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Native diversity buffers against severity of non-native tree invasions

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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.
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Nature | Vol 621 | 28 September 2023 | 773
Article
Native diversity buffers against severity of
non-native tree invasions
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 inuence the establishment of non-native tree species
and the subsequent invasionseverity. We nd 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 inuence of these ecological forces in determining
invasion strategy, we nd 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
inuence 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 globe
1–3,8
. 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 livelihoods
1117
. 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, resi-
dential, 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 invaders4,1921.
Given the prominent roles of trees in shaping the structure and function-
ing of ecosystems, such tree invasions have the capacity to alter plant
composition, productivity, biodiversity and the services provided to
humans
1,4,22
. Previous research in invasion ecology has expanded our
understanding of community-level properties that influence ecosys-
tem susceptibility to invasion2325, as well as traits that make plant spe-
cies more likely to become invasive2630. However, most work has been
restricted to local and regional scales
31,32
, 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 invasions
33
. 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 com-
plex systems are thought to exhibit ‘biotic resistance’ to invasion
23,3439
.
This hypothesis is based on the assumption that greater diversity in the
native community fills the available ecological niches and reduces avail-
able 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 propor-
tion 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 com-
munity 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. None-
theless, 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, inva-
sive species ought to be similar to native species that are adapted for
that region, especially in extreme environments
42
. Under this ‘envi-
ronmental filtering hypothesis’
43,44
(or ‘preadaptation hypothesis’),
invasive species will be more successful if their traits mirror those of
the native community
45
. For example, to be successful in a harsh desert
environment, non-native plants would need to be ecologically similar
https://doi.org/10.1038/s41586-023-06440-7
Received: 2 November 2022
Accepted: 14 July 2023
Published online: 23 August 2023
Open access
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A list of authors and their afiliations appears at the end of the paper.
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774 | Nature | Vol 621 | 28 September 2023
Article
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 overlap
4649
. Here, invaders are thought to be more success-
ful if they can fill unique niche spaces that are not already used by the
native community, reducing competition and enabling their establish-
ment. 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 conundrum
24,50
). 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 environments
51,52
.
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 pat-
terns and mechanisms underpinning plant invasion is that these pro-
cesses are likely strongly influenced by anthropogenic activity, which
may dampen the signal of ecological drivers. Humans drive contempo-
rary plant invasions through highly efficient transport—both intentional
and accidental—of non-native plants, with proximity to ports and air-
ports being associated with increased invasion11,53,54. A constant influx
of non-native species may override a native community’s ability to resist
invasion
55
(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. Account-
ing 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 anthro-
pogenic 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 relationships
56
. 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 database
7
, we deter-
mined native tree status (native or non-native) according to the Global
Naturalized Alien Flora
6
and the KEW Plants of the World databases
5
.
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
Per cent invaded
0
100
Fig. 1 | Dist ribution of t he study data . Distribut ion of the full stud y dataset,
coded for non-native severity (n = 471,888 plot s). The map shows average p er
cent invasio n across a 1-degr ee hexagonal gri d, from non-invaded (0%) p ixels
in green to co mpletely invade d (100%) pixels in purp le. Plots are con sidered
invaded if the re is any non-native tre e present.
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Nature | Vol 621 | 28 September 2023 | 775
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 findings
10,57
(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 visu-
alization 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 popula-
tion 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 pres-
ence, with results being variable across diversity metrics and bioclimatic
zones considered (Supplementary Tables3 and 4). However, popula-
tion 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 inva-
sion56. 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
Absolute bedrock depth
Native functional redundancy
Native functional richness
Mean annual precipitation
Distance to ports
Population density
Sand content
Silt content
Coarse fragments
Mean annual temperature
0 0.01 0.02 0.03
Mean absolute SHAP value
Absolute bedrock depth
Native phylogenetic redundancy
Mean annual precipitation
Native phylogenetic richness
Population density
Distance to ports
Sand content
Silt content
Coarse fragments
Mean annual temperature
0 0.01 0.02 0.03
Mean absolute SHAP value
–0.05
0
0.05
10100 1,000
Inuence on
non-native presence
–0.05
0
0.05
100 300 1,000 3,000
–0.04
0
0.04
100 200 300
–0.06
–0.03
0
0.03
0.06
10100 1,000
Distance to ports (km)
–0.04
0
0.04
31030100
Native richness
–0.04
0
0.04
1234
Native redundancy
Phylogenetic diversity
a
Functional diversity
b
Inuence on
non-native presence
Inuence on
non-native presence
Inuence on
non-native presence
Distance to ports (km)
Inuence on
non-native presence
Native richness
Inuence on
non-native presence
Native redundancy
Fig. 2 | Anth ropogenic d rivers are more i mportan t than native diver sity
in determining invasion occurrence. a,b,Importa nce (Shapley addit ive
explanatio ns (SHAP) values) of all var iables include d in random forest mo dels
ordered from g reatest to le ast import ant, alongside i nfluence o f distance to
ports, n ative richnes s and native redun dancy on non-nati ve presence (whet her
a plot is invade d or not) for global mode ls of phylogeneti c (a) and functional
(b) diversity (phylogenetic diversity, n = 17,640plots; f unctional dive rsity,
n = 17,271plots). All result s shown are from ran dom forest model s. Note that
y-axis range s differ among pan els, with the var iable impor tance plots
represen ting the corre sponding mag nitude. Error ba nds represent 9 5%
confidence intervals.
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776 | Nature | Vol 621 | 28 September 2023
Article
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 R
2
 = 0.14,
functional diversity random forest non-native richness R
2
 = 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 R
2
 = 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 spaces23,3436,61. 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 ranges
62
. 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
a
b
Population density
Mean annual precipitation
Absolute bedrock depth
Silt content
Coarse fragments
Distance to ports
Sand content
Mean annual temperature
Native functional redundancy
Native functional richness
0 0.01 0.02 0.03 0.04
Mean absolute SHAP value
Population density
Absolute bedrock depth
Mean annual precipitation
Silt content
Coarse fragments
Sand content
Distance to ports
Mean annual temperature
Native phylogenetic redundancy
Native phylogenetic richness
0 0.01 0.02 0.03
Mean absolute SHAP value
–0.03
0
0.03
10 100 1,000
Inuence on
non-native abundance
–0.15
–0.10
–0.05
0
0.05
0.10
0.15
100 300 1,000 3,000 300
–0.050
–0.025
0
0.025
0.050
10 100 1,000
Distance to ports (km)
–0.1
0
0.1
31030
Native richness Native redundancy
Phylogenetic diversityFunctional diversity
Inuence on
non-native abundance
Inuence on
non-native abundance
Distance to ports (km) Native richness Native redundancy
Inuence on
non-native abundance
Inuence on
non-native abundance
Inuence on
non-native abundance
0.10
0.05
0
–0.05
–0.10
200100
0.10
0.05
0
–0.05
–0.10
12
34
Fig. 3 | Nati ve diversity is th e most impor tant driver o f invasion severi ty.
