (Ameztegui et al., 2017; Markesteijn et al., 2011). Although alien could also be that abundance in the alien range is not yet in steady species with “acquisitive” trait values (e.g., species with low WD that state and is still increasing due to ongoing invasion. grow faster in diameter and height) are usually most successful in open We expected that multivariate trait dissimilarity to co-occurring and disturbed ecosystems (Kleunen, Weber, et al., 2010; Tecco, Díaz, trees would enhance species abundance through niche complementarity Cabido & Urcelay, 2010), more conservative trait values (e.g., higher 290 | VAN DER SANDE et al. SLA Multivariate H ~ dissimilarity SM WD SLA H ∆SLA ~ SM WD SLA H ∆Height ~ SM WD SLA H ∆SM ~ SM WD SLA H ∆WD ~ SM WD SLA H HII ~ SM WD SLA H SPEI ~ SM WD −0.1 0.0 0.1 0.2 Median standardized estimate F I G U R E 4 The effect of traits (grey: specific leaf area (SLA); orange: adult height (H); purple: seed mass (SM); blue: wood density (WD)) on species-specific differences in the slopes of the relationships of predictors (multivariate trait dissimilarity, competitive differences in each of the four traits (ΔSLA, ΔH, ΔSM, ΔWD), human influence index (HII) and standardized precipitation and evapotranspiration index (SPEI)) with relative abundance. The median estimate of standardized effect size with 95% credible intervals is given. For meaning of abbreviations, see Figure 3 legend. Positive effects (e.g., “∆SLA~SLA”) indicate that the trait (SLA) positively affects the slope of the relationship between the predictor (∆SLA) and abundance. Specifically, the effect of the predictor (∆SLA) on abundance is more positive for species with high values of that trait (SLA). For numerical values of parameter estimates, see Supporting Information Appendix S14 [Colour figure can be viewed at wileyonlinelibrary.com] shade tolerance and WD) may be of general importance in forests range than it is in the native range. This novel finding could indicate (Martin et al., 2009; van der Sande et al., 2017). The effect of ∆WD, that propagule pressure not only initiates, but also sustains, inva- however, tended to be stronger in the alien compared to the native sion. Small seeds enhance invasion in open ecosystems (Taylor et al., range (Figure 3), suggesting a higher importance of shade tolerance, 2016), but could also enhance abundance in forests if dispersal is a possibly because light limitation suppresses tree abundance more limiting factor. The balance between the importance of seed number strongly in the alien range. The underlying reason for this may be that and seed size may also depend on the time since establishment in the trees are typically younger and smaller in the alien range, and therefore alien range. With longer time since arrival to a new area, abundance suffer more light limitation than in the native range. Further research may be less limited by the lack of arriving seeds (i.e., seed number) needs to demonstrate whether and why light limitation is a stronger and relatively more by establishment of seedlings (i.e., seed size). constraint in the alien compared to the native range. The positive effect of ∆SM in the native range is consistent with the idea that light limitation creates a strong filter, as larger seeds 4.3 | Human influence is not associated with local are associated with enhanced recruitment success in the forest un- tree abundance derstorey (Dalling & Hubbell, 2002). ∆SM did not, however, affect abundance in the alien range. There is a known trade-off between We expected that tree abundance in the alien range would be highest seed number and seed size, and this could suggest that seed num- with strong human influence in the region, as human influence may ber is