Human impact, climate and dispersal strategies
determine plant invasion on islands

Severin D. H. Irl, Andreas H. Schweiger, Manuel J. Steinbauer, Claudine Ah-Peng, José Ramón Arévalo, Carl Beierkuhnlein, Alessandro Chiarucci, Curtis C. Daehler, José María Fernández-Palacios, Olivier Flores, et al.

► To cite this version:

Severin D. H. Irl, Andreas H. Schweiger, Manuel J. Steinbauer, Claudine Ah-Peng, José Ramón Arévalo, et al. Human impact, climate and dispersal strategies determine plant invasion on islands. Journal of Biogeography, 2021, 48 (8), pp.1889-1903. 10.1111/jbi.14119. hal-04330476

HAL Id: hal-04330476

https://hal.univ-reunion.fr/hal-04330476

Submitted on 2 Feb 2024

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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Human impact, climate and dispersal strategies
determine plant invasion on islands

Severin D. H. Irl, Andreas H. Schweiger, Manuel J. Steinbauer, Claudine Ah-Peng, José Ramón Arévalo, Carl Beierkuhnlein, Alessandro Chiarucci, Curtis C. Daehler, José María Fernández-Palacios, Olivier Flores, et al.

<figure><img src="image_1.png" /></figure>

To cite this version:

Severin D. H. Irl, Andreas H. Schweiger, Manuel J. Steinbauer, Claudine Ah-Peng, José Ramón Arévalo, et al. Human impact, climate and dispersal strategies determine plant invasion on islands. Journal of Biogeography, 2021, 48 (8), pp.1889-1903. 10.1111/jbi.14119. hal-04330476

HAL Id: hal-04330476

https://univ-reunion.hal.science/hal-04330476v1

Submitted on 2 Feb 2024

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Human impact, climate and dispersal strategies determine plant invasion on islands

Severin D. H. Irl<fnref n="1" />,<fnref n="2" /> ID

Andreas H. Schweiger<fnref n="2" />,<fnref n="3" /> ID

Manuel J. Steinbauer<fnref n="2" />,<fnref n="4" /> ID

Claudine Ah-Peng<fnref n="5" />

José Ramón Arévalo<fnref n="6" /> ID

Carl Beierkuhnlein<fnref n="7" /> ID

Alessandro Chiarucci<fnref n="8" /> ID

Curtis C. Daehler<fnref n="9" />

José María Fernández-Palacios<fnref n="6" /> ID

Olivier Flores<fnref n="5" /> ID

Christoph Kueffer<fnref n="10" /> ID

Petr Maděra<fnref n="11" /> ID

Rüdiger Otto<fnref n="6" /> ID

Julienne M.-I. Schweiger<fnref n="2" /> ID

Dominique Strasberg<fnref n="12" /> ID

Anke Jentsch<fnref n="2" />,¹³ ID

## Abstract

**Aim:** Biological invasions are likely determined by species dispersal strategies as well as environmental characteristics of a recipient region, especially climate and human impact. However, the contribution of climatic factors, human impact, and dispersal strategies in driving invasion processes is still controversial and not well embedded in the existing theoretical considerations. Here, we study how climate, species dispersal strategies, and human impact determine plant invasion processes on islands distributed in all major oceans in the context of directional ecological filtering.

**Location:** Six mountainous, tropical, and subtropical islands in three major oceans: Island of Hawai'i and Maui (Pacific), Tenerife and La Palma (Atlantic), and La Réunion and Socotra (Indian Ocean).

**Taxon:** Vascular Plants.

**Methods:** We recorded 360 non-native species in 218 plots along roadside elevational transects covering the major temperature, precipitation and human impact (i.e., road density) gradients of the islands. We collected dispersal strategies for a majority of the recorded species and calculated the environmental niche per species using a hypervolume approach.

**Results:** Non-native species' generalism (i.e., mean community niche width) increased with precipitation, elevation and human impact but showed no relationship with temperature. Increasing precipitation led to environmental filtering of non-native species resulting in more generalist species under high precipitation conditions. We found no directional filtering for temperature but an optimum range of most species between 10 and 20°C. Niche widths of non-native species increased with the prevalence of certain dispersal strategies, particularly anemochory and anthropochory.

<footnote n="1">¹Biogeography and Biodiversity Lab, Institute of Physical Geography, Goethe-University, Frankfurt, Germany</footnote>

<footnote n="2">²Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany</footnote>

<footnote n="3">³Plant Ecology, University of Hohenheim, Hohenheim, Germany</footnote>

<footnote n="4">⁴Sport Ecology, University of Bayreuth, Bayreuth, Germany</footnote>

<footnote n="5">⁵UMR PVBMT, Pôle de Protection des Plantes, Université de La Réunion, Saint Pierre, France</footnote>

<footnote n="6">⁶Departamento de Botánica, Ecología y Fisiología Vegetal, Universidad de La Laguna, La Laguna, Spain</footnote>

<footnote n="7">⁷Biogeography, University of Bayreuth, Bayreuth, Germany</footnote>

<footnote n="8">⁸Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, Bologna, Italy</footnote>

<footnote n="9">⁹Department of Botany, University of Hawai'i, Honolulu, HI, USA</footnote>

<footnote n="10">¹⁰Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland</footnote>

<footnote n="11">¹¹Faculty of Forestry and Wood Technology, Department of the Forest Botany, Dendrology and Geobiocoenology, Mendel University, Brno, Czech Republic</footnote>

<footnote n="12">¹²UMR PVBMT, Université de La Réunion, Saint-Denis Messag, La Réunion, France</footnote>

<footnote>Severin D. H. Irl and Andreas H. Schweiger are equal contributions.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2021 The Authors. Journal of Biogeography published by John Wiley & Sons Ltd.</footnote>
# Human impact, climate and dispersal strategies determine plant invasion on islands

Severin D. H. Irl<fnref n="1" />,<fnref n="2" /> ID | Andreas H. Schweiger<fnref n="2" />,<fnref n="3" /> ID | Manuel J. Steinbauer<fnref n="2" />,<fnref n="4" /> ID |
Claudine Ah-Peng<fnref n="5" /> | José Ramón Arévalo<fnref n="6" /> ID | Carl Beierkuhnlein<fnref n="7" /> ID |
Alessandro Chiarucci<fnref n="8" /> ID | Curtis C. Daehler<fnref n="9" /> | José María Fernández-Palacios<fnref n="6" /> ID |
Olivier Flores<fnref n="5" /> ID | Christoph Kueffer<fnref n="10" /> ID | Petr Maděra<fnref n="11" /> ID | Rüdiger Otto<fnref n="6" /> ID |
Julienne M.-I. Schweiger<fnref n="2" /> ID | Dominique Strasberg<fnref n="12" /> ID | Anke Jentsch<fnref n="2" />,¹³ ID |

## Abstract

**Aim:** Biological invasions are likely determined by species dispersal strategies as well as environmental characteristics of a recipient region, especially climate and human impact. However, the contribution of climatic factors, human impact, and dispersal strategies in driving invasion processes is still controversial and not well embedded in the existing theoretical considerations. Here, we study how climate, species dispersal strategies, and human impact determine plant invasion processes on islands distributed in all major oceans in the context of directional ecological filtering.

**Location:** Six mountainous, tropical, and subtropical islands in three major oceans: Island of Hawai'i and Maui (Pacific), Tenerife and La Palma (Atlantic), and La Réunion and Socotra (Indian Ocean).

**Taxon:** Vascular Plants.

**Methods:** We recorded 360 non-native species in 218 plots along roadside elevational transects covering the major temperature, precipitation and human impact (i.e., road density) gradients of the islands. We collected dispersal strategies for a majority of the recorded species and calculated the environmental niche per species using a hypervolume approach.

**Results:** Non-native species' generalism (i.e., mean community niche width) increased with precipitation, elevation and human impact but showed no relationship with temperature. Increasing precipitation led to environmental filtering of non-native species resulting in more generalist species under high precipitation conditions. We found no directional filtering for temperature but an optimum range of most species between 10 and 20°C. Niche widths of non-native species increased with the prevalence of certain dispersal strategies, particularly anemochory and anthropochory.

<footnote n="1">¹Biogeography and Biodiversity Lab, Institute of Physical Geography, Goethe-University, Frankfurt, Germany</footnote>

<footnote n="2">²Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany</footnote>

<footnote n="3">³Plant Ecology, University of Hohenheim, Hohenheim, Germany</footnote>

<footnote n="4">⁴Sport Ecology, University of Bayreuth, Bayreuth, Germany</footnote>

<footnote n="5">⁵UMR PVBMT, Pôle de Protection des Plantes, Université de La Réunion, Saint Pierre, France</footnote>

<footnote n="6">⁶Departamento de Botánica, Ecología y Fisiología Vegetal, Universidad de La Laguna, La Laguna, Spain</footnote>

<footnote n="7">⁷Biogeography, University of Bayreuth, Bayreuth, Germany</footnote>

<footnote n="8">⁸Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum University of Bologna, Bologna, Italy</footnote>

<footnote n="9">⁹Department of Botany, University of Hawai'i, Honolulu, HI, USA</footnote>

<footnote n="10">¹⁰Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland</footnote>

<footnote n="11">¹¹Faculty of Forestry and Wood Technology, Department of the Forest Botany, Dendrology and Geobiocoenology, Mendel University, Brno, Czech Republic</footnote>

<footnote n="12">¹²UMR PVBMT, Université de La Réunion, Saint-Denis Messag, La Réunion, France</footnote>

<footnote>Severin D. H. Irl and Andreas H. Schweiger are equal contributions.</footnote>

<footnote>This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.</footnote>

<footnote>© 2021 The Authors. *Journal of Biogeography* published by John Wiley & Sons Ltd.</footnote>
<figure><img src="image_2.png" /></figure>

¹³Disturbance Ecology, University of Bayreuth, Bayreuth, Germany

**Correspondence**

Severin D. H. Irl, Biogeography and Biodiversity Lab, Institute of Physical Geography, Goethe-University Frankfurt, Frankfurt am Main, Germany.
Email: irl@geo.uni-frankfurt.de

**Funding information**

European H2020 Project 641762
ECOPOTENTIAL; Ministry of Education of the Czech Republic (project CZ.1.07/2.3.00/20.0004 LANDTEAM)

Handling Editor: Sebastien Lavergne

**Main conclusions:** Plant invasion on tropical and subtropical islands seems to be mainly driven by precipitation and human impact, while temperature seems to be of little importance. Furthermore, anemochory and anthropochory are dispersal strategies associated with large niche widths of non-native species. Our study allows a more detailed look at the mechanisms behind directional ecological filtering of non-native plant species in non-temperature-limited ecosystems.

**KEYWORDS**

biogeography, climate, dispersal, invasion ecology, island, niche, plant functional traits, roads

# 1 | INTRODUCTION

The erosion of biogeographic barriers by human activities is a defining element of the Anthropocene that has led to an unprecedented exchange of species across oceans and continents (van Kleunen et al., 2015; Kueffer, 2017; Lewis & Maslin, 2015). Abiotic, especially climatic, characteristics of the recipient region strongly control the establishment and spread of non-native plant species (Alexander et al., 2011; Petitpierre et al., 2012; Zefferman et al., 2015) besides propagule pressure and biotic characteristics (Catford et al., 2009) as well as anthropogenic activities (Blackburn et al., 2016; Moser et al., 2018). However, our general understanding of the interplay between climatic influences and human activities on plant invasion between as well as within recipient regions is insufficient so far, although theoretical considerations like the concept of *directional ecological filtering* (Alexander et al., 2011), the generalization of invasions along steep environmental gradients as *invasion syndromes* (Kueffer et al., 2013) and the notion of natural and anthropogenic disturbance regimes acting as catalysts of invasion dynamics (Jentsch & White, 2019) provide important conceptual cornerstones regarding abiotic filtering of non-native species along environmental gradients. Furthermore, the relative contribution of climatic factors versus dispersal strategies in driving plant invasion processes is still controversial and equivocally embedded in the existing theoretical considerations so far.

The theoretical concept of *directional ecological filtering* stresses the importance of abiotic conditions in filtering non-native species along environmental gradients (Alexander et al., 2011). The authors demonstrate that with increasing elevation (i.e., increasing geographic and ecological distance from the lowland introduction source) non-native species with a wide environmental amplitude (i.e., generalists) become more and more important in the non-native species pool because non-native specialists (i.e., species with a narrow environmental amplitude) are selectively filtered out along the environmental gradient. As a consequence, under unfavorable conditions at high elevations, non-native high elevation specialists are underrepresented in comparison to non-native generalists. Our study sets out to disentangle the different layers included in

directional ecological filtering: (a) the indirect but integrative effect of elevation *per se* and (b) direct environmental effects, e.g., driven by climate and the intensity of human impact, that change along an elevational gradient, often in a non-linear way.

The process of species sorting along elevational gradients described by directional ecological filtering has been an important contribution to plant invasion biology. Nevertheless, the integrative nature of elevation reflects a wide series of independent and partly non-linear trajectories and thresholds in multiple abiotic conditions, e.g., temperature, frost, wind speed, precipitation, UV radiation, cloudiness, atmospheric pressure, etc. (Körner, 2007). This complicates identifying the actual abiotic determinants of ecological filtering. Therefore, for a better mechanistic understanding of the processes behind directional ecological filtering, it is paramount to disentangle elevation from biotic and abiotic drivers behind species sorting (Jakobs et al., 2010; Lembrechts et al., 2017; Seipel et al., 2015). We choose high elevation islands as model systems because of their isolated and replicative character (MacArthur & Wilson, 1967), their recognized position as hotspots of global plant invasion (Dawson et al., 2017) and their often steep and non-linear climatic and disturbance gradients within the recipient regions on small spatial scale driven by differences in trade wind exposure (Irl et al., 2015; Weigel et al., 2018) and human activity. This allows us to separate the influence of different abiotic drivers (i.e., temperature and precipitation), human impact, and species dispersal strategies (Seipel et al., 2015; Steinbauer et al., 2017). Particularly, the environmental and climatic diversity found on high elevation islands characterize these systems as condensed continents, referred to as *mini-continents* (Irl et al., 2015) as well as a wide range and non-random distribution of human influences and activities (Caujapé-Castells et al., 2010).

How human activities on the scale of plant communities affect and alter the spread and establishment of non-native species within a recipient region (e.g., an island) is still under scrutiny. This is because geographic entities (e.g., islands or countries) are often taken as a reference by invasion biologists for large-scale assessments (e.g. Blackburn et al., 2016; Kueffer et al., 2010; Moser et al., 2018). Independent of scale, areas of high human densities will increase the likelihood of non-native species to establish (Spear et al.,
<figure><img src="image_3.png" /></figure>

¹³Disturbance Ecology, University of Bayreuth, Bayreuth, Germany

**Correspondence**

Severin D. H. Irl, Biogeography and Biodiversity Lab, Institute of Physical Geography, Goethe-University Frankfurt, Frankfurt am Main, Germany.
Email: irl@geo.uni-frankfurt.de

**Funding information**

European H2020 Project 641762
ECOPOTENTIAL; Ministry of Education of the Czech Republic (project CZ.1.07/2.3.00/20.0004 LANDTEAM)

Handling Editor: Sebastien Lavergne

**Main conclusions:** Plant invasion on tropical and subtropical islands seems to be mainly driven by precipitation and human impact, while temperature seems to be of little importance. Furthermore, anemochory and anthropochory are dispersal strategies associated with large niche widths of non-native species. Our study allows a more detailed look at the mechanisms behind directional ecological filtering of non-native plant species in non-temperature-limited ecosystems.

**KEYWORDS**

biogeography, climate, dispersal, invasion ecology, island, niche, plant functional traits, roads

# 1 | INTRODUCTION

The erosion of biogeographic barriers by human activities is a defining element of the Anthropocene that has led to an unprecedented exchange of species across oceans and continents (van Kleunen et al., 2015; Kueffer, 2017; Lewis & Maslin, 2015). Abiotic, especially climatic, characteristics of the recipient region strongly control the establishment and spread of non-native plant species (Alexander et al., 2011; Petitpierre et al., 2012; Zefferman et al., 2015) besides propagule pressure and biotic characteristics (Catford et al., 2009) as well as anthropogenic activities (Blackburn et al., 2016; Moser et al., 2018). However, our general understanding of the interplay between climatic influences and human activities on plant invasion between as well as within recipient regions is insufficient so far, although theoretical considerations like the concept of *directional ecological filtering* (Alexander et al., 2011), the generalization of invasions along steep environmental gradients as *invasion syndromes* (Kueffer et al., 2013) and the notion of natural and anthropogenic disturbance regimes acting as catalysts of invasion dynamics (Jentsch & White, 2019) provide important conceptual cornerstones regarding abiotic filtering of non-native species along environmental gradients. Furthermore, the relative contribution of climatic factors versus dispersal strategies in driving plant invasion processes is still controversial and equivocally embedded in the existing theoretical considerations so far.

The theoretical concept of *directional ecological filtering* stresses the importance of abiotic conditions in filtering non-native species along environmental gradients (Alexander et al., 2011). The authors demonstrate that with increasing elevation (i.e., increasing geographic and ecological distance from the lowland introduction source) non-native species with a wide environmental amplitude (i.e., generalists) become more and more important in the non-native species pool because non-native specialists (i.e., species with a narrow environmental amplitude) are selectively filtered out along the environmental gradient. As a consequence, under unfavorable conditions at high elevations, non-native high elevation specialists are underrepresented in comparison to non-native generalists. Our study sets out to disentangle the different layers included in

directional ecological filtering: (a) the indirect but integrative effect of elevation *per se* and (b) direct environmental effects, e.g., driven by climate and the intensity of human impact, that change along an elevational gradient, often in a non-linear way.

