Mountain roads and non-native species 
modify elevational patterns of plant diversity
Authors: Sylvia Haider, Christoph Kueffer, Helge 
Bruelheide, Tim Seipel, Jake M. Alexander, Lisa J. 
Rew, Jose Ramon Arevalo, Lohengrin A. Cavieres, 
Keith L. McDougall, Ann Milbau, Bridgett J. Naylor, 
Karina Speziale, and Anibal Pauchard
This is the peer reviewed version of the following article: see full citation below, which has been 
published in final form at https://dx.doi.org/10.1111/geb.12727. This article may be used for non-
commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Haider, Sylvia, Christoph Kueffer, Helge Bruelheide, Tim Seipel, Jake M. Alexander, Lisa J. 
Rew, Jose Ramon Arevalo, Lohengrin A. Cavieres, Keith L. McDougall, Ann Milbau, Bridgett J. 
Naylor, Karina Speziale, and Anibal Pauchard. "Mountain roads and non-native species modify 
elevational patterns of plant diversity." Global Ecology & Biogeography 27, no. 6 (June 2018): 
667-678. DOI:10.1111/geb.12727.
Made available through Montana State University’s ScholarWorks 
scholarworks.montana.edu 
Mountain roads and non-native species modify elevational
patterns of plant diversity
Sylvia Haider1,2 | Christoph Kueffer3,4 | Helge Bruelheide1,2 | Tim Seipel5 |
Jake M. Alexander6 | Lisa J. Rew5 | Jos
e Ram
on Ar
evalo7 |
Lohengrin A. Cavieres8,9 | Keith L. McDougall10,11 | Ann Milbau12 |
Bridgett J. Naylor13 | Karina Speziale14 | Aníbal Pauchard15,9
1Institute of Biology/Geobotany and Botanical Garden, Martin Luther University Halle-Wittenberg, Halle, Germany
2German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
3Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland
4Department of Botany and Zoology, Centre for Invasion Biology, Stellenbosch University, Matieland, South Africa
5Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana
6Department of Ecology and Evolution, University of Lausanne, Lausanne, Switzerland
7Department of Botany, Ecology and Plant Physiology, University of La Laguna, La Laguna, Tenerife, Spain
8Departamento de Bot
anica, Facultad de Ciencias Naturales y Oceanogr
aficas, Universidad de Concepci
on, Concepci
on, Chile
9Institute of Ecology and Biodiversity (IEB), Concepci
on, Chile
10National Parks and Wildlife Service, Queanbeyan, New South Wales, Australia
11Department of Ecology, Environment and Evolution, La Trobe University, Wodonga, Victoria, Australia
12Research Institute for Nature and Forest (INBO), Brussels, Belgium
13US Forest Service, PNW Research Station, Forestry and Range Sciences Lab, La Grande, Oregon
14Laboratorio Ecotono, INIBIOMA (CONICET-Universidad Nacional del Comahue), Río Negro, Argentina
15Laboratorio de Invasiones Biol
ogicas, Facultad de Ciencias Forestales, Universidad de Concepci
on, Concepci
on, Chile
Abstract
Aim: We investigated patterns of species richness and community dissimilarity along elevation
gradients using globally replicated, standardized surveys of vascular plants. We asked how these
patterns of diversity are influenced by anthropogenic pressures (road construction and non-native
species).
Location: Global.
Time period: 2008–2015.
Major taxa studied: Vascular plants.
Methods: Native and non-native vascular plant species were recorded in 943 plots along 25 eleva-
tion gradients, in nine mountain regions, on four continents. Sampling took place in plots along and
away from roads. We analysed the effects of elevation and distance from road on species richness
patterns and community dissimilarity (beta-diversity), and assessed how non-native species modi-
fied such elevational diversity patterns.
Results: Globally, native and total species richness showed a unimodal relationship with elevation
that peaked at lower-mid elevations, but these patterns were altered along roads and due to
non-native species. Differences in elevational species richness patterns between regions
disappeared along roadsides, and non-native species changed the patterns’ character in all study
regions. Community dissimilarity was reduced along roadsides and through non-native species. We
also found a significant elevational decay of beta-diversity, which however was not affected by
roads or non-native species.
Main conclusions: Idiosyncratic native species richness patterns in plots away from roads
implicate region-specific mechanisms underlying these patterns. However, along roadsides a
clearer elevational signal emerged and species richness mostly peaked at mid-elevations. We
conclude that both roads and non-native species lead to a homogenization of species richness pat-
terns and plant communities in mountains.
K E YWORD S
alien, altitude, beta-diversity, elevational decay, exotic, homogenization, hump-shaped pattern,
roadsides, species replacement, species turnover
1 | INTRODUCTION
In the last two decades, research into elevational species richness pat-
terns has gained renewed attention, and in a large number of regional
studies (e.g. Grytnes, Beaman, Romdal, & Rahbek, 2008; Siefert, Lesser,
& Fridley, 2015) and several global meta-analyses (e.g. Guo et al., 2013;
Steinbauer et al., 2016) a variety of different patterns have been
observed. The different patterns could result from a range of different
drivers (see e.g. Romdal & Grytnes, 2007 and references therein;
McCain & Grytnes, 2010), but also from differences in survey
protocols.