more important to achieving higher abundance in the alien increase propagule pressure (Gallardo et al., 2015) through higher VAN DER SANDE et al. | 291 planting intensity of alien species in the surrounding region, which The negative effect of seed mass on the relationship between then disperse their seeds to nearby forests, and/or through higher HII and abundance (Figure 4) indicates that species with small seeds disturbance and human-facilitated seed dispersal (intentional or un- experience more positive effects of human influence, possibly be- intentional) (Dainese et al., 2017; Hulme, 2014). We found, however, cause smaller seeded species tend to be better adapted to human that the relationship between abundance of alien species and human disturbances. influence was not significant (Figure 3). This indicates that, although propagule pressure is known to be important for enhancing regional abundance and invasiveness of woody alien species (Bucharova & van 5 | CO N C LU S I O N S A N D A PPLI C ATI O N S Kleunen, 2009; Feng & van Kleunen, 2016), and for local-scale abun- dance of life forms other than trees (Chytrý et al., 2008), human influ- We show that tree species have similar levels of abundance in their ence (at least as quantified in this study) may not predict local-scale native and alien ranges, and show striking similarities in the factors abundance of alien tree species in forests. The lack of effect of human that are associated with abundance in both ranges. This contradicts influence on tree abundance could be caused by a weaker relation- hypotheses and results for other life forms in the invasion literature, ship between human influence and propagule pressure for trees than which suggest that alien species are most successful when their for other life forms, because many alien tree species were first intro- niches differ from those of the resident species or they have novel duced in plantations that were far from built-up areas and night-time traits. Instead, our results indicate that tree species that are broadly lights (which are key components of the HII). Our measure of human functionally similar to co-occurring native trees, but have higher val- influence might not capture the presence of nearby plantations (and ues for traits linked with competitiveness, become most abundant hence, seed source areas), or key aspects of human influence on in both the native and alien ranges. These effects were especially secondary forests. Proximity of plantations could be a predictor of strong for species with high wood density, suggesting a general im- invasive alien tree species abundance, explaining the weaker role of portance of light limitation as a strong filter in forest ecosystems. environmental–biotic filtering and height differences in accounting These results are consistent with the notion that similar ecologi- for their abundance (Supporting Information Appendix S9a). cal mechanisms apply to native and alien species (Oduor, Leimu, Kleunen & Mack, 2016), and suggest that we can use information on species from their native range to predict how locally dominant they 4.4 | Species-specific differences in mechanisms may become elsewhere. underlying abundance AC K N OW L E D G M E N T S We expected that species may vary in the importance of environ- This work was supported by funds designated to T.M.K. mental–biotic filtering, niche differentiation effects and competitive under the Helmholtz Recruitment Initiative of the Helmholtz trait differences for their local abundance. Specifically, abundance Association. M.T.v.d.S. was supported by the research pro- of species with a conservative growth strategy (e.g., with high WD gramme Rubicon with project number 019.171LW.023, financed and