The process of species sorting along elevational gradients described by directional ecological filtering has been an important contribution to plant invasion biology. Nevertheless, the integrative nature of elevation reflects a wide series of independent and partly non-linear trajectories and thresholds in multiple abiotic conditions, e.g., temperature, frost, wind speed, precipitation, UV radiation, cloudiness, atmospheric pressure, etc. (Körner, 2007). This complicates identifying the actual abiotic determinants of ecological filtering. Therefore, for a better mechanistic understanding of the processes behind directional ecological filtering, it is paramount to disentangle elevation from biotic and abiotic drivers behind species sorting (Jakobs et al., 2010; Lembrechts et al., 2017; Seipel et al., 2015). We choose high elevation islands as model systems because of their isolated and replicative character (MacArthur & Wilson, 1967), their recognized position as hotspots of global plant invasion (Dawson et al., 2017) and their often steep and non-linear climatic and disturbance gradients within the recipient regions on small spatial scale driven by differences in trade wind exposure (Irl et al., 2015; Weigel et al., 2018) and human activity. This allows us to separate the influence of different abiotic drivers (i.e., temperature and precipitation), human impact, and species dispersal strategies (Seipel et al., 2015; Steinbauer et al., 2017). Particularly, the environmental and climatic diversity found on high elevation islands characterize these systems as condensed continents, referred to as *mini-continents* (Irl et al., 2015) as well as a wide range and non-random distribution of human influences and activities (Caujapé-Castells et al., 2010).

How human activities on the scale of plant communities affect and alter the spread and establishment of non-native species within a recipient region (e.g., an island) is still under scrutiny. This is because geographic entities (e.g., islands or countries) are often taken as a reference by invasion biologists for large-scale assessments (e.g. Blackburn et al., 2016; Kueffer et al., 2010; Moser et al., 2018). Independent of scale, areas of high human densities will increase the likelihood of non-native species to establish (Spear et al.,
2013). More specifically, non-native specialists establish and persist in areas of high human densities because such areas are generally the core areas of introduction and establishment for a wide range of non-natives (Pauchard et al., 2009). We assume that non-native generalists will, as a result of their environmental tolerance and possibly also better dispersal strategies (Moravcová et al., 2015), be able to spread to areas that have a lower human footprint, leading to larger niche widths of non-natives in natural and undisturbed areas. Indeed, it is likely that both abiotic as well as anthropogenic influences act simultaneously on processes shaping biological invasions in plant communities. Deciphering their individual contributions will help our mechanistic understanding of biological invasions.

We argue that by using roadsides on oceanic islands across various archipelagos, we have standardized, repeatable settings that reduce the blurring effect of biotic interactions on the abiotic signal (Lembrechts et al., 2017; Poll et al., 2009). This allows identifying the potential climatic conditions that non-native species could spread into before they invade natural areas where biotic interactions such as competition (Haider et al., 2018; McDougall et al., 2018) but also plant associated mutualisms such as mycorrhizal fungi (Clavel et al., 2020) become more relevant. In this study, we use the aforementioned characteristics of islands as model systems (Burns, 2015) and roads across elevational gradients as experimental entities to study the drivers and dynamics of invasion processes to address the following research questions: (a) Which abiotic variables drive increasing generalism of non-native plant species described by the directional ecological filtering concept within and between oceanic islands? (b) Do anthropogenic drivers alter the pattern identified by directional ecological filtering within and between oceanic islands? (c) Does the prevalence of certain dispersal strategies lead to increasing generalism in non-native plant species?

2 | MATERIALS AND METHODS

2.1 | Study areas

In a coordinated effort, we studied 13 elevational roadside transects on 6 subtropical and tropical islands in 4 archipelagos across 3 oceans and major bioclimatic regions (Figure 1, Table 1, 218 vegetation records covering 360 non-natives species of 762 species). All roads that were sampled are two-lane paved mountain roads. Three of the four sampled archipelagos are of volcanic origin (Hawai'i, Canary Islands and La Réunion). Socotra is a continental fragment. All islands are high elevation islands covering a wide range of climatic conditions (Irl, 2014). Across all studied islands, mean annual temperature (MAT) ranges from around 5 to around 28°C and mean annual precipitation (MAP) from around 100 to almost 6000 mm, respectively (Table 1). All islands are affected by an asymmetric distribution of precipitation along the elevational gradient, depending on the orientation to trade winds, i.e., Canary Islands, Hawai'i and La Réunion, or monsoonal winds (Socotra). This asymmetry facilitates disentangling the key environmental drivers changing along

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

FIGURE 1 (a) Map of study regions represented by six islands from four archipelagos, including plot locations. (b) Although located on islands, our plots span climatic space of global magnitude, as illustrated by the Whittaker plot (Weigelt et al., 2013). The four outlier points on the mean annual precipitation axis are located in the hyper-humid zone of trade wind clouds on the windward side of Hawai'i Island, Hawai'i. Note the differing scale bars per island. All maps drawn using the WGS 84 projection.

elevation. Humans populate all islands. As a result, all islands have a relatively large but varying network of roads.

2.2 | Sampling design

Biotic interactions in natural communities can strongly blur the effects of climate forcing on the establishment and spread of non-native plant species (Catford et al., 2009). For this reason, we focus on roadside communities, where frequent disturbances strongly diminish the effects of biotic interactions (Lembrechts et al., 2017; Poll et al., 2009). To standardize for local disturbances, we sampled elevational transects in roadside plots (Bacaro et al., 2015; Jakobs et al., 2010; Madëra et al., 2013) because they provide not only repeatable
2013). More specifically, non-native specialists establish and persist in areas of high human densities because such areas are generally the core areas of introduction and establishment for a wide range of non-natives (Pauchard et al., 2009). We assume that non-native generalists will, as a result of their environmental tolerance and possibly also better dispersal strategies (Moravcová et al., 2015), be able to spread to areas that have a lower human footprint, leading to larger niche widths of non-natives in natural and undisturbed areas. Indeed, it is likely that both abiotic as well as anthropogenic influences act simultaneously on processes shaping biological invasions in plant communities. Deciphering their individual contributions will help our mechanistic understanding of biological invasions.

We argue that by using roadsides on oceanic islands across various archipelagos, we have standardized, repeatable settings that reduce the blurring effect of biotic interactions on the abiotic signal (Lembrechts et al., 2017; Poll et al., 2009). This allows identifying the potential climatic conditions that non-native species could spread into before they invade natural areas where biotic interactions such as competition (Haider et al., 2018; McDougall et al., 2018) but also plant associated mutualisms such as mycorrhizal fungi (Clavel et al., 2020) become more relevant. In this study, we use the aforementioned characteristics of islands as model systems (Burns, 2015) and roads across elevational gradients as experimental entities to study the drivers and dynamics of invasion processes to address the following research questions: (a) Which abiotic variables drive increasing generalism of non-native plant species described by the directional ecological filtering concept within and between oceanic islands? (b) Do anthropogenic drivers alter the pattern identified by directional ecological filtering within and between oceanic islands? (c) Does the prevalence of certain dispersal strategies lead to increasing generalism in non-native plant species?

2 | MATERIALS AND METHODS

2.1 | Study areas

In a coordinated effort, we studied 13 elevational roadside transects on 6 subtropical and tropical islands in 4 archipelagos across 3 oceans and major bioclimatic regions (Figure 1, Table 1, 218 vegetation records covering 360 non-natives species of 762 species). All roads that were sampled are two-lane paved mountain roads. Three of the four sampled archipelagos are of volcanic origin (Hawai'i, Canary Islands and La Réunion). Socotra is a continental fragment. All islands are high elevation islands covering a wide range of climatic conditions (Irl, 2014). Across all studied islands, mean annual temperature (MAT) ranges from around 5 to around 28°C and mean annual precipitation (MAP) from around 100 to almost 6000 mm, respectively (Table 1). All islands are affected by an asymmetric distribution of precipitation along the elevational gradient, depending on the orientation to trade winds, i.e., Canary Islands, Hawai'i and La Réunion, or monsoonal winds (Socotra). This asymmetry facilitates disentangling the key environmental drivers changing along

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

FIGURE 1 (a) Map of study regions represented by six islands from four archipelagos, including plot locations. (b) Although located on islands, our plots span climatic space of global magnitude, as illustrated by the Whittaker plot (Weigelt et al., 2013). The four outlier points on the mean annual precipitation axis are located in the hyper-humid zone of trade wind clouds on the windward side of Hawai'i Island, Hawai'i. Note the differing scale bars per island. All maps drawn using the WGS 84 projection.

elevation. Humans populate all islands. As a result, all islands have a relatively large but varying network of roads.

2.2 | Sampling design

Biotic interactions in natural communities can strongly blur the effects of climate forcing on the establishment and spread of non-native plant species (Catford et al., 2009). For this reason, we focus on roadside communities, where frequent disturbances strongly diminish the effects of biotic interactions (Lembrechts et al., 2017; Poll et al., 2009). To standardize for local disturbances, we sampled elevational transects in roadside plots (Bacaro et al., 2015; Jakobs et al., 2010; Madëra et al., 2013) because they provide not only repeatable
<figure><img src="image_5.png" /><figcaption>TABLE 1 List of all sampled islands and the number of non-native species, all species collected, and % non-native species on each respective island. Additionally, we provide selected environmental features of all transects within each island (minimum and maximum mean annual temperature, mean annual precipitation and elevation)</figcaption></figure>

<table><thead><tr><th>Archipelago (Ocean)</th><th>Island</th><th>Transects per island</th><th>Non-native species collected</th><th>All species collected</th><th>% Non-natives</th><th>Min.-Max. mean annual temperature (°C)</th><th>Min.-Max. mean annual precipitation (mm)</th><th>Min.-Max. elevation (m a.s.l.)</th><th>Island area (km²)</th><th>Island age (My)</th></tr></thead><tbody><tr><td>Canary Islands (Atlantic)</td><td>Tenerife</td><td>3</td><td>61</td><td>232</td><td>26</td><td>8.8-20.1</td><td>96-799</td><td>5-2250</td><td>2034</td><td>11.5</td></tr><tr><td>Canary Islands (Atlantic)</td><td>La Palma</td><td>2</td><td>40</td><td>244</td><td>16</td><td>9.2-19.4</td><td>287-1312</td><td>161-2281</td><td>706</td><td>1.7</td></tr><tr><td>Hawaii'i (Pacific)</td><td>Maui</td><td>1</td><td>88</td><td>102</td><td>86</td><td>7.3-21.1</td><td>789-1617</td><td>364-2958</td><td>1883</td><td>1.5</td></tr><tr><td>Hawaii'i (Pacific)</td><td>Big Island</td><td>3</td><td>146</td><td>168</td><td>87</td><td>4.9-23.7</td><td>272-5964</td><td>21-3747</td><td>10430</td><td>0.6</td></tr><tr><td>Socotra (Indian Ocean)</td><td>Socotra</td><td>2</td><td>7</td><td>135</td><td>5</td><td>19.4-28.3</td><td>150-410</td><td>3-986</td><td>3625</td><td>Continental fragment</td></tr><tr><td>Mascarenes (Indian Ocean)</td><td>La Réunion</td><td>2</td><td>140</td><td>229</td><td>61</td><td>11.5-21.6</td><td>522-2588</td><td>382-2366</td><td>2512</td><td>3</td></tr></tbody></table>

settings ideal for comparing non-native species establishment even across biogeographic regions (Alexander et al., 2011; Seipel et al., 2012) but also provide habitat for endemic plant species such as roadside cliffs that shelter endemics from anthropogenic disturbances and introduced herbivores (Irl et al., 2014). Roads are often regarded as important vectors for the spread of non-native species (Walentowitz et al., 2019). In this context, human impact, which is measured as road density per km², represents the probability of introduction of non-native species to a certain location of an island. Figuratively speaking, where human impact is high, the 'introduction gateway' is farther open for a given non-native species.

We used standardized roadside plots to ensure comparability between study areas. We collected data from previously published data for Hawai'i from Jakobs et al. (2010), for Tenerife from Bacaro et al., (2015), and for Socotra from Madèra et al., (2013). Data that were collected exclusively for this study and previously unpublished can be found in Table S2a. On Hawaii, the Canary Islands and La Réunion roadsides were sampled using plots of 2 × 50 m (=100 m²) with the longer side being placed parallel to the road. A different plot shape (20 × 5 m = 100 m²) was chosen on Socotra with the longer side placed along the road (see Madèra et al., 2013 for details). We recorded all plant species within each plot. Data collection took place in 2007 (Tenerife, Hawai'i), 2011 (Socotra), 2012 (Maui), 2013 (La Palma), and 2015 (La Réunion). The sampling design resulted in 218 roadside plots with 16.8 ± 7.0 roadside plots per elevational transect. Species nomenclature was harmonized using Kew's Plant List (The Plant List, 2015). We determined the status as non-native using Acebes Ginovés et al., (2010) for the Canary Islands, Wagner et al., (2005) for Hawai'i, Boulet (2015) for La Réunion, and Miller and Morris (2004) and Senan et al., (2012) for Socotra.

Transects covered substantial elevational and climate gradients of continental magnitude (Figure 1, Table 1), highlighting the representability of this study. Overlap in non-native species composition was generally quite low between archipelagos (Table S2b). We recorded 360 non-native species (47% of all recorded species) in 218 plots. We found most non-native species to be restricted to one archipelago (314 species, 87.2%), while only few occurred on two (42 species, 11.7%) or three archipelagos (2 species, <1%). The two most widespread species were *Bidens pilosa* and *Rumex acetosella* occurring on Hawai'i, La Réunion, and the Canary Islands—both considered global invasive species in mountainous areas (Seipel et al., 2012). Six species had >30 occurrence records (*Hypochaeris radicata*: 40 [18% of plots], *Plantago lanceolata*: 38 [17% of plots], *Pennisetum clandestinum*: 35 [16% of plots], *Holcus lanatus*: 32 [15% of plots], *Senecio madagascariensis*: 32 [15% of plots], *Sonchus oleraceus*: 31 [14% of plots]). For a list of all species' occurrence records see Table S2b.

## 2.3 | Dispersal strategies

We obtained information on dispersal strategies (anemochory, zoochory, hydrochory, and anthropochory) for 191 of the 360 non-native plant species in our data set (53%). Anthropochory here
<figure><img src="image_6.png" /><figcaption>TABLE 1 List of all sampled islands and the number of non-native species, all species collected, and % non-native species on each respective island. Additionally, we provide selected environmental features of all transects within each island (minimum and maximum mean annual temperature, mean annual precipitation and elevation)</figcaption></figure>

<table><thead><tr><th>Archipelago (Ocean)</th><th>Island</th><th>Transects per island</th><th>Non-native species collected</th><th>All species collected</th><th>% Non-natives</th><th>Min.-Max. mean annual temperature (°C)</th><th>Min.-Max. mean annual precipitation (mm)</th><th>Min.-Max. elevation (m a.s.l.)</th><th>Island area (km²)</th><th>Island age (My)</th></tr></thead><tbody><tr><td>Canary Islands (Atlantic)</td><td>Tenerife</td><td>3</td><td>61</td><td>232</td><td>26</td><td>8.8-20.1</td><td>96-799</td><td>5-2250</td><td>2034</td><td>11.5</td></tr><tr><td>Canary Islands (Atlantic)</td><td>La Palma</td><td>2</td><td>40</td><td>244</td><td>16</td><td>9.2-19.4</td><td>287-1312</td><td>161-2281</td><td>706</td><td>1.7</td></tr><tr><td>Hawaii'i (Pacific)</td><td>Maui</td><td>1</td><td>88</td><td>102</td><td>86</td><td>7.3-21.1</td><td>789-1617</td><td>364-2958</td><td>1883</td><td>1.5</td></tr><tr><td>Hawaii'i (Pacific)</td><td>Big Island</td><td>3</td><td>146</td><td>168</td><td>87</td><td>4.9-23.7</td><td>272-5964</td><td>21-3747</td><td>10430</td><td>0.6</td></tr><tr><td>Socotra (Indian Ocean)</td><td>Socotra</td><td>2</td><td>7</td><td>135</td><td>5</td><td>19.4-28.3</td><td>150-410</td><td>3-986</td><td>3625</td><td>Continental fragment</td></tr><tr><td>Mascarenes (Indian Ocean)</td><td>La Réunion</td><td>2</td><td>140</td><td>229</td><td>61</td><td>11.5-21.6</td><td>522-2588</td><td>382-2366</td><td>2512</td><td>3</td></tr></tbody></table>

settings ideal for comparing non-native species establishment even across biogeographic regions (Alexander et al., 2011; Seipel et al., 2012) but also provide habitat for endemic plant species such as roadside cliffs that shelter endemics from anthropogenic disturbances and introduced herbivores (Irl et al., 2014). Roads are often regarded as important vectors for the spread of non-native species (Walentowitz et al., 2019). In this context, human impact, which is measured as road density per km², represents the probability of introduction of non-native species to a certain location of an island. Figuratively speaking, where human impact is high, the 'introduction gateway' is farther open for a given non-native species.