Unimodal patterns of species richness along elevation gradients
have often been detected around the world (Rahbek, 2005). Mid-
elevational peaks in species richness could result from geometrical con-
straints on the random placement of species’ ranges within a bounded
elevational domain, causing greatest overlap of species’ ranges at the
centre of the gradient (known as the mid-domain effect; Colwell &
Lees, 2000; Grytnes et al., 2008). But such a pattern could also have
biotic explanations, for example if fewer environmental specialists
occur towards the extremes of the gradient (Kammer & M€ohl, 2002).
Beta-diversity, that is community dissimilarity, is expected to
increase with elevational distance between sampling units because
increasing environmental (e.g. climatic) differences select for different
suites of species, and because geographic distance imposes dispersal
constraints to species exchange (distance decay; Nekola & White,
1999). Few studies based on empirical data have simultaneously
addressed the response of species richness and community dissimilarity
to environmental variation across large geographic extents (e.g. Vald
es
et al., 2015). Moreover, neither have elevation gradients been
considered.
Historically, biodiversity patterns have been predominantly shaped
by natural factors such as climatic gradients, but natural environments
are now intensely modified by human activities (Sanderson et al.,
2002). Increasing evidence indicates that mountain biodiversity is
affected by climate change (Gottfried et al., 2012; Lenoir & Svenning,
2013), human land use (Dainese & Poldini, 2012) and non-native spe-
cies invasion (Marini et al., 2012; Pauchard et al., 2009, 2016; Pysek,
Jarosík, Pergl, & Wild, 2011; van Kleunen et al., 2015). A feature of
many mountains is that they have roads that connect lowlands with
high elevation sites. Compared to semi-natural habitats more distant to
roads, the habitat directly next to the road is often characterized by
high rates of disturbance and reduced competition (Forman &
Alexander, 1998; Spellerberg, 1998), plus increased propagule pressure
and potential for long-distance dispersal by vehicles (Rew et al., 2018;
Taylor, Brummer, Taper, Wing, & Rew, 2012; von der Lippe & Kowarik,
2007). Such habitats also have more homogenous abiotic conditions as
a result of similar construction techniques and management practices
(e.g. mowing). As a result, species have been shown to have larger
elevational ranges along roadsides compared to adjacent semi-natural
habitats (Lembrechts et al., 2017; Pollnac, Seipel, Repath, & Rew,
2012).
Contrary to native species, non-native plant richness has been
shown to consistently decrease with elevation, at least from the lowest
third of the gradient upwards (Haider et al., 2010; Seipel et al., 2012;
Tanaka & Sato, 2016; Zhang et al., 2015). This pattern is likely caused
by range expansion from low to high elevations, coupled with ecologi-
cal filtering on a pool of predominantly low-elevation non-native
species (Alexander et al., 2011). Thus, non-native species at high eleva-
tions are a subset of the low-elevation non-native species pool (Haider
et al., 2010; Alexander et al., 2011; Averett et al., 2016). We expect
non-native species to increase similarity between the communities
they invade across elevation gradients, because they tend to be
environmental generalists (Alexander et al., 2011) and typically have
good dispersal abilities (Vicente et al., 2014).
We conducted, to our knowledge, the first multi-region survey of
native and non-native plants along elevation gradients that is based on
a systematic design in a multi-scale hierarchical framework (Kueffer
et al., 2014). Our standardized approach allows us to directly compare
elevational species richness patterns of vascular plants across regions,
and to test how elevation affects differences in the species composi-
tion of plant communities. We simultaneously addressed the effects of
roads, non-native plant species and their interaction on diversity pat-
terns across nine mountain regions and tested the following
hypotheses:
1. Native species richness peaks at mid-elevations. More specifically,
we expect the unimodal pattern to be clearer along roadsides
compared to plots away from roads, because roadsides enable
higher migration rates (stronger range overlap) and have more
homogenous environmental conditions.
2. Non-native species shift the peak of species richness to lower
elevations because they predominantly spread from low to high
elevation: we expect the effect to be stronger along roadsides.
3. Community dissimilarity is reduced by non-native species,
especially along roadsides.
4. The decline in community dissimilarity in response to elevational
distance is more gradual along roadsides and when non-native
species are included in the analysis.
2 | METHODS
2.1 | Study regions
Standardized vegetation surveys were conducted in nine regions:
Norway (northern Scandes), Switzerland (European Alps; Canton
Valais), Canary Islands (Mount Teide; Tenerife), Montana (Beartooth
Mountains and Yellowstone National Park), and Oregon (Blue
Mountains) in the northern hemisphere, and Australia (Australian
Alps; New South Wales), central Chile and southern Chile (both
Andes), and southern Argentina (southern Andes) in the southern
hemisphere. The study regions comprise a range of climatic zones,
stretching from a subarctic to Mediterranean climate (Table 1, Sup-
porting Information Figure S1). In all regions three roads were
selected (except for central Chile with only one road) along which the
vegetation sampling took place, giving a total of 25 roads. Most of
the roads started in lowland agricultural settings, followed by an
extensive forested montane zone. In five regions, the elevation gradi-
ent reached beyond the tree line and thus into alpine vegetation
(Table 1). Roads were both paved and unpaved, but all were open to
vehicular traffic at least part of the year.
2.2 | Sampling design
The elevation gradient of each road was divided into 19 equally spaced
elevation bands, giving 20 sample locations (‘transects’; see also Seipel
et al., 2012). Exceptions were Switzerland where roads were divided
into 20 bands, and central Chile with only 12 bands. (As the elevation
gradients differed in length, a standardized elevational distance
between transects would have resulted in large differences in the num-
ber of sample locations per road and region, and was thus not applied.)