low SLA) may be most strongly affected by environmental–bi- by the Netherlands Organisation for Scientific Research (NWO). otic filtering, because strong filtering limits the abundance of other, sPlot was initiated by sDiv, the Synthesis Centre of the German potentially more competitive species. The abundance of acquisi- Centre for Integrative Biodiversity Research (iDiv) Halle-Jena- tive species (e.g., with low WD and high SLA), however, may mostly Leipzig, funded by the German Research Foundation (FZT 118), depend on competitive trait differences, which can confer an ad- and now is a platform of iDiv. H.B., J.D., O.P., B.J.-A., U.J., J.K., vantage over slower growing conservative species in high-light en- M.W. and T.M.K. received funding through iDiv; M.v.K. was sup- vironments. The positive effect of SLA on the relationship between ported by the German Research Foundation DFG (project num- ∆SLA and abundance indicates that especially acquisitive species ber 264740629), A.G.G. by FONDECYT 11150835, CONICYT-PAI may indeed gain advantage from competitive trait differences (even 82130046 and a Marie Curie Intra European Fellowship within though, on average across species, ∆SLA did not affect abundance, the 7th European Community Framework Programme (Project Figure 3). The positive effect of WD on the relationship between PIEF-GA-2010–274798), J.P. by the European Research Council ∆WD and abundance indicates that especially conservative species Synergy grant ERC-SyG-2013-610028 IMBALANCE-P; F.E. with dense wood may gain an advantage. These results may indicate by a grant from the Austrian Science Foundation (FWF, grant that two different strategies—being more acquisitive and being more I2086-B16); M.C., P. Petřík, J. Pergl and P. Pyšek by the Plant durable than co-occurring species—can both provide competitive Diversity Analysis and Synthesis Centre PLADIAS, no. 14-15414S advantage and increase abundance (Guo et al., 2018). Alternatively, (Czech Science Foundation) and long-term research development these results, and the absence of effects of SLA and WD on other project Rozvoj výzkumných organizací RVO 67985939 (The Czech trait effects (e.g., multivariate dissimilarity, ∆H, ∆SM) on abundance, Academy of Sciences), P. Pyšek additionally by Grant projects of may indicate that species with quite extreme values of SLA or WD excellence in basic research EXPRO no. 19-28807X (Czech Science may gain the strongest advantage of ∆SLA and ∆WD on abundance. Foundation), and H.B. by VEGA 0119/19. We are grateful to all 292 | VAN DER SANDE et al. scientists who sampled vegetation plots or measured traits and Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, provided their data. We thank Sonja Knapp for comments on ear- 1–48. lier versions of the manuscript. Begueria, S., & Serrano, V. (2015). Package “SPEI”: Calculation of stan- dardised precipitation-evapotranspiration index. Version 1.6. Retrieved AU T H O R C O N T R I B U T I O N S from https​://cran.r-proje​c t.org/web/packa​ges/SPEI/index.html Broennimann, O., Treier, U. A., Müller-Schärer, H., Thuiller, W., Peterson, MTvdS and TMK conceived the idea of the study; HB, WD, JD, FE, A. T., & Guisan, A. (2007). Evidence of climatic niche shift during SH, MvK, HK, J.Pergl, OP, P.Pyšek, PW, MW, FJ, UJ, JK and BJA were biological invasion. Ecology Letters, 10, 701–709. https​://doi. involved in establishing the GloNAF, sPlot and TRY databases and org/10.1111/j.1461-0248.2007.01060.x helped develop the idea of the study; FA, IA, EB, MC, MD, MDS, Bruelheide, H., Dengler, J., Jiménez-Alfaro, B., Purschke, O., Hennekens, JF, RF, VG, GRG, AGG, BJA, UJ, EK, SK, KK, MM, ÜN, RKP, JP, MS, S. M., Chytrý, M., … Zverev, A. (2019). sPlot—A new tool for global vegetation analyses. Journal of Vegetation Science, 30, 161–186. MS, P.Petřík, PBR, BS, MS, CV, TJSW and TW provided data; MTvdS https​://doi.org/10.1111/jvs.12710​ analysed the data and J.Pagel assisted with data analyses; MTvdS Bruun, H. H., & Ten Brink, D.