We used standardized roadside plots to ensure comparability between study areas. We collected data from previously published data for Hawai'i from Jakobs et al. (2010), for Tenerife from Bacaro et al., (2015), and for Socotra from Madèra et al., (2013). Data that were collected exclusively for this study and previously unpublished can be found in Table S2a. On Hawaii, the Canary Islands and La Réunion roadsides were sampled using plots of 2 × 50 m (=100 m²) with the longer side being placed parallel to the road. A different plot shape (20 × 5 m = 100 m²) was chosen on Socotra with the longer side placed along the road (see Madèra et al., 2013 for details). We recorded all plant species within each plot. Data collection took place in 2007 (Tenerife, Hawai'i), 2011 (Socotra), 2012 (Maui), 2013 (La Palma), and 2015 (La Réunion). The sampling design resulted in 218 roadside plots with 16.8 ± 7.0 roadside plots per elevational transect. Species nomenclature was harmonized using Kew's Plant List (The Plant List, 2015). We determined the status as non-native using Acebes Ginovés et al., (2010) for the Canary Islands, Wagner et al., (2005) for Hawai'i, Boullet (2015) for La Réunion, and Miller and Morris (2004) and Senan et al., (2012) for Socotra.

Transects covered substantial elevational and climate gradients of continental magnitude (Figure 1, Table 1), highlighting the representability of this study. Overlap in non-native species composition was generally quite low between archipelagos (Table S2b). We recorded 360 non-native species (47% of all recorded species) in 218 plots. We found most non-native species to be restricted to one archipelago (314 species, 87.2%), while only few occurred on two (42 species, 11.7%) or three archipelagos (2 species, <1%). The two most widespread species were *Bidens pilosa* and *Rumex acetosella* occurring on Hawai'i, La Réunion, and the Canary Islands—both considered global invasive species in mountainous areas (Seipel et al., 2012). Six species had >30 occurrence records (*Hypochaeris radicata*: 40 [18% of plots], *Plantago lanceolata*: 38 [17% of plots], *Pennisetum clandestinum*: 35 [16% of plots], *Holcus lanatus*: 32 [15% of plots], *Senecio madagascariensis*: 32 [15% of plots], *Sonchus oleraceus*: 31 [14% of plots]). For a list of all species' occurrence records see Table S2b.

## 2.3 | Dispersal strategies

We obtained information on dispersal strategies (anemochory, zoochory, hydrochory, and anthropochory) for 191 of the 360 non-native plant species in our data set (53%). Anthropochory here
defines species dispersal by humans that can be either accidental when seeds easily attach to clothing, footwear, tires, vehicles or through agricultural practices or intentional species spread through planting for ornamental and agricultural purposes (Faliński, 1972). Data for most species were extracted from CABI invasive species compendium (www.cabi.org/isc), a comprehensive, global database on biological and ecological characteristics of invasive species. A list of all dispersal strategies per species can be found in Table S3. When no information was available from this data base, we conducted web searches (these additional web sources are listed in Table S3). If necessary, multiple dispersal categories were assigned per species because many species have more than one dispersal strategy.

## 2.4 | Environmental data

Climate data, i.e., MAT and MAP, for the Canary Islands were taken from interpolated meteorological stations presented in Irl et al., (2015) with a resolution of 100 × 100 m. For Hawai'i, we downloaded the corresponding GIS layers and extracted the point values for each georeferenced plot with a resolution of 250 × 250 m (Giambelluca et al., 2013, 2014). The climate data for La Réunion were provided by Météo France (French National Institute for Meteorology and Climatology) and for Socotra by Scholte and de Geest (2010) and unpublished data with a resolution of 900 × 900 m. Per plot climatic data can be accessed in Table S2c.

We approximated human impact as road density (Foxcroft et al., 2011) by measuring per 1 km² raster cell the length of all roads. The more roads are located in a raster cell, the higher the road density value, and thus the higher the human impact (Basille et al., 2013). Here, high road density and thus human impact is associated with a high probability of introduction of non-native species (Guido et al., 2016; Pauchard et al., 2009). We assigned a road density value to each plot by using its location per raster cell to extract the road density value in a GIS framework. Other factors of human impact such as human population size or socioeconomic variables that have been shown to be important for invasion processes on the island scale (e.g., Kueffer et al., 2010 or Blackburn et al., 2016) are too coarse to be applied on the plot scale we are working on and, thus, were not considered for the analyses.

## 2.5 | Estimation of niche width

Climatic niche width of the particular species was estimated by following Hutchinson's concept where species niche is defined as an n-dimensional space (hypervolume) in which a species is able to exist under consideration of biotic interactions (Hutchinson, 1957). Following this concept, we quantified niche space for each species by using the hypervolume approach of Blonder and Harris (2019) based on MAP and MAT as input variables. Hypervolumes were calculated for each species with more than three records based on a box kernel density estimation using the `hypervolume()` command

of the hypervolume R package (4.0.3, Blonder & Harris, 2019). The Silverman bandwidth estimator was used to estimate the bandwidths for the box kernel density estimation of each species. This estimator is defined as $1.06 \cdot sd(X) \cdot m^{-1/5}$ with m being the number of observations and X is the data vector in each input variable. Both input variables, MAP and MAT, were standardized for hypervolume estimation to ensure that hypervolume dimensions are comparable. Elevational range of each species was calculated by subtracting the lowest elevational occurrences per species from the highest. Mean community niche width of the investigated non-native plant communities was subsequently calculated as the mean of the individual hypervolumes or elevational ranges of all species per plot.

We calculated temperature and precipitation range realized by each species across the six investigated islands as well as the global temperature and precipitation range realized by the investigated species. Species' global temperature and precipitation ranges were estimated by combining species distribution information obtained from the Botanical Information and Ecology Network (BIEN) database (via the BIEN r package, 1.2.4, see Figure S7; Maitner, 2020) with climatic information on MAT and MAP obtained from the Worldclim (bioclim) dataset with a 2.5 min resolution (Fick & Hijmans, 2017).

## 2.6 | Statistical analysis

We used simple and multiple linear regression models to assess the effect of climate, human influence and dispersal strategy on species richness and mean community niche width of non-native plants. Predictor variables were not transformed unless the model plots and diagnostics indicated the need of predictor transformation. For this we tested quadratic ($x^2$ and $x^2+x$) as well as log-transformed data against untransformed data.

We applied linear mixed-effect models by using geographic sampling location (expressed by transect ID) as random effect (random slope and intercept). With this we account for island-specific differences in how climate, human impact and dispersal strategies influence invasion processes. We quantified the proportion of explained variation for the linear mixed-effect models by calculating Pseudo-$R^2$ for generalized mixed-effect models using the `r.squaredGLMM()` function from the *MuMln-R*-package (1.43.17; Barton, 2020). The proportion of explained variation is thereby deconstructed into a Marginal $R^2$ that is the contribution of variance that is explained by the fixed effect alone (e.g., MAT or MAP) and a Conditional $R^2$ that quantifies the variance explained by the fixed effect plus the random effect.

We used variance partitioning to decipher the contribution of dispersal strategies versus climate drivers versus human impact in explaining mean community niche width of non-native species. We therefore selected the best predictors based on Marginal $R^2$ and AIC form the linear mixed-effect models and aggregated these variables to the three grouped predictor variables dispersal strategies, climate and human impact. Variation partitioning was performed using the `varpart()` function included in the *vegan* R-package (2.56; Oksanen
defines species dispersal by humans that can be either accidental when seeds easily attach to clothing, footwear, tires, vehicles or through agricultural practices or intentional species spread through planting for ornamental and agricultural purposes (Faliński, 1972). Data for most species were extracted from CABI invasive species compendium (www.cabi.org/isc), a comprehensive, global database on biological and ecological characteristics of invasive species. A list of all dispersal strategies per species can be found in Table S3. When no information was available from this data base, we conducted web searches (these additional web sources are listed in Table S3). If necessary, multiple dispersal categories were assigned per species because many species have more than one dispersal strategy.

## 2.4 | Environmental data

Climate data, i.e., MAT and MAP, for the Canary Islands were taken from interpolated meteorological stations presented in Irl et al., (2015) with a resolution of 100 × 100 m. For Hawai'i, we downloaded the corresponding GIS layers and extracted the point values for each georeferenced plot with a resolution of 250 × 250 m (Giambelluca et al., 2013, 2014). The climate data for La Réunion were provided by Météo France (French National Institute for Meteorology and Climatology) and for Socotra by Scholte and de Geest (2010) and unpublished data with a resolution of 900 × 900 m. Per plot climatic data can be accessed in Table S2c.

We approximated human impact as road density (Foxcroft et al., 2011) by measuring per 1 km² raster cell the length of all roads. The more roads are located in a raster cell, the higher the road density value, and thus the higher the human impact (Basille et al., 2013). Here, high road density and thus human impact is associated with a high probability of introduction of non-native species (Guido et al., 2016; Pauchard et al., 2009). We assigned a road density value to each plot by using its location per raster cell to extract the road density value in a GIS framework. Other factors of human impact such as human population size or socioeconomic variables that have been shown to be important for invasion processes on the island scale (e.g., Kueffer et al., 2010 or Blackburn et al., 2016) are too coarse to be applied on the plot scale we are working on and, thus, were not considered for the analyses.

## 2.5 | Estimation of niche width

Climatic niche width of the particular species was estimated by following Hutchinson's concept where species niche is defined as an n-dimensional space (hypervolume) in which a species is able to exist under consideration of biotic interactions (Hutchinson, 1957). Following this concept, we quantified niche space for each species by using the hypervolume approach of Blonder and Harris (2019) based on MAP and MAT as input variables. Hypervolumes were calculated for each species with more than three records based on a box kernel density estimation using the `hypervolume()` command

of the *hypervolume* R package (4.0.3, Blonder & Harris, 2019). The Silverman bandwidth estimator was used to estimate the bandwidths for the box kernel density estimation of each species. This estimator is defined as $1.06 \cdot sd(X) \cdot m^{-1/5}$ with *m* being the number of observations and *X* is the data vector in each input variable. Both input variables, MAP and MAT, were standardized for hypervolume estimation to ensure that hypervolume dimensions are comparable. Elevational range of each species was calculated by subtracting the lowest elevational occurrences per species from the highest. Mean community niche width of the investigated non-native plant communities was subsequently calculated as the mean of the individual hypervolumes or elevational ranges of all species per plot.

We calculated temperature and precipitation range realized by each species across the six investigated islands as well as the global temperature and precipitation range realized by the investigated species. Species' global temperature and precipitation ranges were estimated by combining species distribution information obtained from the Botanical Information and Ecology Network (BIEN) database (via the *BIEN* r package, 1.2.4, see Figure S7; Maitner, 2020) with climatic information on MAT and MAP obtained from the Worldclim (bioclim) dataset with a 2.5 min resolution (Fick & Hijmans, 2017).

## 2.6 | Statistical analysis

We used simple and multiple linear regression models to assess the effect of climate, human influence and dispersal strategy on species richness and mean community niche width of non-native plants. Predictor variables were not transformed unless the model plots and diagnostics indicated the need of predictor transformation. For this we tested quadratic ($x^2$ and $x^2+x$) as well as log-transformed data against untransformed data.

We applied linear mixed-effect models by using geographic sampling location (expressed by transect ID) as random effect (random slope and intercept). With this we account for island-specific differences in how climate, human impact and dispersal strategies influence invasion processes. We quantified the proportion of explained variation for the linear mixed-effect models by calculating Pseudo-$R^2$ for generalized mixed-effect models using the *r.squaredG-LMM()` function from the *MuMln-R*-package (1.43.17; Barton, 2020). The proportion of explained variation is thereby deconstructed into a Marginal $R^2$ that is the contribution of variance that is explained by the fixed effect alone (e.g., MAT or MAP) and a Conditional $R^2$ that quantifies the variance explained by the fixed effect plus the random effect.

We used variance partitioning to decipher the contribution of dispersal strategies versus climate drivers versus human impact in explaining mean community niche width of non-native species. We therefore selected the best predictors based on Marginal $R^2$ and AIC form the linear mixed-effect models and aggregated these variables to the three grouped predictor variables dispersal strategies, climate and human impact. Variation partitioning was performed using the *varpart()` function included in the *vegan* R-package (2.56; Oksanen
<figure><img src="image_6.png" /></figure>

et al., 2019). The proportional Venn diagram was drawn with *euler-APE* (Micallef & Rodgers, 2014).

We used non-parametric ANOVA (Kruskal-Wallis Test) to test
differences in species' climatic niche (hypervolume) and elevational
range between the different dispersal strategies. We used general-
ized linear models with a binomial distribution to test the effects of
species-specific niche width (i.e., hypervolume) on the probability of
being dispersed by wind, water, animals, and humans, respectively.

All analyses and statistical tests were performed in the R envi-
ronment (4.0.2; R Core Team, 2020) with a level of significance of
alpha =0.05.

3 | RESULTS

3.1 | The effects of climate and human impact on non-native species richness and niche width

Non-native species richness showed a positive effect on non-native
community niche width (Adjusted R² = 0.07, *p* = 0.001) but showed
no effect on the community mean elevational range (Adjusted
R² = 0.01, *p* = 0.12) when pooling all data from all islands. The posi-
tive effect of non-native species richness on non-native commu-
nity niche width disappeared while its effect on mean elevational
range was significant for the partially pooled data, i.e., when ac-
counting for transect and island identity in a linear mixed-effect
model (Marginal R² = 4.96 × 10⁻⁵, *p* = 0.93 and marginal R = 0.08,
*p* = .0015, respectively, Figure 2a,b). Non-native species richness
furthermore had a hump-shaped correlation with MAT for the
partially pooled data but no significant relationship was detected
for the pooled data (marginal R² = 0.13, *p* < 0.01 for the partially
pooled data and adjusted R² = 0.002, *p* > 0.3 for the pooled data,
Figure 2c, Table 2 for more details). A strong positive relationship
was observed between non-native species richness and precipita-
tion (log-transformed) which was significant for the pooled and par-
tially pooled data (Adjusted R² = 0.2, *p* = 1.49 × 10⁻⁸ for the pooled
data and marginal R² = 0.17, *p* = 0.003 for the partially pooled data,
Figure 2d). Non-native species richness showed a negative rela-
tion to road density only for the pooled data (Adjusted R² = 0.02,
*p* = 0.044, Figure 2e).

Mean community niche width had a hump-shaped relation-
ship with elevation (Adjusted R² = 0.12, *p* < 0.05 for the pooled and
marginal R² = 0.044, *p* < 0.01 partially pooled data, Figure 3a, see
Table 2 for more details), a positive relationship with precipitation (log-
transformed, only for the pooled data, adjusted R² = 38, *p* < 2 × 10⁻¹⁶,
Figure3d) and a negative relationship with road density (log-
transformed, adjusted R² = 57, *p* < 2 × 10⁻¹⁶ for the pooled and mar-
ginal R² = 0.19, *p* = 1.12 × 10⁻¹⁶ for the partially pooled data, Figure 3e).
Community mean elevational range showed a positive relationship
with elevation (adjusted R² = 49, *p* < 2 × 10⁻¹⁶ for the pooled and mar-
ginal R² = 0.47, *p* = 2.26 × 10⁻⁵ partially pooled data, Figure 3b).

The range of precipitation realized by the individual spe-
cies showed a pattern of environmental filtering with increasing

<figure><img src="image_7.png" /></figure>

**FIGURE 2** (a) Mean community niche width of non-native plant species and (b) mean elevational range versus number of non-native species. Number of non-native species versus (c) mean annual temperature, (d) mean annual precipitation and (e) road density. LM stands for the results from a simple univariate linear regression model using the predictor variable shown in the respective graph (pooled data). The regression line for the LM is shown in dark grey. LME stands for a univariate linear mixed-effects model using the predictor variable shown in the respective graph. The regression lines from a partial pooling LME approach for each transect are shown in the respective colors of the islands. Asterisks indicate level of significance: \* *p* < .05, \*\* *p* < .01 and \*\*\* *p* < .001. Solid lines indicate significant relationships, dashed lines non-significant relationships. See Table 2 for results of the corresponding linear mixed effects models using transect ID as random factor

precipitation, i.e., species with broad precipitation ranges (gener-
alists) were more frequent in humid conditions, while precipitation
specialists dominated in dry conditions (Figure 4a). In contrast, tem-
perature filtering was not directional but characterized by an opti-
mum temperature range of 10-20°C with temperature-specialist
filtering out toward low and high temperatures (Figure 4b). Both,
precipitation and temperature filtering observed for the investi-
gated islands are not related to global temperature and precipitation
ranges realized by the investigated species (see Figure S4).
<figure><img src="image_7.png" /></figure>

et al., 2019). The proportional Venn diagram was drawn with *euler-APE* (Micallef & Rodgers, 2014).

We used non-parametric ANOVA (Kruskal-Wallis Test) to test
differences in species' climatic niche (hypervolume) and elevational
range between the different dispersal strategies. We used general-
ized linear models with a binomial distribution to test the effects of
species-specific niche width (i.e., hypervolume) on the probability of
being dispersed by wind, water, animals, and humans, respectively.

All analyses and statistical tests were performed in the R envi-
ronment (4.0.2; R Core Team, 2020) with a level of significance of
alpha =0.05.

