# Native diversity buffers against severity of non-native tree invasions

Camille S Delavaux, Thomas W Crowther, Constantin M Zohner, Niamh M Robmann, Thomas Lauber, Johan van den Hoogen, Sara Kuebbing, Jingjing Liang, Sergio De-Miguel, Gert-Jan Nabuurs, et al.

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Camille S Delavaux, Thomas W Crowther, Constantin M Zohner, Niamh M Robmann, Thomas Lauber, et al.. Native diversity buffers against severity of non-native tree invasions. Nature, 2023, 621, pp.773-781. 10.1038/s41586-023-06440-7 . hal-04188507

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# Native diversity buffers against severity of non-native tree invasions

https://doi.org/10.1038/s41586-023-06440-7

Received: 2 November 2022

Accepted: 14 July 2023

Published online: 23 August 2023

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Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species¹,². Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies³,⁴. Here, leveraging global tree databases⁵⁻⁷, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

Plant invasions have multifaceted impacts on ecosystems and human wellbeing across the globe¹⁻³,⁸. It is expected that plant invasions will continue to increase in the coming decades owing to human-assisted introduction and naturalization of these species, with ever-growing impacts on biodiversity within native forest ecosystems¹,⁹,¹⁰. These invasions will undoubtedly also have considerable economic impacts in managed landscapes by disrupting timber production, agriculture and human livelihoods¹¹⁻¹⁷. In particular, non-native trees represent an important and increasing concern globally, as they are often actively planted far outside their native ranges for forestry, reforestation, residential, or ornamental purposes⁴,¹⁸. Along with the passive spread of non-native species, the active propagation of trees by humans can often result in an increased potential to become problematic invaders⁴,¹⁹⁻²¹. Given the prominent roles of trees in shaping the structure and functioning of ecosystems, such tree invasions have the capacity to alter plant composition, productivity, biodiversity and the services provided to humans¹,⁴,²². Previous research in invasion ecology has expanded our understanding of community-level properties that influence ecosystem susceptibility to invasion²³⁻²⁵, as well as traits that make plant species more likely to become invasive²⁶⁻³⁰. However, most work has been restricted to local and regional scales³¹,³², with contrasting ecological mechanisms affecting invasion success in different regions. We thus lack a global unified theory of the human and ecological drivers of tree species invasions³³. Developing an integrated global understanding of ecological and anthropogenic forces that drive non-native tree invasions is critical to improve decision making in conservation and management.

Countless ecological mechanisms have been proposed to explain the susceptibility of different ecosystems to invasion by non-native species in different locations. Traditionally, more diverse or ecologically complex systems are thought to exhibit 'biotic resistance' to invasion²³,³⁴⁻³⁹.

This hypothesis is based on the assumption that greater diversity in the native community fills the available ecological niches and reduces available resources, limiting niche space to novel species. However, most work has focused on testing this hypothesis using species richness as an indicator of niche filling²³,³⁵, which may not fully capture the proportion of niches that are filled in the native community. Instead, more informative metrics for niche filling may be phylogenetic or functional diversity. Phylogenetic diversity accounts for evolutionary similarity and represents a reasonable proxy for similarity between taxa, whereas functional diversity directly addresses the underlying mechanism of biotic resistance (that is, the breadth of ecological niches filled), but may be more difficult to measure. Conversely, there is also evidence for the opposite pattern in some ecosystems, whereby a more diverse community is indicative of a more favourable habitat, where a wide range of invasive species might survive. This 'biotic acceptance'²⁵,⁴⁰,⁴¹ hypothesis leads to the expectation that highly diverse sites are optimal for many plant species and could promote invasion of non-native species. Nonetheless, we still lack a unified understanding of the relative importance of these two competing processes, and their variation across the globe, leading to ongoing calls to resolve this 'invasion paradox'²⁵.

Invasion success is also likely to depend on the ecological strategy of the invading species relative to the recipient native community. One school of thought is that environmental constraints are the primary drivers of plant species distributions. Therefore, to be successful, invasive species ought to be similar to native species that are adapted for that region, especially in extreme environments⁴². Under this 'environmental filtering hypothesis'⁴³,⁴⁴ (or 'preadaptation hypothesis'), invasive species will be more successful if their traits mirror those of the native community⁴⁵. For example, to be successful in a harsh desert environment, non-native plants would need to be ecologically similar
<figure><img src="image_3.png" /><figcaption>**Fig. 1 | Distribution of the study data.** Distribution of the full study dataset, coded for non-native severity (n = 471,888 plots). The map shows average per cent invasion across a 1-degree hexagonal grid, from non-invaded (0%) pixels</figcaption></figure>

in green to completely invaded (100%) pixels in purple. Plots are considered invaded if there is any non-native tree present.

to native plants to survive, possessing traits that protect them against high heat and water loss. By contrast, the 'limiting similarity hypothesis' (also known as 'Darwin's naturalization hypothesis') postulates that invasive species need to be ecologically distinct from native species to avoid niche overlap⁴⁶⁻⁴⁹. Here, invaders are thought to be more successful if they can fill unique niche spaces that are not already used by the native community, reducing competition and enabling their establishment. These two processes suggest contrasting mechanisms for how species invade: either species invade by being similar or dissimilar to the native community (Darwin's naturalization conundrum²⁴,⁵⁰). It is possible that the relative importance of these opposing ecological mechanisms varies under different environmental conditions, with greater importance of environmental filtering in harsh conditions and greater niche differentiation in more moderate environments³¹,⁵². Such regional variation in the relative importance of these mechanisms might help to explain the opposing responses observed across studies. However, until now, we lack a broad-scale analysis of these different invasion mechanisms that can help us to see past the idiosyncrasy of local-scale observations to identify unifying trends.

A key challenge hindering a global consensus of the ecological patterns and mechanisms underpinning plant invasion is that these processes are likely strongly influenced by anthropogenic activity, which may dampen the signal of ecological drivers. Humans drive contemporary plant invasions through highly efficient transport—both intentional and accidental—of non-native plants, with proximity to ports and airports being associated with increased invasion¹¹,⁵³,⁵⁴. A constant influx of non-native species may override a native community's ability to resist invasion⁵⁵ (biotic resistance) and obscure the impacts and importance of specific ecological drivers, such as native diversity, particularly at early stages of invasion. That is, with increased propagule pressure of non-natives species exerted by humans, the relative importance of ecological drivers may be reduced. Moreover, sites with high levels of non-native propagule pressure due to human activity are also likely to be heavily disturbed, compounding this anthropogenic influence. Accounting for human global change drivers may be particularly important when considering the role of invasion strategy, with the potential for

anthropogenic drivers and human propagule pressure to overwhelm the impact of ecological drivers. This could occur through an increase in the frequency and magnitude of introductions, which would be expected to increase stochastic variation and dampen ecological signals. So far, these hypotheses have been tested only at local and regional scales, with few studies integrating ecological and anthropogenic drivers of invasion at the global scale to disentangle the relative importance of human activity, environmental conditions and biological diversity³³.

Here, by combining global datasets of local-scale forest inventories, native status, environmental climate variables and anthropogenic drivers, we test for the relative importance of ecological and anthropogenic influence on non-native tree invasion. Using this large-scale approach, we search for a unifying perspective of the environmental and anthropogenic contexts driving non-native invasion and invasion severity, via both relative richness and abundance of non-natives, as well as invasion strategy. We consider three hypotheses: (H1) greater native diversity reduces non-native invasion²³; (H2) high levels of environmental filtering in extreme environmental conditions leads to similarity of non-natives with the surrounding natives, and moderate conditions are associated with greater levels of niche differentiation and dissimilarity²⁴; and (H3) human drivers, specifically proximity to ports and areas of high human population density, will mediate and potentially override these ecological relationships⁵⁶. We explore these hypotheses through the lens of different biodiversity metrics (phyloge-netic diversity, functional diversity and species richness), providing a comprehensive view of the interactions between ecological processes and human influence on invasion. Addressing these hypotheses is important to highlight generalizations in the field for prevention and management of non-native tree invasions, which is key to mitigating the potential severe ecological and socio-economic toll of these invasions.

Using the Global Forest Biodiversity Initiative database⁷, we determined native tree status (native or non-native) according to the Global Naturalized Alien Flora⁶ and the KEW Plants of the World databases⁵. This dataset encompassed 471,888 plots, of which 4.9% of plots were invaded, or contained at least one non-native tree species (Fig. 1 and Supplementary Table 1a). Moreover, this dataset contained a larger
<figure><img src="image_4.png" /><figcaption>**Fig. 2 |** Anthropogenic drivers are more important than native diversity in determining invasion occurrence. **a,b.** Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on non-native presence (whether a plot is invaded or not) for global models of phylogenetic (a) and functional</figcaption></figure>

(b) diversity (phylogenetic diversity, *n* = 17,640 plots; functional diversity, *n* = 17,271 plots). All results shown are from random forest models. Note that y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.

proportion of invaded plots in tropical (15.2%) than in temperate systems (5.2%). Overall, 249 individual non-native tree species were identified, with the most frequent being *Robinia pseudoacacia*, *Pinus sylvestris*, *Maclura pomifera*, *Picea abies* and *Ailanthus altissima* labelled as non-native in 3,976, 2,603, 2,493, 2,468 and 1,597 plots, respectively (Supplementary Table 2). Regions with the greatest like- lihood of being invaded include North America, Europe and East Asia (Extended Data Fig. 1), consistent with previous findings¹⁰,⁵⁷ (but see ref. 58). To test for drivers of non-native tree invasion and invasion strategy, we used a down-sampled version of the dataset consisting of 17,738 forest plots, distributed across 14 biomes proportional to their global land cover.