a,b,Importan ce (Shapley additive ex planations (SH AP) values) of all variable s
included in r andom forest mode ls ordered from g reatest to lea st importa nt,
alongside i nfluence o f distance to p orts, native r ichness and na tive redundanc y
on invasion se verity for global mo dels of phylogene tic (a) and functional
(b) diversity (phylogenetic diversity, n = 3,498plots; function al diversity,
n = 3,368 pl ots). Plots are shown for the s everity of invasi on measured as
non-native sp ecies abunda nce (proport ion of basal area w ith non-native pla nt
species); plot s for non-native spe cies richne ss (proportio n of non-native plant
species) are sh own in Extende d Data Fig.4. All re sults shown are fr om random
forest mode ls. Note that the y-a xis ranges diffe r among panels, w ith the
variable importance plots represent ing the corresponding magnitude. Error
bands represent 95% confidence intervals.
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Nature | Vol 621 | 28 September 2023 | 777
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 Sup-
plementary Table6; phylogenetic diversity random forest R2 = 0.084,
functional diversity random forest R2 = 0.099; H2), with our global analy-
sis indicating that non-native trees were more similar to the native com
-
munity 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 temper-
ature 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 coexist
51
. For functional diversity, inva-
sion 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 America
63
. 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 pri-
marily 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 anthro-
pogenic activity weakened the environmental filtering pattern for phylo-
genetic 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 strat
-
egies. 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
b
Functional diversity
d
Similar Dissimilar
Similar Dissimilar
–0.6
–0.3
0
0.3
0.6
01020
Mean annual temperature (°C)
Invasion strategy
–1.0
0
1.0
01020
Invasion strategy
Bioclimatic zone
Temperate Tropical Other
a
c
Absolute bedrock depth
Soil pH
Population density
Distance to ports
Native phylogenetic diversity
Mean annual precipitation
Mean annual temperature
–1.5 –1.0 –0.5 0 0.5
Absolute bedrock depth
Soil pH
Population density
Distance to ports
Native phylogenetic diversity
Mean annual precipitation
Mean annual temperature
–0.25 0 0.25
Model estimate
Phylogenetic diversity
Mean annual temperature (°C)Model estimate
Fig. 4 | Enviro nmental f ilterin g at temperat ure extremes . a,c, Estimates of
overlapping var iables include d in temperate a nd tropical GLM mo dels (forest
plot) for phylogenetic (a) and functional (c) diversi ty models (phylo genetic
diversity, n = 3,498; functional diver sity, n = 3,368). Values to the l eft of the zero
line indica te negative mod el estimate s, and those to the r ight indicate p ositive
estimates. b,d, Relationship b etween mea n annual temper ature and invasion
strateg y for phylogeneti c (b) and functio nal (d) diversity mod els, showing tha t
at extreme te mperatures inv asion occurs th rough similarit y (Supplement ary
Table7; phylogenetic diversity: P(1) = 9.69 × 10−14, P(2) = 2.13 × 10−1 1; funct ional
diversity: P(1) < 2 × 10−16, P(2) = 1.07 × 10−4, where P(1) and P(2) repre sent each
temperature and temperature squared P values, respe ctively). Note for
functio nal diversity, this pat tern only holds a t low temperature s. Error bars
and bands represent standard error.
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778 | Nature | Vol 621 | 28 September 2023
Article
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. Never-
theless, 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 fac-
tors 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 impor-
tant 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 plantings
3
, understanding the ecological drivers pro-
moting 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 analy-
sis 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 ecosystems
58
. 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 fair
65
tropical forest inventory data are critical
for gaining better insight into these ecologically and socially important
ecosystems.
–1.0
–0.5
0
0.5
1.0
1.5
0510 15 20 25
Invasion strategy
–1.0
–0.5
0
0.5
1.0
1.5
0510 15 20 25
Mean annual temperature (°C)
Invasion strategy
aPhylogenetic diversity b
Near to ports Far fromports
Similar Dissimilar
Similar
–0.5
–0.2
0
0.2
0.5
0.8
050 100 150 200 250
–0.5
–0.2
0
0.2
0.5
0.8
050 100 150 200 250
Mean annual precipitation
Functional diversity
cd
Mean annual temperature (°C) Mean annual precipitation
Invasion strategyInvasion strategy
Dissimilar
Fig. 5 | Proxi mity to por ts weakens envi ronmental f ilteri ng in the
temperate bioclimate zone. a,b, In temperate p lots far from por ts,
tempe rature i s posit ively cor related with an inv asion strateg y of increasin g
dissimilarity for phylogenetic (a) and functi onal (b) diversity (phylogenetic
diversity: n = 2,710 plot s, P = 6.37 × 10−6; func tional divers ity: n = 2,603,
P < 2 × 10−16). c,d, This rela tionship bet ween temper ature and invasion s trategy
weakens for phylogenetic (c) and functional (d) diver sity with proxi mity to
ports (Supplementary Table7; phylogenetic diversity: P = 0.0001; funct ional
diversity: P = 2.71 × 10−13). Lines and p oints repres ent the lowest (c,d) and
highest (a,b) 10% of data. Error band s represent st andard error.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature | Vol 621 | 28 September 2023 | 779
Many tree species are intentionally introduced for forestry or wood
products and may be managed
4
, 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 mono-
culture 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 regime
66
, 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 range
62,66
.
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 tan-
gible 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 introductions
68,69
.
Ultimately, this emerging understanding of global tree invasions pro-
vides 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.
