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## VIII. SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of the article. **Appendix S1.** List of main originality indices cited, with references, abbreviations and some of their key properties.

**Appendix S2.** Illustration associated with Table 1.
## Appendix S1. List of main originality indices cited in this paper, with references, abbreviations and some of their key properties.

In Table S1.1, we describe the most used indices that measure species originality, but we are aware that other indices exist and many have been reviewed and compared elsewhere (e.g., Redding *et al.*, 2014; Cadotte & Davies, 2016). Our choice was structured based on the goal of giving an overview of the variety of originality indices and showing their link with diversity, which is a property of a set of species, and with abundance-based rarity, which is a property of individual species. We describe those links in detail in part III of the main text.

<table><caption>**Table S1.1.** Indices developed for measuring originality. The first column distinguishes among four different types of data used to evaluate species originality: tree structures (phylogenetic trees or trait-based dendrograms), dissimilarity matrices (phylogenetic or trait-based distances), multidimensional space axes and indices based on generalized entropy.</caption><thead><tr><th>Data type</th><th>Method description</th><th>Reference</th><th>Name¹ (Examples of abbreviations used in the literature)</th><th>Code used in our paper</th><th>Some known properties</th></tr></thead><tbody><tr><td rowspan="2">Tree-based approach, no branch length²</td><td>Inversely proportional to the number of branches descending from each internal node between the focal species (tip) and the root</td><td>May, 1990</td><td>May index (<i>MVW</i> [Redding <i>et al.</i>, 2014]; <i>M</i> [Pavoine <i>et al.</i>, 2017])</td><td><i>M</i></td><td>Compared to <i>VW</i>, it accounts for polytomies in phylogenetic trees (May, 1990)</td></tr><tr><td>Inversely proportional to the number of internal nodes between the focal species (tip) and the root</td><td>Vane-Wright <i>et al.</i>, 1991</td><td>Vane-Wright <i>et al.</i> index (<i>VW</i> [e.g., Redding <i>et al.</i>, 2014])</td><td><i>VW</i></td><td>Compared to <i>M</i>, it does not account for polytomies in phylogenetic trees (May, 1990)</td></tr></tbody></table>
<table><tr><td></td><td>Inversely related to the sum of scores attributed to the nodes between the focal species (tip) and the root. A score is attributed to each node depending on whether it has more or less species descending from it than its sister nodes</td><td>Nixon &amp; Wheeler, 1992; Redding et al., 2014</td><td>NWU=BPD<sup>1</sup> (Redding et al., 2014; BPD is the unweighted binary phylogenetic diversity of Nixon &amp; Wheeler, 1992)</td><td>NW U</td><td>Ranks all members of a clade as having the same priority relative to all members of its sister clade (Nixon &amp; Wheeler, 1992)</td></tr><tr><td></td><td>Inversely related to the sum of the number of species for each clade to which the focal species belongs</td><td></td><td>NWW=WPD<sup>1</sup> (Redding et al., 2014; WPD is the weighted phylogenetic diversity of Nixon &amp; Wheeler, 1992)</td><td>NW W</td><td>Can hardly distinguish the originality between species from the same clade (Huang et al., 2011)</td></tr><tr><td rowspan="3">Tree-based approach, with branch lengths<sup>2</sup></td><td>Apportions the evolutionary history represented by a branch length equally among descending daughter branches</td><td>Redding &amp; Mooers, 2006</td><td>Equal splits (ES [Redding &amp; Mooers, 2006])</td><td>ES</td><td rowspan="2">Sensitive to phylogenetic revisions and strongly dependent on the terminal branch lengths (Redding et al., 2014)</td></tr><tr><td>Apportions the evolutionary history represented by a branch length fairly among descending daughter species</td><td>Redding, 2003</td><td>Fair proportion (FP [Redding, 2003]); evolutionary distinctiveness (ED [Isaac et al., 2007])</td><td>FP</td></tr><tr><td>Distance from a focal species to where it subtends the tree</td><td>Altschul &amp; Lipman, 1990</td><td>Pendant edge (PE [Redding et al., 2008])</td><td>PE</td><td>Overweights unique species near the tips of a tree (Redding et al., 2008); ignores the pattern of shared evolution between species</td></tr></table>
<table><tr><td></td><td>Expected average rarity of the characters of a species under a framework of genome evolution</td><td>Huang et al., 2011</td><td>Character rarity (CHR [Huang et al., 2011])</td><td>CHR</td><td>If the phylogenetic tree is ultrametric, then CHR is equal to the APD and is correlated with NWW; incorporates models of dynamic processes of trait evolution (Huang et al., 2011)</td></tr><tr><td></td><td>Average pairwise phylogenetic distance from a focal species to all the others in a reference species set</td><td>Redding et al., 2014</td><td>Average phylogenetic distance (APD [Redding et al., 2014])<sup>3</sup></td><td>APD</td><td>Less sensitive to the terminal branch length changes than are FP and ES (Redding et al., 2014)</td></tr><tr><td rowspan="2">Dissimilarity-based approach</td><td>Shortest distance between the focal species and all the others in the set</td><td>Pavoine et al., 2017</td><td>Nearest neighbour (NN [Pavoine et al., 2017])</td><td>NN</td><td rowspan="2">With dissimilarity-based approaches, trait-based and phylogenetic originality can be compared with less distortion of trait information than can tree-based approaches</td></tr><tr><td>Average pairwise dissimilarities between the focal species and all the others in the set</td><td>Eiswerth & Haney, 1992</td><td>Genetic distinctiveness (Eiswerth & Haney, 1992); average<sup>3</sup> (AV, Pavoine et al., 2017)</td><td>AV</td></tr><tr><td>Originality in a multidimensional space<sup>4</sup></td><td>Distance from a focal species' point to the hypothetical average centroid of all species</td><td>Magnuson-Ford et al., 2009</td><td>Magnusson et al. index</td><td>5</td><td>May discriminate the traits responsible for the originality of identified species</td></tr></table>
<table><tr><td rowspan="2">Entropy-based approach</td><td rowspan="2">Estimates the abundances species should have to maximize the diversity of a set; these abundances reflect species' originalities (see section III.3.c)</td><td>Pavoine et al., 2005</td><td>Quadratic entropy-based (QE-based [Pavoine et al., 2005]; Qb [Pavoine et al., 2017])</td><td>Qb</td><td>Depends on ultrametric distances between species and on an entropy index (the quadratic entropy, Rao, 1982) that weights commonness over rarity (Pavoine et al., 2017)</td></tr><tr><td>Pavoine et al., 2017</td><td>Pavoine et al., index (Rb [Pavoine et al., 2017])</td><td>Rb</td><td>Can handle any matrix of dissimilarity between species (does not depend on ultrametric distances); depends on an entropy index (the R index) that weights rarity over commonness; is similar to AV, but better discriminates original from redundant species than does AV (Pavoine et al., 2017)</td></tr></table>

<footnote n="1">¹ When we did not find a specific name, we used the name of the authors who developed the index.</footnote>

<footnote n="2">² Tree-based approaches were generally applied to phylogenies. However, they have also been applied to functional dendrograms (e.g.; Sobral et al., 2016; Pavoine et al., 2017). In the latter case, the originality indices are sensitive to the way in which the dendrograms have been built, with a risk of distorting the information contained in the traits.</footnote>

<footnote n="3">³ APD is actually a special application of AV. However, in APD, the dissimilarities between species are calculated from a phylogenetic tree; we thus classified APD as belonging to the tree-based indices.</footnote>

<footnote n="4">⁴ Ways of multidimensional space construction: a functional multidimensional space can be obtained by at least three approaches. First, each axis in the functional space can be a functional continuous trait. Second, axes can be calculated with a principal component analysis (PCA) applied</footnote>
to continuous traits or by a Hill and Smith PCA applied to continuous and categorical traits. Third, axes can be calculated with a principal coordinate analysis (PCoA) or non-metric multidimensional scaling (nMDS) applied to trait-based dissimilarities between species. A phylogenetic space can be obtained by applying the PCoA or nMDS to phylogenetic dissimilarities between species. Magnusson *et al.* (2009) originally used a PCA on a matrix of species' morphological traits; they defined their index as the Euclidean distance from the origin of the PCA (the origin being the centroid of all points; we propose here to define species originality more generally as the distance from a focal species' point to the hypothetical average centroid of all species).