3 | RESULTS

3.1 | The effects of climate and human impact on non-native species richness and niche width

Non-native species richness showed a positive effect on non-native
community niche width (Adjusted R² = 0.07, *p* = 0.001) but showed
no effect on the community mean elevational range (Adjusted
R² = 0.01, *p* = 0.12) when pooling all data from all islands. The posi-
tive effect of non-native species richness on non-native commu-
nity niche width disappeared while its effect on mean elevational
range was significant for the partially pooled data, i.e., when ac-
counting for transect and island identity in a linear mixed-effect
model (Marginal R² = 4.96 × 10⁻⁵, *p* = 0.93 and marginal R = 0.08,
*p* =.0015, respectively, Figure 2a,b). Non-native species richness
furthermore had a hump-shaped correlation with MAT for the
partially pooled data but no significant relationship was detected
for the pooled data (marginal R² = 0.13, *p* < 0.01 for the partially
pooled data and adjusted R² = 0.002, *p* > 0.3 for the pooled data,
Figure 2c, Table 2 for more details). A strong positive relationship
was observed between non-native species richness and precipita-
tion (log-transformed) which was significant for the pooled and par-
tially pooled data (Adjusted R² = 0.2, *p* = 1.49 × 10⁻⁸ for the pooled
data and marginal R² = 0.17, *p* = 0.003 for the partially pooled data,
Figure 2d). Non-native species richness showed a negative rela-
tion to road density only for the pooled data (Adjusted R² = 0.02,
*p* = 0.044, Figure 2e).

Mean community niche width had a hump-shaped relation-
ship with elevation (Adjusted R² = 0.12, *p* < 0.05 for the pooled and
marginal R² = 0.044, *p* < 0.01 partially pooled data, Figure 3a, see
Table 2 for more details), a positive relationship with precipitation (log-
transformed, only for the pooled data, adjusted R² = 38, *p* < 2 × 10⁻¹⁶,
Figure3d) and a negative relationship with road density (log-
transformed, adjusted R² = 57, *p* < 2 × 10⁻¹⁶ for the pooled and mar-
ginal R² = 0.19, *p* = 1.12 × 10⁻¹⁶ for the partially pooled data, Figure 3e).
Community mean elevational range showed a positive relationship
with elevation (adjusted R² = 49, *p* < 2 × 10⁻¹⁶ for the pooled and mar-
ginal R² = 0.47, *p* = 2.26 × 10⁻⁵ partially pooled data, Figure 3b).

The range of precipitation realized by the individual spe-
cies showed a pattern of environmental filtering with increasing

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

FIGURE 2 (a) Mean community niche width of non-native plant species and (b) mean elevational range versus number of non-native species. Number of non-native species versus (c) mean annual temperature, (d) mean annual precipitation and (e) road density. LM stands for the results from a simple univariate linear regression model using the predictor variable shown in the respective graph (pooled data). The regression line for the LM is shown in dark grey. LME stands for a univariate linear mixed-effects model using the predictor variable shown in the respective graph. The regression lines from a partial pooling LME approach for each transect are shown in the respective colors of the islands. Asterisks indicate level of significance: \* *p* < .05, \*\* *p* < .01 and \*\*\* *p* < .001. Solid lines indicate significant relationships, dashed lines non-significant relationships. See Table 2 for results of the corresponding linear mixed effects models using transect ID as random factor

precipitation, i.e., species with broad precipitation ranges (gener-
alists) were more frequent in humid conditions, while precipitation
specialists dominated in dry conditions (Figure 4a). In contrast, tem-
perature filtering was not directional but characterized by an opti-
mum temperature range of 10-20°C with temperature-specialist
filtering out toward low and high temperatures (Figure 4b). Both,
precipitation and temperature filtering observed for the investi-
gated islands are not related to global temperature and precipitation
ranges realized by the investigated species (see Figure S4).
<table><caption>TABLE 2 Model results for a simple linear model and a linear mixed-effect model corresponding to Figures 2, 3, and 6. MAT = Mean annual temperature, MAP = Mean annual precipitation, # Non-native species = Number of non-native species. Transect was used as random effect (slope + intercept) for each model. Marginal R², Conditional R², AIC and *p*-values are given. Thereby, the Marginal R² is the contribution of variance that is explained by the fixed effect alone and the Conditional R² is the variance explained by the fixed effect plus the random effect</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">Simple linear model</th><th colspan="4">Linear mixed-effect model</th><th rowspan="2">Figure</th></tr><tr><th>Adjusted R<sup>2</sup></th><th>AIC</th><th>p-value</th><th>Marginal R<sup>2</sup></th><th>Conditional R<sup>2</sup></th><th>AIC</th><th>p-value</th></tr></thead><tbody><tr><td>Mean community niche width ~ # Non-native species</td><td>0.07</td><td>-477.14</td><td>0.001</td><td>4.96 x 10<sup>-5</sup></td><td>0.75</td><td>-609.27</td><td>0.93</td><td>2a</td></tr><tr><td>Mean elevational range ~ # Non-native species</td><td>0.01</td><td>2130.40</td><td>0.12</td><td>0.077</td><td>0.33</td><td>2113.64</td><td>0.0015</td><td>2b</td></tr><tr><td># Non-native species ~ MAT<sup>2</sup> + MAT</td><td>0.002</td><td>918.84</td><td>0.51/0.40</td><td>0.13</td><td>0.63</td><td>855.70</td><td>0.008/0.002</td><td>2c</td></tr><tr><td># Non-native species ~ log(MAP)</td><td>0.20</td><td>886.51</td><td>1.49 x 10<sup>-8</sup></td><td>0.17</td><td>0.34</td><td>875.97</td><td>0.003</td><td>2d</td></tr><tr><td># Non-native species ~ Road density</td><td>0.02</td><td>915.02</td><td>0.044</td><td>0.051</td><td>0.45</td><td>891.41</td><td>0.14</td><td>2e</td></tr><tr><td>Mean community niche width ~ Elevation<sup>2</sup> + Elevation</td><td>0.12</td><td>-484.25</td><td>0.037/0.002</td><td>0.044</td><td>0.80</td><td>-649.56</td><td>0.007/0.0009</td><td>3a</td></tr><tr><td>Mean elevational range ~ Elevation</td><td>0.49</td><td>2036.15</td><td>&lt;2 x 10<sup>-16</sup></td><td>0.47</td><td>0.69</td><td>2016.91</td><td>2.26 x 10<sup>-5</sup></td><td>3b</td></tr><tr><td>Mean community niche width ~ MAT</td><td>0.015</td><td>-469.47</td><td>0.076</td><td>0.02</td><td>0.84</td><td>-641.96</td><td>0.15</td><td>3c</td></tr><tr><td>Mean community niche width ~ log(MAP)</td><td>0.38</td><td>-535.78</td><td>&lt;2 x 10<sup>-16</sup></td><td>0.083</td><td>0.77</td><td>-632.16</td><td>0.065</td><td>3d</td></tr><tr><td>Mean community niche width ~ log(Road density)</td><td>0.57</td><td>-588.75</td><td>&lt;2 x 10<sup>-16</sup></td><td>0.19</td><td>0.64</td><td>-623.08</td><td>1.12 x 10<sup>-6</sup></td><td>3e</td></tr><tr><td>Mean community niche width ~ % Anemochory</td><td>0.062</td><td>-476.55</td><td>0.0015</td><td>0.014</td><td>0.77</td><td>-616.61</td><td>0.13</td><td>6a</td></tr><tr><td>Mean community niche width ~ % Anthropochory</td><td>-0.004</td><td>-466.63</td><td>0.54</td><td>0.004</td><td>0.74</td><td>-605.47</td><td>0.20</td><td>6b</td></tr><tr><td>Mean community niche width ~ % Hydrochory</td><td>0.081</td><td>-479.37</td><td>0.0003</td><td>0.014</td><td>0.70</td><td>-608.70</td><td>0.019</td><td>6c</td></tr><tr><td>Mean community niche width ~ % Zoochory</td><td>0.031</td><td>-471.87</td><td>0.019</td><td>0.002</td><td>0.76</td><td>-604.70</td><td>0.32</td><td>6d</td></tr></tbody></table>

Mean annual precipitation showed a hump-shaped relationship with elevation when accounting for transect and island identity but not for the pooled data (Fig S1a and Table S1 for statistics). MAT showed a strong negative relationship with increasing elevation (for the pooled and partially pooled data, Figure S1b and Table S1 for statistics) and road density decreased with increasing elevation (log-transformed, for pooled and partially pooled data, Figure S1c, Table S1). MAP showed a negative relationship with road density while MAT had a positive correlation with road density (both only for the pooled data, Figure S1d and e, Table S1).

## 3.2 | Dispersal strategies and niche width of non-natives

The probability of a species being wind dispersed increased with species-specific niche width (anemochory, *p* = 0.00284, Figure 5a). The same was true for probability of a species being dispersed by humans (anthropochory, *p* = 0.0251, Figure 5b). However, species niche width showed no significant effect for hydrochory and zoochory (*p* = 0.806 and *p* = 0.2097, respectively, Figure 5c,d). We found

no significant differences for species-specific niche width (i.e., environmental hypervolume, Figure S5a) and elevational range (Figure S5b) between the four dispersal strategies (anemochory, zoochory, hydrochory, and anthropochory; *p* = 0.19 for both, environmental hypervolume and elevational range).

At the community level, we observed effects of dispersal strategies on the mean community niche width (i.e., hypervolume). Mean community niche width increased with percent anemochory and percent zoochory species in the communities (only for the pooled data, adjusted R² = 0.062, *p* = 0.0015, and adjusted R² = 0.031, *p* = 0.019, respectively, Figure 6a,d) but decreased with percent hydrochory (Adjusted R² = 0.081, *p* = 0.0003 for the pooled and marginal R² = 0.014, *p* = 0.019 partially pooled data, Figure 6c). Percent anthropochory had no effect on the mean community niche (Figure 6b, Table 2 for statistics).

## 3.3 | Relative importance of climate, human impact and dispersal strategies

Altogether climate, human impact and dispersal strategies explained a substantial proportion of mean community niche width
<table><caption>TABLE 2 Model results for a simple linear model and a linear mixed-effect model corresponding to Figures 2, 3, and 6. MAT = Mean annual temperature, MAP = Mean annual precipitation, # Non-native species = Number of non-native species. Transect was used as random effect (slope + intercept) for each model. Marginal R², Conditional R², AIC and p-values are given. Thereby, the Marginal R² is the contribution of variance that is explained by the fixed effect alone and the Conditional R² is the variance explained by the fixed effect plus the random effect</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">Simple linear model</th><th colspan="4">Linear mixed-effect model</th><th rowspan="2">Figure</th></tr><tr><th>Adjusted R<sup>2</sup></th><th>AIC</th><th>p-value</th><th>Marginal R<sup>2</sup></th><th>Conditional R<sup>2</sup></th><th>AIC</th><th>p-value</th></tr></thead><tbody><tr><td>Mean community niche width ~ # Non-native species</td><td>0.07</td><td>-477.14</td><td>0.001</td><td>4.96 x 10<sup>-5</sup></td><td>0.75</td><td>-609.27</td><td>0.93</td><td>2a</td></tr><tr><td>Mean elevational range ~ # Non-native species</td><td>0.01</td><td>2130.40</td><td>0.12</td><td>0.077</td><td>0.33</td><td>2113.64</td><td>0.0015</td><td>2b</td></tr><tr><td># Non-native species ~ MAT<sup>2</sup> + MAT</td><td>0.002</td><td>918.84</td><td>0.51/0.40</td><td>0.13</td><td>0.63</td><td>855.70</td><td>0.008/0.002</td><td>2c</td></tr><tr><td># Non-native species ~ log(MAP)</td><td>0.20</td><td>886.51</td><td>1.49 x 10<sup>-8</sup></td><td>0.17</td><td>0.34</td><td>875.97</td><td>0.003</td><td>2d</td></tr><tr><td># Non-native species ~ Road density</td><td>0.02</td><td>915.02</td><td>0.044</td><td>0.051</td><td>0.45</td><td>891.41</td><td>0.14</td><td>2e</td></tr><tr><td>Mean community niche width ~ Elevation<sup>2</sup> + Elevation</td><td>0.12</td><td>-484.25</td><td>0.037/0.002</td><td>0.044</td><td>0.80</td><td>-649.56</td><td>0.007/0.0009</td><td>3a</td></tr><tr><td>Mean elevational range ~ Elevation</td><td>0.49</td><td>2036.15</td><td>&lt;2 x 10<sup>-16</sup></td><td>0.47</td><td>0.69</td><td>2016.91</td><td>2.26 x 10<sup>-5</sup></td><td>3b</td></tr><tr><td>Mean community niche width ~ MAT</td><td>0.015</td><td>-469.47</td><td>0.076</td><td>0.02</td><td>0.84</td><td>-641.96</td><td>0.15</td><td>3c</td></tr><tr><td>Mean community niche width ~ log(MAP)</td><td>0.38</td><td>-535.78</td><td>&lt;2 x 10<sup>-16</sup></td><td>0.083</td><td>0.77</td><td>-632.16</td><td>0.065</td><td>3d</td></tr><tr><td>Mean community niche width ~ log(Road density)</td><td>0.57</td><td>-588.75</td><td>&lt;2 x 10<sup>-16</sup></td><td>0.19</td><td>0.64</td><td>-623.08</td><td>1.12 x 10<sup>-6</sup></td><td>3e</td></tr><tr><td>Mean community niche width ~ % Anemochory</td><td>0.062</td><td>-476.55</td><td>0.0015</td><td>0.014</td><td>0.77</td><td>-616.61</td><td>0.13</td><td>6a</td></tr><tr><td>Mean community niche width ~ % Anthropochory</td><td>-0.004</td><td>-466.63</td><td>0.54</td><td>0.004</td><td>0.74</td><td>-605.47</td><td>0.20</td><td>6b</td></tr><tr><td>Mean community niche width ~ % Hydrochory</td><td>0.081</td><td>-479.37</td><td>0.0003</td><td>0.014</td><td>0.70</td><td>-608.70</td><td>0.019</td><td>6c</td></tr><tr><td>Mean community niche width ~ % Zoochory</td><td>0.031</td><td>-471.87</td><td>0.019</td><td>0.002</td><td>0.76</td><td>-604.70</td><td>0.32</td><td>6d</td></tr></tbody></table>

Mean annual precipitation showed a hump-shaped relationship with elevation when accounting for transect and island identity but not for the pooled data (Fig S1a and Table S1 for statistics). MAT showed a strong negative relationship with increasing elevation (for the pooled and partially pooled data, Figure S1b and Table S1 for statistics) and road density decreased with increasing elevation (log-transformed, for pooled and partially pooled data, Figure S1c, Table S1). MAP showed a negative relationship with road density while MAT had a positive correlation with road density (both only for the pooled data, Figure S1d and e, Table S1).

## 3.2 | Dispersal strategies and niche width of non-natives

The probability of a species being wind dispersed increased with species-specific niche width (anemochory, *p* = 0.00284, Figure 5a). The same was true for probability of a species being dispersed by humans (anthropochory, *p* = 0.0251, Figure 5b). However, species niche width showed no significant effect for hydrochory and zoochory (*p* = 0.806 and *p* = 0.2097, respectively, Figure 5c,d). We found

no significant differences for species-specific niche width (i.e., environmental hypervolume, Figure S5a) and elevational range (Figure S5b) between the four dispersal strategies (anemochory, zoochory, hydrochory, and anthropochory; *p* = 0.19 for both, environmental hypervolume and elevational range).

At the community level, we observed effects of dispersal strategies on the mean community niche width (i.e., hypervolume). Mean community niche width increased with percent anemochory and percent zoochory species in the communities (only for the pooled data, adjusted R² = 0.062, *p* = 0.0015, and adjusted R² = 0.031, *p* = 0.019, respectively, Figure 6a,d) but decreased with percent hydrochory (Adjusted R² = 0.081, *p* = 0.0003 for the pooled and marginal R² = 0.014, *p* = 0.019 partially pooled data, Figure 6c). Percent anthropochory had no effect on the mean community niche (Figure 6b, Table 2 for statistics).

## 3.3 | Relative importance of climate, human impact and dispersal strategies

Altogether climate, human impact and dispersal strategies explained a substantial proportion of mean community niche width
<figure><img src="image_8.png" /></figure>

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

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

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

FIGURE 3 (a) Mean community niche width and (b) mean elevational range of non-native plant species per plot against elevation. Mean community niche width per plot versus (c) mean annual temperature, (d) mean annual precipitation and (e) road density. We calculated mean community niche width by applying a kernel density-based hypervolume approach based on temperature and precipitation regime (see methods for details). LM stands for the results from a simple univariate linear regression model using the predictor variable shown in the respective graph (pooled data). The regression line for the LM is shown in dark grey. LME stands for a univariate linear mixed effects model using the predictor variable shown in the respective graph. The regression lines from a partial pooling LME approach for each transect are shown in the respective colors of the islands. Asterisks indicate level of significance: \*p < .05, \*\*p < .01 and \*\*\*p < .001. Solid lines indicate significant relationships, dashed lines non-significant relationships. See Table 2 for results of the corresponding linear mixed effects models using transect ID as random factor

(R² = 0.74; Figure 7). The major share of the explained variance was explained by human impact and climatic variables, i.e., the combined effects of MAT, MAP (log-transformed) and road density (human 28% + climate 20% + human/climate 28% = 76% of explained variance). Dispersal strategy was only subordinate in explaining mean community niche width independently (4%), while the jointly explained variance of dispersal strategies and road density amounted to 11% and of all three groups to 8%.