We calculated three metrics of invasion: (1) presence of non-natives in the plot ('non-native presence'); (2) relative proportion of non-native species richness to total tree richness ('non-native richness'); and (3) relative proportion of non-native species basal area to total tree basal area ('non-native abundance'). The first metric (non-native presence) is simply a measure of the presence or absence of invasion, whereas the latter two metrics (relative abundance and richness) provide insight into the subsequent severity of the invasion.

To test how hypothesized human and environmental drivers affected the probability a forest plot was invaded or the invasion severity within invaded plots, we built generalized linear models (GLMs) and random forest models using either phylogenetic or functional diversity metrics (both as richness and redundancy) as predictor variables (Extended Data Fig. 3). For both functional and phylogenetic diversity, we used random forest models to determine variable importance and for visualization purposes, whereas GLMs were used to test for significance and directionality of relationships. Our models also included human drivers (distance to shipping ports (hereafter referred to as ports) and population density) and accounted for several additional soil chemical and climate variables. Next, to test whether non-native tree species invade by being similar or dissimilar to the native community (termed 'invasion strategy'), we again built models predicting non-native similarity from either native phylogenetic or functional diversity metrics, along with the same environmental and human impact variables. The non-native

invasion strategy was defined as the change in redundancy due to addition of non-native trees, with values below zero and values above zero indicating invasion via similarity and dissimilarity, respectively, to the native community.

## Diversity limits invasion severity

We found that anthropogenic drivers were more important than local native tree diversity in determining non-native invasion (presence) globally (H3), whereas native diversity—both phylogenetic and functional—was most important in determining invasion severity (H1; Fig. 2 and Supplementary Tables 3 and 4; phylogenetic diversity random forest area under the curve (AUC) = 0.634, functional diversity random forest AUC = 0.631). These results indicate the importance of human-induced propagule pressure in initiating invasion of forests and of native biodiversity moderating the severity of the invasion. We found that forest plots closer to ports are more likely to be invaded (Supplementary Tables 3 and 4; linear model *P* < 0.001). Notably, these results are consistent whether we analyse all data together at the global level or separate data into either the temperate and tropical bioclimatic zones (Supplementary Tables 3 and 4). By contrast, we did not find that human population density was consistently related to non-native presence, with results being variable across diversity metrics and bioclimatic zones considered (Supplementary Tables 3 and 4). However, population density was always positively correlated with invasion probability; population density may be a weaker predictor as it only measures human presence, which is not necessarily related to propagule pressure.

Proximity to ports has long been known to influence invasion¹¹,⁵³,⁵⁴, with locations closer to a port being likely to experience greater propagule pressure. Moreover, proximity to ports may serve as a proxy for residence time, where plots closer to ports are more likely to have longer exposure to non-native propagule pressure, thus increasing the likelihood of invasion⁵⁶. Yet, at far enough distances, stochastic processes and historical land-use patterns may begin to weaken the role of ports (Fig. 3, distances greater than 500 km). For example, the third most frequent non-native tree in our dataset, *M. pomifera*, is widely naturalized throughout the
<figure><img src="image_5.png" /><figcaption>**Fig. 3** | Native diversity is the most important driver of invasion severity. **a,b,** Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on invasion severity for global models of phylogenetic (a) and functional (b) diversity (phylogenetic diversity, *n* = 3,498 plots; functional diversity, *n* = 3,368 plots). Plots are shown for the severity of invasion measured as</figcaption></figure>

non-native species abundance (proportion of basal area with non-native plant species); plots for non-native species richness (proportion of non-native plant species) are shown in Extended Data Fig. 4. All results shown are from random forest models. Note that the y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.

interior of North America, where it has been used for various agricul-
tural purposes dating back to the 1850s⁵⁹. Such results highlight the
idiosyncratic use of trees across the globe, leading to unique invasion
trends relative to herbaceous plants. Nevertheless, at more local scales,
this strong signal of anthropogenic activity and associated propagule
pressure relative to native diversity driving non-native presence is in
agreement with previous work that considers invasion across stages⁵⁶ and
recent assessments of regional and global tree invasion⁵⁷,⁶⁰, and highlights
the prominent role of humans in reshaping biological communities.

Although proximity to ports determined the probability a forest
plot was invaded, native tree communities with higher phylogenetic
and functional diversity exhibited lower invasion severity (Fig. 3,
Extended Data Fig. 4 and Supplementary Tables 3 and 4; phyloge-
netic diversity random forest non-native richness R² = 0.68, phy-
logenetic diversity random forest non-native abundance R² = 0.14,
functional diversity random forest non-native richness R² = 0.69 and
functional diversity random forest non-native abundance R² = 0.07;
GLM phylogenetic and functional diversity P < 0.001). Addition-
ally, distance to ports was no longer significant in linear models
predicting invasion severity (Supplementary Tables 3 and 4) for
both phylogenetic (P = 0.16 and 0.28 for non-native richness and
abundance, respectively) and functional diversity models (P = 0.63
and 0.86 for non-native richness and abundance, respectively), and
showed reduced variable importance in the random forest models
(Fig. 3 and Extended Data Fig. 4). When investigating these patterns
using conventionally analysed species richness instead of phylo-
genetic or functional richness, we find similar qualitative results
(Supplementary Table 5, random forest non-native richness R² = 0.71
and random forest non-native abundance R² = 0.14), suggesting that
species diversity may be a useful proxy for projecting invasion sever-
ity in the absence of functional and phylogenetic information. Our
results are consistent with the hypothesis of biotic resistance (H1),
where increased native diversity reduces invasion success, which is
probably driven by the native community utilizing more available
niche spaces²³,³⁴⁻³⁶,⁶¹. These results are also consistent with work

investigating tree migration drivers that suggests that migration is
slower into more diverse communities owing to greater resource use
(fewer available niches) in these systems⁵⁷.

Overall, these results show that anthropogenic drivers, particu-
larly distance to shipping centres (ports), are more important in
determining which locations will experience non-native invasions
compared with traditionally studied native diversity (H3). However,
it is the intrinsic ecological drivers, including native tree community
phylogenetic and functional diversity (richness and redundancy), that
are more important in determining invasion severity (H1). Repeated
human introduction of plant species has a more important role in the
initial invasion process, but invasion severity is predominantly a result
of native intrinsic diversity. Notably, both distance to ports and native
diversity show patterns of saturation of effects, suggesting a thresh-
old at which plots that are far enough from ports, or high enough in
native diversity, will not benefit from further distance or diversity with
regard to reduced invasion or invasion severity. Although our focus
here is on the relative importance of human versus biotic drivers of
introduction, we find that environmental variables—especially mean
annual temperature—correlate strongly with patterns of non-native
invasion, which may reflect resource availability²⁶, belowground
microorganism composition³⁰ or potential climate compatibility
between donor and recipient ranges⁶². Together, our results sug-
gest that locations near human activity are more likely to experience
non-native invasions in part due to increased propagule pressure,
whereas those with lower diversity are more likely to experience more
severe non-native invasions once non-natives are present. These
results may suggest that managing forests to maintain high native
tree diversity may be a good strategy to buffer communities against
invasion, particularly for locations that are far from human activity.

**Evidence for environmental filtering**

When considering a range of climate, soil and anthropogenic variables, we find evidence for environmental filtering as a driver of invasion
<figure><img src="image_6.png" /><figcaption>**Fig. 4 | Environmental filtering at temperature extremes.** a,c, Estimates of overlapping variables included in temperate and tropical GLM models (forest plot) for phylogenetic (a) and functional (c) diversity models (phylogenetic diversity, *n* = 3,498; functional diversity, *n* = 3,368). Values to the left of the zero line indicate negative model estimates, and those to the right indicate positive estimates. b,d, Relationship between mean annual temperature and invasion strategy for phylogenetic (b) and functional (d) diversity models, showing that</figcaption></figure>

at extreme temperatures invasion occurs through similarity (Supplementary Table 7; phylogenetic diversity: *P*₁ = 9.69 × 10⁻¹⁴, *P*₂ = 2.13 × 10⁻¹¹; functional diversity: *P*₁ < 2 × 10⁻¹⁶, *P*₂ = 1.07 × 10⁻⁴, where *P*₁ and *P*₂ represent each temperature and temperature squared *P* values, respectively). Note for functional diversity, this pattern only holds at low temperatures. Error bars and bands represent standard error.