⁵ We did not find abbreviation for this index and did not use any in our paper.

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Appendix S2. Illustration associated with Table 1.

Consider a community with *S* species and a phylogenetic tree with the species as tips. As an illustration, we will consider the data set in Fig. S2.1. Pavoine, Love and Bonsall (2009) developed the following parametric index of phylogenetic diversity for the community:

$$I_q = \sum_{k=1}^{K} T_k \left[ \frac{1 - \sum_{b \in P_k} p_b^q}{q-1} \right] \quad (\text{eq. 1})$$

where *q* is the parameter of the diversity index. It controls the importance given to lineage abundances. *k* is a period in the phylogeny. Periods are defined between two successive speciation events (see Fig. S1.1(B) for an example). *T*<sub>*k*</sub> is the length of period *k* (time elapsed between the two speciation events). *P*<sub>*k*</sub> is the set of branches that cross period *k*. For example in Fig. S1.1(C), we highlighted in red the set of branches that cross the second period. *b* is a branch of the phylogenetic tree. *L*<sub>*b*</sub> is the length of branch *b*. *p*<sub>*b*</sub> is the summed relative abundance of all species descending from branch *b*.

Applied to the data set of Fig. S1.1, *I*<sub>*q*</sub> is equal to:

$$I_q = T_1 \left[ \frac{1 - (p_A + p_B + p_C)^q - (p_D + p_E)^q}{q-1} \right] + T_2 \left[ \frac{1 - (p_A + p_B)^q - p_C^q - (p_D + p_E)^q}{q-1} \right] + T_3 \left[ \frac{1 - (p_A + p_B)^q - p_C^q - p_D^q - p_E^q}{q-1} \right] + T_4 \left[ \frac{1 - p_A^q - p_B^q - p_C^q - p_D^q - p_E^q}{q-1} \right] \quad (\text{eq. 2})$$

This can also be written as:

$$I_q = \frac{1}{q-1} \left[ \begin{aligned} & (p_A + p_B + p_C)L_1(1 - (p_A + p_B + p_C)^{q-1}) + (p_D + p_E)L_{6,1}(1 - (p_D + p_E)^{q-1}) \\ & + (p_A + p_B)L_{2,2}(1 - (p_A + p_B)^{q-1}) + p_C L_{3,2}(1 - p_C^{q-1}) + (p_D + p_E)L_{6,2}(1 - (p_D + p_E)^{q-1}) \\ & + (p_A + p_B)L_{2,3}(1 - (p_A + p_B)^{q-1}) + p_C L_{3,3}(1 - p_C^{q-1}) + p_D L_{7,3}(1 - p_D^{q-1}) + p_E L_{8,3}(1 - p_E^{q-1}) \\ & p_A L_4(1 - p_A^{q-1}) + p_B L_5(1 - p_B^{q-1}) + p_C L_{3,4}(1 - p_C^{q-1}) + p_D L_{7,4}(1 - p_D^{q-1}) + p_E L_{8,4}(1 - p_E^{q-1}) \end{aligned} \right]$$

(eq. 3)
and thus

$$I_q = \frac{1}{q-1} \left[ \begin{array}{l} (p_A + p_B + p_C)L_1(1-(p_A + p_B + p_C)^{q-1}) \\ + (p_A + p_B)L_2(1-(p_A + p_B)^{q-1}) + p_C L_3(1-p_C^{q-1}) + p_A L_4(1-p_A^{q-1}) \\ + p_B L_5(1-p_B^{q-1}) + (p_D + p_E)L_6(1-(p_D + p_E)^{q-1}) \\ + p_D L_7(1-p_D^{q-1}) + p_E L_8(1-p_E^{q-1}) \end{array} \right] \quad (\text{eq. 4})$$

Index $I_q$ can thus also be written more generally as

$$I_q = \sum_b L_b p_b \left( \frac{1-p_b^{q-1}}{q-1} \right) \quad (\text{eq. 5})$$

which, for ultrametric trees, is equivalent to the general equation in Chiu and Chao (2014), as follows:

$$I_q = (T_1 + T_2 + T_3 + T_4 - \sum_b L_b p_b^q) / (q-1) \quad (\text{eq. 6})$$

Let $p_i$ be the relative abundance of species $i$, and let $C(i, Root)$ be the shortest path from species $i$ (tip) to the root of the tree. In Table 1, we highlighted that $I_q$ can also be written as a mean of abundance-weighted originalities:

$$I_q = \sum_{i=1}^{S} p_i O_q \quad (\text{eq. 7})$$

with

$$O_q = \left[ \sum_{b \in C(i, \text{Root})} L_b \left( \frac{1 - p_b^{q-1}}{q-1} \right) \right] \quad (\text{eq. 8})$$

Applied to the theoretical example of Fig. S1.1, eq. 7 corresponds to reorganizing eq. 4 as follows:
$$I_q = \frac{1}{q-1} \left\{ \begin{aligned} & p_A \left[ L_1 (1 - (p_A + p_B + p_C)^{q-1}) + L_2 (1 - (p_A + p_B)^{q-1}) + L_4 (1 - p_A^{q-1}) \right] \\ & p_B \left[ L_1 (1 - (p_A + p_B + p_C)^{q-1}) + L_2 (1 - (p_A + p_B)^{q-1}) + L_5 (1 - p_B^{q-1}) \right] \\ & + p_C \left[ L_1 (1 - (p_A + p_B + p_C)^{q-1}) + L_3 (1 - p_C^{q-1}) \right] \\ & + p_D \left[ L_6 (1 - (p_D + p_E)^{q-1}) + L_7 (1 - p_D^{q-1}) \right] \\ & + p_E \left[ L_6 (1 - (p_D + p_E)^{q-1}) + L_8 (1 - p_E^{q-1}) \right] \end{aligned} \right\}$$

<figure><img src="image_15.png" /><figcaption>**Fig. S2-1.** Theoretical data set with five species, named from A to E, and an ultrametric phylogenetic tree with the species as tips. In panel (A), the main aspects of the data set are given. The red circle represents the root node. Branch lengths of the tree are indicated and denoted by $L_i$ to $L_8$. The relative abundances of the species in a community are noted from $p_A$ for species A to $p_E$ for species E. In (B), the phylogeny is split into periods, from $T_1$ (the oldest) to $T_4$ (the most recent). These periods are defined between successive speciation events. The length of the branches that cross several periods is also split per period. The notation $L_{ij}$ means the length of the section of branch i in period j. This leads to $L_2 = L_{2,2} + L_{2,3}$, $L_3 = L_{3,2} + L_{3,3} + L_{3,4}$, $L_6 = L_{6,1} + L_{6,2}$, $L_7 = L_{7,3} + L_{7,4}$, $L_8 = L_{8,3} + L_{8,4}$, $L_{6,1} = T_1$, $L_{2,2} = L_{3,2} = L_{6,2} = T_2$, $L_{2,3} = L_{3,3} = L_{7,3} = L_{8,3} = T_3$, and $L_{3,4} = L_{7,4} = L_{8,4} = T_4$. In (C), as an example, we overlined the branches that cross the second period of length $T_2$ in red.</figcaption></figure>
References

CHIU, C.-H., JOST, L. & CHAO, A. (2014). Phylogenetic beta diversity, similarity, and differentiation measures based on Hill numbers. *Ecological Monographs* **84**, 21–44.