4 | DISCUSSION

Our study sets out to understand the major drivers of directional ecological filtering acting on non-native plant species on islands. We use roadside communities spanning elevational gradients on islands distributed in all major oceans in the tropics and subtropics and test the effects of climate, human influence and species dispersal strategy. Although directional ecological filtering, as proposed by Alexander et al., (2011), provides a valuable framework for biological invasions along environmental gradients, our study highlights the importance to look more in detail at the underlying environmental drivers related to climate, human impact and dispersal strategies. First, we confirm that the elevational range of non-native species increases with elevation as proposed by directional ecological filtering (Figure 3b). Second, when looking more in detail at the underlying drivers of directional ecological filtering, we see that precipitation and human impact (i.e., road density) are the main drivers, while temperature that has previously been proposed as a major driver of invasion processes on islands (Arévalo et al., 2005; Daehler, 2005) is of subordinate importance for the ecological amplitude (environmental niche width) of non-native species (Figure 3, Table 2). Third, temperature and precipitation specialists are restricted to areas of moderate-temperature conditions of 10–20°C and low-to-moderately high precipitation values of 100–1500 mm/year (38% of the non-native species in our study fall in this category), while generalist species also occur outside of this corridor (Figure 4, 62% of the non-native species). This supports the optimal environmental window hypothesis of Cury and Roy (1989). Fourth, while dispersal strategy overall has little influence on niche width (Figure 7), we see that generalist species favor specific dispersal strategies (namely anemochory and anthropochory, Figure 5). This indicates that these two dispersal strategies increase species’ niche width because they are better adapted to disperse along roads.

4.1 | Directional ecological filtering on tropical and
subtropical islands

We observed no unidirectional response of species' temperature range along the temperature gradient, but rather a bidirectional effect. Specialists subsequently drop out when moving out of the thermal optimum toward low- as well as high-temperature extremes. The observed pattern with maximum species occurrence in the center of the realized temperature range might be comparable to a mid-domain effect (sensu Colwell & Lees, 2000), where species occurrence is restricted by the limits of the realized temperature conditions. However, we observed a unidirectional, non-linear filtering effect for precipitation with regard to the range of precipitation realized by non-native plant species. For this climatic filter, we observed specialists to occur under low precipitation conditions, which can occur at both ends of the elevational gradient. With increasing precipitation and, thus water availability, also species' precipitations range increase and specialists are increasingly
<figure><img src="image_9.png" /></figure>

FIGURE 3 (a) Mean community niche width and (b) mean elevational range of non-native plant species per plot against elevation. Mean community niche width per plot versus (c) mean annual temperature, (d) mean annual precipitation and (e) road density. We calculated mean community niche width by applying a kernel density-based hypervolume approach based on temperature and precipitation regime (see methods for details). LM stands for the results from a simple univariate linear regression model using the predictor variable shown in the respective graph (pooled data). The regression line for the LM is shown in dark grey. LME stands for a univariate linear mixed effects model using the predictor variable shown in the respective graph. The regression lines from a partial pooling LME approach for each transect are shown in the respective colors of the islands. Asterisks indicate level of significance: \*p < .05, \*\*p < .01 and \*\*\*p < .001. Solid lines indicate significant relationships, dashed lines non-significant relationships. See Table 2 for results of the corresponding linear mixed effects models using transect ID as random factor

(R² = 0.74; Figure 7). The major share of the explained variance was explained by human impact and climatic variables, i.e., the combined effects of MAT, MAP (log-transformed) and road density (human 28% + climate 20% + human/climate 28% = 76% of explained variance). Dispersal strategy was only subordinate in explaining mean community niche width independently (4%), while the jointly explained variance of dispersal strategies and road density amounted to 11% and of all three groups to 8%.

4 | DISCUSSION

Our study sets out to understand the major drivers of directional ecological filtering acting on non-native plant species on islands. We use roadside communities spanning elevational gradients on islands distributed in all major oceans in the tropics and subtropics and test the effects of climate, human influence and species dispersal strategy. Although directional ecological filtering, as proposed by Alexander et al., (2011), provides a valuable framework for biological invasions along environmental gradients, our study highlights the importance to look more in detail at the underlying environmental drivers related to climate, human impact and dispersal strategies. First, we confirm that the elevational range of non-native species increases with elevation as proposed by directional ecological filtering (Figure 3b). Second, when looking more in detail at the underlying drivers of directional ecological filtering, we see that precipitation and human impact (i.e., road density) are the main drivers, while temperature that has previously been proposed as a major driver of invasion processes on islands (Arévalo et al., 2005; Daehler, 2005) is of subordinate importance for the ecological amplitude (environmental niche width) of non-native species (Figure 3, Table 2). Third, temperature and precipitation specialists are restricted to areas of moderate-temperature conditions of 10–20°C and low-to-moderately high precipitation values of 100–1500 mm/year (38% of the non-native species in our study fall in this category), while generalist species also occur outside of this corridor (Figure 4, 62% of the non-native species). This supports the optimal environmental window hypothesis of Cury and Roy (1989). Fourth, while dispersal strategy overall has little influence on niche width (Figure 7), we see that generalist species favor specific dispersal strategies (namely anemochory and anthropochory, Figure 5). This indicates that these two dispersal strategies increase species’ niche width because they are better adapted to disperse along roads.

4.1 | Directional ecological filtering on tropical and
subtropical islands

We observed no unidirectional response of species' temperature range along the temperature gradient, but rather a bidirectional effect. Specialists subsequently drop out when moving out of the thermal optimum toward low- as well as high-temperature extremes. The observed pattern with maximum species occurrence in the center of the realized temperature range might be comparable to a mid-domain effect (sensu Colwell & Lees, 2000), where species occurrence is restricted by the limits of the realized temperature conditions. However, we observed a unidirectional, non-linear filtering effect for precipitation with regard to the range of precipitation realized by non-native plant species. For this climatic filter, we observed specialists to occur under low precipitation conditions, which can occur at both ends of the elevational gradient. With increasing precipitation and, thus water availability, also species' precipitations range increase and specialists are increasingly
<figure><img src="image_14.png" /><figcaption>FIGURE 4 Range of (a) mean annual precipitation and (b) mean annual temperature realized by non-native plant species on islands. Each species is ranked from narrowest to widest climatic niche on the respective climate gradient. Niche width is here quantified as the range of temperature and precipitation covered by the species on all islands in our study (shown in black). The grey bar indicates for each species the global precipitation and temperature range, respectively. Black ticks indicate species with missing information on global distribution, i.e., global precipitation and temperature niche range</figcaption></figure>

filtered out (see Figures 3d and 4). We find that precipitation is par-
ticularly important on the cross-island scale, but also seems to have
an ecological relevance on the within-island scale because the slope
of all transects is positive in the partial pooling (although strictly
speaking not statistically significant, Figure 3d and Table 2). Both,
the observed precipitation and temperature filtering are not related
to the global temperature and precipitation ranges realized by the
investigated species—a fact that reinforces the importance of cli-
matic filtering as a structuring force for non-native plant communi-
ties on islands.

As suggested by Alexander et al., (2011), a thermal filter at low elevations (where most non-natives arrive due to high human impact; Pauchard et al., 2009) leads to a strong negative selection of non-native high elevation specialists, resulting in an effective increase in overall generalism in the arriving non-native species pool. However, human impact (quantified as road density) also had an important impact on structuring the niche width of non-native species. In fact, human impact was the best overall predictor of niche width of non-native species, nearly explaining 1/3 of the explained variance (Figure 7). The local influence of human impact can obviously override climatic filters that might be more dominant on larger scales, by producing site conditions more favorable for non-native species than expected by climate alone (Catford et al., 2011).

Interestingly, our results show that, by taking advantage of the unique climatic conditions of trade wind and monsoonal islands, di-rectional ecological filtering is not driven by temperature but actu-ally by precipitation and human impact on tropical and subtropical islands—an important addition to the theoretical framework pro-posed by Alexander et al., (2011). Within islands, increasing amounts of precipitation lead to a subsequent dropping out of xerophytic

specialists, while precipitation generalists with a wide tolerance of
moisture conditions are not affected.

4.2 | The effect of dispersal strategies on non-native niche width

When looking at the effect of species' dispersal strategies we observed anemochory (dispersal by wind) and anthropochory (dispersal by humans) to be significantly correlated with species' environmental niche width. In other words, species dispersed by wind and humans occupy larger environmental niches on islands than species which do not use these dispersal strategies. In contrast, dispersal by water and animals did not show any effects on non-native niche width. Thus, besides climatic filtering and habitat accessibility (i.e., road density), dispersal by wind and humans seems to be an important driver of niche occupation for non-native species on tropical and subtropical islands. This is in line with von der Lippe and Kowarik (2012) who argue that roadsides offer specific conditions that favor certain types of dispersal strategies that are adapted to wind drag by cars (e.g., small seed size) and other human activities. However, in comparison to climate and human impact, dispersal strategies were the weakest predictor of non-native niche width (Figure 7). This leads to the conclusion that dispersal strategies are only of subordinate importance for plant invasion processes on tropical and subtropical oceanic islands, also because previous studies have shown that the altitudinal limits of non-native species along roadsides are not dispersal limited but rather in equilibrium with their climatic limits (Alexander et al., 2009).
<figure><img src="image_10.png" /><figcaption>FIGURE 4 Range of (a) mean annual precipitation and (b) mean annual temperature realized by non-native plant species on islands. Each species is ranked from narrowest to widest climatic niche on the respective climate gradient. Niche width is here quantified as the range of temperature and precipitation covered by the species on all islands in our study (shown in black). The grey bar indicates for each species the global precipitation and temperature range, respectively. Black ticks indicate species with missing information on global distribution, i.e., global precipitation and temperature niche range</figcaption></figure>

filtered out (see Figures 3d and 4). We find that precipitation is par-
ticularly important on the cross-island scale, but also seems to have
an ecological relevance on the within-island scale because the slope
of all transects is positive in the partial pooling (although strictly
speaking not statistically significant, Figure 3d and Table 2). Both,
the observed precipitation and temperature filtering are not related
to the global temperature and precipitation ranges realized by the
investigated species—a fact that reinforces the importance of cli-
matic filtering as a structuring force for non-native plant communi-
ties on islands.

As suggested by Alexander et al., (2011), a thermal filter at low elevations (where most non-natives arrive due to high human impact; Pauchard et al., 2009) leads to a strong negative selection of non-native high elevation specialists, resulting in an effective increase in overall generalism in the arriving non-native species pool. However, human impact (quantified as road density) also had an important impact on structuring the niche width of non-native species. In fact, human impact was the best overall predictor of niche width of non-native species, nearly explaining 1/3 of the explained variance (Figure 7). The local influence of human impact can obviously override climatic filters that might be more dominant on larger scales, by producing site conditions more favorable for non-native species than expected by climate alone (Catford et al., 2011).

Interestingly, our results show that, by taking advantage of the unique climatic conditions of trade wind and monsoonal islands, di-rectional ecological filtering is not driven by temperature but actu-ally by precipitation and human impact on tropical and subtropical islands—an important addition to the theoretical framework pro-posed by Alexander et al., (2011). Within islands, increasing amounts of precipitation lead to a subsequent dropping out of xerophytic

specialists, while precipitation generalists with a wide tolerance of
moisture conditions are not affected.

## 4.2 | The effect of dispersal strategies on non-native niche width

When looking at the effect of species' dispersal strategies we observed anemochory (dispersal by wind) and anthropochory (dispersal by humans) to be significantly correlated with species' environmental niche width. In other words, species dispersed by wind and humans occupy larger environmental niches on islands than species which do not use these dispersal strategies. In contrast, dispersal by water and animals did not show any effects on non-native niche width. Thus, besides climatic filtering and habitat accessibility (i.e., road density), dispersal by wind and humans seems to be an important driver of niche occupation for non-native species on tropical and subtropical islands. This is in line with von der Lippe and Kowarik (2012) who argue that roadsides offer specific conditions that favor certain types of dispersal strategies that are adapted to wind drag by cars (e.g., small seed size) and other human activities. However, in comparison to climate and human impact, dispersal strategies were the weakest predictor of non-native niche width (Figure 7). This leads to the conclusion that dispersal strategies are only of subordinate importance for plant invasion processes on tropical and subtropical oceanic islands, also because previous studies have shown that the altitudinal limits of non-native species along roadsides are not dispersal limited but rather in equilibrium with their climatic limits (Alexander et al., 2009).
<figure><img src="image_15.png" /></figure>

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

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

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

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

FIGURE 5 Relationship between dispersal trait occurrence and environmental niche width of the investigated non-native plant species for (a) anemochory, (b) anthropochory, (c) hydrochory and (d) zoochory. Blue lines and *p*-values depict the predictions and statistics of generalized linear models with a binomial distribution which were used to test the effects of species niche width (i.e., hypervolume) on the probability of being dispersed by wind, water, animals, and humans, respectively. See full statistical results in Table S6

In general, information on dispersal strategies was more abundant for species with bigger environmental niches (Figure S5c), which might bias our observations to a certain degree. However, the contrasting trends we observed between species-specific niche width and the different dispersal strategies made us rather confident about the ecological significances of these observations, indicating an ecological trend rather than a bias as a result of deficient data.

## 4.3 | Generalists versus specialists

In summary, our results suggest that generalist species are favored by directional ecological filtering because (a) of the higher probability

that species with large environmental niches will randomly overlap with (at least some part of) the main introductory regions at high human impact where introduction probability is high, and (b) once established in these introductory regions generalists can spread within the climate space of the recipient island. This spread seems to be mainly driven by the environmental tolerance of the species in combination with human-mediated habitat accessibility and dispersal. In contrast to generalists, specialists that have established under high human impact conditions, will have a lower probability of spreading into other climatic and human impact conditions found on the recipient island because their environmental requirements are confined to the conditions found at or similar to the conditions of the introductory region.
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

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

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

FIGURE 5 Relationship between dispersal trait occurrence and environmental niche width of the investigated non-native plant species for (a) anemochory, (b) anthropochory, (c) hydrochory and (d) zoochory. Blue lines and *p*-values depict the predictions and statistics of generalized linear models with a binomial distribution which were used to test the effects of species niche width (i.e., hypervolume) on the probability of being dispersed by wind, water, animals, and humans, respectively. See full statistical results in Table S6

In general, information on dispersal strategies was more abundant for species with bigger environmental niches (Figure S5c), which might bias our observations to a certain degree. However, the contrasting trends we observed between species-specific niche width and the different dispersal strategies made us rather confident about the ecological significances of these observations, indicating an ecological trend rather than a bias as a result of deficient data.

## 4.3 | Generalists versus specialists

In summary, our results suggest that generalist species are favored by directional ecological filtering because (a) of the higher probability

that species with large environmental niches will randomly overlap with (at least some part of) the main introductory regions at high human impact where introduction probability is high, and (b) once established in these introductory regions generalists can spread within the climate space of the recipient island. This spread seems to be mainly driven by the environmental tolerance of the species in combination with human-mediated habitat accessibility and dispersal. In contrast to generalists, specialists that have established under high human impact conditions, will have a lower probability of spreading into other climatic and human impact conditions found on the recipient island because their environmental requirements are confined to the conditions found at or similar to the conditions of the introductory region.
<figure><img src="image_20.png" /></figure>

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

<figure><img src="image_22.png" /></figure>

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

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

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

FIGURE 6 Effect of four different dispersal strategies on mean community niche width. Dispersal strategies are given in relative contribution per plot (%). We calculated mean community niche width by applying a kernel density-based hypervolume approach based on temperature and precipitation regime (see methods for details). LM stands for the results from a simple univariate linear regression model using the predictor variable shown in the respective graph (pooled data). The regression line for the LM is shown in dark grey. LME stands for a univariate linear mixed effects model using the predictor variable shown in the respective graph. The regression lines from a partial pooling LME approach for each transect are shown in the respective colors of the islands. Asterisks indicate level of significance: \*p < .05, \*\*p < .01, and \*\*\*p < .001. Solid lines indicate significant relationships, dashed lines non-significant relationships. See Table 2 for results of the corresponding linear mixed effects models using transect ID as random factor

4.4 | Island-specific idiosyncrasies in the observed patterns

By adding elevational transect as random factor to the model, we can explain a substantial portion of variance in niche width of non-native plant species that is not covered by climate or human impact factors as well as dispersal alone. On the one hand, this might have to do with the fact that we used relatively simple climate factors (MAT and MAP) that only describe certain (albeit important) aspects of the climatic conditions affecting plant invasion. Annual means, for example, neglect temporal climate dynamics such as seasonality effects or inter-annual variability, which might additionally influence plant invasion (Parepa et al., 2013) as well as the contribution of fog drip that can be quite substantial in certain locations on trade wind-influenced islands (Ritter et al., 2008). However, the temporally and spatially high-resolution climate data necessary for such

analyses are often insufficient or missing on high elevation islands with complex, small-scale topography where climatic conditions can dramatically change within a few hundred meters—a caveat highlighted by Harter et al., (2015). On the other hand, the factor transect integrates across a wide range of environmental (e.g., indicating if a transect is located on windward or leeward side of an island) and spatial information (spatial proximity of plots along the transect) but also local road conditions (e.g., regarding age, traffic frequency, management, etc.), which are not covered by large-scale climatic or human impact factors.