At each elevation, 50 m 3 2 m plots were sampled. One plot was paral-
lel and next to the road (hereafter ‘roadside’). Another 50 m 3 2 m plot
was placed perpendicular to the roadside plot in semi-natural habitat,
and started 50 m, and ended 100 m, away from the road (hereafter
‘interior’). Due to natural obstacles (like canyons or very steep slopes)
not all plots could be established. Particularly in Switzerland,
rough topography and private property adjacent to the roads often
prevented sampling in the interior plots. Overall, 943 plots were
sampled (Table 1).
In each plot we recorded the presence of all vascular plant species.
Species names across regions were standardized with the Taxonomic
Name Resolution Service (Boyle et al., 2013). We assigned the status
of native or non-native at a regional scale for all species, based on
regional databases and literature (Supporting Information Table S1).
2.3 | Diversity measures
Species richness represents the number of species recorded in a plot
and was calculated for native, non-native, and native and non-native
species combined (hereafter ‘all’). To assess the change in community
dissimilarity between plots (beta-diversity), we applied the framework
of Carvalho, Cardoso, and Gomes (2012), which divides total beta-
diversity into two additive components (species richness differences
and species replacement) and thus allows insights into ecological proc-
esses shaping a change of total beta-diversity (Carvalho et al., 2012;
Marini et al., 2013; Podani & Schmera, 2011).
For all pairwise comparisons of species composition among plots
that were located within the same plot type (interior or roadside) within
TABLE 1 Location and climatic conditions of the nine study regions (Abbr.5 abbreviation, Lat.5 latitude, Long.5 longitude), vertical range of
the sampled elevation gradient, elevation of the tree line, and number of recorded interior and roadside plots
Region Abbr. Mountain(s) Lat. Long.
Climatic
zone
Elevation
gradient
(m a.s.l.)
Gradient
length (m)
Tree line
(m a.s.l.)
Recorded
plots interior/
roadside
Northern hemisphere:
Norway NOR Northern Scandes 68.247 17.654 Subarctic 13–696 683 600 60/60
Switzerland CH European Alps (Canton Valais) 46.263 7.509 Temperate 415–1800 1385 2200 28/63
Canary Islands IC Mount Teide (Tenerife) 28.250 216.603 Mediterranean 5–2250 2245 1900 53/55
Montana MT Beartooth Mountains,
Yellowstone National Park
44.777 2110.196 Temperate 1803–3315 1512 3000 60/60
Oregon OR Blue Mountains 45.237 2117.531 Temperate 902–2264 1362 2750 60/60
Southern hemisphere:
Australia AUS Australian Alps
(New South Wales)
236.038 148.359 Temperate 410–2125 1715 1900 60/60
Central Chile CLC Andes 233.338 270.292 Mediterranean 1900–3585 1685 2200 13/13
Southern Chile CLS Southern Andes 237.568 271.565 Temperate 274–1686 1412 1800 58/60
Argentina ARG Southern Andes 241.011 271.530 Temperate 857–1678 821 1700 60/60
a region, total beta-diversity was calculated using the Jaccard dissimi-
larity index, where values range from 0 (identical species composition)
to 1 (no shared species) (Colwell & Coddington, 1994). Total beta-
diversity5 (b1c)/(a1b1c), where a indicates the number of species
shared between plots, and b and c are the number of species that occur
in just one of the two plots, respectively. The species richness
component of beta-diversity was calculated as the absolute difference
in species richness between two plots, beta-diversity (richness
differences)5 |b-c|/(a1b1c). The species replacement component of
beta-diversity is given by the substitution of n species in the first plot
from n species in the second plot (Cardoso, Borges, & Veech, 2009).
Beta-diversity (replacement)52 3 min(b,c)/(a1b1c), with min(b,c)
being the lower number of species per plot between the two plots.
Total beta-diversity and its two components of species richness
differences and species replacement were computed with the ‘beta’
function in the package ‘BAT’ (Cardoso, Rigal, Carvalho, & Kembel,
2015) in the R statistical environment (R Core Team, 2016).
2.4 | Data analysis
2.4.1 | Global analysis of species richness patterns
To describe patterns of species richness along the elevation gradient
we fitted a global model using data from all regions. The model was
fitted using a linear mixed-effects model (‘lmer’ in the R package
‘lmerTest’; Kuznetsova, Brockhoff, & Bojesen Christensen, 2016) with
species richness as the response. The predictor variables included a
second-order polynomial of elevation, species status that indicated
whether the species richness value was for all species combined (native
and non-native species) or just the native species, plot type (interior or
roadside), and all interactions as fixed effects. For the second-order
polynomial, we summarized the linear and the quadratic term of eleva-
tion making use of the ‘poly’ function in R, which uses QR factorization
to generate monic orthogonal polynomials. Using polynomials made
sure that the linear and quadratic terms were uncorrelated (i.e. orthog-
onal) and allowed us to show their combined effects in the result
tables. A quadratic term was included to account for unimodal patterns
of species richness along the elevation gradient. Elevation and species
richness were scaled between 0 and 1 in every region to allow compar-
ison among regions with different elevational extents and different
ranges of species richness. Species richness was scaled separately for
native species and all species combined. For scaling we used the
‘decostand’ function with the method ‘range’ in the R package ‘vegan’
(Oksanen et al., 2016). We added the nested terms region, road, tran-
sect and plot as random effects to account for the nested structure of
the sampling design and for the fact that richness of native species and
all species combined were not independent because records were
made in the same plot. p-values were calculated from F-statistics of
type III sum of squares with Satterthwaite approximation to estimate
the denominator degrees of freedom (R package ‘lmerTest’).