strategy, in particular, with respect to mean annual temperature and precipitation. In all global models, temperature was important for predicting tree invasion strategy (Fig. 4, Extended Data Fig. 5 and Supplementary Table 6; phylogenetic diversity random forest R² = 0.084, functional diversity random forest R² = 0.099; H2), with our global analysis indicating that non-native trees were more similar to the native community in environments at cold and hot temperature extremes (Fig. 5 and Supplementary Table 6, *P* < 0.001). That is, in order to invade into a cold or hot environment, non-native plants are more successful if they share similar traits with native plants to survive in these harsher temperature conditions. By contrast, at locations with moderate temperatures, non-natives are neither more nor less similar to native communities, potentially because these less harsh environmental conditions allow a wider range of life strategies to coexist⁵¹. For functional diversity, invasion strategy at high temperatures is relatively neutral, with the line approaching a value of zero, suggesting that although phylogenetically similar, these communities show some level of functional divergence, highlighting the importance of including functional diversity in future studies. When separating the data into temperate and tropical systems, we found divergent temperature patterns (Supplementary Table 6; temperate *P* < 0.001, tropical *P* = 0.01). In temperate systems, non-native trees were more likely to be similar to the native tree community in colder environments relative to hot environments, in line with previous results in temperate North America⁶³. In tropical systems, we found the opposite pattern, with non-native trees being more likely to be similar to the native tree community in hotter tropical environments. At the lowest temperatures, non-natives invading through similarity were primarily gymnosperms (fir, spruce and pine species) invading into native

communities containing species in the same genus; by contrast, at the highest temperatures, non-natives invading through similarity were angiosperms, with a high prevalence of palms and legumes. Further, we detect a similar pattern of environmental filtering for mean annual precipitation when analysing phylogenetic and functional diversity with random forest models, where lower or higher precipitation is associated with non-native invasion through similarity (Extended Data Fig. 5). This suggests that the most likely invaders at low or high temperature or precipitation may be ecologically similar to the host communities, which could inform invasion risk checklists at ports.

Within the temperate bioclimatic zone, we found evidence that anthropogenic activity weakened the environmental filtering pattern for phylogenetic and functional diversity seen for temperature and precipitation, respectively (H3). In particular, proximity to ports modified the signal of environmental filtering due to temperature, weakening the influence of temperature on invasion strategy with respect to phylogenetic similarity (Fig. 5 and Supplementary Table 6; *P* < 0.001). Colder ecosystems show evidence of environmental filtering of invasion; however, increased proximity to ports reduces the prevalence of this strategy. We suggest that this may be due to increased introductions around shipping ports, which would increase stochastic variation and dampen ecological strategies. However, we did not detect a similar interaction governing the tropical bioclimatic zone, potentially owing to relatively lower human pressure, and particularly lower ship traffic⁶⁴, compared to temperate systems. Alternatively, this pattern may also reflect the fact that some temperate plots occur at greater distances to ports than tropical sites (95th percentile of 784 km versus 311 km for temperate and tropical, respectively), increasing statistical power for detecting this trend in
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

**Fig. 5 | Proximity to ports weakens environmental filtering in the temperate bioclimate zone.** a,b, In temperate plots far from ports, temperature is positively correlated with an invasion strategy of increasing dissimilarity for phylogenetic (a) and functional (b) diversity (phylogenetic diversity: *n* = 2,710 plots, *P* = 6.37 × 10⁻⁶; functional diversity: *n* = 2,603, *P* < 2 × 10⁻¹⁶). c,d, This

relationship between temperature and invasion strategy weakens for phylogenetic (c) and functional (d) diversity with proximity to ports (Supplementary Table 7; phylogenetic diversity: *P* = 0.0001; functional diversity: *P* = 2.71 × 10⁻¹³). Lines and points represent the lowest (c,d) and highest (a,b) 10% of data. Error bands represent standard error.

temperate regions. Furthermore, proximity to ports also marginally weakened the signal of environmental filtering due to precipitation for functional invasion strategy (Supplementary Table 6; *P* = 0.07). These results illustrate that human influence can override the ecological factors driving invasion, suggesting that at high enough propagule pressure, the phylogenetic and functional similarity of a non-native becomes less important in predicting its ability to invade a native community. Nevertheless, as our analyses are not causal, these results could also reflect correlations between port locations and invasion strategy. However, when we investigated the same effect with human population density, we did not see this weakening effect, demonstrating that distance to ports seems to be a particularly relevant mediator of these patterns. These results suggest that human activity may overwhelm ecological drivers of non-native invasion strategies and reduce the influence of ecological processes, making inclusion of human impacts critical for studying global invasion strategies.

Collectively, our work integrates biotic and anthropogenic factors across phylogenetic and functional diversity for both invasion presence and invasion severity of non-native tree species worldwide. Although non-native trees have been relatively overlooked relative to herbaceous plants, their large size, long lifespans and important history in forestry, food, reforestation and city landscaping exposes trees to unique ecological and anthropogenic factors that shape their worldwide distributions. Moreover, given that many tree invasions are in their infancy, with substantial 'invasion debts' of recent tree plantings³, understanding the ecological drivers promoting spread has the potential to provide real-time feedback for

the preventative management of invasive trees. However, there are important considerations when interpreting these findings, many of which could be addressed with increased data resolution and increased sampling within under-sampled geographic regions. First, our analysis is largely observational, whereas community composition would ideally be compared before and after invasion to better understand the causality of the trends observed here. We can gain some insight into this question by conducting a sensitivity analysis on the subset of invaded plots that were measured at multiple time points and that had no initial invasion. Doing so reveals that the reduction in native diversity due to invasion can potentially account for as much as 10.4% (mean of 6.7%) of the observed biotic resistance (Supplementary Table 9), but that the remainder of this effect is attributable to difference in native diversity (that is, biotic resistance) across plots. Additional long-term data on plots that are uninvaded and become invaded will be useful in further addressing the influence of invasion on native diversity. Second, many tree species in our analysis were only identified to genus level or were not present in the master plant phylogeny, which may lead to an underestimation of native diversity or invasive species richness in some plots, particularly in species-rich forests. Indeed, a key challenge in global analyses such as ours is the underrepresentation of certain ecosystems, for example, tropical ecosystems⁵⁸. This is addressed to some extent by our down-sampling approach, as well as our spatial cross-validation approach (Methods), but ongoing efforts to fund and develop open-access and fair⁶⁵ tropical forest inventory data are critical for gaining better insight into these ecologically and socially important ecosystems.
Many tree species are intentionally introduced for forestry or wood products and may be managed⁴, generating variation in the drivers underpinning invasion that are unique to trees. To minimize the influence of heavily managed forests, we included only plots with a minimum of three species and thus our dataset does not include monoculture forestry plantations. In addition, when restricting our analysis to the subset of global plots that occur in protected areas with minimal human footprint, our core results and inferences remain unchanged (Supplementary Table 7). Having additional high-quality data on the human role in invasion, including the type and time of management, and overall level in disturbance regime⁶⁶, would refine our results and better separate ecological versus human drivers. Future work should also focus on drivers of tree invasion and invasion strategies across scales²⁵,⁶³,⁶⁷, as patterns may differ at scales larger than the local plot level that we include here, which may be important for regional versus local management of non-native trees. Finally, emerging work shows that the consideration of native range size and change in environment and/or disturbance from donor to recipient community may be more helpful in understanding introduction and invasion success than simply quantifying these variables in the novel, recipient range⁶²,⁶⁶. Therefore, including the change in environmental and human impact variables would also be a fruitful avenue for future research.

Together, these results provide important unifying insights into the global drivers of non-native tree invasions and the ecological strategies that might be most successful in different regions. The trends and ecological mechanisms identified here can provide tangible guidelines to support forest management of non-native tree invasions around the globe. However, because non-native trees are introduced purposefully for forestry or to support local livelihoods, which can lead to differences in forest management objectives and strategies⁴, it is critical that local stakeholders are included when making decisions about how to best manage these introductions⁶⁸,⁶⁹. Ultimately, this emerging understanding of global tree invasions provides fundamental insights that are needed to understand how forest composition is being reshaped under global change, and for forest management practices to limit the spread and impacts of non-native tree invasions worldwide.

## Online content

Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-023-06440-7.