PAVOINE, S., LOVE, M. & BONSALL, M. (2009). Hierarchical partitioning of evolutionary and ecological patterns in the organization of phylogenetically-structured species assemblages: application to rockfish (genus: *Sebastes*) in the Southern California Bight. *Ecology Letters* **12**, 898–908.
DIVERSITY IN THE CITY:
URBANIZATION AFFECTS
DIFFERENTLY TRAIT-BASED AND
PHYLOGENETIC PLANT
ORIGINALITY AT LOCAL AND
REGIONAL SCALES
# INTRODUCTION TO CHAPTER 2

It is clear from Chapter 1 that species originality and diversity measures and concepts are tightly related. However, there were very few studies investigating this relation with a real dataset. In this chapter, we investigated species diversity dependence on species originality and their respective variation along an urbanization gradient. Urbanization is currently considered as one of the major threats to biodiversity worldwide, challenging species by habitat fragmentation and land-cover changes. Although, urban ecologists studied the biological diversity of cities, there is still a lack of information about how species contribute to the diversity observed in a given community and how it is affected by urbanization. In this study, we propose a methodological framework to analyze species originality and diversity at two spatial scales: local communities and regional species pool. We assessed species diversity as the mean of species local originalities, which represents a measure of community diversity, and the mean of regional originalities, which is viewed as the amount of the regional diversity expressed by species in the plot. Finally, we calculated the skewness of originalities which represents the fraction of original species in the community, i.e. the distribution of originality along redundancy-uniqueness gradient.

In the light of plant species importance for ecosystem services, we chose to apply our originality-diversity framework to the herbaceous plant communities in Paris and its surrounding region, Ile-de-France. We demonstrate that our framework can determine at which spatial scale, local or regional, urbanization induces variations in the diversity of the biological characteristics and evolutionary history of species, while identifying which species are responsible for these variations depending on their originality values. To our knowledge, this is the first real case study of plant originality measure in an urbanization context. We believe that the results of this study may assist urban policy-makers in efficient management of biodiversity.

This research work is a result of my collaboration with four research structures:

* Research team from Germany, based at **Helmholtz Centre for Environmental Research** – UFZ, Department Community Ecology, Halle (Saale) where I spent two months, in
January and February 2019, working with **Sonja Knapp**, **Walter Durka** and **Ingolf Kühn**.
At that stage, I had already started plant data analysis, but there was still some missing data
in plant traits and mismatches in taxonomical names between trait databases and species
occurrences database. **SK**, **WD** and **IK** considerably improved the trait data, and their
botanical expertise allowed to identify accidental species or obviously misidentified species
(e.g., alpine species that occur only once in a dataset are more likely to be identification
mistakes). **WD** also showed me methods he used to construct DaPhnE phylogeny of
vascular plants so I could add species from my dataset missing in the tree. **IK** contributed
significantly to the statistical analysis and improved it by adding spatial autocorrelation
analysis between species plots.

* **Vigie-flore** citizen science program, which is supervised by researchers from my own research unit and by the Vigie-Nature program: **Gabrielle Martin**, **Nathalie Machon** and **Emmanuelle Porcher** managed Vigie-flore data; **Eric Motard** supervised data collection and input to the database; **GM** substantially helped at the very beginning of this study, by guiding me through data structure and providing ideas for traits use.

* **Conservatoire Botanique National du Bassin Parisien** is represented by **Jeanne Vallet**, who provided several ideas for the originality framework and helped me with plant species taxonomy and occurrence probabilities.

* My PhD supervisors, **Sandrine Pavoine** and **Philippe Grandcolas**, who were invested at every stage of this study, bringing their expertise in statistical analysis and general context information.
Diversity in the city: urbanization affects trait-based and
phylogenetic plant originality at local and regional scales differently

Anna Kondratyeva<fnref n="1" />,<fnref n="2" />*, Sonja Knapp<fnref n="3" />, Walter Durka<fnref n="3" />,<fnref n="4" />, Ingolf Kühn<fnref n="3" />,<fnref n="4" />,<fnref n="5" />, Jeanne Vallet<fnref n="6" />, Nathalie Machon<fnref n="1" />, Gabrielle Martin<fnref n="7" />, Eric Motard<fnref n="8" />, Philippe Grandcolas<fnref n="2" />, Sandrine Pavoine<fnref n="1" />*

* Correspondence:

Anna Kondratyeva: anna.kondratyeva@edu.mnhn.fr

Sandrine Pavoine: sandrine.pavoine@mnhn.fr

**Keywords:** biodiversity measure, citizen science, functional trait, originality measure, phylogeny, spatial scale, urbanization

This version is in revision for Frontiers Ecology and Evolution

<footnote n="1">¹Centre d'Ecologie et des Sciences de la Conservation (CESCO), Muséum National d'Histoire Naturelle, Centre National de la Recherche Scientifique, Sorbonne Université, Paris, France</footnote>

<footnote n="2">²Institut Systématique Evolution Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Paris, France</footnote>

<footnote n="3">³Helmholtz Centre for Environmental Research – UFZ, Department Community Ecology, Halle (Saale), Germany</footnote>

<footnote n="4">⁴German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany.</footnote>

<footnote n="5">⁵Martin Luther University Halle-Wittenberg, Geobotany and Botanical Garden, Halle, Germany</footnote>

<footnote n="6">⁶Conservatoire Botanique National du Bassin Parisien (CBNBP), Muséum National d'Histoire Naturelle, Sorbonne Université, Paris, France</footnote>

<footnote n="7">⁷Institut Méditerranéen de Biodiversité et d'Ecologie marine et continentale (IMBE), Univ Avignon, Aix Marseille Univ, CNRS, IRD, IUT site Agroparc, Avignon Cedex 09, France</footnote>

<footnote n="8">⁸Institut d'Ecologie et des Sciences de l'Environnement de Paris (IEES), Sorbonne Université, Paris, France</footnote>
ABSTRACT

Urbanization is one of the most intensive and rapid human-driven factors that influence
biodiversity. Thus, society needs appropriate methods for understanding species responses to
urbanization processes. To find the appropriate methods, species diversity must be surveyed and
measured. Although a number of biodiversity metrics exist in ecology, little is known about the
contributions of different species to urban community diversity. In urban contexts, studies have
mostly analyzed the influence of urbanization on the spatial distributions of species and on their
functional traits and phylogenetic community structures. Here, we develop a framework that relies
on the originality of a species. A species is original if it possesses rare trait values compared to all
others in the community of species or if it is distantly related to the other species in the community.
The most original species have the greatest contributions to the diversity of that community. We
studied the influence of urbanization on species originality using Vigie-flore, the French citizen
science dataset of plant occurrences in the Île-de-France region. To visualize the urbanization
gradient, we calculated the percentage of urbanized land cover around the species plots. Since
urbanization acts simultaneously at multiple spatial levels, we considered both the local and regional
spatial scales as areas for the originality calculations. Using several trait databases and recent plant
phylogeny, we measured trait-based and phylogenetic originality. Then, for each plot, we calculated
the mean of the local species originalities, which represented a measure of community diversity,
and the mean of the regional originalities, which represented the amount of the regional diversity
expressed by species in the plot. While the trait-based originality increased along the urbanization
gradient at both scales, the phylogenetic originality decreased with urbanization at only the regional
scale, meaning that urban communities do not contribute to regional phylodiversity. We also found
that urbanization made some species original at the local scale, although they were not at the
regional scale. Our conceptual framework will help to identify the species responsible for variations
in diversity along environmental gradients and to determine the most relevant spatial scale for
urban sustainable management.
# INTRODUCTION

The United Nations (2018) predicted that by the year 2050, 68% of the global human population will live in urban areas. This process comes with a complex mix of challenges, including land-use modifications and habitat fragmentation, which increase the pressure on local remnant species diversity (Gaston, 2010). Urbanizing processes such as residential and commercial development are listed in the International Union for the Conservation of Nature (IUCN) Unified Classification of Direct Threats scheme (version 3.2) as threats with substantial impacts on biodiversity. In addition, some of the components of biodiversity have the potential to provide ecosystem services to humanity (Millennium Ecosystem Assessment (MEA), 2005; Robinson and Lundholm, 2012; Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), 2019). Therefore, it is now recognized that biodiversity needs to be fully considered in urban planning and landscape design (Ahern, 2013).