2.4.2 | Regional species richness patterns
We also compared elevational patterns of species richness within each
region separately. Within each region two mixed-effects models with
species richness as the response were fitted that included a second-
order polynomial of elevation (see explanations above), species status,
plot type, and all interactions as predictor variables (fixed effects). The
two regional models differed in the species status to be compared: the
first model compared species richness of native species and all species
combined, while the second model compared native and non-native
species richness. Elevation was scaled and centred by using the ‘scale’
function. Random effects were the nested terms road (except for cen-
tral Chile with only one road), transect and plot. As before, p-values
were calculated from F-statistics of type III sum of squares with Sat-
terthwaite approximation.
All models (global and regional) were simplified by consecutively
deleting non-significant terms, beginning with the three-way interac-
tion. By using likelihood ratio tests based on Chi-squared, we further
tested if species richness was better explained by a linear or a parabolic
relationship with elevation for the resulting most parsimonious models.
For model comparisons we used maximum likelihood (ML), while the
final best models were re-fitted with restricted maximum likelihood
(REML).
To assess the peak species richness individually for each plot type
(interior or roadside) and species status (all species combined, native
species or non-native species), we fitted for each region six generalized
linear mixed-effects models (‘glmer’ in R package ‘lme4’; Bates,
Maechler, Bolker, & Walker, 2013). Species richness was the response
(for one of the six combinations of plot type and species status) and
elevation was the only explanatory variable. Road identity was added
as a random effect. For central Chile generalized linear models (‘glm’)
were used without the random effect because there was only one
road. Models were fitted with a Poisson family and log link, or in the
case of overdispersion (residual deviance/residual degrees of freedom
>1.2) with a negative binomial distribution (quasi-Poisson and log link
for the glms for central Chile). Each model was initially fitted with the
linear and the quadratic term of elevation (‘poly’ function in R), and
then compared to a simpler model containing only the linear term of
elevation by using a likelihood ratio test based on Chi-squared. When
there was no significant difference between these two models, the
simpler model was also compared to a model containing only the
intercept and random effects. We extracted the peak of species
richness and its elevation from the model predictions for models with a
significant quadratic elevation effect. With a binomial test we sought
statistical evidence that the richness peak of all species combined was
lower than that of native species.
2.4.3 | Analyses of beta-diversity
We tested with global and regional linear mixed-effects models
(R package ‘lmerTest’) whether beta-diversity depended on species
status (native or all species combined), plot type (interior or roadside)
or their interactions. To avoid pseudoreplication, as all plots are
included in numerous plot pairs, we calculated for each plot the mean
beta-diversity from all respective plot pairs that contained that plot.
This was done separately for native species and all species combined.
In the models, random effects included plot nested in region for the
global model, and only plot for the regional models. We fitted models
separately for the response of total beta-diversity and its components
of species richness differences and species replacement.
To analyse the effect of elevational distance between plots on
beta-diversity, matrix regression models (MRMs) were fitted using the
R package ‘ecodist’ (Goslee & Urban, 2007). The explanatory variable
was Euclidean distance of plot elevation. Response matrices were total
beta-diversity and its components of species richness differences and
species replacement. Statistical significance of elevational distance was
achieved by using permutation tests with 10,000 runs. Matrix regres-
sion models were fitted separately for interior and roadside plots and
for native species and all species combined. From these 108 models
(nine regions 3 two plot types 3 two species status 3 three beta-
diversity response matrices) we extracted the slope between the
response and elevational distance. To test if the steepness of the slope
depended on plot type and species status, we fitted linear mixed-
effects models with these variables and their interaction as fixed
effects and region as a random effect (R package ‘lmerTest’). However,
as the interaction was not significant for any of the response matrices,
we fitted the models again without the interaction. For all mixed-
effects models, model statistics were taken from type III sum of
squares with Satterthwaite approximation to estimate the denominator
degrees of freedom.
3 | RESULTS
Overall, we recorded 2,410 vascular plant species, with regional total
species richness ranging from 159 to 604. In all regions, we recorded a
greater total number of native than non-native species, and across plot
types native species were on average also more numerous at the plot
level (Supporting Information Table S2). The total number of native
species was larger for interior plots than roadside plots (except for
Norway, Switzerland and Argentina), while the mean number of native
species per plot was greater along roadsides in five of the nine regions
(Supporting Information Table S2). The total number of non-native
species as well as mean non-native species richness per plot were
larger for roadside plots compared to interior plots in all regions
(Supporting Information Table S2). Norway and Switzerland had the
highest ratios of native to non-native species, while the four regions in
the southern hemisphere had the lowest ratios (Supporting Information
Table S2).