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Camille S. Delavaux¹²⁵, Thomas W. Crowther¹, Constantin M. Zohner¹, Niamh M. Robmann¹, Thomas Lauber¹, Johan van den Haagen¹, Sara Kuebbing², Jingjing Liang¹, Sergio de Miguel⁴,⁵, Gert-Jan Nabuurs⁶, Peter B. Reich⁷,⁸,⁹, Meirnad Abegg¹⁰, Yves C. Adou Yao¹⁰, Giorgio Alberti¹²,¹³, Angelica M. Almeyda Zambrano¹⁴, Braulio Vilchez Alvarado¹⁵, Esteban Alvarez-Dávila¹⁶, Patricia Alvarez-Loayza¹⁷, Luciana Alves¹⁸, Christian Ammer¹⁹, Clara Antón-Fernández²⁰, Alejandro Araujo-Murakami²¹, Luzmila Arroyo²¹, Valerio Avitabile²², Gerardo A. Aymerd²³,²⁴, Timothy R. Baker²⁵, Radomir Batay²⁶, Olaf Banki²⁷, Jorcyce G. Barroso²⁸, Meredith L. Bastian²⁹,³⁰, Jean-François Bastin³¹, Luca Birigazzi³², Philippe Birnboon³³,³⁴,³⁵, Robert Bitarihio³⁶, Pascal Boeckx³⁷, Frans Bongers⁶, Olivier Bouriaud³⁸, Pedro H. S. Brancalio³⁹, Susanne Brandl⁴⁰, Roel Brienen²⁹, Eben N. Broadbent¹, Helge Bruelheide⁴²,⁴³, Filippo Bussotti⁴⁴, Roberto Cazzolla Gatti⁴⁵, Ricardo C. César³⁹, Goran Ceslár⁴⁶, Robin Chazdani⁴⁷,⁴⁸, Han Y. H. Chen⁴⁹, Chelsea Chisholm¹, Hyunkook Cho⁵⁰, Emil Ciencia¹⁵,¹⁹,¹⁰, Connie Clark⁵³, David Clark⁵⁴, Gabriel D. Colletta⁵⁵, David A. Coomes⁵⁶, Fernando Cornejo Valverde⁵⁷, José J. Corral-Rivas⁵⁸, Philip M. Crim⁵⁹,⁶⁰, Jonathan R. Cumming⁵⁹, Selvadurai Dayanandan⁶¹, André L. de Gasper⁶², Mathieu Decuyper⁶,⁶³, Gérardine Derroire⁶⁴, Ben DeVries⁶⁵, Ilja Djordjevic⁶⁶, Jiri Dolezal⁶⁷,⁶⁸, Aurélie Dourожin⁶⁸, Nestor Laurier Engne Obiang⁶⁹, Brian J. Enquist⁷⁰,⁷¹, Teresa J. Eyre⁷², Adandé Belarmain Fandohan⁷³, Tom M. Fayle⁷⁴,⁷⁵, Ted R. Feldpausch⁷⁶, Leandro V. Ferreira⁷⁷, Markus Fischer⁷⁸, Christine Fletcher⁷⁹, Lorenzo Frizzera⁸⁰, Javier G. P. Gamarra⁸¹, Damiano Gianelle⁸⁰, Henry B. Glick⁸², David J. Harris⁸³, Andrew Hector⁸⁴, Andreas Hemp⁸⁵, Geerten Hengeveld⁸⁶, Bruno Hérault⁸⁶,⁸⁷, John L. Herbohn⁸,⁸⁸, Martin Herold⁸⁶, Anika Hillers⁹,⁹⁰, Euridice N. Honorio Coronado⁹¹, Cang Hui⁹,⁹²,⁹³, Thomas T. Ibanez⁹⁴, lédé Amaral⁹⁵, Nobuo Imai⁹⁶, Andrzej M. Jagodziński⁹⁷,⁹⁸, Bogdan Jaroszewicz⁹⁹, Vivian Kvist Johannsen¹⁰⁰, Carlos A. Joly¹⁰¹, Tommaso Jucker¹⁰², Ilbin Jung¹⁰⁵, Viktor Karminov¹⁰³, Kuswata Kartawinata¹⁰⁴, Elizabeth Kearsley¹⁰⁵, David Kenfack¹⁰⁶, Deborah K. Kennard¹⁰⁷, Sebastian Kepfer-Rojas¹⁰⁰, Gunnar Keppel¹⁰⁸, Mohammed Latif Khan¹⁰⁹, Timothy J. Killeen²¹, Hyun Seok Kim¹¹⁰,¹¹¹,¹¹¹,¹¹¹, Kanehiro Kitayama¹¹⁴, Michael Köhl¹¹⁵, Henn Korjus¹¹⁶, Florian Kraxner¹¹⁷, Diana Laarmann¹¹⁶, Mait Lang¹¹⁶, Simon L. Lewis²⁵,¹¹⁸, Huicui Lu¹¹⁹, Natalia V. Lukina¹²⁰, Brian S. Maitner¹⁷⁰, Yadvinder Malhi¹²¹, Eric Marcon¹²², Beatriz Schwantes Marimon¹²³, Ben Hur Marimon-Junior¹²³, Andrew R. Marshall⁴⁸,¹²⁴,¹²⁵, Emanuel H. Martin¹²⁶, Olga Martynenko¹³, Jorge A. Meave¹²⁷, Omar Melo-Cruz¹²⁹, Casimiro Mendoza¹²⁹, Cory Morow⁴⁷, Abel Monteagudo Mendoza¹³⁰,¹³¹, Vanessa S. Moreno³⁰, Sharif A. Mukul⁴⁸,¹³², Philip Mundhenk¹¹⁵, Maria Guadalupe Nava-Miranda¹³³,¹³⁴,¹³⁵, David Neill¹³⁶, Victor J. Neldner⁷², Radovan V. Nevenic⁶⁶, Michael A. Nguji²⁷, Pascal A. Niklaus¹³⁷,

Jacek Oleksyn⁹⁷, Petr Ontikov¹⁰³, Edgar Ortiz-Malavasi¹⁵, Yude Pan¹³⁸, Alain Paquette¹³⁹, Alexander Parada-Gutierrez²¹, Elena I. Parfenova¹⁴⁰, Minjee Park³¹¹⁰, Marc Parren¹⁴¹, Narayanaswamy Parthasarathy¹⁴², Pablo L. Perl¹⁴³, Sebastian Pfautsch¹⁴⁴, Oliver L. Phillips²⁵, Nicolas Picard¹⁴⁵, Maria Teresa T. F. Piedade¹⁴⁶, Daniel Piotto¹⁴⁷, Nigel C. A. Pitman¹⁰⁴, Irina Polo¹⁴⁸, Lourens Poorter⁵, Axel D. Poulsen⁸³, Hans Pretzsch¹⁴⁹, Nigel Ramirez Arevalo¹⁵⁰, Zorayda Restrepo-Correa¹⁵¹, Mirco Rodeghieri⁶⁰,¹⁵², Samir G. Rolim¹⁴⁷, Anand Roopsind¹⁵³, Francesco Rovero¹⁵⁴,¹⁵⁵, Eran Rutishauer¹⁵⁶, Purabi Saikia¹⁶⁰, Christian Salas-Eljatbi¹⁶⁰,¹⁶⁹,¹⁶⁰, Philippe Saner¹⁶¹, Peter Schall¹⁶¹, Dmitry Schepaschenko¹⁷,¹⁷,¹⁴⁰,¹⁶², Michael Scherer-Lorenzen¹⁶³, Bernhard Schmid¹³⁷, Jochen Schöngart¹⁴⁶, Eric B. Searle¹³⁹, Vladimir Seben¹⁶⁴, Josep M. Serra-Diaz¹⁶⁵,¹⁶⁶, Douglas Shell¹⁶,¹⁷,¹⁸, Anatole Z. Shidenko¹¹⁷, Javier E. Silva-Espejo¹⁶⁹, Marcos Silveira¹⁷⁰, James Singh¹⁷¹, Plinio Sist⁶⁶, Ferry Slik¹⁷², Bonaventure Sonké¹⁷³, Alexandre F. Souza¹⁷⁴, Miscicki Stanislaw¹⁷⁵, Krzysztof J. Stereńczak²⁸, Jens-Christian Svenning¹⁸⁶,¹⁷⁶, Miroslav Svoboda¹⁷⁷, Ben Swanepoel¹⁷⁸, Natalia Targhetta¹⁴⁶, Nadja Tchebakova¹⁴⁰, Hans ter Steege²⁷,¹⁷⁹, Raquel Thomas¹⁸⁰, Elena Tikhonova¹²⁰, Peter M. Umunay¹⁸¹, Vladimir A. Usoltsev¹⁸², Renato Valencia¹⁸³, Fernando Valladares¹⁸⁴, Fons van der Plas¹⁸⁵, Tran Van Do¹⁸⁶, Michael E. van Nuland¹⁸⁷, Rodolfo M. Vasquez¹³⁰, Hans Verbeeck¹⁰⁵, Helder Viana¹⁸⁸,¹⁸⁹, Alexander C. Vibrans²⁶,¹⁹⁰, Simone Vieira¹⁹¹, Klaus von Gadow¹⁹², Hua-Feng Wang¹⁹³, James V. Watson¹⁹⁴, Gijsbert D. A. Werner¹⁹⁵, Susan K. Wiser¹⁹⁶, Florian Wittmann¹⁹⁷, Hannsjoerg Woell¹⁹⁸, Virginia Wortel¹⁹⁹, Roderik Zagt²⁰⁰, Tomasz Zawita-Niedźwiemie²⁰¹, Chunyu Zhang²⁰², Xiuhai Zhao²⁰², Mo Zhou³, Zhi-Xin Zhu¹⁹³, Irie C. Zo-Bi⁸⁷ & Daniel S. Maynard¹,²⁰³