5 | CONCLUSION

The large coverage in global climate space of our islands (Weigelt et al., 2013) suggests that the findings in our study are likely not
<figure><img src="image_16.png" /></figure>

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

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

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

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

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

FIGURE 6 Effect of four different dispersal strategies on mean community niche width. Dispersal strategies are given in relative contribution per plot (%). We calculated mean community niche width by applying a kernel density-based hypervolume approach based on temperature and precipitation regime (see methods for details). LM stands for the results from a simple univariate linear regression model using the predictor variable shown in the respective graph (pooled data). The regression line for the LM is shown in dark grey. LME stands for a univariate linear mixed effects model using the predictor variable shown in the respective graph. The regression lines from a partial pooling LME approach for each transect are shown in the respective colors of the islands. Asterisks indicate level of significance: \*p < .05, \*\*p < .01, and \*\*\*p < .001. Solid lines indicate significant relationships, dashed lines non-significant relationships. See Table 2 for results of the corresponding linear mixed effects models using transect ID as random factor

## 4.4 | Island-specific idiosyncrasies in the observed patterns

By adding elevational transect as random factor to the model, we can explain a substantial portion of variance in niche width of non-native plant species that is not covered by climate or human impact factors as well as dispersal alone. On the one hand, this might have to do with the fact that we used relatively simple climate factors (MAT and MAP) that only describe certain (albeit important) aspects of the climatic conditions affecting plant invasion. Annual means, for example, neglect temporal climate dynamics such as seasonality effects or inter-annual variability, which might additionally influence plant invasion (Parepa et al., 2013) as well as the contribution of fog drip that can be quite substantial in certain locations on trade wind-influenced islands (Ritter et al., 2008). However, the temporally and spatially high-resolution climate data necessary for such

analyses are often insufficient or missing on high elevation islands with complex, small-scale topography where climatic conditions can dramatically change within a few hundred meters—a caveat highlighted by Harter et al., (2015). On the other hand, the factor transect integrates across a wide range of environmental (e.g., indicating if a transect is located on windward or leeward side of an island) and spatial information (spatial proximity of plots along the transect) but also local road conditions (e.g., regarding age, traffic frequency, management, etc.), which are not covered by large-scale climatic or human impact factors.

## 5 | CONCLUSION

The large coverage in global climate space of our islands (Weigelt et al., 2013) suggests that the findings in our study are likely not
<figure><img src="image_26.png" /></figure>

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

FIGURE 7 Independent and joint variance of mean community niche width of non-native plant species explained by grouped climatic drivers, dispersal strategies and human influence as a result of variance partitioning. The circles and overlap of the Venn diagram are drawn proportionally to scale. The climate group consists of mean annual precipitation (log-transformed) and mean annual temperature, the dispersal group of percent anemochory and percent hydrochory, and human impact is quantified by road density (log-transformed). Non-significant variables were excluded. The independent and joint contribution per group is given as the percentage of the total explained variance

restricted to islands but might be applied to plant invasion phenomena where comparable environmental gradients exist (see Zefferman et al., 2015). Interestingly, our results show that patterns of plant species invasion across islands as well as within-islands can be largely quantified with species-climate and species-human interactions, i.e., human-mediated habitat accessibility, invasibility and dispersal, at least for roadside communities that are often the starting point for invasions into natural habitat (Haider et al., 2018; McDougall et al., 2018). The strong dependence of plant invasion on climatic and human drivers likely means that with ongoing climate change (Harter et al., 2015), interactions with other neobiota (Cubas et al., 2019) and land use change (Caujapé-Castells et al., 2010), plant invasion processes on islands will be strongly affected.

## ACKNOWLEDGEMENTS

We would like to thank Rémy Poncet, Nicolas Juillet, Jean-Noël Rivière and Jacques Fournel for field assistance in La Réunion, Pavel Kovar, Jaroslav Koblízek, Alena Salasova and Petr Jelinek on Socotra and Gabi Jakobs in Hawaii. Fieldwork in Hawaii and on Tenerife was done in the framework of the Mountain Invasion Research Network (MIREN). Fieldwork on Socotra was supported by the Ministry of Education of the Czech Republic (project CZ.1.07/2.3.00/20.0004

LANDTEAM). Carl Beierkuhnlein was supported by the European H2020 Project 641762 ECOPOTENTIAL: Improving future ecosystem benefits through Earth Observations. No permits were required for this research.

## DATA AVAILABILITY STATEMENT

The raw data for all previously unpublished datasets (La Réunion and La Palma) are published in DRYAD Online repository (Irl et al., 2021). All other datasets are cited accordingly. Also, a list of all environmental and anthropogenic variables as well as a list regarding each species status (alien vs native) and dispersal trait is published in DRYAD Online repository (Irl et al., 2021).

## ORCID

- Severin D. H. Irl https://orcid.org/0000-0002-1734-8607

- Andreas H. Schweiger https://orcid.org/0000-0003-2656-5918

- Manuel J. Steinbauer https://orcid.org/0000-0002-7142-9272

- José Ramón Arévalo https://orcid.org/0000-0003-2152-5212

- Carl Beierkuhnlein https://orcid.org/0000-0002-6456-4628

- Alessandro Chiarucci https://orcid.org/0000-0003-1160-235X

- José María Fernández-Palacios https://orcid.org/0000-0001-9741-6878

- Olivier Flores https://orcid.org/0000-0002-1416-0449

- Christoph Kueffer https://orcid.org/0000-0001-6701-0703

- Petr Maděra https://orcid.org/0000-0001-5415-8290

- Rüdiger Otto https://orcid.org/0000-0002-2764-8443

- Julienne M.-I. Schweiger https://orcid.org/0000-0002-3090-2924

- Dominique Strasberg https://orcid.org/0000-0003-3012-252X

- Anke Jentsch https://orcid.org/0000-0002-2345-8300

## REFERENCES

- Acebes Ginovés, J. R., León Arencibia, M. C., Rodríguez Navarro, M. L., del Arco Aguilar, M. García Gallo, A., Pérez de Paz, P. L., Rodríguez Delgado, O., Martín Osorio, V. E., & Wildpret de la Torre, W. (2010). Spermatophyta. In M. Arechavaleta, S. Rodríguez, N. Zurita, & A. García (Eds.), *Lista de especies silvestres de Canarias. Hongos, plantas y animales terrestres*. 2009 (pp. 122-172).: Gobierno de Canarias.

- Alexander, J. M., Kueffer, C., Daehler, C. C., Edwards, P. J., Pauchard, A., & Seipel, T.; MIREN Consortium (2011). Assembly of nonnative floras along elevational gradients explained by directional ecological filtering. *Proceedings of the National Academy of Sciences of the United States of America*, 108, 656-661. https://doi.org/10.1073/pnas.1013136108

- Alexander, J. M., Naylor, B., Poll, M., Edwards, P. J., & Dietz, H. (2009). Plant invasions along mountain roads: The altitudinal amplitude of alien Asteraceae forbs in their native and introduced ranges. *Ecography*, 3, 334-344. https://doi.org/10.1111/j.1600-0587.2008.05605.x

- Arévalo, J. R., Delgado, J. D., Otto, R., Naranjo, A., Salas, M., & Fernández-Palacios, J. M. (2005). Distribution of alien vs. native plant species in roadside communities along an altitudinal gradient in Tenerife and Gran Canaria (Canary Islands). *Perspectives in Plant Ecology, Evolution and Systematics*, 7, 185-202. https://doi.org/10.1016/j.ppees.2005.09.003

- Bacaro, G., Maccherini, S., Chiarucci, A., Jentsch, A., Rocchini, D., Torri, D., Gioria, M., Tordoni, E., Martellos, S., Altobelli, A., Otto, R., Escudero, C. G., Fernández-Lugo, S., Fernández-Palacios, J. M., & Arévalo, J. R. (2015). Distributional patterns of endemic, native
<figure><img src="image_22.png" /></figure>

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

FIGURE 7 Independent and joint variance of mean community niche width of non-native plant species explained by grouped climatic drivers, dispersal strategies and human influence as a result of variance partitioning. The circles and overlap of the Venn diagram are drawn proportionally to scale. The climate group consists of mean annual precipitation (log-transformed) and mean annual temperature, the dispersal group of percent anemochory and percent hydrochory, and human impact is quantified by road density (log-transformed). Non-significant variables were excluded. The independent and joint contribution per group is given as the percentage of the total explained variance

restricted to islands but might be applied to plant invasion phenomena where comparable environmental gradients exist (see Zefferman et al., 2015). Interestingly, our results show that patterns of plant species invasion across islands as well as within-islands can be largely quantified with species-climate and species-human interactions, i.e., human-mediated habitat accessibility, invasibility and dispersal, at least for roadside communities that are often the starting point for invasions into natural habitat (Haider et al., 2018; McDougall et al., 2018). The strong dependence of plant invasion on climatic and human drivers likely means that with ongoing climate change (Harter et al., 2015), interactions with other neobiota (Cubas et al., 2019) and land use change (Caujapé-Castells et al., 2010), plant invasion processes on islands will be strongly affected.

## ACKNOWLEDGEMENTS

We would like to thank Rémy Poncet, Nicolas Juillet, Jean-Noël Rivière and Jacques Fournel for field assistance in La Réunion, Pavel Kovar, Jaroslav Koblízek, Alena Salasova and Petr Jelinek on Socotra and Gabi Jakobs in Hawaii. Fieldwork in Hawaii and on Tenerife was done in the framework of the Mountain Invasion Research Network (MIREN). Fieldwork on Socotra was supported by the Ministry of Education of the Czech Republic (project CZ.1.07/2.3.00/20.0004

LANDTEAM). Carl Beierkuhnlein was supported by the European H2020 Project 641762 ECOPOTENTIAL: Improving future ecosystem benefits through Earth Observations. No permits were required for this research.

## DATA AVAILABILITY STATEMENT

The raw data for all previously unpublished datasets (La Réunion and La Palma) are published in DRYAD Online repository (Irl et al., 2021). All other datasets are cited accordingly. Also, a list of all environmental and anthropogenic variables as well as a list regarding each species status (alien vs native) and dispersal trait is published in DRYAD Online repository (Irl et al., 2021).

## ORCID

- Severin D. H. Irl https://orcid.org/0000-0002-1734-8607

- Andreas H. Schweiger https://orcid.org/0000-0003-2656-5918

- Manuel J. Steinbauer https://orcid.org/0000-0002-7142-9272

- José Ramón Arévalo https://orcid.org/0000-0003-2152-5212

- Carl Beierkuhnlein https://orcid.org/0000-0002-6456-4628

- Alessandro Chiarucci https://orcid.org/0000-0003-1160-235X

- José María Fernández-Palacios https://orcid.org/0000-0001-9741-6878

- Olivier Flores https://orcid.org/0000-0002-1416-0449

- Christoph Kueffer https://orcid.org/0000-0001-6701-0703

- Petr Maděra https://orcid.org/0000-0001-5415-8290

- Rüdiger Otto https://orcid.org/0000-0002-2764-8443

- Julienne M.-I. Schweiger https://orcid.org/0000-0002-3090-2924

- Dominique Strasberg https://orcid.org/0000-0003-3012-252X

- Anke Jentsch https://orcid.org/0000-0002-2345-8300

## REFERENCES

- Acebes Ginovés, J. R., León Arencibia, M. C., Rodríguez Navarro, M. L., del Arco Aguilar, M. García Gallo, A., Pérez de Paz, P. L., Rodríguez Delgado, O., Martín Osorio, V. E., & Wildpret de la Torre, W. (2010). Spermatophyta. In M. Arechavaleta, S. Rodríguez, N. Zurita, & A. García (Eds.), *Lista de especies silvestres de Canarias. Hongos, plantas y animales terrestres*. 2009 (pp. 122-172).: Gobierno de Canarias.

- Alexander, J. M., Kueffer, C., Daehler, C. C., Edwards, P. J., Pauchard, A., & Seipel, T.; MIREN Consortium (2011). Assembly of nonnative flora along elevational gradients explained by directional ecological filtering. *Proceedings of the National Academy of Sciences of the United States of America*, 108, 656-661. https://doi.org/10.1073/pnas.1013136108

- Alexander, J. M., Naylor, B., Poll, M., Edwards, P. J., & Dietz, H. (2009). Plant invasions along mountain roads: The altitudinal amplitude of alien Asteraceae forbs in their native and introduced ranges. *Ecography*, 3, 334-344. https://doi.org/10.1111/j.1600-0587.2008.05605.x

- Arévalo, J. R., Delgado, J. D., Otto, R., Naranjo, A., Salas, M., & Fernández-Palacios, J. M. (2005). Distribution of alien vs. native plant species in roadside communities along an altitudinal gradient in Tenerife and Gran Canaria (Canary Islands). *Perspectives in Plant Ecology, Evolution and Systematics*, 7, 185-202. https://doi.org/10.1016/j.ppees.2005.09.003

- Bacaro, G., Maccherini, S., Chiarucci, A., Jentsch, A., Rocchini, D., Torri, D., Gioria, M., Tordoni, E., Martellos, S., Altobelli, A., Otto, R., Escudero, C. G., Fernández-Lugo, S., Fernández-Palacios, J. M., & Arévalo, J. R. (2015). Distributional patterns of endemic, native
<figure><img src="image_28.png" /></figure>

and alien species along a roadside elevation gradient in Tenerife, Canary Islands. *Community Ecology*, **16**, 223-234. https://doi.org/10.1556/168.2015.16.2.10

Barton, K. (2020). MuMln: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMln. https://CRAN.R-project.org/package=MuMln

Basille, M., Van Moorter, B., Herfindal, I., Martin, J., Linnell, J. D., Odden, J., Andersen, R., & Gaillard, J.-M. (2013). Selecting habitat to survive: The impact of road density on survival in a large carnivore. *PLoS One*, **8**, e65493. https://doi.org/10.1371/journal.pone.0065493

Blackburn, T. M., Delean, S., Pyšek, P., & Cassey, P. (2016). On the island biogeography of aliens: A global analysis of the richness of plant and bird species on oceanic islands. *Global Ecology and Biogeography*, **25**, 859-868. https://doi.org/10.1111/geb.12339

Blonder, B., & Harris, D. J. (2019). Hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.12. https://CRAN.R-project.org/package=hypervolume.

Boullet, V. (2015). Index de la flore vasculaire de la Réunion. Conservatoire Botanique National de Mascarin, Digital version 2015. Last update January 6 2015.

Burns, K. C. (2015). A theory of island biogeography for exotic species. *The American Naturalist*, **186**, 441-451. https://doi.org/10.1086/682934

Catford, J. A., Jansson, R., & Nilsson, C. (2009). Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. *Diversity and Distributions*, **15**, 22-40. https://doi.org/10.1111/j.1472-4642.2008.00521.x

Catford, J. A., Vesk, P. A., White, M. D., & Wintle, B. A. (2011). Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics. *Diversity and Distributions*, **17**, 1099-1110. https://doi.org/10.1111/j.1472-4642.2011.00794.x

Caujapé-Castells, J., Tye, A., Crawford, D. J., Santos-Guerra, A., Sakai, A., Beaver, K., Lobin, W., Florens, F.B.V., Moura, M., Jardim, R., Gómes, I., & Kueffer, C. (2010). Conservation of oceanic island floras: Present and future global challenges. *Perspectives in Plant Ecology, Evolution and Systematics*, **12**, 107-129. https://doi.org/10.1016/j.ppees.2009.10.001.

Clavel, J., Lembrechts, J., Alexander, J., Haider, S., Lenoir, J., Milbau, A., Nuñez, M. A., Pauchard, A., Nijs, I., & Verbruggen, E. (2020). The role of arbuscular mycorrhizal fungi in nonnative plant invasion along mountain roads. *New Phytologist*, Early View. https://doi.org/10.1111/nph.16954.

Colwell, R. K., & Lees, D. C. (2000). The mid-domain effect: Geometric constraints on the geography of species richness. *Trends in Ecology and Evolution*, **15**, 70-76. https://doi.org/10.1016/S0169-5347(99)01767-X

Cubas, J., Irl, S. D. H., Villafuerte, R., Bello-Rodríguez, V., Rodríguez-Luengo, J. L., del Arco, M., Martín-Esquivel, J. L., & González-Mancebo, J. M. (2019). Endemic plant species are more palatable to introduced herbivores than non-endemics. *Proceedings of the Royal Society B*, **286**, 20190136. https://doi.org/10.1098/rspb.2019.0136

Cury, P. M., & Roy, C. (1989). Optimal environmental window and pelagic fish recruitment success in upwelling areas. *Canadian Journal of Fisheries and Aquatic Science*, **46**, 670-680. https://doi.org/10.1139/f89-086

Daehler, C. C. (2005). Upper-montane plant invasions in the Hawaiian Islands: Patterns and opportunities. *Perspectives in Plant Ecology, Evolution and Systematics*, **7**, 203-216. https://doi.org/10.1016/j.ppees.2005.08.002

Dawson, W., Moser, D., Van Kleunen, M., Kreft, H., Pergl, J., Pysek, P., Weigelt, P., Winter, M., Lenzner, B., Blackburn, T. M., Dyer, E. E., Cassey, P., Scrivens, S. L., Economo, E. P., Guénard, B., Capinha, C., Seebens, H., García-Díaz, P., Nentwig, W., ... Essl, F. (2017). Global

hotspots and correlates of alien species richness across taxonomic groups. *Nature Ecology & Evolution*, **1**, 1-7. https://doi.org/10.1038/s41559-017-0186

Faliński, J. B. (1972). Anthropochory in xerothermic grasslands in the light of experimental data. *Acta Societatis Botanicorum Poloniae*, **41**, 357-368.

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. *International Journal of Climatology*, **37**, 4302-4315. https://doi.org/10.1002/joc.5086

Foxcroft, L. C., Jarošík, V., Pyšek, P., Richardson, D. M., & Rouget, M. (2011). Protected-area boundaries as filters of plant invasions. *Conservation Biology*, **25**, 400-405. https://doi.org/10.1111/j.1523-1739.2010.01617.x

Giambelluca, T. W., Chen, Q. I., Frazier, A. G., Price, J. P., Chen, Y.-L., Chu, P.-S., Eischeid, J. K., & Delparte, D. M. (2013). Online Rainfall Atlas of Hawai'i. *Bulletin of the American Meteorological Society*, **94**, 313-316. https://doi.org/10.1175/BAMS-D-11-00228.1

Giambelluca, T. W., Shuai, X., Barnes, M. L., Alliss, R. J., Longman, R. J., Miura, T., Businger, A. D. (2014). Evapotranspiration of Hawai'i. Final report submitted to the U.S. Army Corps of Engineers-Honolulu District, and the Commission on Water Resource Management, State of Hawai'i.