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edgements, 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. Delavaux1 ✉, Thomas W. Crowther1, Constantin M. Zohner1, Niamh M. Robmann1,
Thomas Lauber1, Johan van den Hoogen1, Sara Kuebbing2, Jingjing Liang3, Sergio de-Miguel4,5,
Gert-Jan Nabuurs6, Peter B. Reich7,8,9, Meinrad Abegg10, Yves C. Adou Yao11,
Giorgio Alberti12,13, Angelica M. Almeyda Zambrano14, Braulio Vilchez Alvarado15,
Esteban Alvarez-Dávila16, Patricia Alvarez-Loayza17, Luciana F. Alves18, Christian Ammer19,
Clara Antón-Fernández20, Alejandro Araujo-Murakami21, Luzmila Arroyo21,
Valerio Avitabile22, Gerardo A. Aymard23,24, Timothy R. Baker25, Radomir Bałazy26,
Olaf Banki27, Jorcely G. Barroso28, Meredith L. Bastian29,30, Jean-Francois Bastin31,
Luca Birigazzi32, Philippe Birnbaum33,34,35, Robert Bitariho36, Pascal Boeckx37,
Frans Bongers6, Olivier Bouriaud38, Pedro H. S. Brancalion39, Susanne Brandl40,
Roel Brienen25, Eben N. Broadbent41, Helge Bruelheide42,43, Filippo Bussotti44,
Roberto Cazzolla Gatti45, Ricardo G. César39, Goran Cesljar46, Robin Chazdon47,48 ,
Han Y. H. Chen49, Chelsea Chisholm1, Hyunkook Cho50, Emil Cienciala51,52, 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, Géraldine Derroire64, Ben DeVries65,
Ilija Djordjevic66, Jiri Dolezal67,6 8, Aurélie Dourdain64, Nestor Laurier Engone 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 Hengeveld6,
Bruno Hérault86,87, John L. Herbohn48,88, Martin Herold6, Annika Hillers89,90 ,
Eurídice N. Honorio Coronado91, Cang Hui92,93, Thomas T. Ibanez34,35, Iêda Amaral94,
Nobuo Imai95, Andrzej M. Jagodziński96,97, Bogdan Jaroszewicz98, Vivian Kvist Johannsen99,
Carlos A. Joly100, Tommaso Jucker101, Ilbin Jung50, Viktor Karminov102,
Kuswata Kartawinata103, Elizabeth Kearsley104, David Kenfack105, Deborah K. Kennard106,
Sebastian Kepfer-Rojas99, Gunnar Keppel107, Mohammed Latif Khan108, Timothy J. Killeen21,
Hyun Seok Kim109,110,111,112, Kanehiro Kitayama113, Michael Köhl114, Henn Korjus115,
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Natalia V. Lukina119, Brian S. Maitner70, Yadvinder Malhi120, Eric Marcon121,
Beatriz Schwantes Marimon122, Ben Hur Marimon-Junior122, Andrew R. Marshall48,123,124,
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Casimiro Mendoza128, Cory Merow47, Abel Monteagudo Mendoza129,130, Vanessa S. Moreno39,
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David Neill135, Victor J. Neldner72, Radovan V. Nevenic66, Michael R. Ngugi72,
Pascal A. Niklaus136, Jacek Oleksyn96, Petr Ontikov102, Edgar Ortiz-Malavasi15, Yude Pan137,
Alain Paquette138, Alexander Parada-Gutierrez21, Elena I. Parfenova139, Minjee Park3,10 9,
Marc Parren140, Narayanaswamy Parthasarathy141, Pablo L. Peri142, Sebastian Pfautsch143,
Oliver L. Phillips25, Nicolas Picard144, Maria Teresa T. F. Piedade145, Daniel Piotto146,
Nigel C. A. Pitman103, Irina Polo147, Lourens Poorter6, Axel D. Poulsen83, Hans Pretzsch148,
Freddy Ramirez Arevalo149, Zorayda Restrepo-Correa150, Mirco Rodeghiero80,151,
Samir G. Rolim146, Anand Roopsind152, Francesco Rovero153,154, Ervan Rutishauser155,
Purabi Saikia156, Christian Salas-Eljatib157,158,159, Philippe Saner160, Peter Schall19,
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Jochen Schöngart145, Eric B. Searle138, Vladimír Seben163, Josep M. Serra-Diaz164,165,
Douglas Sheil166,167, Anatoly Z. Shvidenko116, Javier E. Silva-Espejo168, Marcos Silveira169,
James Singh170, Plinio Sist86, Ferry Slik171, Bonaventure Sonké172, Alexandre F. Souza173,
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Vladimir A. Usoltsev181, Renato Valencia182, Fernando Valladares183, Fons van der Plas184,
Tran Van Do185, Michael E. van Nuland186, Rodolfo M. Vasquez129, Hans Verbeeck104,
Helder Viana187,188, A le xa nd er C. Vibrans62,189, Simone Vieira190, Klaus von Gadow191,
Hua-Feng Wang192, James V. Watson193, G ij sb er t D. A. Werner194, Susan K. Wiser195,
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Irie C. Zo-Bi87 & D an ie l S. Maynard1,202
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 Houphouët-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 Histiory, 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 Cruz, Bolivia. 22European Commission, Joint Research Center, Ispra,
Italy. 23UNELLEZ-Guanare, Programa de Ciencias 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, Raszyn, 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, USA. 30Department of
Evolutionary Anthropology, Duke University, Durham, NC, USA. 31TERRA Teach and Research
Centre, Gembloux Agro Bio-Tech, University of Liege, Liege, Belgium. 32United Nation
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 ( iD iv) Halle-Jena-Leipzig, Leipzig,
Germany. 44Department of Agriculture, Food, E nv ir on ment a n d F o r est ( D A GR I ) , U n i ve r s ity
o f F i r en z e , F l o re n c e, I t a ly . 4 5 D e p ar t m ent o f B i o lo g i cal, Geological, and Environmental
Sciences, University of Bologna, Bologna, Italy. 46Department of Spatial Regulation, GIS and
Forest Policy, Institute of Forestry, Belgrade, 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 de Pós-graduação em Biologia Vegetal, Instituto de Biologia, Universidade
Estadual de Campinas, Campinas, Brazil. 56Department of Plant Sciences and Conservation
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Nature | Vol 621 | 28 September 2023 | 781
Research Institute, University of Cambridge, Cambridge, UK. 57Andes to Amazon Biodiversity
Program, Madre de Dios, Peru. 58Facultad de Ciencias Forestales y Ambientales, Universidad
Juárez del Estado de Durango, Durango, Mexico. 59Department of Biology, West Virginia
University, Morgantown, WV, USA. 60Department of Physical and Biological Sciences, The
College of Saint Rose, Albany, NY, USA. 61Biology Department, Centre for Structural and
Functional Genomics, Concordia University, Montreal, Quebec, Canada. 62Natural Science
Department, Universidade Regional de Blumenau, Blumenau, Brazil. 63World Agroforestry
(ICRAF), Nairobi, Kenya. 64Cirad, UMR EcoFoG (AgroParisTech, CNRS, INRAE), Université des
Antilles, Université dela Guyane, Campus Agronomique, Kourou, France. 65Department of
Geographical Sciences, University of Maryland, College Park, MD, USA. 66Institute of Forestry,
Belgrade, Serbia. 67Institute of Botany, The Czech Academy of Sciences, Třeboň, Czech
Republic. 68Department of Botany, Faculty of Science, University of South Bohemia, České
Budějovice, Czech Republic. 69IRET, Herbier National du Gabon (CENAREST), Libreville,
Gabon. 70Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ,
USA. 71The Santa Fe Institute, Santa Fe, NM, USA. 72Queensland Herbarium, Department
of Environment and Science, Toowong, Queensland, Australia. 73Ecole de Foresterie et
Ingénierie du Bois, Université Nationale d’Agriculture, Kétou, Benin. 74School of Biological and
Behavioural Sciences, Queen Mary University of London, London, UK. 75Biology Centre of the
Czech Academy of Sciences, Institute of Entomology, Ceske Budejovice, Czech Republic.
76Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK.
77Museu Paraense Emílio Goeldi. Coordenação de Ciências da Terra e Ecologia, Belém, Pará,
Brazil. 78Institute of Plant Sciences, University of Bern, Bern, Switzerland. 79Forest Research
Institute Malaysia, Kuala Lumpur, Malaysia. 80Research and Innovation Center, Fondazione
Edmund Mach, San Michele All’adige, Italy. 81Forestry Division, Food and Agriculture
Organization of the United Nations, Rome, Italy. 82Glick Designs LLC, Hadley, MA, USA. 83Royal
Botanic Garden Edinburgh, Edinburgh, UK. 84Department of Plant Sciences, University of
Oxford, Oxford, UK. 85Department of Plant Systematics, University of Bayreuth, Bayreuth,
Germany. 86Cirad, UPR Forêts et Sociétés, University of Montpellier, Montpellier, France.
87Department of Forestry and Environment, National Polytechnic Institute (INP-HB),
Yamoussoukro, Côte d’Ivoire. 88Tropical Forests and People Research Centre, University of the
Sunshine Coast, Maroochydore, Queensland, Australia. 89Centre for Conservation Science,
The Royal Society for the Protection of Birds, Sandy, UK. 90Wild Chimpanzee Foundation,
Liberia Ofice, Monrovia, Liberia. 91Instituto de Investigaciones dela Amazonía Peruana,
Iquitos, Peru. 92Centre for Invasion Biology, Department of Mathematical Sciences,
Stellenbosch University, Stellenbosch, South Africa. 93Theoretical Ecology Unit, African
Institute for Mathematical Sciences, Cape Town, South Africa. 94National Institute of
Amazonian Research, Manaus, Brazil. 95Department of Forest Science, Tokyo University of
Agriculture, Tokyo, Japan. 96Institute of Dendrology, Polish Academy of Sciences, Kórnik,
Poland. 97Poznań University of Life Sciences, Department of Game Management and Forest
Protection, Poznań, Poland. 98Faculty of Biology, Białowieża Geobotanical Station, University
of Warsaw, Białowieża, Poland. 99Department of Geosciences and Natural Resource
Management, University of Copenhagen, Copenhagen, Denmark. 100Department of Plant
Biology, Institute of Biology, University of Campinas, UNICAMP, Campinas, Brazil. 101School of
Biological Sciences, University of Bristol, Bristol, UK. 102Forestry Faculty, Bauman Moscow
State Technical University, Mytischi, Russia. 103Field Museum of Natural History, Chicago, IL,
USA. 104CAVElab-Computational and Applied Vegetation Ecology, Department of Environment,
Ghent University, Ghent, Belgium. 105CTFS-ForestGEO, Smithsonian Tropical Research
Institute, Balboa, Panama. 106Department of Physical and Environmental Sciences, Colorado
Mesa University, Grand Junction, CO, USA. 107UniSA STEM and Future Industries Institute,
University of South Australia, Adelaide, South Australia, Australia. 108Department of Botany,
DrHarisingh Gour Vishwavidyalaya (A Central University), Sagar, India. 109Department of
Agriculture, Forestry and Bioresources, Seoul National University, Seoul, South Korea.
110Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul National University,
Seoul, South Korea. 111National Center for Agro Meteorology, Seoul, South Korea. 112Research
Institute for Agriculture and Life Sciences, Seoul National University, Seoul, South Korea.
113Graduate School of Agriculture, Kyoto University, Kyoto, Japan. 114Institute for World
Forestry, University of Hamburg, Hamburg, Germany. 115Institute of Forestry and Engineering,
Estonian University of Life Sciences, Tartu, Estonia. 116Biodiversity and Natural Resources
Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria.
117Department of Geography, University College London, London, UK. 118Faculty of Forestry,
Qingdao Agricultural University, Qingdao, China. 119Center for Forest Ecology and
Productivity, Russian Academy of Sciences, Moscow, Russia. 120Environmental Change
Institute, School of Geography and the Environment, University of Oxford, Oxford, UK.
121AgroParisTech, UMR-AMAP, Cirad, CNRS, INRA, IRD, Université de Montpellier, Montpellier,
France. 122Departamento de Ciências Biológicas, Universidade do Estado de Mato Grosso,
Nova Xavantina, Brazil. 123Department of Environment and Geography, University of York, York,
UK. 124Flamingo Land, Malton, UK. 125Department of Wildlife Management, College of African
Wildlife Management, Mweka, Tanzania. 126Departamento de Ecología y Recursos Naturales,
Facultad de Ciencias, Universidad Nacional Autónoma de México, Mexico City, Mexico.
127Universidad del Tolima, Ibagué, Colombia. 128Colegio de Profesionales Forestales de
Cochabamba, Cochabamba, Bolivia. 129Jardín Botánico de Missouri, Pasco, Peru. 130Universidad
Nacional de San Antonio Abad del Cusco, Cusco, Peru. 131Department of Environment and
Development Studies, United International University, Dhaka, Bangladesh. 132Laboratorio de
geomática, Instituto de Silvicultura e Industria dela Madera, Universidad Juárez del Estado
de Durango, Durango, Mexico. 133Programa de doctorado en Ingeniería para el desarrollo
rural y civil, Escuela de Doctorado Internacional dela Universidad de Santiago de Compostela,
Santiago de Compostela, Spain. 134Department of Environment and Development Studies,
United International University, Dhaka, Bangladesh. 135Universidad Estatal Amazónica, Puyo,
Pastaza, Ecuador. 136Department of Evolutionary Biology and Environmental Studies, University
of Zürich, Zurich, Switzerland. 137Climate, Fire, and Carbon Cycle Sciences, USDA Forest
Service, Durham, NC, USA. 138Centre for Forest Research, Université du Québec à Montréal,
Montreal, Quebec, Canada. 139V. N. Sukachev Institute of Forest, FRC KSC, Siberian Branch
of the Russian Academy of Sciences, Krasnoyarsk, Russia. 140Forest Ecology and Forest
Management Group, Wageningen University and Research, Wageningen, The Netherlands.