-J. (2008). Recruitment advantage of large and TMK interpreted the results; MTvdS wrote the main draft of seeds is greater in shaded habitats. Ecoscience, 15, 498–507. https​:// the manuscript; all authors discussed the results and revised the doi.org/10.2980/15-4-3147 Bucharova, A., & van Kleunen, M. (2009). Introduction history and spe- manuscript. cies characteristics partly explain naturalization success of North American woody species in Europe. Journal of Ecology, 97, 230–238. ORCID https​://doi.org/10.1111/j.1365-2745.2008.01469.x Masha T. van der Sande https://orcid.org/0000-0002-6845-2308 Callaway, R. M., & Aschehoug, E. T. (2000). Invasive plants versus their new and old neighbors: A mechanism for exotic invasion. Science, Helge Bruelheide https://orcid.org/0000-0003-3135-0356 290, 521–523. https​://doi.org/10.1126/scien​ce.290.5491.521 Jürgen Dengler https://orcid.org/0000-0003-3221-660X Carboni, M., Münkemüller, T., Gallien, L., Lavergne, S., Acosta, A., & Franz Essl https://orcid.org/0000-0001-8253-2112 Thuiller, W. (2013). Darwin’s naturalization hypothesis: Scale matters Richard Field https://orcid.org/0000-0003-2613-2688 in coastal plant communities. Ecography, 36, 560–568. https​://doi. org/10.1111/j.1600-0587.2012.07479.x Sylvia Haider https://orcid.org/0000-0002-2966-0534 Carboni, M., Münkemüller, T., Lavergne, S., Choler, P., Borgy, B., Violle, C., Mark van Kleunen https://orcid.org/0000-0002-2861-3701 … Thuiller, W. (2016). What it takes to invade grassland ecosystems: Holger Kreft https://orcid.org/0000-0003-4471-8236 Traits, introduction history and filtering processes. Ecology Letters, Jan Pergl https://orcid.org/0000-0002-0045-1974 19, 219–229. https​://doi.org/10.1111/ele.12556​ Chesson, P. (2000). Mechanisms of maintenance of species diversity. Petr Pyšek https://orcid.org/0000-0001-8500-442X Annual Review of Ecology and Systematics, 31, 343–366. https​://doi. Patrick Weigelt https://orcid.org/0000-0002-2485-3708 org/10.1146/annur​ev.ecols​ys.31.1.343 Marten Winter https://orcid.org/0000-0002-9593-7300 Chytrý, M., Jarošík, V., Pyšek, P., Hájek, O., Knollová, I., Tichý, L., & Isabelle Aubin https://orcid.org/0000-0002-5953-1012 Danihelka, J. (2008). Separating habitat invasibility by alien plants Milan Chytrý https://orcid.org/0000-0002-8122-3075 from the actual level of invasion. Ecology, 89, 1541–1553. https​://doi. org/10.1890/07-0682.1 Michele De Sanctis https://orcid.org/0000-0002-7280-6199 Colautti, R.I., Parker, J. D., Cadotte, M. W., Pyšek, P., Brown, C. S., … Ülo Niinemets https://orcid.org/0000-0002-3078-2192 Richardson, D. (2014). Quantifying the invasiveness of species. Robert K. Peet https://orcid.org/0000-0003-2823-6587 Neobiota, 21, 7–27. https​://doi.org/10.3897/neobi​ota.21.5310 Petr Petřík https://orcid.org/0000-0001-8518-6737 Colautti, R. I., Ricciardi, A., Grigorovich, I. A., & MacIsaac, H. J. (2004). Is invasion success explained by the enemy release hypothesis? Brody Sandel https://orcid.org/0000-0003-2162-6902 Ecology Letters, 7, 721–733. https​://doi.org/10.1111/j.1461-0248. Cyrille Violle https://orcid.org/0000-0002-2471-9226 2004.00616.x Tiffany M. Knight https://orcid.org/0000-0003-0318-1567 Conedera, M., Wohlgemuth, T., Tanadini, M., & Pezzatti, G. B. (2018). Drivers of broadleaved evergreen species spread into deciduous for- ests in the southern Swiss Alps. Regional Environmental Change, 18, DATA ACC E S S I B I L I T Y 425–436. https​://doi.org/10.1007/s1011​3-017-1212-7 The data used in this manuscript