3.1 | Elevational species richness patterns
At the global scale, there was a unimodal relationship between species
richness and elevation, with maximum species richness occurring in the
lower-mid section of the elevation gradient for native and all species
and for interior and roadside plots (Table 2, Figure 1). At the regional
scale, this pattern was found for all species combined in interior plots
in three regions and for roadside plots in six regions (Figure 2, Support-
ing Information Table S3 and Figure S2). Also for native species, the
most common shape of the elevational species richness pattern was a
unimodal distribution, which we found in four and six regions for inte-
rior and roadside plots, respectively (Figure 2, Supporting Information
Table S3 and Figure S2). Non-native species richness declined with ele-
vation starting in the lowest fifth of the gradient in all regions, except
for interior plots in Norway and Argentina. Roadsides had a strong
effect on the shape of the species richness patterns, and the curve’s
shape differed between interior and roadside plots for native species in
TABLE 2 Results from the global linear mixed-effects model for
species richness as response of elevation, plot type (interior or
roadside) and species status (native species or all species combined)
d.f. F-value p-value
Elevation 473.19 20.064 <.001
Plot type 466.24 1.998 .1581
Species status 936.99 102.935 <.001
Elevation : plot type 470.95 1.549 .2136
Elevation : species status 936.99 64.465 <.001
Plot type : species status 936.99 236.416 <.001
Elevation : plot type : species status 936.99 17.536 <.001
Note. For elevation, we summarized the linear and quadratic term of
elevation using orthogonal polynomials. As random effects we added the
nested terms region, road, transect and plot. p-values were taken from
type III sum of squares with Satterthwaite approximation to estimate the
denominator degrees of freedom (d.f.). Significant p-values (p< .05) are
indicated in bold.
FIGURE 1 Global pattern of species richness along elevation
gradients. Curves and peaks of species richness are based on model
predictions (see Methods and Table 2). Note that elevation and
species richness were scaled prior to modelling, with species richness
scaled separately for native species and all species combined. Native
species are indicated in red, all species combined (native and non-
native species) are indicated in black. Symbols represent the peak of
species richness. Filled symbols and solid lines represent interior plots,
unfilled symbols and dashed lines represent roadsides. The peak of
native species richness is close to mid-elevations, while, for all species
combined, the peak is in the lowest third of the elevation gradient.
The shift of the species richness peak towards lower elevations
through non-native species is stronger along roadsides compared to
interior plots
six regions, for all species combined in seven regions and for non-
native species in six regions (Figure 2).
Including non-native species resulted at the global scale in a
displacement of the peak of species richness to lower elevations
compared to the peak of species richness of native species. This shift
was stronger along roadsides (Figure 1). At the regional scale, the
species richness peak of all species combined was at lower elevation
than that of native species in three regions in interior plots and in six
regions in roadside plots (Supporting Information Table S4). However,
these differences did not result in significant p-values in the binomial
tests (p51 and p5 .289, respectively).
Elevational species richness patterns differed in character for native
and non-native species in seven regions, and non-native species changed
the character of the native species’ pattern in all nine study regions and
also at the global scale. This was evident in a significant elevation-by-
status interaction (Table 2, Supporting Information Table S5), which is
brought about by a different slope of the richness curve, a shift of the ele-
vation at which species richness is maximal or a combination of both. In
addition, including non-native species resulted in a fundamentally different
shape of the richness curves compared to the shape of native species
richness in Oregon, central Chile and Argentina in interior plots and in
Australia in roadside plots (Figure 2, Supporting Information Figure S2).
3.2 | Effects of roadsides and non-native species on
beta-diversity
At the global scale, community dissimilarity (total beta-diversity,
measured as Jaccard dissimilarity) was significantly greater in interior
plots compared to roadside plots (Table 3; Figure 3a). Regionally, greater
beta-diversity in interior plots was found in Switzerland, Montana,
Australia, central and southern Chile (Supporting Information Table S6
and Figure S3a). At the global scale, this difference between plot types
was mainly driven by a stronger decline in dissimilarity for roadsides
when non-native species were included (significant species status-by-
plot type interaction; Table 3). This was found also in Australia, Mon-
tana, Oregon and southern Chile (Supporting Information Table S6 and
Figure S3a). In these regions, except Oregon, total beta-diversity of
native species was significantly higher than that of all species combined,
which also corresponded to the results at the global scale.
The beta-diversity component of species richness differences was
greater for native species compared to all species combined at the
global scale (Table 3, Figure 3b) and in five of the nine regions
(Switzerland, Montana, Oregon, southern Chile, Argentina; Supporting
Information Table S6 and Figure S3b). At the global scale, species rich-
ness differences for native species were greater along roadsides, but
for all species combined they were larger in interior plots (significant
species status-by-plot type interaction; Table 3, Figure 3b). However,
the same pattern was not detected at the regional scale (Supporting
Information Figure S3b). In Norway, Switzerland, Montana and
southern Chile, differences in beta-diversity based on species richness
differences were larger in interior plots, while it was the contrary in
Australia, central Chile and Argentina (Supporting Information Table S6
and Figure S3b). In consequence, at the global scale there was no
significant effect of plot type on species richness differences (Table 3).
Including non-native species increased species replacement
between plots at the global scale (Table 3, Figure 3c) and in six out of
nine regions (Norway, Switzerland, Montana, Oregon, Australia, south-
ern Chile; Supporting Information Table S6 and Figure S3c). At the
global scale, species replacement was significantly lower along
roadsides (Table 3, Supporting Information Figure S3c), which we found
also at the regional scale for Montana, Australia, Argentina and central
Chile (Supporting Information Table S6 and Figure S3c).