Urbanization incorporates a set of anthropogenic filters and modifies natural ecological filters that determine species diversity and composition of urban areas (Williams et al., 2009; Aronson et al., 2016). Now, it has been largely recognized that cities, representing a high spatial heterogeneity of habitats, can harbor an important amount of plant and animal species (McKinney, 2002; Kühn et al., 2004; Godefroid and Koedam, 2007; Pautasso et al., 2011) and that cities play an important role in biodiversity conservation (Kowarik, 2011; Ives et al., 2016; Soanes and Lentini, 2019). In contrast, many studies show negative impacts of urbanization on biological diversity mostly by biotic homogenization (Kühn and Klotz 2006; McKinney, 2006; Devictor et al., 2007, 2008; Morelli et al., 2016). These different viewpoints might be explained by scale-dependency of the urbanization effect on biodiversity (Pautasso, 2007). However, there is still a lack of understanding of the responses of species to urbanization at different spatial scales.

One of the most investigated taxonomic groups in urban ecology is composed of vascular plant species. These species have an important role in primary productivity (Lieth, 1975) and provide habitat for other taxa (Tews et al., 2004). Moreover, vegetation structure and vascular plant diversity
have been used as general indicators of urban environment conditions (Sukopp and Weiner, 1983; Tzoulas and James, 2010). The role of urban areas in biodiversity conservation has been increasingly recognized by policy makers (Grimm et al., 2000; Gill et al., 2007; Lundholm et al., 2014). For example, the development of green infrastructure has been encouraged in cities as a means to increase the resilience of cities to global changes and to improve human well-being (European Commission's Directorate-General Environment, 2012; Kabisch et al., 2016). Plant species are the focus of this development. To guide such developments, efforts to deepen the collective understanding of the responses of plant species to urbanization are the center of urban biodiversity research (Werner and Zahner, 2009; Palma et al., 2017), conservation (McKinney, 2006) and the socioecological sciences (Elands et al., 2015).

To guide sustainable urban development, biodiversity must be evaluated first. Measures of biodiversity should ideally represent all of its multifaceted and hierarchical complexity. In the urban context, studies have mainly focused on describing species distributions and abundances (Aronson et al., 2016; Deguines et al., 2016; Guetté et al., 2017), species trait distributions (Knapp et al., 2009, 2010; Vallet et al., 2010; Williams et al., 2015; Kalusová et al., 2017) and the phylogenetic structures of urban communities (Ricotta et al., 2012, 2015; Morelli et al., 2016; Knapp et al., 2017; Sol et al., 2017). Several measures can thus be used to evaluate urban biodiversity, including species richness (McKinney, 2002, 2008; Cervelli et al., 2013), trait-based diversity (Petchey and Gaston, 2002; Mason et al., 2005; Villéger et al., 2008) and phylodiversity (Faith, 1992; Webb, 2000; Pavoine and Bonsall, 2011). Trait-based approaches allow the measurement of community trait structure (Laliberté and Legendre, 2010), and phylogenetic approaches divulge the evolutionary history of a given community (Tucker et al., 2017, 2019). However, these measures represent the whole diversity of a community and give no information about the contributions of individual species to this diversity (Kondratyeva et al., 2019).

In urbanized areas, species constantly interact with their rapidly changing biotic environment (e.g., non-native species, Gross et al., 2013; Aronson et al., 2014; Kühn et al., 2017). Identifying the
species that are the most threatened by urbanization and estimating the consequences their local extinction could have on biodiversity could help in the prioritization of conservation actions. Thus, there is a need for a metric that evaluates the contribution of each species to the diversity of a reference set of species using pairwise differences among species based on their biological characteristics. Originality measures are such a tool (see Pavoine et al., 2017; Kondratyeva et al., 2019 for reviews on originality measures). A species is original if it has rare biological characteristics within a reference set of species (Pavoine et al., 2005). Such rare biological characteristics can be the distant position of the reference species on a phylogenetic tree (Isaac et al., 2007; Jetz et al., 2014; Redding et al., 2015a; Sol et al., 2017) or they can be trait values that few species share (Mouillot et al., 2013a; Brandl et al., 2016; Pavoine et al., 2017). Thus, in a species assemblage, the less a given species shares its trait values with the other species or the more distantly related it is to the other species, the more original it is and the more it contributes to the diversity of that species set. Inversely, a species is redundant and contributes less to community diversity when it shares its trait values with many other species and/or is closely related to the other species. Species originality and redundancy are often used as indicators of ecosystem functioning (Mouillot et al., 2013a; Brandl et al., 2016). For example, higher trait-based redundancy among species would increase interspecific competition for a limiting resource but, at the same time, would ensure higher ecosystem stability in changing environments (Walker et al., 1999; Tilman et al., 2014).

Biodiversity and originality measures are scale dependent. Moreover, urbanization acts simultaneously at various spatial scales (Aronson et al., 2016), with the response of species varying across scales, according to their evolutionary histories, trait values and the ecological processes they are involved in (Levin, 1992; Chave, 2013). Thus, studying species responses to urbanization necessitates data for local communities across multiple locations. Today, an increasingly burgeoning citizen science program, particularly in ecology, allows the assembly of large amounts of monitoring data (Kobori et al., 2016; Silvertown, 2016). In France, Vigie-Nature (www.vigienature.fr) is one of the largest national initiatives for citizen science in biodiversity
monitoring. It has already proven to be valuable in conservation policy-making (Couvet and Prevot, 2015). One of Vigie-Nature national programs, called Vigie-flore (http://www.vigienature.fr/fr/vigie-flore), has gathered information on plant occurrences across the country for every year since 2009. This long-term dataset has recently allowed the reporting of rapid shifts in plant community composition in response to climate change (Martin et al., 2019).

Therefore, as citizen science data accumulate across space and time, their usefulness in ecology and conservation studies is increasingly acknowledged (Dickinson et al., 2010; McKinley et al., 2017).

Here, using the Vigie-flore program, we analyzed the impact of urbanization on species originality at two spatial scales: local plant communities and the regional species pool. We used the mean of species-specific originality values, which represent a measure of local trait-based (functional) diversity and phylodiversity within a plant community. We investigated three questions as follows:

1. How do regional and local species originality contribute to variations in community diversity along an urbanization gradient? 2. How do non-native species contribute to community diversity?

3. Which species in particular drive community diversity across spatial scales? Since urban environments affect both the trait-based and phylogenetic compositions of communities (Knapp et al., 2008), we measured phylogenetic and trait-based originality for plant species. Finally, with this study, we show that originality measures are worth integrating into urban biodiversity management as metrics that are capable of integrating differences among the species of a community.

# MATERIALS & METHODS

## Study Area

We used plant community data sampled across the Île-de-France region by the participants of the national citizen science survey, Vigie-flore, which constitute a large national dataset of plant occurrences in diverse habitats. The Île-de-France region occupies a territory of 12 100 km² and is the most inhabited region of France, with 12.1 million inhabitants. It includes the city of Paris, which is the French capital and the nation's largest city with 2.2 million inhabitants in 2016, and
approximately 1300 other cities and towns with varying human populations (min = 27, max = 119 645, median = 1318, Institut d'Aménagement et d'Urbanisme (IAU), 2019). This region benefits from having the highest sampling effort within the Vigie-flore program (188 grid cells out of total of 586) as well as a detailed land cover geodatabase.