are published in the data repository Dainese, M., Aikio, S., Hulme, P. E., Bertolli, A., Prosser, F., & Marini, L. of the German Centre for Integrative Biodiversity Research (iDiv): (2017). Human disturbance and upward expansion of plants in a https​://doi.org/10.25829/​idiv.1805-12-2697. warming climate. Nature Climate Change, 7, 577–580. https​://doi. org/10.1038/nclim​ate3337 Dallas, T., Decker, R. R., & Hastings, A. (2017). Species are not most abun- REFERENCES dant in the centre of their geographic range or climatic niche. Ecology Ameztegui, A., Paquette, A., Shipley, B., Heym, M., Messier, C., & Gravel, Letters, 20, 1526–1533. https​://doi.org/10.1111/ele.12860​ D. (2017). Shade tolerance and the functional trait: Demography Dalling, J. W., & Hubbell, S. P. (2002). Seed size, growth rate and gap relationship in temperate and boreal forests. Functional Ecology, 31, microsite conditions as determinants of recruitment success 821–830. https​://doi.org/10.1111/1365-2435.12804​ for pioneer species. Journal of Ecology, 90, 557–568. https​://doi. Aubin, I., Munson, A. D., Cardou, F., Burton, P. J., Isabel, N., Pedlar, J. H., org/10.1046/j.1365-2745.2002.00695.x … Mckenney, D. (2016). Traits to stay, traits to move: A review of Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., functional traits to assess sensitivity and adaptive capacity of tem- Dray, S., … Gorné, L. D. (2015). The global spectrum of plant form perate and boreal trees to climate change. Environmental Reviews, 24, and function. Nature, 529, 167–171. https​://doi.org/10.1038/ 164–186. https​://doi.org/10.1139/er-2015-0072 natur​e16489 VAN DER SANDE et al. | 293 Donaldson, J. E., Hui, C., Richardson, D. M., Robertson, M. P., Webber, Mack, R. N., Simberloff, D., Lonsdale, M. W., Evans, H., Clout, M., & Bazzaz, B. L., & Wilson, J. R. U. (2014). Invasion trajectory of alien trees: The F. A. (2000). Biotic invasions: Causes, epidemiology, global conse- role of introduction pathway and planting history. Global Change quences, and control. Ecological Applications, 10, 689–710. https​:// Biology, 20, 1527–1537. https​://doi.org/10.1111/gcb.12486​ doi.org/10.1890/1051-0761(2000)010[0689:BICEG​C]2.0.CO;2 Feng, Y., & van Kleunen, M. (2016). Introduction history, climatic suitabil- Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., & Hornik, K. (2017). ity, native range size, species traits and their interactions explain es- Cluster: Cluster analysis basics and extensions. R package version 2.0.6. tablishment of Chinese woody species in Europe. Global Ecology and Retrieved from https​://cran.r-proje​c t.org/web/packa​ges/clust​er/ Biogeography, 25, 1356–1366. https​://doi.org/10.1111/geb.12497​ clust​er.pdf Firn, J., Moore, J. L., MacDougall, A. S., Borer, E. T., Seabloom, E. W., Markesteijn, L., Poorter, L., Bongers, F., Paz, H., & Sack, L. (2011). Hydraulics HilleRisLambers, J., … Buckley, Y. M. (2011). Abundance of in- and life history of tropical dry forest tree species: Coordination of troduced species at home predicts abundance away in herba- species’ drought and shade tolerance. New Phytologist, 191, 480–495. ceous communities. Ecology Letters, 14, 274–281. https​://doi. https​://doi.org/10.1111/j.1469-8137.2011.03708.x org/10.1111/j.1461-0248.2010.01584.x Martin, P. H., Canham, C. D., & Marks, P. L. (2009). Why forests appear Gallardo, B., Zieritz, A., & Aldridge, D. C. (2015). The importance of the resistant to exotic plant invasions: Intentional introductions, stand human footprint in shaping the global distribution of terrestrial, dynamics, and the role of shade tolerance. Frontiers in Ecology and the freshwater and marine invaders. PLoS ONE, 10, 1–17. https​://doi. Environment, 7, 142–149. https​://doi.org/10.1890/070096 