141Department of Ecology and Environmental Sciences, Pondicherry University, Puducherry,
India. 142Instituto Nacional de Tecnología Agropecuaria (INTA), Universidad Nacional dela
Patagonia Austral (UNPA), Consejo Nacional de Investigaciones Cientíicas y Tecnicas
(CONICET), Río Gallegos, Argentina. 143School of Social Sciences (Urban Studies), Western
Sydney University, Penrith, New South Wales, Australia. 144Forestry Department, Food and
Agriculture Organization of the United Nations, Rome, Italy. 145Instituto Nacional de Pesquisas
da Amazônia, Manaus, Brazil. 146Laboratório de Dendrologia e Silvicultura Tropical, Centro de
Formação em Ciências Agrolorestais, Universidade Federal do Sul da Bahia, Itabuna, Brazil.
147Jardín Botánico de Medellín, Medellin, Colombia. 148Chair for Forest Growth and Yield
Science, TUM School for Life Sciences, Technical University of Munich, Munich, Germany.
149Universidad Nacional dela Amazonía Peruana, Iquitos, Peru. 150Servicios Ecosistémicos y
Cambio Climático (SECC), Fundación Con Vida & Corporación COL-TREE, Medellín, Colombia.
151Centro Agricoltura, Alimenti, Ambiente, University of Trento, San Michele All’adige, Italy.
152Department of Biological Sciences, Boise State University, Boise, ID, USA. 153Department of
Biology, University of Florence, Florence, Italy. 154Tropical Biodiversity, MUSE–Museo delle
Scienze, Trento, Italy. 155Info Flora, Geneva, Switzerland. 156Department of Environmental
Sciences, Central University of Jharkhand, Ranchi, Jharkhand, India. 157Centro de Modelación y
Monitoreo de Ecosistemas, Universidad Mayor, Santiago, Chile. 158Vicerrectoria de Investigacion
y Postgrado, Universidad de La Frontera, Temuco, Chile. 159Depto. de Silvicultura y Conservacion
dela Naturaleza, Universidad de Chile, Temuco, Chile. 160Datascientist.ch, Wallisellen,
Switzerland. 161Siberian Federal University, Krasnoyarsk Russian Federation, Krasnoyarsk,
Russia. 162Geobotany, Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany.
163National Forest Centre, Forest Research Institute Zvolen, Zvolen, Slovakia. 164Université de
Lorraine, AgroParisTech, INRAE, Silva, Nancy, France. 165Center for Ecological Dynamics in a
Novel Biosphere (ECONOVO) and Center for Biodiversity Dynamics in a Changing World
(BIOCHANGE), Department of Biology, Aarhus University, Aarhus, Denmark. 166Forest Ecology
and Forest Management, Wageningen University and Research, Wageningen, The Netherlands.
167Faculty of Environmental Sciences and Natural Resource Management, Norwegian University
of Life Sciences, Ås, Norway. 168Departamento de Biología, Universidad dela Serena,
La Serena, Chile. 169Centro de Ciências Biológicas e da Natureza, Universidade Federal do Acre,
Rio Branco, Acre, Brazil. 170Guyana Forestry Commission, Georgetown, France. 171Environmental
and Life Sciences, Faculty of Science, Universiti Brunei Darussalam, Bandar Seri Begawan,
Brunei. 172Plant Systematic and Ecology Laboratory, Department of Biology, Higher Teachers’
Training College, University of Yaoundé I, Yaoundé, Cameroon. 173Departamento de Ecologia,
Universidade Federal do Rio Grande do Norte, Natal, Rio Grande do Norte, Brazil. 174Warsaw
University of Life Sciences, Warsaw, Poland. 175Section for Ecoinformatics & Biodiversity,
Department of Biology, Aarhus University, Aarhus, Denmark. 176Faculty of Forestry and Wood
Sciences, Czech University of Life Sciences, Prague, Czech Republic. 177Wildlife Conservation
Society, Vientiane, Laos. 178Quantitative Biodiversity Dynamics, Betafaculty, Utrecht University,
Utrecht, The Netherlands. 179Iwokrama International Centre for Rainforest Conservation and
Development (IIC), Georgetown, Guyana. 180School of Forestry and Environmental Studies,
Yale University, New Haven, CT, USA. 181Botanical Garden of Ural Branch of Russian Academy
of Sciences, Ural State Forest Engineering University, Yekaterinburg, Russia. 182Pontiicia
Universidad Católica del Ecuador, Quito, Ecuador. 183LINCGlobal, Museo Nacional de Ciencias
Naturales, CSIC, Madrid, Spain. 184Plant Ecology and Nature Conservation Group, Wageningen
University, Wageningen, The Netherlands. 185Silviculture Research Institute, Vietnamese
Academy of Forest Sciences, Hanoi, Vietnam. 186Department of Biology, Stanford University,
Stanford, CA, USA. 187Centre for the Research and Technology of Agro-Environmental and
Biological Sciences, CITAB, University of Trás-os-Montes and Alto Douro, UTAD, Viseu, Portugal.
188Department of Ecology and Sustainable Agriculture, Agricultural High School, Polytechnic
Institute of Viseu, Viseu, Portugal. 189Department of Forest Engineering Universidade Regional
de Blumenau, Blumenau, Brazil. 190Environmental Studies and Research Center, University
of Campinas, UNICAMP, Campinas, Brazil. 191Department of Forest and Wood Science,
University of Stellenbosch, Stellenbosch, South Africa. 192Key Laboratory of Tropical Biological
Resources, Ministry of Education, School of Life and Pharmaceutical Sciences, Hainan
University, Haikou, China. 193Division of Forestry and Natural Resources, West Virginia University,
Morgantown, WV, USA. 194Department of Zoology, University of Oxford, Oxford, UK. 195Manaaki
Whenua–Landcare Research, Lincoln, New Zealand. 196Department of Wetland Ecology,
Institute for Geography and Geoecology, Karlsruhe Institute for Technology, Karlsruhe,
Germany. 197Independent Researcher, Bad Aussee, Austria. 198Centre for Agricultural Research
in Suriname (CELOS), Paramaribo, Suriname. 199Tropenbos International, Wageningen,
The Netherlands. 200Polish State Forests, Coordination Center for Environmental Projects,
Warsaw, Poland. 201Research Center of Forest Management Engineering of State Forestry
and Grassland Administration, Beijing Forestry University, Beijing, China. 202Department
of Genetics, Evolution, and Environment, University College London, London, UK.
e-mail: Cam il le .d el av au x@ us ys .ethz.ch
Content courtesy of Springer Nature, terms of use apply. Rights reserved
Article
Methods
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, contain-
ing 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 World
70
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 represent-
ing 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 pref-
erentially 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’. Specif ically, 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 species
74,75
.