## Biological Data

Following the standardized protocol of the Vigie-flore program, skilled amateur botanists investigate randomly selected 1 km² grid cells. Each grid cell contains eight systematically distributed 10 m² plots. In each plot, the presence of all vascular plants is recorded. We considered the set of species in each 10 m² plot as a local community assemblage. We analyzed a subset of 2362 plots collected over the years 2009 to 2017 in all types of habitats (1231 in agricultural areas, 533 in forests, 281 in urbanized areas, 261 in artificial open areas and 56 in semi-natural areas). For computational reasons, only plots with a minimum of three species were retained. Indeed, it is meaningless to measure originality in a plot with one species where there are no other species, and in a plot with two species, each of the species would have equal originality. A total of 322 plots missing geolocalization were also excluded from the study. Out of 812 species recorded by Vigie-flore and found in Île-de-France, 593 herbaceous angiosperm species were retained. To assemble as much trait data as possible, we standardized species taxonomic nomenclature among datasets by using several synonymous names and updating them by using The Plant List (http://www.theplantlist.org/), the Tela Botanica website of the French botanist's network (https://www.tela-botanica.org/) and Flora Gallica (Tison and de Foucault, 2017). For further analysis, we kept species names as found in Vigie-flore data with a taxonomical reference TAXREF (V2.0, Gargominy, 2008). We removed some data from the initial species dataset: 1. species occurring only in plots of 1 or 2 species (see explanation above); 2. species identified as accidental or obviously misidentified (e.g., alpine species that occur only once in a dataset are more likely to be identification mistakes); 3. woody species, as they seem to be affected less strongly by urbanization than other groups (Neil and Wu, 2006) and because of their outlying trait values; 4.
Bryophyta and Pteridophyta species; and 5. species that rely on human assistance (no cultivated or ornamental species). Woody and herbaceous species were distinguished following the LEDA trait database (Kleyer et al., 2008).

## Land Cover Data

We used vectorized maps of land cover in Île-de-France from Institut d'Aménagement et d'Urbanisme, with a spatial resolution of 12.5 meters available for two time periods (2009 to 2011 and 2012 to 2017). We used six land cover categories (Figure 1): 1. forest (24% of Île-de-France area), 2. semi-natural area (2%), 3. agricultural area (50%), 4. artificial open area (6%), 5. water (2%) and 6. urbanized area (16%, including individual housing (8%), collective housing (2%), industrial and business areas (2%), facilities (1%), quarries, dumps and worksites (0.5%) and transportation infrastructures (2.5%)). A detailed description of each category is available online (http://www.iau-idf.fr/). Using the ArcGIS® software for geographical data manipulation (Environmental Systems Research Institute, Esri, 2018, ArcGIS release 10.5.1), we linked the Vigie-flore plots with land cover data and calculated the proportion of urbanized areas as a surrogate for the urbanization gradient, i.e., we calculated the percentage of urbanized area inside each 1x1 km² grid cell where Vigie-flore plots occurred.

## Species Biological Characteristics and Phylogeny

We collected information on the biological traits of each species from several databases (Table S1): the BiolFlor database on biological and ecological traits of the German flora (Klotz et al., 2002), LEDA database of life-history traits of the Northwest European flora (Kleyer et al., 2008), TRY global database of plant traits (Kattge et al., 2011), Ecoflora database of British Isles (http://www.ecoflora.co.uk, Fitter and Peat, 1994) and Catminat database of French flora (Julve, 1998) along with numerous botanical garden resources across Europe (e.g., the Royal Botanic Gardens, Kew, POWO, 2019). The data included three binary (life span, pollination vector, seed dispersal mode), one ordinal (type of reproduction), one circular (beginning of flowering) and six
quantitative traits (seed weight, leaf dry mass content, leaf size, specific leaf area, plant height and duration of flowering) related to the plant species dispersal, establishment, reproduction and persistence (Table S2). We selected traits that are widely used in functional ecology studies for their presumed roles in the context of urbanization (Knapp et al., 2009, 2010; Vallet et al., 2010; Williams et al., 2015; Kalusová et al., 2017) and that were available for the majority of our species. However, the Grime's strategies (Grime et al., 1988) or the Raunkiær life forms (Raunkiær, 1932) that were largely available for our species are synthetic traits and were not retained. Similarly, the Ellenberg indicator values were not retained either because they are not functional traits *per se* but represent the ecological preferences of species (Ellenberg et al., 1991) by characterizing a nonrandom association of plant species with particular characteristics of the environment (Garnier et al., 2017). We investigated the correlations among all retained biological traits to avoid potential collinearity. Highly correlated traits were not retained if the absolute values of Pearson correlation coefficient were superior to 0.7 (Dormann et al., 2013). In complement to the biological traits, we calculated species urbanity following Hill et al. (2002), which, like the Ellenberg indicator values, is an environmental association (sensu Garnier et al., 2017). We used species urbanity *a posteriori* to link species urban preferences with their trait-based and phylogenetic originality values. We calculated species urbanity as the mean percentage of urbanized area in all grid cells where the species occurred. In addition, we used the native status of the species that was extracted from the Île-de-France plant species list established by the Conservatoire Botanique National du Bassin Parisien (CBNBP, 2016) to separate the species into two groups of native and non-native species. A species was defined as non-native if it was introduced by humans into the region after the year 1500.

We used DaPhnE (Durka and Michalski, 2012), a dated phylogeny that is resolved to the species level and covers the vascular flora of the British Isles, Germany, the Netherlands and Switzerland and thus all taxa from the LEBA and BiolFlor trait databases. The final tree was pruned to our species pool (Table S2) in R (R Core Team 2019).
# DATA ANALYSES

## Community and Species Metrics

To measure originality, we used the metric named *AV* for "average" as it represents the average dissimilarity between a focal species and all others in a set (Pavoine et al., 2017). The *AV* metric requires a dissimilarity matrix based on species phylogeny or the species trait values. To form a trait-based dissimilarity matrix, we first log-transformed the most asymmetrically distributed quantitative traits: canopy height, leaf size, and mean seed weight to ensure a bell-shaped distribution of values. We then used the mixed-variable coefficient of dissimilarity (Pavoine et al., 2009) associated with the most appropriate coefficients to estimate trait-based distances between pairs of species: the Jaccard metric (Jaccard, 1901) for binary traits, the Podani metric (Podani, 1999) for ordinal traits and the Manhattan metric (Farris, 1972; Nelson and Horn, 1975) for quantitative traits. To form a phylogenetic dissimilarity matrix, we calculated the patristic distances between pairs of species (sum of the branch lengths on the shortest path between two species in the phylogenetic tree).