¹Institute of Integrative Biology, ETH Zurich (Swiss Federal Institute of Technology), Zurich, Switzerland. ²The Forest School at The Yale School of the Environment, Yale University, New Haven, CT, USA. ³Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA. ⁴Department of Crop and Forest Sciences, University of Lleida, Lleida, Spain. ⁵Joint Research Unit CTFC-AGROTECNIO-CERCA, Solsona, Spain. ⁶Wageningen University and Research, Wageningen, The Netherlands. ⁷Department of Forest Resources, University of Minnesota, St Paul, MN, USA. ⁸Hawkesbury Institute for the Environment, Western Sydney University, Penrith, New South Wales, Australia. ⁹Institute for Global Change Biology, and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA. ¹⁰Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland. ¹¹UFR Biosciences, University Félix Houpouënt-Boigny, Abidjan, Côte d'Ivoire. ¹²Department of Agricultural, Food, Environmental and Animal Sciences, University of Udine, Udine, Italy. ¹³Faculty of Science and Technology, Free University of Bolzano, Bolzano, Italy. ¹⁴Spatial Ecology and Conservation Laboratory, Department of Tourism, Recreation and Sport Management, University of Florida, Gainesville, FL, USA. ¹⁵Forestry School, Tecnológico de Costa Rica TEC, Cartago, Costa Rica. ¹⁶Fundacion ConVida, Universidad Nacional Abierta y a Distancia, UNAD, Medellin, Colombia. ¹⁷Field Museum of Natural History, Chicago, IL, USA. ¹⁸Center for Tropical Research, Institute of the Environment and Sustainability, UCLA, Los Angeles, CA, USA. ¹⁹Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Göttingen, Germany. ²⁰Division of Forest and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), Ås, Norway. ²¹Museo de Historia Natural Noel Kempff Mercado, Santa Santa, Bolivia. ²²European Commission, Joint Research Center, Ispra, Italy. ²³UNELLEZ-Guanare, Programa de Científicos del Agro y el Mar, Herbario Universitario (PORT), Portuguesa, Venezuela. ²⁴Compensation International S. A. Ci Progress-GreenLife, Bogotá, Colombia. ²⁵School of Geography, University of Leeds, Leeds, UK. ²⁶Department of Geomatics, Forest Research Institute, Raszyń, Poland. ²⁷Naturalis Biodiversity Center, Leiden, The Netherlands. ²⁸Centro Multidisciplinar, Universidade Federal do Acre, Rio Branco, Brazil. ²⁹Proceedings of the National Academy of Sciences, Washington, DC. ³⁰Department of Evolutionary Anthropology, Duke University, Durham, NC, USA. ³¹TERRA Teach and Research Centre, Gembloux Agro Bio-Tech, University of Liege, Liège, Belgium. ³²United National Framework Convention on Climate Change, Bonn, Germany. ³³Institut Agronomique néo-Calédonien (IAC), Nouméa, New Caledonia. ³⁴AMAP, University of Montpellier, Montpellier, France. ³⁵CIRAD, CNRS, INRAE, IRD, Montpellier, France. ³⁶Institute of Tropical Forest Conservation, Mbarara University of Sciences and Technology, Mbarara, Uganda. ³⁷Isotope Bioscience Laboratory-ISOFYS, Ghent University, Ghent, Belgium. ³⁸Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control (MANSiD), Stefan cel Mare University of Suceava, Suceava, Romania. ³⁹Department of Forest Sciences, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil. ⁴⁰Bavarian State Institute of Forestry, Freising, Germany. ⁴¹Spatial Ecology and Conservation Laboratory, School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA. ⁴²Institute of Biology, Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle-Wittenberg, Germany. ⁴³German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. ⁴⁴Department of Agriculture, Food, Environment and Forest (DAGRI), University of Firenze, Florence, Italy. ⁴⁵Department of Biological, Geological, and Environmental Sciences, University of Bologna, Bologna, Italy. ⁴⁶Department of Spatial Regulation, GIS and Forest Policy, Institute of Forestry, Brugge, Serbia. ⁴⁷Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA. ⁴⁸Forest Research Institute, University of the Sunshine Coast, Sippy Downs, Queensland, Australia. ⁴⁹Faculty of Natural Resources Management, Lakehead University, Thunder Bay, Ontario, Canada. ⁵⁰Division of Forest Resources Information, Korea Forest Promotion Institute, Seoul, South Korea. ⁵¹IFER-Institute of Forest Ecosystem Research, Jilove u Prahy, Czech Republic. ⁵²Global Change Research Institute CAS, Brno, Czech Republic. ⁵³Nicholas School of the Environment, Duke University, Durham, NC, USA. ⁵⁴Department of Biology, University of Missouri-St Louis, St Louis, MO, USA. ⁵⁵Programa
### Tree inventory and non-native status

For tree inventory data, we used the Global Forest Biodiversity Initiative (GFBI) database⁷, which contains tree-level abundance data for more than 1.2 million forest plots on all continents across the globe, containing more than 31 million unique georeferenced records of tree size and density dating from 1958. Each observation in the dataset consists of a unique tree ID, plot ID, plot coordinates, tree diameter at breast height (DBH), tree-per-hectare expansion factors, year of measurement, and binomial species names. In this study, we applied several filters to these data before analyses. First, where plots had multiple years of data, we kept only the most recent year of census data. We then subset the data to include only plots with at least three species as required for our phylogenetic metrics, excluding monoculture forest plantations from the study.

To assign native status to each tree species (native or non-native, representing naturalized and invasive), we established a consensus status between the Global Naturalized Alien Flora (GloNAF)⁶ and the KEW Plants of the World⁷⁰ databases. All databases were standardized to The Plant List taxonomy⁷¹. The GloNAF database contains detailed, georeferenced information on the naturalized status of more than 10,000 plant species in each of 1,029 regions across the globe representing countries or federal states; the KEW database outlines native ranges of vascular plant species for over 1.2 million plant species⁷⁰. The GFBI and GloNAF datasets were joined by matching each unique species by location in GFBI to a GloNAF region polygon and species status. Then, for each GFBI plot, we extracted the GloNAF region identifier using Google Earth Engine⁷². This process was then repeated for the KEW database. We then filtered out plots that included any species with disagreement between GloNAF and KEW databases (that is, conflicting native status), and only included trees with a minimum diameter of 5 cm and a minimum height of 1.3 m to allow for DBH measurements. All trees identified as ‘non-native’ were verified to be listed in the BGCI Tree List, which defines a tree as, “A woody plant with usually a single stem growing to a height of at least two metres, or if multi-stemmed, then at least one vertical stem five centimetres in diameter at breast height”⁷³. Note that this is an inclusive definition which includes monocots and tree ferns, as well as species that can occur both as tall single-stem and shrub-like multi-stem phenotypes.

To account for unequal representation of plots across biomes (Fig. 1), we used a reduced version of this database, down-sampled to a number of plots proportional to the land area covered by each of 14 biomes (Supplementary Table 1), while conserving as many tropical plots as possible. This ensured that we were not overrepresenting historically oversampled biomes, particularly in temperate regions. In addition, we preferentially retained invaded plots during this down-sampling to ensure adequate representation of invaded plots in the final dataset, with a maximum of half of the plots within a biome being invaded. This oversampling of invaded plots allowed for adequate representation of invaded and non-invaded plots in our analyses of non-native presence, and allowed sufficient data for our analyses of invasion severity, as these analyses only used data from plots that had non-native species invasions. Results were not qualitatively different if we did not preferentially retain invaded plots in our down-sampling (Extended Data Fig. 6 and Supplementary Table 8). Note also that the global mapping used the full dataset, with no subsampling. Prior to analyses, we also collapsed locations with multiple replicate plots and removed plots where phylogenetic of functional diversity could not be calculated for both native and full communities due to less than three species being present (see below).

### Non-native invasion metrics

We split our invasion metrics into the two broad categories of ‘non-native invasion’ (presence) and ‘invasion severity’. Specifically, using

our data, we were able to determine for each plot (1) whether any non-native tree species were present (non-native presence); (2) the proportion of tree species that were non-native relative to total tree species (invasion severity, assessed via non-native richness)²³; and (3) the proportion basal area of non-native tree species relative to total tree species basal area (invasion severity, assessed via non-native abundance). These metrics are congruent with recently proposed frameworks for measuring and reporting invasive plant species⁷⁴,⁷⁵. The metric of relative introduced species richness may be hypothesized to lead to a bias in detection of biotic resistance, with greater biotic resistance falsely detected in diverse communities, as these communities will have a lower proportion of non-native trees due to the higher denominator (total site diversity). However, use of the binomial approach in our GLM modelling of this proportion, as opposed to direct proportion, overcomes this limitation, as it uses raw counts of proportion, effectively weighting observations by the total species number in the community²³.

### Climatic and anthropogenic variables

For climatic and anthropogenic variables, we relied on the Global Environmental Composite⁷⁶,⁷⁷. This global database contains spatially explicit geographic information system (GIS) layers of more than 260 unique environmental variables, encompassing climate, soil, land cover and land use, plant biomass, topography, human footprint, and disturbance⁷⁸,⁷⁹. Climate variables were extracted from the CHELSA (climatologies at high resolution for the earth's land surface areas) dataset⁷⁸, whereas soil variables were from the SoilGrids⁸⁰ dataset. In addition, we created distance measures by calculating the spherical distance to shipping ports⁸¹ and airports⁸². All layers were standardized to a 30 arcsec resolution (-1 km² at the equator), a resolution at which these variables have been shown to have an influence on plant biogeography and assembly patterns⁸³,⁸⁴. We chose model variables to represent both climate and soil properties that exhibited low collinearity for each of three datasets: global (all 14 biomes from Supplementary Table 1), temperate (temperate broadleaf, coniferous, grassland biomes) and tropical (tropical moist broadleaf, deciduous broadleaf, coniferous, and grassland biomes). We chose to use distinct variables rather than transforming them into principal component analysis axes for increased interpretability of these variables and their effects. Because variables exhibiting collinearity varied between the three datasets, the resulting models include different variable combinations. For all models, we used mean annual temperature (MAT), mean annual precipitation (MAP), distance to shipping ports⁸¹ (hereafter ‘ports’) and human population density⁸⁵. For the global models, we used the following additional environmental variables: absolute depth to bedrock, coarse fragments, sand content and soil pH. For temperate models, we used absolute depth to bedrock, clay content, and soil pH as additional variables; for tropical models we used absolute depth to bedrock, soil organic content, and soil pH as additional variables. All soil variables used were determined at a depth of 0 cm, or the top layer of soil.