org/10.1371/journ​al.pone.0125801 Montesinos-Navarro, A., Estrada, A., Font, X., Matias, M. G., Meireles, Gallien, L., & Carboni, M. (2017). The community ecology of invasive spe- C., Mendoza, M., … Early, R. (2018). Community structure informs cies: Where are we and what’s next? Ecography, 40, 335–352. https​:// species geographic distributions. PLoS ONE, 13, 1–16. doi.org/10.1111/ecog.02446​ Niklas, K. J., & Spatz, H. (2010). Worldwide correlations of mechanical Gallien, L., Carboni, M., & Münkemüller, T. (2014). Identifying the properties and green wood density. American Journal of Botany, 97, signal of environmental filtering and competition in invasion 1587–1594. https​://doi.org/10.3732/ajb.1000150 patterns—A contest of approaches from community ecology. Oduor, A. M. O., Leimu, R., van Kleunen, M., & Mack, R. (2016). Invasive Methods in Ecology and Evolution, 5, 1002–1011. https​://doi. plant species are locally adapted just as frequently and at least as org/10.1111/2041-210X.12257​ strongly as native plant species. Journal of Ecology, 104, 957–968. Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation https​://doi.org/10.1111/1365-2745.12578​ using multiple sequences. Statistical Science, 7, 457–472. https​://doi. Parker, J. D., Torchin, M. E., Hufbauer, R. A., Lemoine, N. P., Alba, C., org/10.1214/ss/11770​11136​ Blumenthal, D. M., … Wolfe, L. M. (2013). Do invasive species per- Gower, J. C. (1971). A general coefficient of similarity and some of its prop- form better in their new ranges? Ecology, 5, 985–994. https​://doi. erties. Biometrics, 27, 857–871. https​://doi.org/10.2307/2528823 org/10.1890/12-1810.1 Guo, W. Y., van Kleunen, M., Winter, M., Weigelt, P., Stein, A., Pierce, Petit, R. J., Bialozyt, R., Garnier-Géré, P., & Hampe, A. (2004). Ecology and S., … Pyšek, P. (2018). The role of adaptive strategies in plant natu- genetics of tree invasions: From recent introductions to Quaternary ralization. Ecology Letters, 21, 1380–1389. https​://doi.org/10.1111/ migrations. Forest Ecology and Management, 197, 117–137. https​:// ele.13104​ doi.org/10.1016/j.foreco.2004.05.009 Hierro, J. L., Maron, J. L., & Callaway, R. M. (2005). A biogeographical Plummer, M. (2013). rjags: Bayesian graphical models using MCMC. R approach to plant invasions: The importance of studying exotics package version 4.9. in their introduced and native range. Journal of Ecology, 93, 5–15. Plummer, M., Best, N., Cowles, K., & Vines, K. (2006). CODA: Convergence https​://doi.org/10.1111/j.0022-0477.2004.00953.x diagnosis and output analysis for MCMC. R News, 6, 7–11. Houslay, T. M., & Wilson, A. J. (2017). Avoiding the misuse of BLUP in Plummer, M., Stukalov, A., & Denwood, M.. (2016). Bayesian graphical behavioural ecology. Behavioral Ecology, 28, 948–952. https​://doi. models using MCMC. org/10.1093/behec​o/arx023 Poorter, H., Niinemets, Ü., Poorter, L., Wright, I. J., & Villar, R. (2009). Hulme, P. E. (2014). Alien plants confront expectations of climate Causes and consequences of variation in leaf mass per area (LMA): change impacts. Trends in Plant Science, 19, 547–549. https​://doi. A meta-analysis. New Phytologist, 182, 565–588. https​://doi. org/10.1016/j.tplan​t s.2014.05.003 org/10.1111/j.1469-8137.2009.02830.x Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, Pyšek, P., Manceur, A. M., Alba, C., McGregor, K. F., Pergl, J., Štajerová, G., … Wirth, C. (2011). TRY—A global database of plant K., … Kühn, I. (2015). Naturalization of central European plants in traits. Global Change Biology, 17, 2905–2935. https​://doi. North America: Species traits, habitats, propagule pressure, resi- org/10.1111/j.1365-2486.2011.02451.x dence time. Ecology, 96, 762–774. https​://doi.org/10.1890/14-1005.1 