Using the dissimilarity matrices, we calculated the phylogenetic and trait-based originality scores of each species with the *distinctDis* function of the R package *adiv* (Pavoine, 2018). Following Redding et al. (2015a), for each species, we calculated a regional originality score considering all species in our dataset (*rAV*) and local originality scores considering only the species from a given plot (*LAV*). Thus, for the same species, local originality varies depending on the composition of a given plot, whereas regional originality will be constant for a given species over all plots (Figure 2). We named a given plot *P* and the region *R* (union of all sampled plots). *N*<sub>P</sub> is the number of species in plot *P*, *N*<sub>R</sub> is the number of species in region *R*, and *d*<sub>ij</sub> is the functional or phylogenetic distance between species *i* and *j*. The regional originality of species *i* is as follows:

$$rAV_i = \frac{1}{N_R - 1} \sum_{j \in R} d_{ij}$$
If species *i* occurs in plot *P*, its local originality in plot *P* is as follows:

$$lAV_{iP} = \frac{1}{N_P - 1} \sum_{j \in P} d_{ij}$$

We defined the diversity of a given plot as the average of the local originalities of its species. This diversity metric gives a unique value to each set of species. Thus, in each plot *P*, we calculated the mean of the local originalities of the species (*M/O*<sub>*P*</sub>), which represents a measure of the local trait-based diversity or phylodiversity within that plot, depending on whether trait-based or phylogenetic dissimilarities (*d*<sub>*ij*</sub>) were considered; *M/O*<sub>*P*</sub> can be calculated as follows:

$$MlO_P = \frac{1}{N_P} \sum_{i \in P} lAV_{iP} = \frac{1}{N_P} \frac{1}{N_P - 1} \sum_{i,j \in P} d_{ij}$$

In each plot *P*, we also calculated the mean of the regional species originalities (*MrO*<sub>*P*</sub>), taken from regionally calculated values (*rAV*) but only for species composing plot *P* (Figure 2). *MrO*<sub>*P*</sub> can be viewed as the average contribution of a species from plot *P* to the (trait-based or phylogenetic) regional diversity and can be calculated as follows:

$$MrO_P = \frac{1}{N_P} \sum_{i \in P} rAV_i = \frac{1}{N_P} \sum_{i \in P} \frac{1}{N_R - 1} \sum_{j \in R} d_{ij}$$

To test if the observed variations in the mean trait-based and phylogenetic originality along the urbanization gradient were due to changes in the originality values of many species or were carried by a few very original (or very redundant) species, we calculated the skewness of the phylogenetic and trait-based originalities in each plot. We did so for the local and regional originality values.
Negative skewness of originality or left-skewed originality represented scenarios with many species of high originality and few redundant species. In contrast, positive skewness of originality or right-skewed originality represented many redundant species and a few highly original species.

We then created a dataset without 85 non-native species and recalculated all local and regional
originality scores and all plot-level metrics (species richness and mean and skewness of the
originalities) to assess the contributions of non-native species to originality. The species richness
was calculated per plot for both datasets. Finally, we ranked species from the complete dataset
(native and non-native species) by their regional and local originality values and selected the top
scoring 5% of the species (the first 30 in the list). We also tested how many species become original
at the local level although they were not at the regional level. To do so, we calculated the ratio of
all local originality values to the regional originality value ($LAV_{IP} / rAV_i$) for each species.

Statistical and Spatial Modeling

We modeled the variation in community metrics with the complete dataset and the only native
species dataset with generalized mixed linear models in the R package *glmmTMB* (Millar, 2011); the
mean, skewness and ratio of trait-based and phylogenetic species originality at local and regional
levels were calculated with a Gaussian distribution, while the species richness was calculated with
a negative binomial distribution and a quadratic link between the mean and the variance. We
included as fixed explanatory variables: the urbanization percentage in the 1 km² cell surrounding
a given plot, the species richness (except for the model where species richness was a response
variable) and the survey year, which was transformed by subtracting its minimum value to allow
the comparison between units of variables. We also included an interaction effect between
urbanization and species richness. The IDs of the grid cells and the IDs of the plots were modeled
as random nested variables to account for the pseudoreplication of the plots at the same location.
Species-specific local originality was tested as a response by adding to the previous mixed models
the species name as a random factor to control for species that appeared in several plots. Backward
selection of variables was based on Akaike information criterion (AIC) values, where the lowest
AIC value denoted the best model. We calculated the marginal and conditional R² values as proposed in Nakagawa and Schielzeth (2013). The marginal R² value describes the proportion of variance explained by fixed covariables only. The conditional R² value describes the proportion of variance explained by fixed and random covariables.

As our data were distributed in space, we tested for spatial independence in the residuals of each model. We used the local Moran's I test, or the local indicator of spatial association (LISA, Anselin, 1995), and visualized it with a correlogram of 500 meter increments for neighborhood definition. The significance was assessed based on 999 randomizations with the R package *ncf* (Bjørnstad and Falck, 2001). No spatial autocorrelation was detected in the model residuals, and thus, no correction was needed (Figure S1). All statistical analyses were performed with R software version 3.6.0 (R Core Team, 2019), and statistical significance was considered to be present at α = 0.05.

## RESULTS

### Community Level Metrics

All model results with the complete dataset are presented in Table 1. We observed a significant positive effect of urbanization on the mean trait-based originality, or trait-based diversity, of plots at both the regional and local scales. Species richness had a significant positive effect only on local-scale trait-based diversity. That is, the higher species richness was, the more the species differed locally in their traits. There was, however, a significant interaction effect between urbanization and species richness at both scales, such that the positive effect of urbanization on trait-based diversity (*M/O* and *MrO* with trait-based *d*<sub>ij</sub> values) tended to decrease with increasing species richness and *vice versa* to the point of becoming negative for the highest levels of species richness (Figure 3A,B).

Trait-based diversity at the local scale significantly decreased with the survey year. The mean phylogenetic originality of the plots decreased with urbanization and species richness only when evaluated at the regional scale (*MrO* with phylogenetic *d*<sub>ij</sub> values, Figure 3C). However, there was also a significant interaction between species richness and urbanization: the negative effect of urbanization decreased with increasing species richness and *vice versa*. The mean phylogenetic
originality of the plots increased with the sample year at the regional scale only.

The skewness of trait-based originality at the local scale and phylogenetic originality at the local and regional scales did not change with urbanization. We found that the skewness of local trait-based originality significantly changed with species richness from close to zero to positive values (Figure 4A). The skewness of local phylogenetic originality changed accordingly. The interaction between urbanization and species richness was not significant, and models were simplified to the main effects only, except for the skewness of the local phylogenetic originality. A significant interaction effect on the skewness of local phylogenetic originality was such that the positive effect of species richness decreased with urbanization to the point of becoming negative for the highest levels of urbanization (Figure 4D). The skewness of regional trait-based originality changed from near-zero values to positive values with urbanization and species richness (Figure 4B). For the skewness of regional phylogenetic originality, only species richness had a significant positive effect (Figure 4C). The sampling year was not linked with trait-based and phylogenetic originality skewness at both scales. Finally, the species richness of the plots increased along the urbanization gradient and did not change significantly across the years.

## Species Level Metrics

For each species, we calculated its local and regional trait-based and phylogenetic originalities, which allowed the identification of the most original species. Among them, we found common species such as the annual bluegrass *Poa annua* and the dandelion *Taraxacum* sect. *Ruderalia* (Table S3). When we modeled the species-specific originalities directly, only local originality was investigated. The trait-based originality for each species increased significantly with both urbanization and species richness and decreased across the years. The significant interaction between urbanization and species richness on the species-specific trait-based originality decreased the positive urbanization effect in plots with greater species richness (Figure S2A). Species phylogenetic originality was not influenced by urbanization but increased with species richness and decreased with the sampling year. There was, however, a significant effect of the interaction
between urbanization and species richness on species phylogenetic originality; the positive effect
of species richness decreased in the most urbanized plots (Figure S2B). Finally, Spearman's
correlation was not significant between the per-species local phylogenetic and trait-based
originalities (r = 0.04, p = 0.20).

The ratio of local to regional trait-based originality increased significantly with both urbanization
and species richness. Due to the interaction with species richness, the positive effect of
urbanization on the trait-based ratio of originality decreased in richer plots and *vice versa* (Figure
S3A). The sampling year was excluded from the model after AIC backward variable selection. We
found that Spearman's correlation between the mean of the local trait-based originalities of a
species and its regional trait-based score was highly significant (r = 0.78, p < 0.001). Finally, species
urbanity increased with the local trait-based originalities of native and non-native species (Figure
S4A, Spearman's correlation coefficient r = 0.17, p < 0.001) but not with the regional originality (r
= -0.01, p = 0.80). Species urbanity was higher in species that became more original locally than
they were regionally (Figure S4A).