The metric of relative introduced species richness may be hypoth-
esized 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 bino-
mial 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 Composite
76,77
. 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 distur-
bance78,79. Climate variables were extracted from the CHELSA (clima-
tologies 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 ports
81
and airports
82
. All layers were standardized to a
30 arcsec resolution (~1 km
2
at the equator), a resolution at which these
variables have been shown to have an influence on plant biogeography
and assembly patterns
83,84
. 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 trans-
forming 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 ports
81
(hereafter ‘ports’) and human population
density
85
. For the global models, we used the following additional envi-
ronmental 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 func-
tional similarity across species within a community assemblage. Yet,
congruency between these two metrics remains under debate
86,87
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 space
88
, 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).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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 best
24,90
. 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 trans-
formed to increasingly larger numbers, with higher MNTD values indi-
cating 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 commu-
nity 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 repre-
sented the major clusters of functional trait diversity, thereby cap-
turing 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 esti-
mate 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
species
91
. 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 Gaston
93
. 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 phylo-
genetic 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 phylogeneti-
cally 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 forest
100
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 inva-
sion, 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 anthropo-
genic 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 resistance
23,34,35
. Finally, we used an
adapted version of the random forest models, removing diversity vari-
ables, 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 regres-
sion 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 correlelo-
gram plots using the ncf
103
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 pres-
ence). 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 autocorrela-
tion correction, into a variable that is included in each model
101,102
. 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 impor-
tance and influence. Specifically, we usedShapley additive explanations
(SHAP) values to infer variable importance in the model outcome
105,106
.
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
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Article
values, random forest models were fit in R using the ranger package
107
,
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 package
108
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 measures
109
. 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 remain-
ing 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 VEcv
110
), 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 scaling
111,112
. 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 lme4
116
, lmerTest
117
, and betareg
118
, 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) database
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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
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Acknowledgements The authors acknowledge the Bernina Foundation and DOB Ecology
for inancial 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 16549). 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.
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Article
Extended Data Fig. 1 | Map of non-native invasion probability. Map showing
probabilit y of non-native tree p resence base d on the probabilit y output
of the random for est classif ier (A, total n = 36 8,030plots, n per ite ration =
10,000plots) along side maps showin g uncertain ty in predict ions (B) including
local unc ertainty o f invasion probabil ity via boot strapped co efficie nt of
variation (i) an d extent of extrap olation as perc entage of bands o utside
univariate (ii) a nd multivariate (ii) t raining range. Re gions outsi de the Area of
Applicab ility are indica ted with dots .
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Extende d Data Fig. 2 | Ma p of non-native inva sion probab ility insi de the
area of applicability. Map showing probabili ty of non-native tr ee presence
based on th e probability ou tput of the rando m forest classif ier (A, total
n = 368,030plot s, n per iteratio n = 10,000plots) alongsid e maps showing
uncert ainty in predic tions (B) includin g local uncer tainty of inva sion
probabilit y via boots trapped coef ficien t of variation (i) and ext ent of
extrapola tion as percen tage of bands out side univariat e (ii) and multivariat e
(ii) training ran ge. Regions ou tside the Area o f Applicabilit y are masked.
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Extende d Data Fig. 3 | Me an nearest t axon distanc e (MNTD). Mean n earest
taxon dist ance is the average di stance to nea rest neighb or by branch leng th on
the tree, whi ch represent s redundancy in t he community ( A). For each specie s i,
the sum of all shor test dist ances d to each ot her taxa j is cal culated; these v alues
are then average d across the tot al species in th e tree (N). If invasion o ccurs via
non-native s being similar to th e native communit y, this would lead to the
expect ation that MNT D decrease s, increasing re dundancy (B). Convers ely,
if non-native inva sion occurs v ia non-natives be ing dissimilar to t he native
communit y, this would lead to the exp ectation t hat MNTD incre ases, reduci ng
redundanc y (C). Taxon D represen ts a non-native add ition to the comm unity.
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Extende d Data Fig. 4 | Nat ive diversity m ediates de gree of non- native
invasion. Variable impor tance (SHAP val ues) of all variables i ncluded in
random fores t models, ordere d from greates t to least impor tance along side
influe nce of distan ce to ports, na tive richness a nd native redunda ncy on
invasion sever ity (propor tion of non-native p lant species) for (A) phyl ogenetic
diversity a nd (B) functional d iversity glob al models (phyloge netic n = 3,498
plots; func tional n = 3,3 68plots). All results s hown are from rando m forest
models. N ote that y-axis ran ges differ among p anels, with th e variable
importance plots represent ing the corresponding magnitude.
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Article
Extende d Data Fig. 5 | Varia ble impor tance for non- native invasion s trategy.
Variable impor tance (SHA P values) of all variabl es included in ran dom forest
models, ord ered from great est to least imp ortance al ongside inf luence of
native richness, mean annual temperature and mean annual precipitation on
invasion str ategy for (A) phylo genetic diversi ty and (B) functi onal diversity
global mod els (phylogenet ic n = 3,498plots; functio nal n = 3,368plots).
All result s shown are from rand om forest models . Note that y-axis ran ges
differ amon g panels, with th e variable impor tance plots re presenting th e
corresponding magnitude. Error bands represent 95% confidence intervals.
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Extende d Data Fig. 6 | Varia ble import ance for analys es using da ta
down-sampled without preferentially retaining invaded plots. Variable
import ance (SHAP value s) for all variables incl uded in random fore st models,
ordered from g reatest to lea st importa nce for (A) non-native pre sence,
(B) richness , and (C) abundance, eac h for (i) phylogenetic diver sity and (ii)
functio nal diversity gl obal models (pre sence: phylogen etic n = 18,898;
functio nal n = 18,611, rich ness: phylogen etic n = 840plots; fu nctional
n = 823plots, ab undance: phylo genetic n = 840plo ts; function al n = 823plots).
All result s shown are from ran dom forest model s with down-sam pled data, but
without pre ferentially ret aining invaded pl ots. Error band s represent 95%
confidence intervals.
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Data analysis All code used to complete analyses for the manuscript is available at the following link: https://github.com/c383d893/GlobalInvasion.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),
MuMIn (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 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
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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.
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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? Yes No
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... Invasions by tree species have been relatively overlooked compared to those of herbaceous plants [1]. Furthermore, invasive woody plants are commonly characterized by trait strategies associated with high resource demands and site disturbances. ...
... However, using the terms wood anatomy, tree growth, tree rings, or invader, the search results were less than 10 publications. Eriobotrya japonica has been mentioned as an invader with a low occurrence frequency in a few localities (e.g., 5 out of 22,994 invaded plots worldwide [1]; recorded in one out of nine islands in the Caribbean [19]). ...
... This enables the species to adopt a "sit-and-wait" strategy, persisting under the low-light conditions of undisturbed, closed-canopy forests and rapidly exploiting disturbances to establish dominance [5]. As many tree invasions remain in early stages, with significant "invasion debts" linked to recent plantings or horticultural escapes [1,7], studying E. japonica during this critical phase of forest invasiveness provides essential insights into invasion mechanisms and informs the development of preventive management strategies to protect the regional cloud forest. Funding: This research received no external funding. ...