Kraft, N. J. B., Adler, P. B., Godoy, O., James, E. C., Fuller, S., & Levine, J. Pyšek, P., Pergl, J., Essl, F., Lenzner, B., Dawson, W., Kreft, H., … Kleunen, M. (2015). Community assembly, coexistence and the environmen- M. V. (2017). Naturalized alien flora of the world: Species diversity, tal filtering metaphor. Functional Ecology, 29, 592–599. https​://doi. taxonomic and phylogenetic patterns, geographic distribution and org/10.1111/1365-2435.12345​ global hotspots of plant invasion. Preslia, 89, 203–274. https​://doi. Kraft, N. J. B., Godoy, O., & Levine, J. M. (2015). Plant functional traits org/10.23855​/presl​ia.2017.203 and the multidimensional nature of species coexistence. Proceedings R Core Team. (2017). R: A language and environment for statistical com- of the National Academy of Sciences USA, 112, 797–802. puting. Vienna, Austria: R Foundation for Statistical Computing. Kunstler, G., Falster, D., Coomes, D. A., Hui, F., Kooyman, R. M., Laughlin, Retrieved from https​://www.R-proje​c t.org/ D. C., … Westoby, M. (2016). Plant functional traits have globally Rejmánek, M., & Richardson, D. M. (1996). What attributes make some consistent effects on competition. Nature, 529, 204–207. https​://doi. plant species more invasive? Ecology, 77, 1655–1661. https​://doi. org/10.1038/natur​e16476 org/10.2307/2265768 Lai, H. R., Mayfield, M. M., Gay-des-combes, J. M., Spiegelberger, T., & Rejmánek, M., & Richardson, D. M. (2013). Trees and shrubs as invasive Dwyer, J. M. (2015). Distinct invasion strategies operating within a alien species—2013 update of the global database. Diversity and natural annual plant system. Ecology Letters, 18, 336–346. https​:// Distributions, 19, 1093–1094. https​://doi.org/10.1111/ddi.12075​ doi.org/10.1111/ele.12414​ Richardson, D. M., Hui, C., Nuñez, M. A., & Pauchard, A. (2014). Tree Lockwood, J. L., Cassey, P., & Blackburn, T. (2005). The role of propa- invasions: Patterns, processes, challenges and opportunities. gule pressure in explaining species invasions. Trends in Ecology and Biological Invasions, 16, 473–481. https​://doi.org/10.1007/s1053​ Evolution, 20, 223–228. https​://doi.org/10.1016/j.tree.2005.02.004 0-013-0606-9 294 | VAN DER SANDE et al. Rundel, P. W., Dickie, I. A., & Richardson, D. M. (2014). Tree invasions van Kleunen, M., Weber, E., & Fischer, M. (2010). A meta-analysis of into treeless areas: Mechanisms and ecosystem processes. Biological trait differences between invasive and non-invasive plant species. Invasions, 16, 663–675. https​://doi.org/10.1007/s1053​0 -013-0614-9 Ecology Letters, 13, 235–245. https​://doi.org/10.1111/j.1461-0248. Schielzeth, H. (2010). Simple means to improve the interpretability of 2009.01418.x regression coefficients. Methods in Ecology and Evolution, 1, 103–113. van Kleunen, M., Pyšek, P., Dawson, W., Essl, F., Kreft, H., Pergl, J., … https​://doi.org/10.1111/j.2041-210X.2010.00012.x Winter, M. (2019). The global naturalized alien flora (GloNAF) data- Schrodt, F., Kattge, J., Shan, H., Fazayeli, F., Joswig, J., Banerjee, A., … base. Ecology, 100, 1–2. Reich, P. B. (2015). BHPMF—A hierarchical Bayesian approach to Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A gap-filling and trait prediction for macroecology and functional bio- multiscalar drought index sensitive to global warming: The standard- geography. Global Ecology and Biogeography, 24, 1510–1521. https​:// ized precipitation evapotranspiration index. Journal of Climate, 23, doi.org/10.1111/geb.12335​ 1696–1718. https​://doi.org/10.1175/2009J​CLI29​09.1 Slik, J. W. F., Paoli, G., McGuire, K., Amaral, I., Barroso, J., Bastian, M., … Vilà, M., Basnou, C., Pyšek, P., Josefsson, M., Genovesi, P., Gollasch, S., … Zweifel, N. (2013). Large trees drive forest aboveground