Concerning the ratio of local to regional phylogenetic originality, no effect of urbanization alone
was detected, but there was a significant increase with species richness. Moreover, there was a
significant effect of the interaction between urbanization and species richness on the ratio of local
to regional phylogenetic originality; the positive effect of species richness decreased in the most
urbanized plots (Figure S3B). The phylogenetic originality ratio decreased with the sampling year.
We also found that the most phylogenetically original species were not the same at the local and
regional scales. In addition, Spearman's correlation was low and not significant (r = -0.06, p = 0.11)
between the average of the local phylogenetic originalities of a species and its regional phylogenetic
originality score. Finally, species urbanity was higher in less phylogenetically original species at the
regional scale in both native and non-native species (Spearman's correlation coefficient r = -0.15,
p < 0.001, Figure S4B).
## Dataset without Non-native Species

Removing 85 non-native species (14.6% of the total regional pool), which occurred in 709 out of 2362 plots, had a minimal effect on the results of the previous models (Table S4). The only differences we found were that 1. the year of sampling had a significant negative effect on mean regional trait-based originality (the effect was not significant with the complete dataset); 2. inversely, it had no significant effect on the mean regional phylogenetic originality (positive effect with the complete dataset); and 3. the skewness of the local phylogenetic originality was not impacted by species richness (positive effect with the complete dataset).

At the local scale, non-native species were significantly more original than native species due to their trait values (Wilcoxon paired rank test W=5819854, p < 0.001, Figure S5A) and less original due to their phylogeny (W=11347166, p < 0.001, Figure S5B). Twelve out of thirty species in the top 5% of trait-based original species at the local scale were non-native. At the regional level, non-native species were significantly more original than native species in their traits (W = 18069, p < 0.001, Figure S5C) and not significantly different by their phylogenetic relations (W = 24375, p = 0.62, Figure S5D).

## DISCUSSION

Urbanization affects ecosystems at multiple scales (Aronson et al. 2016) by changing community phylogenetic structure (Ricotta et al., 2009; Knapp et al., 2017; Sol et al., 2017) and species trait distributions (Williams et al., 2015). While urban ecosystem structure is often studied at the community scale, it is still difficult to evaluate the role and contribution of each species to local diversity. We provide a promising tool that relies on the originality values calculated for each species relative to the co-occurring species within its community. Our approach is conceptually simple: species originality is measured as an average distance from a focal species to all the others in the community. It has recently been proposed to use the originality indices for diversity evaluations (see Kondratyeva et al., 2019, section II.3.b). Species originality indices can indeed be used for evaluation of local diversity by calculating the mean of species originality scores in a local set of
species (Ricotta et al., 2016). Thus, our study is a first application of this framework to the urban context that considers the two spatial scales, which are the area over which originality is calculated.

## Trait-based Originality of Urban Communities

In our case study, the regional trait-based originality of a species increased the local trait-based diversity of communities with increasing urbanization. In other words, species that were highly original at the regional scale were also the most original at the local scale relative to their trait-based dissimilarities, and original species were more frequent in urbanized areas than in non-urbanized areas. Species in urbanized areas were even more original at the local scale than at the regional scale, rendering them the unique representatives of some traits. However, the most important ecosystem processes are usually ensured by common redundant species (Mouillot et al., 2013b; Brandl et al., 2016). To a certain level of species richness, plant community trait-based diversity (and species trait-based originality) continued to increase with urbanization. Beyond that level, the number of redundant species started to increase, accordingly leading to positive skewness of the originality values and making the communities more resilient to environmental changes (McLean et al., 2019). Therefore, in more-urbanized areas, a few species with high trait-based originality contributed to the increase of community trait-based diversity but did not participate much in ecosystem stability. However, throughout the years of the study, trait-based originality decreased at both scales, and fewer species became original at the local scale compared to at the regional scale. Urban land modification may intensify with time and indicate a growing biotic homogenization of local plant communities (McKinney, 2006).

The species with the highest trait-based originality occurred rarely, only appearing once or twice in the whole 10-year dataset. However, three of these species have been found in many different communities and in the top 5% of original species: *Erigeron sumatrensis*, which is a highly original non-native species with high urbanity; and species of the *Taraxacum* sect. *Ruderalia* and *Poa annua*, both of which are native and highly common in the Île-de-France region. Thus, these three widespread species greatly contributed to the trait-based diversity of local communities and of the
whole region. At the same time, the omnipresence of the same species in urban habitats can lead
to biological homogenization, such shown in birds (Devictor et al., 2008) and plants (McKinney,
2006) across cities. Indeed, *T. sect. ruderalia* and *P. annua* are urbanoneutral species (Hill et al., 2002);
in other words, they are less affected than other species by urbanization disturbances (CBNBP,
2016). Such highly common and dominant species could take over the majority of urban ecosystem
resources, pushing other less frequent species to adapt different trait values, giving the
urbanoneutral species higher trait-based originality.

Phylogenetic Originality of Urban Communities

The patterns we obtained with phylogenetic originality were different from those obtained with trait data. Knapp et al. (2008) found that urbanophilic species with well-adapted traits for urban environmental conditions are phylogenetically more distinct in urban areas than maladapted species. In our dataset, a highly phylogenetically original plant species was not necessarily original in its traits, and *vice versa*. This corresponds to findings of Lososová et al. (2016) that phylogenetic diversity is only a weak proxy for the functional diversity of urban plant communities. Overall, the use of phylodiversity as a proxy for trait-based diversity (feature diversity, Faith, 1992) has been recently challenged (Mouquet et al., 2012; Cadotte et al., 2017) and has received mixed empirical evidence (e.g., Mazel et al., 2017, 2018, 2019; but see Owen et al., 2019). However, there is emerging evidence of rapid urbanization-driven trait evolution that stresses the need to include the eco-evolutionary implications of urbanization, especially for species that may play key roles in ecosystem functioning (Alberti, 2015).

Contrary to the trends seen in trait-based originality, local and regional phylogenetic originalities
were not correlated in our dataset. There was no straightforward evidence for an effect of
urbanization on the phylogenetic diversity calculated with local originality values. It seems that
urbanization levels predominantly affect the contribution of local plots to the regional
phylodiversity. This is in line with the results of Knapp et al. (2012), who found a decrease in
phylodiversity on a large scale but no clear trend in phylodiversity on the small scale of household
yards across a gradient of housing density in the US. Therefore, we proposed to study the effects
of urbanization on herbaceous plant community phylodiversity through regionally measured
originality, where all relations in the species pool were considered. In our data, more-related species
(less original) at the regional scale constituted local urban communities of the Île-de-France region,
thus decreasing regional phylodiversity at the local scale. Many species became phylogenetically
original in rich local communities compared to their regional originality; however, increasing
urbanization slowed down this increase. A community with a few distantly related species can have
a higher phylodiversity than richer communities with more closely related species (Knapp et al.,
2012). Indeed, in our dataset, the mean regional phylogenetic originality represented in a single plot
decreased with growing species richness. Moreover, there was a global gain in regional phylogenetic
originality through the years, but a loss of phylogenetically original species at a local scale, again
showing the importance of the spatial scale of a study.

Species found in the top 5% of the most phylogenetically original species at local and regional
scales were mostly monocotyledons (Table S3). This is primarily an effect of the lower number of
monocotyledon species than dicotyledon species in the regional pool. Because of the large
phylogenetic age of the split between these two plant groups (Chaw et al., 2004), a lower number
of monocotyledons makes these species more distantly related on average to all species other than
those that are classified as dicotyledons.