Article
Full-text available
The presence of shade-tolerant tree invaders has been recently noted in tropical and temperate forest understories. Maximum growth rate is an important trait for exotic trees becoming invaders in a forest. This study aimed to determine the growth rate of Eriobotrya japonica in a secondary cloud forest in central Veracruz, Mexico. The objectives of this study were to determine wood density, tree ring boundaries and number, and their relationship to diameter at breast height (DBH) and climate data. Tree ring counts were obtained using Python-based software with subsequent visual validation. Growth rates were measured using diametric tape, dendrometric bands, and the pinning method. The number of rings increased with DBH, presenting values ranging from 14 to 27. Tree rings were delimited by fibers that were five times narrower in the ring limit zone than in the intra-ring zone. The growth ring delimitation zones were formed when vascular cambium activity stalled during the relatively dry-cold season (January–February). The growth rate of E. japonica was statistically similar (ca. 0.2 mm yr−1) regardless of the method employed to measure it. Relative growth rate was low (0.02 cm cm−1 yr−1). Wood density (0.76 g cm−3) values lay within the upper values for mature forest trees. Eriobotrya japonica is a potential invader, with traits such as high wood density and a relatively low growth rate, which are characteristic of the shade-tolerant tree species.
... Numerical studies have demonstrated that more phylogenetically and/or functionally diverse communities are less invaded (e.g., Byun et al., 2020;Delavaux et al., 2023), while others have suggested that an increase in certain functional traits can resist plant invasion (e.g., Ernst et al., 2022;Li et al., 2022). ...
... We focus on two main questions: (a) how do the performance metrics of invasive plants (i.e., coverage, relative coverage, and relative richness) change with local community structure, macroclimate, soil nutrients, and human activities? Our expectations are: (1) native plant community structure factors may limit (Rossignaud et al., 2022;Delavaux et al., 2023;Rossignaud & Hulme, 2023). ...
... We calculated relative richness as the invasive-plant richness divided by total herbaceous plant richness in each 30 × 30 m plot. These three invasive metrics reflect the invasive severity in terms of invasive-plant richness and coverage (Guo et al., 2015;Delavaux et al., 2023). To model the occurrence of invasive plants, we used binomial distribution of the relative richness. ...
Article
Question Understanding the factors influencing plant invasions is essential for effective prevention and control actions. However, the relative importance of the biotic resistance, resource availability, and propagule pressures on invasive plants in fragmented grasslands of humid and semi‐humid regions remains unclear. Locations Shandong Province, eastern coastal China. Method This study is based on community composition and soil nutrient data from 42 grassland plots of 30 × 30 m surveyed between 2021 and 2022. In each plot, we sampled six sub‐plots of 2 × 2 m, totaling 24 m ² . We used beta regressions and general linear models to examine the coverage, relative coverage and relative richness of invasive plants in relation to community structure (such as richness and coverage of native shrubs, species richness, phylogenetic diversity and functional traits of native herbaceous plant), macroclimate, soil nutrients and human activities. Partial regressions and Random Forest analyses were used to assess the relative importance of different predictors. Results The coverage, relative coverage and relative richness of invasive plants decreased with the richness and coverage of shrubs, but increased with phylogenetic diversity of native herbaceous plant. Community structure factors, particularly shrub richness and coverage, exhibited higher relative importance on invasive‐plant performance compared to other variables. Conclusions Our results indicate that diversity–invasibility relationships are sensitive to the choice of diversity index. Overall, native shrubs within the local community play a crucial role in buffering against the spread and establishment of invasive plants in humid grasslands, underscoring the significance of biotic resistance in plant invasion.
... Additionally, canopy height plays a pivotal role in characterizing habitat structural heterogeneity as an important factor in explaining biodiversity spatial patterns Marselis et al., 2022;Torresani et al., 2023). Endemic forests represent one of the global biodiversity hotspots and must-preserved ecosystems (Delavaux et al., 2023), but climate change and human pressure are jeopardizing the capability of species to adapt fast enough to resist disturbances due to stand replacement or prolonged heat waves (Anderegg et al., 2015;Hartmann et al., 2018). In the Mediterranean basin, the landscape is undergoing transformations driven by droughts, extreme heat episodes and increasingly recurrent wildfires, impacting carbon fluxes and threatening the habitats of endemic species (Grünig et al., 2023;Moreira et al., 2011;Ruffault et al., 2020). ...
... The lowest level of plant invasion was associated with greater native canopy cover and this is consistent with previous studies that focussed on the role of tree richness or density on non-native richness (Delavaux et al. 2023;Ibanez et al. 2019;Rossignaud et al. 2022). These results support the importance of native canopy cover in the resistance of forest habitats to plant invasions. ...
Article
Full-text available
Aim Identifying habitats vulnerable to plant invasions is essential for developing efficient management programmes. We assessed trends in richness and cover of non‐native plants in indigenous shrublands and forests across New Zealand. We investigated whether species classed as invasive species exhibited higher levels of plant invasion than naturalised species and the extent to which this reflected plant life form. Location New Zealand. Time Period From January 2009 to March 2014. Major Taxa Studied Plant. Methods We analysed 839 permanent 20 × 20 m plots spread across New Zealand that could be classified to a recognised forest type: mānuka‐kānuka shrubland, beech, beech‐broadleaved, beech‐broadleaved‐podocarp and broadleaved‐podocarp forests. Generalised additive models were run with native canopy richness or cover and spatial coordinates as co‐variables in order to compare non‐native plant richness and cover across forest types in relation to their invasive status and growth form. Results Overall, 35% of the plots had at least one non‐native species. Mānuka‐kānuka shrubland exhibited the highest mean non‐native richness (11 species) and cover (32%) with broadleaved‐podocarp forest presenting the next highest invasion level but to a much lesser extent (1.7 species and 3% cover). Despite presenting overall greater non‐native richness, naturalised species had lower cover than invasive species (4.2%, 13.5%, respectively). This pattern was mainly related to non‐native woody species that, despite their low richness, can reach greater cover than herbaceous species once established. Main Conclusions Despite half the New Zealand flora being composed of non‐native plant species, relatively few were found in forest habitats. However, indigenous shrubland and early successional forests showed higher vulnerability to plant invasion. Woody species, which are overrepresented among invasive species, had higher cover than herbaceous species and were less limited by native canopy cover. Such findings highlight the threat posed by non‐native woody species and the need for more targeted management programmes.
... These traits allow it to not only colonise clearings but also persist after plantations replace native forests because it can exhibit some shade tolerance (Guerra et al., 2010;Salgado-Luarte and Gianoli, 2012). This rapid establishment and fast growth of A. chilensis would align with the concept of the "pre-emptive resource effect"a mechanism where early colonising native species can outcompete invasive plants by monopolising essential resources (Byun et al., 2013;Byun and Lee, 2017;Delavaux et al., 2023). Additionally, studies suggest that P. radiata, being a shade-intolerant species, might struggle to establish into a darker understory dominated by A. chilensis and other native species (Goḿez et al., 2019;Becerra and Simonetti, 2020). ...