### Diversity metrics

We analysed data using either phylogenetic or functional diversity; these two approaches were chosen to be as analogous as possible. Phylogenetic alpha diversity explains the genetic relatedness of species within a community and is often assumed to represent a proxy for functional similarity across species within a community assemblage. Yet, congruency between these two metrics remains under debate⁸⁶,⁸⁷ and their role in invasion patterns remains untested; therefore, we focused on two major axes of diversity, explaining richness and divergence in the community across both phylogenetic and functional space⁸⁸, capturing both evolutionary and ecological processes. For each native and entire tree community (native and non-native species), we calculated Faith's phylogenetic diversity (phylogenetic richness) and mean nearest taxon distance (MNTD, phylogenetic redundancy; Extended Data Fig. 2).
Entire tree community metrics were calculated on all species, whether they were matched to GloNAF and KEW or not; this included tree species which were identified to genus level. Faith's phylogenetic diversity was calculated as the sum of the branch lengths on the phylogenetic tree of the species in the community; MNTD was calculated as the average distance to the nearest neighbour across the community. These metrics were calculated based on tree placement of taxa in a recently published reference backbone tree for plants⁸⁹. Out of 13,345 starting taxa, a total of 12,325 were placed on the reference tree, with 4,960 placed at the species level and 7,365 placed at the genus level. We chose MNTD over other available metrics describing community divergence because we were interested in redundancy of the community, and this metric captures this best²⁴,⁹⁰. To enable a more intuitive understanding of this metric, we transformed each community-level value of MNTD to the maximum MNTD across all communities minus calculated MNTD. This transformed the maximum value to zero and all smaller values transformed to increasingly larger numbers, with higher MNTD values indicating a greater native redundancy, similar to the expected increased redundancy with greater phylogenetic richness (Faith's phylogenetic diversity). To determine the non-native invasion strategy, or impact of non-natives on native MNTD, we calculated the difference between the native and non-native community relative to the native community alone. We used the following formula for non-native invasion strategy: (entire community MNTD - native community MNTD)/native community MNTD. When non-native invasion strategy was greater than zero, this indicated that the addition of the non-native species resulted in a more dissimilar community, whereas a non-native invasion strategy less than zero corresponded to the opposite.

For functional diversity, we calculated the analogous metrics using trait distance matrices instead of phylogenetic tree-based distances. We selected eight traits extracted from Maynard et al.⁸³ that represented the major clusters of functional trait diversity, thereby capturing the full spectrum of tree form and function while minimizing correlation between traits. Maynard et al.⁸³ used data from the TRY plant trait database to parametrize machine learning models to estimate the expression of 18 traits as a function of the local environment and/or phylogeny. The observed trait data underlying these models encompassed 491,001 unique observations across 13,189 species from 2,313 genera, with consistent representation across taxonomic orders. The resulting models were then used to generate trait estimates for 52,255 tree species, capturing approximately 80% of documented tree species⁹¹. Using this trait database, we were able to assign trait value to 81% of the tree species in GFBI reported to the species level. The eight traits we included in our metrics were chosen to include traits typically associated with plant invasion²⁸,⁹² including those associated with dispersal, establishment, resource acquisition and competitive ability that represent the major trait clusters encompassing the full dimensionality of trait space from Maynard et al.⁸³ The eight traits included in our study were the following: wood density, root depth, leaf nitrogen, leaf phosphorus, leaf area, tree height, seed dry mass, and bark thickness. All traits were log-transformed and normalized to allow for statistically valid comparisons⁸³. To obtain functional diversity metrics analogous to those used for phylogenetic diversity, we used the dendrogram approach of Petchey and Gaston⁹³. Specifically, for every plot we calculated the species-by-species trait distance matrix encompassing all eight traits, and then used hierarchical clustering to create a functional dendrogram. This dendrogram was subsequently used to calculate 'functional richness' (analogous to Faith's phylogenetic diversity) and 'functional redundancy' (MNTD); we use this terminology for functional diversity to maintain naming of variables between phylogenetic and functional diversity analyses. Metrics were calculated in R using packages ape⁹⁴, tidyverse⁹⁵, abdiv⁹⁶, doParallel⁹⁷, foreach⁹⁸ and pez⁹⁹.

Because both functional and phylogenetic diversity metrics have unique limitations, we considered them both here so as to obtain a

more robust view of underlying patterns and processes. The benefit of phylogenetic diversity is that it does not rely on imputed data, and thus it provides more consistent results with lower uncertainty. However, phylogenetic diversity is only a loose proxy for functioning, depending on the degree to which the functional traits of interest are phylogenetically conserved. Thus, as a complement of this, we also use imputed trait values to estimate functional diversity, which should better capture underlying functional differences across species, but which is subject to higher uncertainty relative to phylogeny (or measured trait values), and may omit rare and potentially functionally unique species. Thus, by simultaneously considering both functional and phylogenetic diversity and showing that these metrics yield consistent global trends, our approach provides consistent evidence that these patterns are robust to the limitations of either approach taken individually.

## Statistical analyses

We combined random forest¹⁰⁰ and GLM approaches to answer our focal questions. Specifically, we used random forest models to visualize patterns and determine variable importance, while GLMs were used to assess statistical significance and directionality of patterns. We first tested for environmental and anthropogenic drivers of non-native invasion, including non-native presence and invasion severity (non-native richness, non-native abundance). Our independent variables included either phylogenetic or functional metrics, climate and soil variables, and human impact variables. Next, we tested the impact of these variables on non-native invasion strategy (difference in MNTD due to non-natives). We focused on addressing specific hypotheses related to drivers of non-native invasion and invasion strategy. We acknowledge the importance of other variables, and therefore included them in our models, but do not interpret each variable.

Random forest models and GLMs used the same model designs. Models predicting non-native presence as well as invasion severity, for both non-native richness and abundance, included independent predictor variables of native diversity and native redundancy, as well as climate and human driver variables detailed in 'Climatic and anthropogenic variables'. For comparison, we repeated these models with native tree species richness in place of both diversity variables (richness and redundancy), as species richness is commonly used in the invasion literature when testing for biotic resistance²³,³,⁴,³⁵. Finally, we used an adapted version of the random forest models, removing diversity variables, to assess probability of locations with non-native trees globally and generate an associated map (Extended Data Fig. 1).

To account for spatial autocorrelation in the modelling step, we used residual autocovariates (RACs)¹⁰¹,¹⁰². First, we used simple linear regression to determine the range of spatial autocorrelation for the models with continuous outcomes (invasion severity and invasion strategy). We then assessed residual spatial autocorrelation using correlogram plots using the ncf¹⁰³ package in R, which showed that residual correlation was consistently negligible beyond 250 km, which was also applied to the models with binary outcomes (non-native presence). Using this buffer distance, we generated RAC values using the autocov_dist() function in the spdep package⁷⁰,¹⁰⁴, which determines an inverse distance weighted residual value for each data point in the 250 km neighbourhood. RAC incorporates the spatial signature of the model residuals, relative to the model without any spatial autocorrelation correction, into a variable that is included in each model¹⁰¹,¹⁰². The result is an inverse distance weighted residual value for each data point in the 250 km neighbourhood, which we used as continuous predictors in both the linear and random forest models.

Random forest models were used primarily to assess variable importance and influence. Specifically, we used Shapley additive explanations (SHAP) values to infer variable importance in the model outcome¹⁰⁵,¹⁰⁶. SHAP values are a machine learning analogue of partial regression, quantifying the relative importance of each variable on the outcome, accounting for all other variables in the model. To estimate the SHAP
values, random forest models were fit in R using the ranger package¹⁰⁷, using default hyperparameters (500 trees, observations sampled with replacement, number of variables per split equal to the square root of the number of predictors, a minimum of 5 observations per node). We then used the fastshap package¹⁰⁸ to estimate approximate SHAP values for each predictor, using *n* = 100 simulations. The overall variable importance was taken as the sum of the absolute value of the SHAP values, and the marginal effect of each variable was visualized by plotting the covariate versus the corresponding SHAP value for each observation.

To account for spatial autocorrelation in the accuracy assessment of random forest models, we implemented spatially-buffered leave-one-out cross-validation (LOO-CV) to obtain conservative lower-bound accuracy measures¹⁰⁹. To do this, we first randomly selected a focal observation as the test data, and then we omitted all observations within a 250 km buffer distance around this observation. The remaining data were used to train the model, and the resulting fit was used to predict outcome for the withheld focal observation. This was repeated 500 times for each model, each time selecting a new focal point and predicting its outcome using the 250 km spatially-buffered training set. The resulting accuracy measures were calculated on the set of 500 out-of-fit predictions. For continuous variables, we estimated accuracy using the cross-validated coefficient of determination relative to the one-to-one line (termed VECv¹¹⁰), denoted simply *R*² here, and for binary outcomes we used area under the ROC curve (AUC), which quantifies the ability of the classifier to distinguish between classes, and serves as an assessment of model performance.