The contribution of species richness differences to total beta-
diversity was on average smaller than that of species replacement at
the global and regional scales (Figure 3, Supporting Information Figure
S3). At the global scale, this difference was particularly strong when
FIGURE 2 Schematic representation of species richness patterns
along elevation gradients in the nine study regions (NOR5Norway,
CH5Switzerland, IC5Canary Islands, MT5Montana,
OR5Oregon, AUS5Australia, CLC5 central Chile, CLS5 southern
Chile, ARG5Argentina). The three columns on the left refer to
plots away from the road, while the three columns on the right
represent roadside plots. In each plot type, patterns for native
species, all species combined and non-native species are displayed.
Empty boxes indicate that there was no significant relationship
between species richness and elevation. Bold curves within dashed
boxes show that non-native species have changed the shape of the
pattern of native species, so that curves of native and all species
combined are different. Grey boxes with bold curves for roadside
plots indicate that the relationship between species richness and
elevation differs from interior plots for the respective species
status. Small arrows pointing to the left show that the peak of
species richness is at lower elevations for all species combined
compared to native species. See Supporting Information Figure S2
for modelled species richness curves
non-native species were included. This trend was also observed in five
regions (Switzerland, Montana, Oregon, Australia, southern Chile;
Supporting Information Figure S3).
3.3 | Elevational decay of beta-diversity
Total beta-diversity (Jaccard dissimilarity) increased significantly with
increasing elevational distance between plots in all regions and for inte-
rior and roadside plots as well as for native species and all species com-
bined (Figure 4a, Supporting Information Table S7 and Figure S4a).
The response of species richness differences to increasing eleva-
tional distance was less consistent across regions (Figure 4b, Supporting
Information Table S7 and Figure S4b). We found a statistically signifi-
cant positive relationship for interior plots in three regions [Canary
Islands, Oregon, southern Chile (only for native species)] and for road-
side plots in seven regions [Canary Islands, Montana, Oregon, Australia,
central Chile (only for all species combined), southern Chile, Argentina
(only for all species combined)]. Norway was the only region where we
found a significant negative relationship between species richness dif-
ferences and elevational distance (Supporting Information Table S7).
Species replacement increased with increasing elevational distance
in seven regions [Norway, Switzerland (only along roadsides), Montana
(in interior plots only for all species combined), Oregon, Australia (only
for interior plots), central Chile (in roadside plots only for native spe-
cies), Argentina (in roadside plots only for native species)], but
decreased in the Canary Islands and southern Chile (only for native
species) (Figure 4c, Supporting Information Table S7 and Figure S4c).
The slope of these regional relationships did not differ significantly
between interior and roadside plots or between native species and all
species combined (Table 4).
4 | DISCUSSION
4.1 | Global and regional species richness patterns
Globally, in interior and roadside plots, species richness of native
species as well as of all species combined showed a unimodal pattern
that peaked at lower-mid elevations. At the regional scale, a unimodal
relationship was also the most frequent shape, but we also observed
linear or no relationship between species richness and elevation.
TABLE 3 Results from the global linear mixed-effects models for the response of beta-diversity [total beta-diversity (Jaccard dissimilarity
index), species richness differences and species replacement] to species status (native species or all species combined) and plot type (interior
or roadside)
Beta-diversity
Total beta-diversity
(Jaccard dissimilarity) Species richness differences Species replacement
d.f. F-value p-value d.f. F-value p-value d.f. F-value p-value
Species status 940.99 72.71 <.001 941.01 88.53 <.001 941.01 53.52 <.001
Plot type 933.08 39.84 <.001 933.66 0.19 .664 933.68 5.28 .022
Species status : plot type 940.99 130.16 <.001 941.01 26.69 <.001 941.01 0.60 .440
d.f.5denominator’s degrees of freedom.
Note. D.f., F- and p-values are from type III sum of squares and Satterthwaite approximation. Significant p-values (p< .05) are indicated in bold.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
(a) Total beta−diversity (Jaccard)
all native all native
Interior Roadside
Species status
Plot type
Be
ta
−d
ive
rs
ity
(b) Species richness differences
all native all native
Interior Roadside
(c) Species replacement
all native all native
Interior Roadside
FIGURE 3 Total beta-diversity (Jaccard dissimilarity index) and its components of species richness differences and species replacement for
the two different plot types (interior or roadside) and species status (native species or all species combined). Statistical results are given in
Table 3. (a) Total beta-diversity was lower along roadsides, particularly when non-native species were included. (b) Species richness differen-
ces were reduced by non-native species. For native species, richness differences were higher along roadsides, while non-native species led
to higher species richness differences in interior plots. (c) Species replacement was higher in interior plots and for all species combined
The mid-domain effect (Cardel
us, Colwell, & Watkins, 2006;
Colwell & Lees, 2000; Grytnes et al., 2008) might be an underlying
mechanism in our study regions. In addition, biological, non-random
processes might be acting: the unimodal species richness curve was
more frequent along roadsides where dispersal is usually increased due
to vehicle movement (Rew et al., 2018; Taylor et al., 2012; von der
Lippe & Kowarik, 2007), and where competition is likely to be reduced
due to disturbance. Both factors might allow the species to establish
under less favourable abiotic conditions at higher (or lower) elevations,
which might lead to larger species elevational ranges along roadsides
(Lembrechts et al., 2017) and stronger range overlap at mid-elevations
(Brehm, Colwell, & Kluge, 2007; Dunn, McCain, & Sanders, 2006; Wu
et al., 2013). However, our regional analysis suggests that the specific
shape of the unimodal curve is likely also related to other factors,
including climate, vegetation type and human activity.