Article
Full-text available
Coastal Maulino Forest, a biodiversity hotspot, is increasingly threatened by frequent and higher-severity wildfires. Endangered tree species, including Nothofagus spp., inhabit small, isolated native forest fragments surrounded by extensive Pinus radiata plantations, a non-native species that often colonises fire-affected areas. However, the seedling density of the native Chilean wineberry, Aristotelia chilensis, negatively correlates with the abundance of P. radiata seedlings in post-fire areas. This pattern emerged across areas burned at varying fire severities, sampled 8 and 24 months following the 2017 “Las Máquinas” megafire in Chile. The high proportion of plots lacking P. radiata seedlings, coupled with this negative relationship, suggests that A. chilensis may play a role in limiting P. radiata invasion. The negative relationship was most pronounced in areas with moderate fire severity, likely reflecting differences in shade tolerance between the species. While A. chilensis, a light-demanding species with some shade tolerance, can persist in partially shaded environments, P. radiata, a strictly light-demanding species, struggles under significant shade. In low-severity areas, no significant relationship was observed since the substantial native canopy remaining likely limits P. radiata establishment by shading. Conversely, in high-severity fire areas, the absence of a significant relationship might result from the detrimental effects on both species, including potential microbiome dependence for A. chilensis. Given the successful establishment of A. chilensis at low fire severity, enhancing its post-fire recruitment, particularly in moderately burned areas, could be a valuable strategy for mitigating P. radiata invasion and restoring fire-affected Mediterranean ecosystems.
... The interactions between global change drivers such as habitat fragmentation, species invasion and climate warming demonstrably impact tree growth (Bradley and Pregitzer 2007;Pretzsch et al. 2014;Delavaux et al. 2023), and these Communicated by Sebastian Seibold. same forces interactively influence wood decomposition Edman et al. 2021;Seibold et al. 2021). ...
Article
Full-text available
Global change drivers such as habitat fragmentation, species invasion, and climate warming can act synergistically upon native systems; however, global change drivers can be neutralized if they induce antagonistic interactions in ecological communities. Deadwood comprises a considerable portion of forest carbon, and it functions as refuge, nesting habitat and nutrient source for plant, animal and microbial communities. We predicted that thermophilic termites would increase wood decomposition with experimental warming and in forest edge habitat. Alternately, given that predatory ants also are thermophilic, they might limit termite-mediated decomposition regardless of warming. In addition, we predicted that a non-native, putative termite-specialist ant species would decrease termite activity, and consequently decomposition, when replacing native ants. We tested these hypotheses using experimental warming plots (~ 2.5 °C above ambient) where termites, and their ant predators, have full access and vary in abundance at microscales. We found that termite activity was the strongest control on decomposition of field wood assays, with mass loss increasing 20% with each doubling of termite activity. However, both native and non-native ant abundance increased with experimental warming and, in turn, appeared to equally limit termite activity and, consequently, reduced wood decomposition rates. As a result, experimental warming had little net effect on the decomposition rates—likely because, although termite activity increased somewhat in warmed plots, ant abundances increased more than five times as much. Our results suggest that, in temperate southern U.S. forests, the negative top-down effects of predatory ants on termites outweighed the potential positive influences of warming on termite-driven wood decomposition rates.
... Upward shifts in elevation are frequently impacted by trailing-edge extirpations (Wiens, 2016). Most North American species are influenced by the human footprint index (hfp), consistent with findings on other tree species (Delavaux et al., 2023). Conversely, precipitation of wettest month (bio13) significantly affected the distribution of East Asian Carya species (C. ...
Article
Full-text available
The present study determined the current status of understorey invasive plant species (IPSs) and their impact on the forest composition and regeneration of Palamau Tiger Reserve, Eastern India. Tree diversity was sampled in 63 random belt transects (0.50 ha each), while shrubs and herbs in 9 random quadrats of 5 m ×\:\times\: 5 m and 1 m ×\:\times\: 1 m, respectively. A sum of 177 plant species were recorded, of which 18 were IPSs with the predominance of the members of the family Asteraceae (7 spp.). In the shrub layer, IPSs contributed the highest density (~ 52% of the overall density), and their contribution in the herb layer (~ 30%) was also very promising. On the other hand, 2% of the forest patches in the shrub and 3% in the herb layer, showed equal distributions of tree saplings and seedlings with IPSs. In contrast, the majority showed an inversely proportional relationship. In the shrub layer, the diversity index and disturbance index revealed an extremely weak but highly significant negative association (R² = 0.14; p < 0.01), while in the herb layer, the correlation was extremely weak and insignificant (R² = 0.001; p = 0.78). Increased IPSs density poses an alarming threat to the growth and regeneration of tree saplings and seedlings as well as the understorey plant diversity. Therefore, there is an urgent need for policy intervention and also to manage and control IPSs to enhance the population and regeneration of important plant species of both economic and ecological significance to achieve the United Nations’ sustainable development goal (SDG)-15.
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Full-text available
Recent extreme weather conditions in Europe have led to widespread destruction of Norway spruce by storms and bark beetles, creating large clearings that need replanting. The shortage of planting material has shifted focus to natural regeneration processes, with Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) emerging as a potential substitute due to its growth performance and drought tolerance. This study introduces and applies methods for investigating the regeneration ecology of Douglas-fir, focusing on the potential density of established regeneration and its dependence on the distance to the nearest seed source. This dependence is modelled with various classical spatial dispersal kernels, the parameters of which are estimated with a quantile regression approach implemented in a new R package quaxnat. Regeneration data from 44,257 sample plots in the state forest of Lower Saxony, Germany, are combined with remote sensing-based positions of potential seed trees to illustrate these methods. Among the standard dispersal kernels provided by quaxnat, the spatial t distribution proves to be the most suitable. Here, for the .999th quantile, the estimated potential regeneration density reaches almost 11,000 trees per hectare in the immediate vicinity of the seed trees and decreases sharply with increasing distance. A simple simulation model that takes dispersal and establishment into account illustrates how these results can be linked to management scenarios. The study provides valuable information for nature conservation and silviculture, suggesting buffer zones around sensitive habitats and guiding forest management decisions regarding natural regeneration options.
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Full-text available
Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.
Article
Full-text available
Data on tropical forests are in high demand. But ground forest measurements are hard to sustain and the people who make them are extremely disadvantaged compared to those who use them. We propose a new approach to forest data that focuses on the needs of data originators, and ensures users and funders contribute properly.