To create a global map of invasion probability and its local uncer-
tainty, we used a repeated prediction approach in Google Earth Engine⁶⁰
(Extended Data Fig. 1a; AUC of spatial cross-validation = 0.84 ± 0.04,
mean F1 score of non-native presence = 0.36). This repeated prediction
approach used the full dataset without any down-sampling. To our
knowledge, no global maps on phylogenetic or functional diversity
metrics exist, so we were unable to include these diversity metrics in
the random forest model for mapping; therefore, these models include
the same covariates as the other models except diversity metrics. We
thought it reasonable to exclude diversity metrics in this analysis as dis-
tance to ports is the most important driver of invasion probability, while
native diversity is less important. After aggregating samples within the
30-arcsec pixels, 368,030 data points remained for our repeated predic-
tion approach. We first trained 50 random forest models on stratified
bootstrapped samples with a total of 10,000 data points each, using
biome as stratification category; this allowed us to repeatedly predict
the probability of non-native presence for each terrestrial pixel on
Earth. The resulting 50 predictions were used to create per-pixel mean
and coefficient of variation maps of the probability of non-native pres-
ence, with probabilities calibrated using Platt scaling¹¹¹,¹¹². These two
maps allow us to investigate the patterns of invasion and the regions
of uncertainty in the predictions. Next, the extrapolation extent was
estimated as a per-pixel percentage of predictor variables, and interac-
tions of predictor variables, outside of the training range, in univariate
and multivariate space, respectively (Extended Data Fig. 1b)⁶⁰. In addi-
tion, to account for gaps in predictor space, we estimated the Area of
Applicability¹¹³, used to mark regions of extrapolation in this map. All
maps are restricted to regions with a minimum of 10% forest cover¹¹⁴.

GLM models were used to estimate statistical parameters and con-
duct statistical tests. All GLM models included the same variables as
those in the random forest models. In the models predicting non-native
presence, we used a binomial distribution and logit link. For non-native
abundance, we used a beta regression approach to predict the propor-
tion of non-native basal area, as a method of modelling proportions
between 0 and 1. We could not use a binomial GLM analogous to that
used for non-native abundance because basal area measurements were
not whole numbers and we wanted to retain all information in the data.
Finally, to account for spatial autocorrelation and non-independently

distributed residuals, we employed the inclusion of RACs as described
above. These models were repeated separately for temperate and tropi-
cal bioclimatic zones, but results were qualitatively similar to the global
model, so we report only global results here. All GLM results can be
found in Supplementary Tables 3–5. GLMs were run in R (v. 4.2.2)¹¹⁵
using lme4¹¹⁶, lmerTest¹¹⁷, and betareg¹¹⁸, while visualizations for these
models used ggplot2¹¹⁹; tidyverse⁹⁵ was used throughout as well.

Because invasion of non-native species may alter the native diversity
of the site into which they invade, we conducted a sensitivity test using
plots where we had data across two time points to incorporate this
effect. We first took all plots for which we had two time points, where
the first time point represented a fully native community (that is, no
presence of non-natives; *n* = 8,221 plots). We then modelled the per
cent change of species richness in each plot from this uninvaded first
time point to a later time point. Our predictor variables included final
invasion status (non-natives present or not) to determine the impact of
invasion on per cent change of species richness, along with all climate,
soil, and anthropogenic impact variables we included in other global
models. We extracted the coefficient of final invasion status (along
with upper and lower confidence ranges), which quantifies the per
cent change in richness due to invasion, and we used this to update
the native species richness of the full global dataset. We then used
these coefficients to estimate the pre-invasion native diversity for each
plot in the global dataset by adding the corresponding species change
resulting from invasion. Finally, we reran our global analysis with this
updated pre-invasion native diversity. The relative contribution of
native species loss to biotic resistance was calculated by comparing
the relative change in the richness coefficient for each of the updated
models relative to the original model (Supplementary Table 9).

Non-native invasion strategy was predicted using the difference
in redundancy (MNTD) in the tree community due to invasion. We
included the same variables as in the previous set of models, except
native redundancy, as this is integrated in our response variable and
therefore would exhibit high collinearity. In GLM models, we tested for
the interaction between MAP and MAT to detect potential non-additive
environmental filtering effects of these two dominant climate vari-
ables. In addition, we tested for the interaction between each MAP
and MAT with distance to ports, to examine whether this important
anthropogenic driver modified main ecological relationships. Final
reported models are those resulting from a process of first creating a
full model with all interactions, and subsequently removing nonsignifi-
cant interactions. All GLM results for invasion strategy can be found
in Supplementary Table 7.

Reporting summary

Further information on research design is available in the Nature Port-
folio Reporting Summary linked to this article.

Data availability

Data used in this study can be found in cited references for the Global Naturalized Alien Flora (GloNAF) database⁶ (non-native status), the KEW Plants of the World database⁵ (native ranges) and the Global Environmental Composite⁶³,⁷⁷ (environmental data layers). Plant trait data were extracted from Maynard et al.⁷⁸. Data from the Global Forest Biodiversity Initiative (GFBI) database⁵⁷ are not available due to data privacy and sharing restrictions, but can be obtained upon request via Science-I (https://science-i.org/) or GFBI (gfbinitiative.org) and an approval from data contributors.

Code availability

All code used to complete analyses for the manuscript is available at the
following link: https://github.com/thomaslauber/Global-Tree-Invasion.
Data analyses were conducted and were visualizations generated
in R (v. 4.2.2), Python (v. 3.9.7), Google Earth Engine (earthengine-api 0.1.306), QGIS-LTR (v. 3.16.7) and the ETH Zurich Euler cluster.

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**Acknowledgements** The authors acknowledge the Bernina Foundation and DOB Ecology for financial support. C.S.D. thanks the Swiss National Science Foundation (Postdoctoral Fellowship #TMPFP3_209925). C.S.D. also acknowledges funding from the Marc R. Benioff Revocable Trust, which, in collaboration with the World Economic Forum, also made this work possible. D.S.M. thanks the Swiss National Science Foundation (Ambizione Grant #PZ00P3_193612). The authors thank L. Mo for assistance in compiling the author list and G. Smith for early discussions about invasion severity. J.C.S. considers this work a contribution to Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by Danish National Research Foundation (grant DNRF173) and his VILLUM Investigator project 'Biodiversity Dynamics in a Changing World', funded by VILLUM FONDEN (grant 165494). P. Schall thanks the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories. G.A. thanks the French National Forest Inventory and the Italian Forest Inventory. G.A. was supported by the Italian National Recovery Plan through the National Biodiversity Future Center. Financial support from Monafor network in Mexico was funded by the National Forestry Commission (CONAFOR), Council of Science and Technology of the State of Durango (COCYTED), the Natural Environment Research Council, UK (NERC; NE/T011084/1), and local support of Ejidos and Comunidades.

**Author contributions** C.S.D., T.W.C., D.S.M. and C.M.Z. contributed the conceptualization of the project. C.S.D. and D.S.M. contributed methodology, investigation and project administration. All authors contributed to data collection and/or curation. C.S.D., D.S.M., N.M.R. and T.L. contributed visualization. T.W.C., D.S.M. and C.S.D. obtained funding and provided supervision. Writing was led by C.S.D. and D.S.M., with review and editing contributed by all other co-authors.

**Funding** Open access funding provided by Swiss Federal Institute of Technology Zurich.

**Competing interests** The authors declare no competing interests.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41586-023-06440-7.

**Correspondence and requests for materials** should be addressed to Camille S. Delavaux.

**Peer review information** Nature thanks Blas Benito, Kevin Potter, Marcel Rejmánek and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

**Reprints and permissions information** is available at http://www.nature.com/reprints.
<figure><img src="image_12.png" /></figure>

**Extended Data Fig. 1** | Map of non-native invasion probability. Map showing probability of non-native tree presence based on the probability output of the random forest classifier (A, total n = 368,030 plots, n per iteration = 10,000 plots) alongside maps showing uncertainty in predictions (B) including

local uncertainty of invasion probability via bootstrapped coefficient of variation (i) and extent of extrapolation as percentage of bands outside univariate (ii) and multivariate (ii) training range. Regions outside the Area of Applicability are indicated with dots.
<figure><img src="image_13.png" /><figcaption>**Extended Data Fig. 2** | Map of non-native invasion probability inside the area of applicability. Map showing probability of non-native tree presence based on the probability output of the random forest classifier (A, total n = 368,030 plots, n per iteration = 10,000 plots) alongside maps showing</figcaption></figure>

uncertainty in predictions (B) including local uncertainty of invasion probability via bootstrapped coefficient of variation (i) and extent of extrapolation as percentage of bands outside univariate (ii) and multivariate (iii) training range. Regions outside the Area of Applicability are masked.
A.

$$MNTD = \frac{\sum_{i=1}^{N} \min (\sum_{j=1}^{N(j \neq i)} d_{ij})}{N}$$

B.

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

C.