Argentina (interior) and Australia (roadside) showed a negative
relationship between elevation and native species richness. This is the
second most commonly observed pattern of plant species richness
globally (Rahbek, 2005), and in our study regions might be explained by
increasingly harsh abiotic conditions at high elevations (McCain &
Grytnes, 2010). In contrast, native species richness in Norway
increased with elevation, which might be the result of reduced
competition from shrubs at high elevations (Lembrechts, Milbau, &
Nijs, 2014). Also, increasingly positive net interactions in harsher
climates might facilitate species co-existence (Choler, Michalet, &
Callaway, 2001).
4.2 | Human-induced disturbance and dispersal
modify region-specific patterns
We found different shapes of the species richness patterns of native,
non-native and all species combined for interior and roadside plots for
all but two regions. Across regions, patterns were more consistent
along roadsides compared to those in interior plots. This suggests that
disturbance and dispersal caused by road development, maintenance
and use result in a weakening of region-specific effects. Mechanisms
such as environmental filtering by temperature or precipitation, and
increasingly positive biotic interactions that have been shown to be
important drivers in interior plots, might be less important along road-
sides or shifted to higher elevations (Alexander et al., 2011; Seipel
et al., 2012).
4.3 | Consistent effect of non-native species
Non-native species displaced the peak of species richness to lower
elevations, particularly along roadsides. Contrary to native species,
non-native species richness decreased consistently with elevation, in
some regions after a small peak in the lowest fifth of the elevation
gradient. The decreasing richness pattern applied to both plot types
(interior and roadside) and has been described also in other studies
(Bhattarai, Måren, & Subedi, 2014; Seipel et al., 2012; Tanaka & Sato,
2016; Zhang et al., 2015). Thus, while we found region-specific
0.6
0.7
0.8
0.9
1.0
(a) Total beta−diversity
NOR
CH
IC
MT
OR
AUS CLC
CLS
ARG
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8 (b) Species richness differences
Be
ta
−d
ive
rs
ity
NOR
CH
IC
MT
OR
AUS
CLC
CLS
ARG
0 500 1000 1500 2000
0.3
0.4
0.5
0.6
0.7
0.8
0.9 (c) Species replacement
Elevational distance (m)
NOR
CH
IC
MTOR
AUS
CLC
CLS
ARG
FIGURE 4 (a) Total beta-diversity (Jaccard dissimilarity index) and
its two components of (b) species richness differences and (c) spe-
cies replacement as response to elevational distance between plots.
Each line represents one region (for abbreviations see Table 1).
Only beta-diversity for native species in interior plots is displayed,
because there was no statistically significant difference compared
to all species combined and roadside plots, respectively (see
Table 4; for regional details see Supporting Information Table S7
and Figure S4). We found a significant elevational decay of total
beta-diversity in all regions (a), but there was no consistent pattern
for species richness differences (b) and species replacement (c)
patterns for native species richness, non-native species richness
responded consistently across regions.
In all regions, the number of non-native species was higher along
roadsides. This might be explained by greater disturbance, which is
known to be a major promoter of plant invasions (Jauni, Gripenberg, &
Ramula, 2015), and by greater seed and vegetative propagule load
from traffic and road maintenance along roadsides. Either way, road-
sides appear to act as conduits and sources of non-native species for
percolation into semi-natural habitats (Bhattarai et al., 2014; Pollnac
et al., 2012; Seipel et al., 2012; Zhang et al., 2015).
Non-native species changed the character of the elevational native
species richness patterns in all regions by altering the rate of species
richness change with elevation, shifting the location of the species rich-
ness peak, or both. However, a change of the pattern’s general shape
(e.g. from unimodal to decreasing) could only be observed in four
regions (interior plots in Oregon, central Chile and Argentina, and road-
sides in Australia). These regions were among those with the lowest
ratio of native to non-native species. Thus, with continuing invasions,
other regions might be expected to undergo changes in the shape of
the species richness pattern in the future. The spatial shift of the native
species richness peak to lower elevations through invasion of non-
native species – already observed in two-thirds of our regions – might
be considered as a first sign of changing species richness patterns.
4.4 | Roads and non-native species reduced
community dissimilarity
Globally, roads reduced total beta-diversity and species replacement,
which suggests roads homogenize mountain flora. The higher levels of
disturbance along roadsides and associated resource release might
reduce small-scale habitat differences. In combination with higher dis-
persal of plant propagules along roads, this might lead to larger species
ranges (Lembrechts et al., 2017), and thus to reduced differences in
local community composition. In interior plots, greater species replace-
ment between plots points to greater environmental heterogeneity
compared to roadsides. In Norway and Switzerland, higher species
replacement along roadsides might result from the larger pools of
native species found along roadsides, and overall much lower richness
of non-native species.
Non-native species richness has been shown to be highest at low
elevations, and only few or no non-natives were found at high eleva-
tions (Haider, Alexander, & Kueffer, 2011; Seipel et al., 2012; Tanaka &
Sato, 2016; Zhang et al., 2015). Therefore, we expected that the inclu-
sion of non-native species increases differences in species richness. In
line with this assumption, Marini et al. (2013) observed that beta-
diversity of non-native species in Italy was mainly related to differences
in species richness. However, in our study, at the global scale, non-
native species reduced plot dissimilarity by reducing differences in
species richness, an effect that was particularly strong along roadsides.