biomass vari- Hulme, P. E. (2010). How well do we understand the impacts of alien ation in moist lowland forests across the tropics. Global Ecology and species on ecosystem services? A pan-European, cross-taxa assess- Biogeography, 22, 1261–1271. https​://doi.org/10.1111/geb.12092​ ment. Frontiers in Ecology and the Environment, 8, 135–144. https​:// Stohlgren, T. J. (1999). Exotic plant species invade hot spots of native doi.org/10.1890/080083 plant diversity. Ecological Monographs, 69, 25–46. Westoby, M. (1998). A leaf-height-seed (LHS) plant ecology strategy Taylor, K. T., Maxwell, B. D., Pauchard, A., Nuñez, M. A., Peltzer, D. A., scheme. Plant and Soil, 199, 213–227. Terwei, A., & Rew, L. J. (2016). Drivers of plant invasion vary globally: Williamson, M., & Fitter, A. (1996). The varying success of invaders. Evidence from pine invasions within six ecoregions. Global Ecology Ecology, 77, 1661–1666. https​://doi.org/10.2307/2265769 and Biogeography, 25, 96–106. https​://doi.org/10.1111/geb.12391​ Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010). A protocol for data exploration Tecco, P. A., Díaz, S., Cabido, M., & Urcelay, C. (2010). Functional traits to avoid common statistical problems. Methods in Ecology and Evolution, of alien plants across contrasting climatic and land-use regimes: Do 1, 3–14. https​://doi.org/10.1111/j.2041-210X.2009.00001.x aliens join the locals or try harder than them? Journal of Ecology, 98, 17–27. https​://doi.org/10.1111/j.1365-2745.2009.01592.x Theoharides, K. A., & Dukes, J. S. (2007). Plant invasion across space B I O S K E TC H and time: Factors affecting nonindigenous species success during four stages of invasion. New Phytologist, 176, 256–273. https​://doi. This research project was carried out as a collaborative effort org/10.1111/j.1469-8137.2007.02207.x Thomson, F. J., Moles, A. T., Auld, T. D., & Kingsford, R. T. (2011). Seed among three large platforms (GloNAF, sPlot and TRY), and among dispersal distance is more strongly correlated with plant height people interested in understanding the functioning of alien tree than with seed mass. Journal of Ecology, 99, 1299–1307. https​://doi. species at global scale. We think that such a large-scale compara- org/10.1111/j.1365-2745.2011.01867.x tive approach enables the generation of insights and generaliza- Van Couwenberghe, R., Collet, C., Pierrat, J. C., Verheyen, K., & Gégout, tions regarding the functioning of alien trees. J. C. (2013). Can species distribution models be used to describe plant abundance patterns? Ecography, 36, 665–674. https​://doi. org/10.1111/j.1600-0587.2012.07362.x S U P P O R T I N G I N FO R M AT I O N van der Sande, M. T., Poorter, L., Kooistra, L., Balvanera, P., Thonicke, Additional supporting information may be found online in the K., Thompson, J., … Peña-Claros, M. (2017). Biodiversity in species, Supporting Information section. traits and structure determines carbon stocks and uptake in tropical forests. Biotropica, 49, 593–603. https​://doi.org/10.1111/btp.12453​ van Kleunen, M., Dawson, W., Essl, F., Pergl, J., Winter, M., Weber, E., … Pyšek, P. (2015). Global exchange and accumulation of non-native How to cite this article: van der Sande MT, Bruelheide H, plants. Nature, 525, 100–103. https​://doi.org/10.1038/natur​e14910 Dawson W, et al. Similar factors underlie tree abundance in van Kleunen, M., Dawson, W., Schlaepfer, D., Jeschke, J. M., & Fischer, M. (2010). Are invaders different? A conceptual framework of comparative forests in native and alien ranges. Global Ecol Biogeogr. approaches for assessing determinants of invasiveness. Ecology Letters, 2020;29:281–294. https​://doi.org/10.1111/geb.13027​ 13, 947–958. https​://doi.org/10.1111/j.1461-0248.2010.01503.x