Guido, A., Vélez-Martin, E., Overbeck, G. E., & Pillar, V. D. (2016). Landscape structure and climate affect plant invasion in subtropical grasslands. *Applied Vegetation Science*, **19**, 600-610. https://doi.org/10.1111/avsc.12263

Haider, S., Kueffer, C., Bruelheide, H., Seipel, T., Alexander, J. M., Rew, L. J., Arévalo, J. R., Cavières, L. A., McDougall, K. L., Milbau, A., Naylor, B. J., Speziale, K., & Pauchard, A. (2018). Mountain roads and non-native species modify elevational patterns of plant diversity. *Global Ecology and Biogeography*, **27**, 667-678. https://doi.org/10.1111/geb.12727

Harter, D. E. V., Irl, S. D. H., Seo, B., Steinbauer, M. J., Gillespie, R., Triantis, K. A., Fernández-Palacios, J.-M., & Beierkuhnlein, C. (2015). Impacts of global climate change on the floras of oceanic islands—Projections, implications and current knowledge. *Perspectives in Plant Ecology, Evolution and Systematics*, **17**, 160-183. https://doi.org/10.1016/j.ppees.2015.01.003

Hutchinson, G. E. (1957). The multivariate niche. *Cold Spring Harbor Symposium on Quantitative Biology*, **22**, 415-421.

Irl, S. D. H. (2014). Patterns and disturbance-induced drivers of plant diversity and endemism on high elevation islands. Dissertation, University of Bayreuth, Germany, 213 pp.

Irl, S. D. H., Harter, D. E. V., Steinbauer, M. J., Gallego Puyol, D., Fernández-Palacios, J. M., Jentsch, A., & Beierkuhnlein, C. (2015). Climate vs. topography-spatial patterns of plant species diversity and endemism on a high-elevation island. *Journal of Ecology*, **103**, 1621-1633. https://doi.org/10.1111/1365-2745.12463

Irl, S. D. H., Schweiger, A. H., Steinbauer, M. J., Ah-Peng, C., Arévalo, J. R., Beierkuhnlein, C., Chiarucci, A., Daehler, C. C., Fernández-Palacios, J. M., Flores, O., Kueffer, C., Madera, P., Otto, R., Schweiger, J. M. I., Strasberg, D., & Jentsch, A. (2021). Human impact, climate and dispersal strategies determine plant invasion on islands. Dryad Dataset, https://doi.org/10.5061/dryad.nzs7h44r5

Irl, S. D. H., Steinbauer, M. J., Epperlein, L., Harter, D. E. V., Jentsch, A., Pätz, S., Wohlfart, C., & Beierkuhnlein, C. (2014). The Hitchhiker's guide to island endemism: Biodiversity and endemic perennial plant species in roadside and surrounding vegetation. *Biodiversity and Conservation*, **23**, 2273-2287. https://doi.org/10.1007/s10531-014-0722-6

Jakobs, G., Kueffer, C., & Daehler, C. C. (2010). Introduced weed richness across altitudinal gradients in Hawai'i: Humps, humans and water-energy dynamics. *Biological Invasions*, **12**, 4019-4031. https://doi.org/10.1007/s10530-010-9816-6
and alien species along a roadside elevation gradient in Tenerife, Canary Islands. *Community Ecology*, **16**, 223-234. https://doi.org/10.1556/168.2015.16.2.10

Barton, K. (2020). MuMln: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMln. https://CRAN.R-project.org/package=MuMln

Basille, M., Van Moorter, B., Herfindal, I., Martin, J., Linnell, J. D., Odden, J., Andersen, R., & Gaillard, J.-M. (2013). Selecting habitat to survive: The impact of road density on survival in a large carnivore. *PLoS One*, **8**, e65493. https://doi.org/10.1371/journal.pone.0065493

Blackburn, T. M., Delean, S., Pyšek, P., & Cassey, P. (2016). On the island biogeography of aliens: A global analysis of the richness of plant and bird species on oceanic islands. *Global Ecology and Biogeography*, **25**, 859-868. https://doi.org/10.1111/geb.12339

Blonder, B., & Harris, D. J. (2019). Hypervolume: High dimensional geometry and set operations using kernel density estimation, support vector machines, and convex hulls. R package version 2.0.12. https://CRAN.R-project.org/package=hypervolume.

Boullet, V. (2015). Index de la flore vasculaire de la Réunion. Conservatoire Botanique National de Mascarin, Digital version 2015. Last update January 6 2015.

Burns, K. C. (2015). A theory of island biogeography for exotic species. *The American Naturalist*, **186**, 441-451. https://doi.org/10.1086/682934

Catford, J. A., Jansson, R., & Nilsson, C. (2009). Reducing redundancy in invasion ecology by integrating hypotheses into a single theoretical framework. *Diversity and Distributions*, **15**, 22-40. https://doi.org/10.1111/j.1472-4642.2008.00521.x

Catford, J. A., Vesk, P. A., White, M. D., & Wintle, B. A. (2011). Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics. *Diversity and Distributions*, **17**, 1099-1110. https://doi.org/10.1111/j.1472-4642.2011.00794.x

Caujapé-Castells, J., Tye, A., Crawford, D. J., Santos-Guerra, A., Sakai, A., Beaver, K., Lobin, W., Florens, F.B.V., Moura, M., Jardim, R., Gómes, I., & Kueffer, C. (2010). Conservation of oceanic island floras: Present and future global challenges. *Perspectives in Plant Ecology, Evolution and Systematics*, **12**, 107-129. https://doi.org/10.1016/j.ppees.2009.10.001.

Clavel, J., Lembrechts, J., Alexander, J., Haider, S., Lenoir, J., Milbau, A., Nuñez, M. A., Pauchard, A., Nijs, I., & Verbruggen, E. (2020). The role of arbuscular mycorrhizal fungi in nonnative plant invasion along mountain roads. *New Phytologist*, Early View. https://doi.org/10.1111/nph.16954.

Colwell, R. K., & Lees, D. C. (2000). The mid-domain effect: Geometric constraints on the geography of species richness. *Trends in Ecology and Evolution*, **15**, 70-76. https://doi.org/10.1016/S0169-5347(99)01767-X

Cubas, J., Irl, S. D. H., Villafuerte, R., Bello-Rodríguez, V., Rodríguez-Luengo, J. L., del Arco, M., Martín-Esquivel, J. L., & González-Mancebo, J. M. (2019). Endemic plant species are more palatable to introduced herbivores than non-endemics. *Proceedings of the Royal Society B*, **286**, 20190136. https://doi.org/10.1098/rspb.2019.0136

Cury, P. M., & Roy, C. (1989). Optimal environmental window and pelagic fish recruitment success in upwelling areas. *Canadian Journal of Fisheries and Aquatic Science*, **46**, 670-680. https://doi.org/10.1139/f89-086

Daehler, C. C. (2005). Upper-montane plant invasions in the Hawaiian Islands: Patterns and opportunities. *Perspectives in Plant Ecology, Evolution and Systematics*, **7**, 203-216. https://doi.org/10.1016/j.ppees.2005.08.002

Dawson, W., Moser, D., Van Kleunen, M., Kreft, H., Pergl, J., Pysek, P., Weigelt, P., Winter, M., Lenzner, B., Blackburn, T. M., Dyer, E. E., Cassey, P., Scrivens, S. L., Economo, E. P., Guénard, B., Capinha, C., Seebens, H., García-Díaz, P., Nentwig, W., ... Essl, F. (2017). Global

hotspots and correlates of alien species richness across taxonomic groups. *Nature Ecology & Evolution*, **1**, 1-7. https://doi.org/10.1038/s41559-017-0186

Faliński, J. B. (1972). Anthropochory in xerothermic grasslands in the light of experimental data. *Acta Societatis Botanicorum Poloniae*, **41**, 357-368.

Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. *International Journal of Climatology*, **37**, 4302-4315. https://doi.org/10.1002/joc.5086

Foxcroft, L. C., Jarošík, V., Pyšek, P., Richardson, D. M., & Rouget, M. (2011). Protected-area boundaries as filters of plant invasions. *Conservation Biology*, **25**, 400-405. https://doi.org/10.1111/j.1523-1739.2010.01617.x

Giambelluca, T. W., Chen, Q. I., Frazier, A. G., Price, J. P., Chen, Y.-L., Chu, P.-S., Eischeid, J. K., & Delparte, D. M. (2013). Online Rainfall Atlas of Hawai'i. *Bulletin of the American Meteorological Society*, **94**, 313-316. https://doi.org/10.1175/BAMS-D-11-00228.1

Giambelluca, T. W., Shuai, X., Barnes, M. L., Alliss, R. J., Longman, R. J., Miura, T., Businger, A. D. (2014). Evapotranspiration of Hawai'i. Final report submitted to the U.S. Army Corps of Engineers-Honolulu District, and the Commission on Water Resource Management, State of Hawai'i.

Guido, A., Vélez-Martin, E., Overbeck, G. E., & Pillar, V. D. (2016). Landscape structure and climate affect plant invasion in subtropical grasslands. *Applied Vegetation Science*, **19**, 600-610. https://doi.org/10.1111/avsc.12263

Haider, S., Kueffer, C., Bruelheide, H., Seipel, T., Alexander, J. M., Rew, L. J., Arévalo, J. R., Cavières, L. A., McDougall, K. L., Milbau, A., Naylor, B. J., Speziale, K., & Pauchard, A. (2018). Mountain roads and non-native species modify elevational patterns of plant diversity. *Global Ecology and Biogeography*, **27**, 667-678. https://doi.org/10.1111/geb.12727

Harter, D. E. V., Irl, S. D. H., Seo, B., Steinbauer, M. J., Gillespie, R., Triantis, K. A., Fernández-Palacios, J.-M., & Beierkuhnlein, C. (2015). Impacts of global climate change on the floras of oceanic islands—Projections, implications and current knowledge. *Perspectives in Plant Ecology, Evolution and Systematics*, **17**, 160-183. https://doi.org/10.1016/j.ppees.2015.01.003

Hutchinson, G. E. (1957). The multivariate niche. *Cold Spring Harbor Symposium on Quantitative Biology*, **22**, 415-421.

Irl, S. D. H. (2014). Patterns and disturbance-induced drivers of plant diversity and endemism on high elevation islands. Dissertation, University of Bayreuth, Germany, 213 pp.

Irl, S. D. H., Harter, D. E. V., Steinbauer, M. J., Gallego Puyol, D., Fernández-Palacios, J. M., Jentsch, A., & Beierkuhnlein, C. (2015). Climate vs. topography-spatial patterns of plant species diversity and endemism on a high-elevation island. *Journal of Ecology*, **103**, 1621-1633. https://doi.org/10.1111/1365-2745.12463

Irl, S. D. H., Schweiger, A. H., Steinbauer, M. J., Ah-Peng, C., Arévalo, J. R., Beierkuhnlein, C., Chiarucci, A., Daehler, C. C., Fernández-Palacios, J. M., Flores, O., Kueffer, C., Madera, P., Otto, R., Schweiger, J. M. I., Strasberg, D., & Jentsch, A. (2021). Human impact, climate and dispersal strategies determine plant invasion on islands. Dryad Dataset, https://doi.org/10.5061/dryad.nzs7h44r5

Irl, S. D. H., Steinbauer, M. J., Epperlein, L., Harter, D. E. V., Jentsch, A., Pätz, S., Wohlfart, C., & Beierkuhnlein, C. (2014). The Hitchhiker's guide to island endemism: Biodiversity and endemic perennial plant species in roadside and surrounding vegetation. *Biodiversity and Conservation*, **23**, 2273-2287. https://doi.org/10.1007/s10531-014-0722-6

Jakobs, G., Kueffer, C., & Daehler, C. C. (2010). Introduced weed richness across altitudinal gradients in Hawai'i: Humps, humans and water-energy dynamics. *Biological Invasions*, **12**, 4019-4031. https://doi.org/10.1007/s10530-010-9816-6
<figure><img src="image_29.png" /></figure>

Jentsch, A., & White, P. S. (2019). A theory of pulse dynamics and disturbance in ecology. *Ecology*, 100, e02734. https://doi.org/10.1002/ecy.2734

Körner, C. (2007). The use of 'altitude' in ecological research. *Trends in Ecology and Evolution*, 22, 569–574. https://doi.org/10.1016/j.tree.2007.09.006

Kueffer, C. (2017). Plant invasions in the Anthropocene. *Science*, 358, 724–725. https://doi.org/10.1126/science.aao6371

Kueffer, C., Daehler, C. C., Torres-Santana, C. W., Lavergne, C., Meyer, J. Y., Otto, R., & Silva, L. (2010). A global comparison of plant invasions on oceanic islands. *Perspectives in Plant Ecology, Evolution and Systematics*, 12, 145–161. https://doi.org/10.1016/j.ppees.2009.06.002

Kueffer, C., Pyšek, P., & Richardson, D. M. (2013). Integrative invasion science: Model systems, multi-site studies, focused meta-analysis and invasion syndromes. *New Phytologist*, 200, 615–633. https://doi.org/10.1111/nph.12415

Lembrechts, J. J., Alexander, J. M., Cavieres, L. A., Haider, S., Lenoir, J., Kueffer, C., McDougall, K., Naylor, B. J., Nuñez, M. A., Pauchard, A., Rew, L. J., Nijs, I., & Milbau, A. (2017). Mountain roads shift native and non-native plant species' ranges. *Ecography*, 40, 353–364. https://doi.org/10.1111/ecog.02200.

Lewis, S. L., & Maslin, M. A. (2015). Defining the Anthropocene. *Nature*, 171, 171–180. https://doi.org/10.1038/nature14258

MacArthur, R. H., & Wilson, E. O. (1967). *The theory of island biogeography*. Princeton University Press.

Maděra, P., Kovář, P., Vojta, J., Volařík, D., Úradníček, L., Salašová, A., Kobližek, J., & Jelínek, P. (2013). Vegetation succession along new roads at Soqotra Island (Yemen): Effects of invasive plant species and utilization of selected native plant resistance against disturbance. *Journal of Landscape Ecology*, 6, 46–59.

Maitner, B. (2020). BIEN: Tools for accessing the botanical information and ecology network database. R package version 1.2.4. https://CRAN.R-project.org/package=BIEN

McDougall, K. L., Lembrechts, J., Rew, L. J., Haider, S., Cavieres, L. A., Kueffer, C., Milbau, A., Naylor, B. J., Nuñez, M. A., Pauchard, A., Seipel, T., Speziale, K. L., Wright, G. T., & Alexander, J. M. (2018). Running off the road: Roadside non-native plants invading mountain vegetation. *Biological Invasions*, 20, 3461–3473. https://doi.org/10.1007/s10530-018-1787-z

Micallef, L., & Rodgers, P. (2014). eulerAPE: Drawing area-proportional 3-venn diagrams using ellipses. *PLoS One*, 9, e101717. https://doi.org/10.1371/journal.pone.0101717

Miller, A. G., & Morris, M. (2004). Ethnoflora of the Soqotra archipelago. The Royal Botanic Garden Edinburgh.

Moravcová, L., Pyšek, P., Jarošík, V., & Pergl, J. (2015). Getting the right traits: Reproductive and dispersal characteristics predict the invasiveness of herbaceous plant species. *PLoS One*, 10, e0123634. https://doi.org/10.1371/journal.pone.0123634

Moser, D., Lenzner, B., Weigelt, P., Dawson, W., Kreft, H., Pergl, J., Pyšek, P., van Kleunen, M., Winter, M., Capinha, C., Cassey, P., Dullinger, S., Economo, E. P., García-Díaz, P., Guénard, B., Hofhansl, F., Mang, T., Seebens, H., & Essl, F. (2018). Remoteness promotes biological invasions on islands worldwide. *Proceedings of the National Academy of Sciences of the United States of America*, 115, 9270–9275. https://doi.org/10.1073/pnas.1804179115

Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., & Wagner, H. (2019). Vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan

Parepa, M., Fischer, M., & Bossdorf, O. (2013). Environmental variability promotes plant invasion. *Nature Communications*, 4, 1604. https://doi.org/10.1038/ncomms2632

Pauchard, A., Kueffer, C., Dietz, H., Daehler, C. C., Alexander, J., Edwards, P. J., Arévalo, J. R., Cavieres, L. A., Guisan, A., Haider, S., Jakobs, G., McDougall, K., Millar, C. I., Naylor, B. J., Parks, C. G., Rew, L. J., & Seipel, T. (2009). Ain't no mountain high enough: Plant invasions reaching new elevations. *Frontiers in Ecology and the Environment*, 7, 479–486. https://doi.org/10.1890/080072.