A possible explanation is that non-native species increased species
richness in the lower part of the elevation gradient, but not consider-
ably at mid-elevations (i.e. where native species richness peaks), so that
specifically the differences in species richness between low and mid-
elevations were reduced.
Furthermore, species replacement increased through non-native
species at the global scale and in two-thirds of the study regions.
This result, too, was contrary to our expectations. Elevational nested-
ness of non-native species (Alexander et al., 2011; Marini et al., 2013)
and smaller elevational niches of native species (more specialists)
should result in lower species replacement for all species combined
compared to native species. The increase of species replacement
through inclusion of non-native species might have been caused by a
patchy distribution of non-native species, despite their elevational
nestedness.
In summary, the negative effect of non-native species on species
richness differences was stronger than the positive effect on species
replacement, so that non-native species overall led to a homogeniza-
tion of plant communities (Ar
evalo et al., 2010; McKinney, 2004).
4.5 | Elevational decay of community dissimilarity
We found a decline in community dissimilarity in response to eleva-
tional distance in all study regions, both along roadsides and in interior
plots and with or without non-native species. Such elevational decay
has been observed for plants (Chapman & McEwan, 2013), and other
taxonomic groups, for instance aquatic microorganisms (Wang et al.,
2012). The elevational decay was found for species richness differen-
ces as well as for species replacement, but the regional responses for
these two components were less consistent compared to the response
TABLE 4 Results from the global linear mixed-effects models to test if the steepness of the slope between beta-diversity and elevational dis-
tance between plots depends on species status (native species or all species combined) and plot type (interior or roadside)
Slope between beta-diversity and elevational distance (elevational decay)
Total beta-diversity Species richness differences Species replacement
d.f. F-value p-value d.f. F-value p-value d.f. F-value p-value
Species status 25 0.16 .697 25 0.38 .543 25 0.46 .506
Plot type 25 0.78 .387 25 0.01 .932 25 0.04 .839
d.f.5denominator’s degrees of freedom.
Note. Models were fitted separately for total beta-diversity (Jaccard dissimilarity index), species richness differences and species replacement. d.f., F-
and p-values are from type III sum of squares and Satterthwaite approximation. The interaction of species status and plot type was removed from all
models because it was insignificant in all three models.
of total beta-diversity. This suggests that idiosyncratic regional factors
determine species richness differences and replacement.
We did not detect statistically significant differences in the rate of
elevational decay for interior compared to roadside plots, or for native
species compared to all species combined. This suggests that biological
invasions in mountains are not yet such a strong homogenizing factor
as they can be in lowland ecosystems (McKinney, 2004). However,
geographic distance was the most important factor explaining
dissimilarity in non-native species composition in Portugal (Vicente
et al., 2014), and so the impact of non-native species on beta-diversity
may increase in the future.
5 | CONCLUSIONS
Our study showed that human-induced disturbance and non-native
species have a strong impact on native species richness patterns and
community dissimilarity in mountains, partly overruling the global mecha-
nisms that bring about these patterns. We suggest that three mecha-
nisms – increased human-assisted dispersal, higher habitat homogeneity,
and presence of generalist non-native species – drive biotic homogeniza-
tion along elevation gradients at a regional scale. Biotic homogenization
not only leads to a reduced differentiation of plant communities among
elevational zones in a particular mountain system, but also to a reduction
in the diversity of species richness patterns in semi-natural habitats. Our
study is the first global comparison of the effects of roads and non-
native species on diversity patterns in mountains, and suggests that
more attention should be paid to the influence of anthropogenic factors
on broad-scale macroecological patterns.
ACKNOWLEDGMENTS
The authors thank all regions for collecting the data and all Mountain
Invasion Research Network (MIREN) members for fruitful discussions.
Neville Walsh (Royal Botanic Gardens Melbourne) assisted with data
collection in Australia. BJN thanks Josh Averett, Kent Coe, and the rest
of the field crew for all their data collection efforts. Thanks to Jonathan
Lenoir, Oliver Purschke, Erik Welk and Susanne Lachmuth for discussing
statistics, and to Gunnar Seidler for helping with data management and
preparing the map of the study regions. Our thanks also to two anony-
mous referees for their constructive comments, which greatly improved
our paper. AP and LAC were funded by CONICYT PFB-23 and ICM
P05-002. LJR was supported by the National Institute of Food and
Agriculture, U.S. Department of Agriculture Hatch: MONB00363.
DATA ACCESSIBILITY
Data were collected by members of MIREN. Species occurrence data
with sampling date and geographic coordinates will be available on
GBIF (https://www.gbif.org/).
ORCID
Sylvia Haider http://orcid.org/0000-0002-2966-0534
Helge Bruelheide http://orcid.org/0000-0003-3135-0356
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BIOSKETCH
This paper is part of the research carried out by the Mountain Invasion
Research Network (MIREN; www.mountaininvasions.org). The aim of
MIREN is to understand the effects of global change on species’ distri-
butions and biodiversity in mountainous areas. We perform observatio-
nal and experimental studies along elevation gradients to evaluate and
quantify the processes and mechanisms that are shaping mountain
plant communities at regional and global scales.
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the sup-
porting information tab for this article.
How to cite this article: Haider S, Kueffer C, Bruelheide H, et al.
Mountain roads and non-native species modify elevational pat-
terns of plant diversity. Global Ecol Biogeogr. 2018;27:667–678.
https://doi.org/10.1111/geb.12727