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

**Extended Data Fig. 3 | Mean nearest taxon distance (MNTD).** Mean nearest taxon distance is the average distance to nearest neighbor by branch length on the tree, which represents redundancy in the community (A). For each species *i*, the sum of all shortest distances *d* to each other taxa *j* is calculated; these values are then averaged across the total species in the tree (N). If invasion occurs via

non-natives being similar to the native community, this would lead to the expectation that MNTD decreases, increasing redundancy (B). Conversely, if non-native invasion occurs via non-natives being dissimilar to the native community, this would lead to the expectation that MNTD increases, reducing redundancy (C). Taxon D represents a non-native addition to the community.
<figure><img src="image_22.png" /></figure>

**Extended Data Fig. 4** | Native diversity mediates degree of non-native invasion. Variable importance (SHAP values) of all variables included in random forest models, ordered from greatest to least importance alongside influence of distance to ports, native richness and native redundancy on invasion severity (proportion of non-native plant species) for (A) phylogenetic

diversity and (B) functional diversity global models (phylogenetic n = 3,498 plots; functional n = 3,368 plots). All results shown are from random forest models. Note that y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude.
Phylogenetic Diversity

<figure><img src="image_23.png" /><figcaption>A.</figcaption></figure>

<figure><img src="image_24.png" /></figure>

Functional Diversity

<figure><img src="image_25.png" /><figcaption>B.</figcaption></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

**Extended Data Fig. 5 | Variable importance for non-native invasion strategy.**

Variable importance (SHAP values) of all variables included in random forest models, ordered from greatest to least importance alongside influence of native richness, mean annual temperature and mean annual precipitation on invasion strategy for (A) phylogenetic diversity and (B) functional diversity

global models (phylogenetic n = 3,498 plots; functional n = 3,368 plots). All results shown are from random forest models. Note that y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.
A.

Non-native presence

i.

<figure><img src="image_31.png" /></figure>

ii.

<figure><img src="image_32.png" /></figure>

B.

Non-native richness

i.

<figure><img src="image_33.png" /></figure>

ii.

<figure><img src="image_34.png" /></figure>

C.

Non-native abundance

i.

<figure><img src="image_35.png" /></figure>

ii.

<figure><img src="image_36.png" /></figure>

**Extended Data Fig. 6** | Variable importance for analyses using data down-sampled without preferentially retaining invaded plots. Variable importance (SHAP values) for all variables included in random forest models, ordered from greatest to least importance for (A) non-native presence, (B) richness, and (C) abundance, each for (i) phylogenetic diversity and (ii) functional diversity global models (presence: phylogenetic n = 18,898; functional n = 18,611, richness: phylogenetic n = 840 plots; functional n = 823 plots, abundance: phylogenetic n = 840 plots; functional n = 823 plots). All results shown are from random forest models with down-sampled data, but without preferentially retaining invaded plots. Error bands represent 95% confidence intervals.
# Reporting Summary

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## Statistics

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## Software and code

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**Data collection** No software was used for data collection.

**Data analysis** All code used to complete analyses for the manuscript is available at the following link: https://github.com/c383d893/Globallnvasion.git. Data analysis was conducted in R (v. 4.2.2) and Python (v. 3.9.7), Google Earth Engine (earthengine-api 0.1.306), and on the ETH Zurich Euler cluster. R packages used in this study include tidyverse (v. 1.3.2), feather (v 0.3.5), doParallel (v. 1.0.17), foreach (v. 1.5.2), ape (v. 5.6-2), pez (v. 1.2-4), abdiv (v. 0.2.0), sp (v. 1.6.0), ncf (v. 1.3-2), spdep (v. 1.2-7), scales (v. 1.2.1), rsq (v. 2.5), geosphere (v. 1.5-18), lme4 (v. 1.1-31), lmerTest (v. 3.1-3), betareg (v. 3.1-4), ggeffects (v. 1.1.4), ggplot2 (v. 3.4.0), gridExtra (v. 2.3), grid (v. 4.2.2), ClustOfVar (v. 1.1), HH (v. 3.1-49), MuMln (v. 1.47.1), viridis (v. 0.6.2), RColorBrewer (v. 1.1-3), fastshap (v. 0.0.7), ranger (v. 0.13.1), ggbeeswarm (v. 0.7.1), colorspace (v. 2.0-3), caret (v. 6.0-93), fmsb (v. 0.7.5), cowplot (v. 1.1.1), and AUC (v. 0.3.2). Python packages used in this study include numpy (v. 1.20.3), pandas (v. 1.5.3), scipy (v. 1.8.0), sklearn (v. 1.1.1), plotnine (v. 0.10.1), matplotlib (v. 3.5.1).

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# Data

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- Accession codes, unique identifiers, or web links for publicly available datasets

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Data used in this study can be found in cited references for the Global Naturalized Alien Flora (GloNAF) database6 (non-native status), the KEW Plants of the World database5 (native ranges), and the Global Environmental Composite65,82 (environmental data layers). Plant trait data were extracted from Maynard et al.83 Data from Global Forest Biodiversity Initiative (GFBI) database 59 are not available due to data privacy and sharing restrictions, but can be obtained upon request via Science-I (https://science-i.org/) or GFBI (https://www.gfbiinitiative.org/) and an approval from data contributors.

# Human research participants

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Reporting on sex and gender N/A

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Note that full information on the approval of the study protocol must also be provided in the manuscript.

# Field-specific reporting

Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

<figure><img src="image_37.png" /></figure>

Life sciences

<figure><img src="image_38.png" /></figure>

Behavioural & social sciences

<figure><img src="image_39.png" /></figure>

Ecological, evolutionary & environmental sciences

For a reference copy of the document with all sections, see [nature.com/documents/nr-reporting-summary-flat.pdf](https://nature.com/documents/nr-reporting-summary-flat.pdf)

# Ecological, evolutionary & environmental sciences study design

All studies must disclose on these points even when the disclosure is negative.

**Study description** We combined random forest and generalized linear model (GLM) approaches to answer our focal questions. Specifically, we used random forest models to visualize patterns and determine variable importance, while GLMs were used to assess statistical significance and directionality of patterns. We first tested for environmental and anthropogenic drivers of non-native invasion, including non-native presence and invasion severity (non-native richness, non-native abundance). Our independent variables included either phylogenetic or functional metrics, climate and soil variables, and human impact variables. Next, we tested the impact of these variables on non-native invasion strategy (difference in MNTD due to non-natives). We focused on addressing specific hypotheses related to drivers of non-native invasion and invasion strategy. We acknowledge the importance of other variables, and therefore included them in our models, but do not interpret each variable.

**Research sample** The Global Forest Biodiversity Initiative (GFBI) database, which contains tree-level abundance data for more than 1.2 million forest plots on all continents across the globe, containing more than 31 million unique georeferenced records of tree size and density dating from 1958. Each observation in the dataset consists of a unique tree ID, plot ID, plot coordinates, tree diameter at breast height (DBH), tree-per-hectare expansion factors, year of measurement, and binomial species names.

**Sampling strategy** To account for unequal representation of plots across biomes (Figure 1), we used a reduced version of this database, down-sampled to a number of plots proportional to the land area covered by each of 14 biomes (Table S1), while conserving as many tropical plots as possible. This ensured that we were not overrepresenting historically oversampled biomes (down-sampling), particularly in temperate regions. In addition, we preferentially retained invaded plots, or up-sampled to invasion within our down-sampling proportional to biome cover, to ensure that no more than half of the plots within a biome were invaded. This oversampling of invaded plots allowed for adequate representation of invaded and non-invaded plots in our analyses on non-native presence, and allowed sufficient data for our analyses of invasion severity, as these analyses only used data from plots that had non-native species invasions.

**Data collection** Data collection varies across all datasets. Please refer to references for each dataset for more details.

**Timing and spatial scale** Data timing and spatial scale varies across all datasets. Please refer to references for each dataset for more details.
Data exclusions

We applied several filters to this data before analyses. First, where plots had multiple years of data, we kept only the most recent year of census data. We then subset the data to include only plots with at least three species as required for our phylogenetic metrics, excluding monoculture forest plantations from the study.

Reproducibility

This study uses experimental data so was not possible to reproduce.

Randomization

Please see "Sampling strategy" for a detailed explanation of random subsampling.

Blinding

Blinding was not possible during data analysis.

Did the study involve field work?

<figure><img src="image_40.png" /></figure>

Yes

<figure><img src="image_41.png" /></figure>

No

Reporting for specific materials, systems and methods

We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.

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<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ Antibodies</td></tr><tr><td>☑</td><td>☐ Eukaryotic cell lines</td></tr><tr><td>☑</td><td>☐ Palaeontology and archaeology</td></tr><tr><td>☑</td><td>☐ Animals and other organisms</td></tr><tr><td>☑</td><td>☐ Clinical data</td></tr><tr><td>☑</td><td>☐ Dual use research of concern</td></tr></tbody></table>

Methods

<table><thead><tr><th>n/a</th><th>Involved in the study</th></tr></thead><tbody><tr><td>☑</td><td>☐ ChIP-seq</td></tr><tr><td>☑</td><td>☐ Flow cytometry</td></tr><tr><td>☑</td><td>☐ MRI-based neuroimaging</td></tr></tbody></table>