Petitpierre, B., Kueffer, C., Broennimann, O., Randin, C., Daehler, C., & Guisan, A. (2012). Climatic niche shifts are rare among terrestrial plant invaders. *Science*, 335, 1344–1348. https://doi.org/10.1126/science.1215933

Poll, M., Naylor, B. J., Alexander, J. M., Edwards, P. J., & Dietz, H. (2009). Seedling establishment of Asteraceae forbs along altitudinal gradients: A comparison of transplant experiments in the native and introduced ranges. *Diversity and Distributions*, 15, 254–265. https://doi.org/10.1111/j.1472-4642.2008.00540.x

R Core Team (2020). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing. https://www.R-project.org/

Ritter, A., Regalado, C. M., & Aschan, G. (2008). Fog water collection in a subtropical elfin laurel forest of the Garajonay National Park (Canary Islands): A combined approach using artificial fog catchers and a physically based impaction model. *Journal of Hydrometeorology*, 9, 920–935. https://doi.org/10.1175/2008JHM992.1

Scholte, P., & de Geest, P. (2010). The climate of Socotra Island (Yemen): A first-time assessment of the timing of the monsoon wind reversal and its influence on precipitation patterns and vegetation. *Journal of Arid Environments*, 74, 1507–1515. https://doi.org/10.1016/j.jarid.env.2010.05.017

Seipel, T., Alexander, J. M., Daehler, C. C., Rew, L. J., Edwards, P. J., Dar, P. A., McDougall, K., Naylor, B., Parks, C., Pollnac, F. W., Reshi, Z. A., Schroder, M., & Kueffer, C. (2015). Performance of the herb Verbascum thapsus along environmental gradients in its native and non-native ranges. *Journal of Biogeography*, 42, 132–143. https://doi.org/10.1111/jbi.12403

Seipel, T., Kueffer, C., Rew, L. J., Daehler, C. C., Pauchard, A., Naylor, B. J., Alexander, J. M., Edwards, P. J., Parks, C. G., Arévalo, J. R., Cavieres, L. A., Dietz, H., Jakobs, G., McDougall, K., Otto, R., & Walsh, N. (2012). Processes at multiple scales affect richness and similarity of non-native plant species in mountains around the world. *Global Ecology and Biogeography*, 21, 236–246. https://doi.org/10.1111/j.1466-8238.2011.00664.x

Senan, A. S., Tomasetto, F., Farcomeni, A., Somashekar, R. K., & Attorre, F. (2012). Determinants of plant species invasions in an arid island: Evidence from Socotra Island (Yemen). *Plant Ecology*, 213, 1381–1392. https://doi.org/10.1007/s11258-012-0098-1

Spear, D., Foxcroft, L. C., Bezuidenhout, H., & McGeoch, M. A. (2013). Human population density explains alien species richness in protected areas. *Biological Conservation*, 159, 137–147. https://doi.org/10.1016/j.biocon.2012.11.022

Steinbauer, M. J., Irl, S. D. H., González-Mancebo, J. M., Breiner, F. T., Hernández-Hernández, R., Hopfenmüller, S., Kidane, Y., Jentsch, A., & Beierkuhlein, C. (2017). Plant invasion and speciation along elevational gradients on the oceanic island La Palma, Canary Islands. *Ecology and Evolution*, 7, 771–779. https://doi.org/10.1002/ece3.2640

The Plant List 2015. Version 1.1. Published on the Internet; http://www.theplantlist.org/

van Kleunen, M., Dawson, W., Essl, F., Pergl, J., Winter, M., Weber, E., Kreft, H., Weigelt, P., Kartesz, J., Nishino, M., Antonova, L. A., Barcelona, J. F., Cabezas, F. J., Cárdenas, D., Cárdenas-Toro, J., Castaño, N., Chacón, E., Chatelain, C., Ebel, A. L., ... Pyšek, P. (2015). Global exchange and accumulation of non-native plants. *Nature*, 525, 100–103. https://doi.org/10.1038/nature14910.
<figure><img src="image_24.png" /></figure>

Jentsch, A., & White, P. S. (2019). A theory of pulse dynamics and disturbance in ecology. *Ecology*, 100, e02734. https://doi.org/10.1002/ecy.2734

Körner, C. (2007). The use of 'altitude' in ecological research. *Trends in Ecology and Evolution*, 22, 569–574. https://doi.org/10.1016/j.tree.2007.09.006

Kueffer, C. (2017). Plant invasions in the Anthropocene. *Science*, 358, 724–725. https://doi.org/10.1126/science.aao6371

Kueffer, C., Daehler, C. C., Torres-Santana, C. W., Lavergne, C., Meyer, J. Y., Otto, R., & Silva, L. (2010). A global comparison of plant invasions on oceanic islands. *Perspectives in Plant Ecology, Evolution and Systematics*, 12, 145–161. https://doi.org/10.1016/j.ppees.2009.06.002

Kueffer, C., Pyšek, P., & Richardson, D. M. (2013). Integrative invasion science: Model systems, multi-site studies, focused meta-analysis and invasion syndromes. *New Phytologist*, 200, 615–633. https://doi.org/10.1111/nph.12415

Lembrechts, J. J., Alexander, J. M., Cavieres, L. A., Haider, S., Lenoir, J., Kueffer, C., McDougall, K., Naylor, B. J., Nuñez, M. A., Pauchard, A., Rew, L. J., Nijs, I., & Milbau, A. (2017). Mountain roads shift native and non-native plant species' ranges. *Ecography*, 40, 353–364. https://doi.org/10.1111/ecog.02200.

Lewis, S. L., & Maslin, M. A. (2015). Defining the Anthropocene. *Nature*, 171, 171–180. https://doi.org/10.1038/nature14258

MacArthur, R. H., & Wilson, E. O. (1967). *The theory of island biogeography*. Princeton University Press.

Maděra, P., Kovář, P., Vojta, J., Volařík, D., Úradníček, L., Salašová, A., Kobližek, J., & Jelínek, P. (2013). Vegetation succession along new roads at Soqotra Island (Yemen): Effects of invasive plant species and utilization of selected native plant resistance against disturbance. *Journal of Landscape Ecology*, 6, 46–59.

Maitner, B. (2020). BIEN: Tools for accessing the botanical information and ecology network database. R package version 1.2.4. https://CRAN.R-project.org/package=BIEN

McDougall, K. L., Lembrechts, J., Rew, L. J., Haider, S., Cavieres, L. A., Kueffer, C., Milbau, A., Naylor, B. J., Nuñez, M. A., Pauchard, A., Seipel, T., Speziale, K. L., Wright, G. T., & Alexander, J. M. (2018). Running off the road: Roadside non-native plants invading mountain vegetation. *Biological Invasions*, 20, 3461–3473. https://doi.org/10.1007/s10530-018-1787-z

Micallef, L., & Rodgers, P. (2014). eulerAPE: Drawing area-proportional 3-venn diagrams using ellipses. *PLoS One*, 9, e101717. https://doi.org/10.1371/journal.pone.0101717

Miller, A. G., & Morris, M. (2004). Ethnoflora of the Soqotra archipelago. The Royal Botanic Garden Edinburgh.

Moravcová, L., Pyšek, P., Jarošík, V., & Pergl, J. (2015). Getting the right traits: Reproductive and dispersal characteristics predict the invasiveness of herbaceous plant species. *PLoS One*, 10, e0123634. https://doi.org/10.1371/journal.pone.0123634

Moser, D., Lenzner, B., Weigelt, P., Dawson, W., Kreft, H., Pergl, J., Pyšek, P., van Kleunen, M., Winter, M., Capinha, C., Cassey, P., Dullinger, S., Economo, E. P., García-Díaz, P., Guénard, B., Hofhansl, F., Mang, T., Seebens, H., & Essl, F. (2018). Remoteness promotes biological invasions on islands worldwide. *Proceedings of the National Academy of Sciences of the United States of America*, 115, 9270–9275. https://doi.org/10.1073/pnas.1804179115

Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E., & Wagner, H. (2019). Vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan

Parepa, M., Fischer, M., & Bossdorf, O. (2013). Environmental variability promotes plant invasion. *Nature Communications*, 4, 1604. https://doi.org/10.1038/ncomms2632

Pauchard, A., Kueffer, C., Dietz, H., Daehler, C. C., Alexander, J., Edwards, P. J., Arévalo, J. R., Cavieres, L. A., Guisan, A., Haider, S., Jakobs, G., McDougall, K., Millar, C. I., Naylor, B. J., Parks, C. G., Rew, L. J., & Seipel, T. (2009). Ain't no mountain high enough: Plant invasions reaching new elevations. *Frontiers in Ecology and the Environment*, 7, 479–486. https://doi.org/10.1890/080072.

Petitpierre, B., Kueffer, C., Broennimann, O., Randin, C., Daehler, C., & Guisan, A. (2012). Climatic niche shifts are rare among terrestrial plant invaders. *Science*, 335, 1344–1348. https://doi.org/10.1126/science.1215933

Poll, M., Naylor, B. J., Alexander, J. M., Edwards, P. J., & Dietz, H. (2009). Seedling establishment of Asteraceae forbs along altitudinal gradients: A comparison of transplant experiments in the native and introduced ranges. *Diversity and Distributions*, 15, 254–265. https://doi.org/10.1111/j.1472-4642.2008.00540.x

R Core Team (2020). *R: A language and environment for statistical computing*. R Foundation for Statistical Computing. https://www.R-project.org/

Ritter, A., Regalado, C. M., & Aschan, G. (2008). Fog water collection in a subtropical elfin laurel forest of the Garajonay National Park (Canary Islands): A combined approach using artificial fog catchers and a physically based impaction model. *Journal of Hydrometeorology*, 9, 920–935. https://doi.org/10.1175/2008JHM992.1

Scholte, P., & de Geest, P. (2010). The climate of Socotra Island (Yemen): A first-time assessment of the timing of the monsoon wind reversal and its influence on precipitation patterns and vegetation. *Journal of Arid Environments*, 74, 1507–1515. https://doi.org/10.1016/j.jarid.env.2010.05.017

Seipel, T., Alexander, J. M., Daehler, C. C., Rew, L. J., Edwards, P. J., Dar, P. A., McDougall, K., Naylor, B., Parks, C., Pollnac, F. W., Reshi, Z. A., Schroder, M., & Kueffer, C. (2015). Performance of the herb Verbascum thapsus along environmental gradients in its native and non-native ranges. *Journal of Biogeography*, 42, 132–143. https://doi.org/10.1111/jbi.12403

Seipel, T., Kueffer, C., Rew, L. J., Daehler, C. C., Pauchard, A., Naylor, B. J., Alexander, J. M., Edwards, P. J., Parks, C. G., Arévalo, J. R., Cavieres, L. A., Dietz, H., Jakobs, G., McDougall, K., Otto, R., & Walsh, N. (2012). Processes at multiple scales affect richness and similarity of non-native plant species in mountains around the world. *Global Ecology and Biogeography*, 21, 236–246. https://doi.org/10.1111/j.1466-8238.2011.00664.x

Senan, A. S., Tomasetto, F., Farcomeni, A., Somashekar, R. K., & Attorre, F. (2012). Determinants of plant species invasions in an arid island: Evidence from Socotra Island (Yemen). *Plant Ecology*, 213, 1381–1392. https://doi.org/10.1007/s11258-012-0098-1

Spear, D., Foxcroft, L. C., Bezuidenhout, H., & McGeoch, M. A. (2013). Human population density explains alien species richness in protected areas. *Biological Conservation*, 159, 137–147. https://doi.org/10.1016/j.biocon.2012.11.022

Steinbauer, M. J., Irl, S. D. H., González-Mancebo, J. M., Breiner, F. T., Hernández-Hernández, R., Hopfenmüller, S., Kidane, Y., Jentsch, A., & Beierkuhlein, C. (2017). Plant invasion and speciation along elevational gradients on the oceanic island La Palma, Canary Islands. *Ecology and Evolution*, 7, 771–779. https://doi.org/10.1002/ece3.2640

The Plant List 2015. Version 1.1. Published on the Internet; http://www.theplantlist.org/

van Kleunen, M., Dawson, W., Essl, F., Pergl, J., Winter, M., Weber, E., Kreft, H., Weigelt, P., Kartesz, J., Nishino, M., Antonova, L. A., Barcelona, J. F., Cabezas, F. J., Cárdenas, D., Cárdenas-Toro, J., Castaño, N., Chacón, E., Chatelain, C., Ebel, A. L., ... Pyšek, P. (2015). Global exchange and accumulation of non-native plants. *Nature*, 525, 100–103. https://doi.org/10.1038/nature14910.
<figure><img src="image_30.png" /></figure>

von der Lippe, M., & Kowarik, I. (2012). Interactions between propagule pressure and seed traits shape human-mediated seed dispersal along roads. *Perspectives in Plant Ecology, Evolution and Systematics*, 14, 123-130. https://doi.org/10.1016/j.ppees.2011.09.006.

Wagner, W. L., Herbst, D. R., & Lorence, D. H. (2005). Flora of the Hawaiian Islands website. http://botany.si.edu/pacificislandbiodiversity/hawaiianflora/index.htm

Walentowitz, A. J., Irl, S. D. H., Acevedo Rodríguez, A. J., Palomares-Martínez, Á., Vetter, V., Zennaro, B., Medina, F. M., & Beierkuhnlein, C. (2019). Graminoid invasion in an insular endemism hotspot and its protected areas. *Diversity, 11*, 192. https://doi.org/10.1007/s10531-014-0722-6

Weigel, R., Irl, S. D. H., Treydte, K., Beierkuhnlein, C., Berels, J., Field, R., Miranda, J. C., Steinbauer, A., Steinbauer, M. J., & Jentsch, A. (2018). A novel dendroecological method finds a non-linear relationship between elevation and seasonal growth continuity on an island with trade wind-influenced water availability. *AoB Plants*, *10*, ply070. https://doi.org/10.1093/aobpla ply070

Weigelt, P., Jetz, W., & Kreft, H. (2013). Bioclimatic and physical characterization of the world's islands. *Proceedings of the National Academy of Sciences of the United States of America*, 110, 15307-15312. https://doi.org/10.1073/pnas.1306309110.

Zefferman, E., Stevens, J. T., Charles, G. K., Dunbar-Irwin, M., Emam, T., Fick, S., Morales, L. V., Wolf, K. M., Young, D. J. N., & Young, T. P. (2015). Plant communities in harsh sites are less invaded: A summary of observations and proposed explanations. *AoB Plants*, *7*. https://doi.org/10.1093/aobpla/plv056

## BIOSKETCH

The author team around Severin D.H. Irl, Andreas H. Schweiger and Anke Jentsch is interested in the mechanisms behind plant invasion. They use the climatic heterogeneity of islands as model systems to better understand plant invasion processes. To make cross-island comparisons possible, the team used a standardized sampling protocol from the Mountain Invasion Research Network (MIREN; https://www.mountaininvasions.org/) for most islands.

Author contributions: SDHI, AHS and AJ conceived the ideas; AJ, CAP, JRA, AC, CCD, OF, PM, RO, DS, JMIS and AHS provided the data; AHS analyzed the data; SDHI and AHS lead the writing with a substantial contribution from all authors.

## SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section.

**How to cite this article:** Irl SD, Schweiger AH, Steinbauer MJ, et al. Human impact, climate and dispersal strategies determine plant invasion on islands. *J Biogeogr.* 2021;00:1-15. https://doi.org/10.1111/jbi.14119
<figure><img src="image_25.png" /></figure>

von der Lippe, M., & Kowarik, I. (2012). Interactions between propagule pressure and seed traits shape human-mediated seed dispersal along roads. *Perspectives in Plant Ecology, Evolution and Systematics*, 14, 123-130. https://doi.org/10.1016/j.ppees.2011.09.006.

Wagner, W. L., Herbst, D. R., & Lorence, D. H. (2005). Flora of the Hawaiian Islands website. http://botany.si.edu/pacificislandbiodiversity/hawaiianflora/index.htm

Walentowitz, A. J., Irl, S. D. H., Acevedo Rodríguez, A. J., Palomares-Martínez, Á., Vetter, V., Zennaro, B., Medina, F. M., & Beierkuhnlein, C. (2019). Graminoid invasion in an insular endemism hotspot and its protected areas. *Diversity, 11*, 192. https://doi.org/10.1007/s10531-014-0722-6

Weigel, R., Irl, S. D. H., Treydte, K., Beierkuhnlein, C., Berels, J., Field, R., Miranda, J. C., Steinbauer, A., Steinbauer, M. J., & Jentsch, A. (2018). A novel dendroecological method finds a non-linear relationship between elevation and seasonal growth continuity on an island with trade wind-influenced water availability. *AoB Plants*, *10*, ply070. https://doi.org/10.1093/aobpla ply070

Weigelt, P., Jetz, W., & Kreft, H. (2013). Bioclimatic and physical characterization of the world's islands. *Proceedings of the National Academy of Sciences of the United States of America*, 110, 15307-15312. https://doi.org/10.1073/pnas.1306309110.

Zefferman, E., Stevens, J. T., Charles, G. K., Dunbar-Irwin, M., Emam, T., Fick, S., Morales, L. V., Wolf, K. M., Young, D. J. N., & Young, T. P. (2015). Plant communities in harsh sites are less invaded: A summary of observations and proposed explanations. *AoB Plants*, *7*. https://doi.org/10.1093/aobpla/plv056

## BIOSKETCH

The author team around Severin D.H. Irl, Andreas H. Schweiger and Anke Jentsch is interested in the mechanisms behind plant invasion. They use the climatic heterogeneity of islands as model systems to better understand plant invasion processes. To make cross-island comparisons possible, the team used a standardized sampling protocol from the Mountain Invasion Research Network (MIREN; https://www.mountaininvasions.org/) for most islands.

Author contributions: SDHI, AHS and AJ conceived the ideas; AJ, CAP, JRA, AC, CCD, OF, PM, RO, DS, JMIS and AHS provided the data; AHS analyzed the data; SDHI and AHS lead the writing with a substantial contribution from all authors.

## SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section.

**How to cite this article:** Irl SD, Schweiger AH, Steinbauer MJ, et al. Human impact, climate and dispersal strategies determine plant invasion on islands. *J Biogeogr.* 2021;00:1-15. https://doi.org/10.1111/jbi.14119