Species Richness and Non-native Species Originality

Even if our 586 species set was a sample of the whole Île-de-France region flora, where more than
2064 vascular plant species have been recorded (CBNBP, 2016), we found a broadly observable
pattern of species richness increase in local plots with urbanization both in plots with and without
non-native species. Non-native species are often promoted in areas disturbed by urbanization
(Kühn et al., 2017). Nevertheless, non-native species did not contribute more to the community
diversity, and their removal in our data hardly influenced community structure, although non-native
species were more functionally original compared to native species at both the local and regional
levels. Non-native species are also filtered by urbanization and have specific environmental preferences, making them rare specialists within many urban habitat patches (McKinney, 2006; Godefroid and Ricotta, 2018). In contrast, the observed loss in mean regional phylogenetic originality in urbanized areas could not be outweighed by the presence of non-native species, which are less phylogenetically original than natives. Similarly, Knapp et al. (2017) found that changes in the native flora of Halle (Germany), which caused a loss of phylogenetic diversity, could not be compensated by the introduction of non-native species. Moreover, the presence of non-native species decreased the phylodiversity of plant communities in Ricotta et al. (2009) and Čeplová et al. (2015) and the similarly for bird communities where exotic species could not completely compensate for the loss of phylogenetic diversity resulting from the loss of native species (Sol et al., 2017). Finally, our results are also in line with the trend of non-native species promoting homogenization globally (Winter et al., 2009) and contrast the findings of Kühn and Klotz (2006), which indicated that after 1500 AD, non-native species promoted differentiation of the plant species assemblages in cities.

## CONCLUSION AND DIRECTIONS FOR FURTHER RESEARCH

Cities are complex urban ecosystems, and their functioning tends to be different than that of natural ecosystems because of the human element (Rebele, 1994; Grimm et al., 2000). Expanding ecological knowledge and expertise can enhance our understanding of the responses of biodiversity to urbanization. Local ecological knowledge is required for more targeted urban management (McPhearson et al., 2016). Originality measures determine which species contribute the most to local and regional diversities. Applied to the urban context, originality measures can easily evaluate local biodiversity by integrating multiscale biological species information.

Our originality framework helps to determine the most relevant spatial scale for efficient urban management (e.g., plant phylogenetic originality was influenced by urbanization on a regional scale only). Moreover, the originality framework determines which original or redundant species are responsible for variations in trait-based diversity and phylodiversity. Here, we showed notably that
urbanization causes some species to have higher trait-based originality at the local community scale than at the regional scale. Although we observed that original species were more frequent in urbanized areas, we also showed that this originality decreased over time, resulting in less diverse communities and regions. Moreover, we showed that the increase in trait-based originality in urbanized areas may be partly hampered by the presence of redundant species in the richest plots. In our case study, the phylogenetic diversity of local plots was not impacted by urbanization. However, species in local urbanized plots tended to be among the least regionally original species, implying a negative effect of urbanization on regional phylogenetic diversity.

Although our study demonstrates the potential of the originality framework, we do not pretend to affirm any generalizations about urban plant communities based on our case study. Here, the urbanization gradient was characterized by a single measure, i.e., the proportion of urbanized areas (including individual and collective housing, industrial and business areas, facilities, quarries, dumps and worksites and transportation infrastructure). Although this urbanization measure combines diverse types of urban infrastructures and impervious surfaces, it does not distinguish among the range of urban microhabitats and does not account for many urbanization-related changes, such as soil compaction, eutrophication and air, soil and water pollution (McDonnell and Hahs, 2008). Therefore, future urban ecology studies could use multidimensional composite measures of urbanization to provide a more holistic understanding of the effects of urbanization on biological diversity (Moll et al., 2019). In addition, our dataset did not allow us to account for species abundance, which can drastically change conclusions about ecosystem functioning, where a high number of individuals supporting the same trait can ensure greater stability (Walker et al., 1999; D'agata et al., 2016). The weighting of originality measure with species relative abundances could address such an issue, thus regulating the relative importance given to rare vs. abundant species (Kondratyeva et al., 2019).

To our knowledge, originality measures have usually been employed for conservation purposes on a global scale (e.g., Jetz et al., 2014; Redding et al., 2015a; Thévenin et al., 2018). Here, we applied
them at the local scale of urban plant communities. Future research could now aim to develop cross-city comparative research on species originality using urban systems networks, such as the Urban Biodiversity Research Coordination Network (UrBioNet, http://urbanet.weebly.com/). Citizen science programs, which are deployed in urban areas, are also a good starting base for exchange in sustainable city development, which include scientists, policymakers and citizens. To complement these programs, we showed that our conceptual framework of originality measures deserves more attention by city planners working tightly with urban ecologists.

## CONFLICT OF INTEREST STATEMENT

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

## AUTHOR CONTRIBUTIONS

AK and SP conceived the ideas of the research presented herein. AK, SP, and IK designed the analytical methods. WD and AK assembled the project phylogeny. AK, WD and IK assembled the trait data. NM, GM, EM, and JV supervised the Vigie-flore data collection and management. AK analyzed the data and wrote the first version of the manuscript. All authors contributed to the final manuscript edition and approved the submitted version.

## FUNDING

This work was funded by a grant from the Agence Nationale de la Recherche under the LabEx BCDiv project reference 10-LABX-0003 within the framework of the program 'Investing for the future' (11-IDEX-0004).

## ACKNOWLEDGEMENTS

We thank Emmanuelle Porcher for the valuable comments on previous versions of the manuscript and all Vigie-flore volunteers for data collection. The study has been supported by the TRY initiative on Plant Traits (http://www.try-db.org). The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Boenisch (Max Planck Institute for
Biogeochemistry, Jena, Germany). TRY is currently supported by Future Earth/bioDISCOVERY
and the German Center for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig.

DATA AVAILABILITY

This study used plant trait values from several datasets that can be found online:

BiolFlor: data available under registration at www.biolflor.de

LEDA: available at http://www.leda-traitbase.org

Ecoflora: http://www.ecoflora.co.uk

Catminat: http://philippe.julve.pagesperso-orange.fr/catminat.htm.

TRY initiative and database: http://www.try-db.org

Seed Information Database. http://data.kew.org/sid/ (September 2019)

DaPhnE phylogeny: http://esapubs.org/archive/ecol/E093/214/

CBNBP: http://cbnbp.mnhn.fr/cbnbp/

The following datasets are not publicly available. Requests to access these datasets should be
directed to assad.alicherif@iau-idf.fr for land cover data of the Île-de-France region and to vigie-
flore@mnhn.fr for Vigie-flore plant occurrence data.
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**Table 1** Results for the generalized linear mixed models (GLMM) that were run on the complete dataset (native + non-native species), including 9 community level metrics (means and skewness of local and regional trait-based and phylogenetic originalities and species richness) and 4 species-specific measures (trait-based and phylogenetic originality and local to regional ratios).

The Estimated coefficients and their significance levels are shown for the explanatory variables that remained after backward selection of variables based on Akaike information criterion (AIC) values, including urbanization percentage, species richness, the interaction (U×SR) between urbanization and species richness and the sampling year. The value "excluded" means that the explanatory variable was not retained in the final model according to the Akaike criterion. The conditional and marginal R² values describe the proportion of variance explained by the fixed (R²c) and random+fixed (R²m) covariables. P-values: NS = nonsignificant (P>0.050); * = 0.010 < P ≤ 0.050; ** = 0.001 < P ≤ 0.010; *** = P ≤ 0.001
<table><thead><tr><th>Dependent variable</th><th>Intercept</th><th>Urbanization (U)</th><th>Species richness (SR)</th><th>Sampling year</th><th>U×SR</th><th>R<sup>2</sup><sub>c</sub></th><th>R<sup>2</sup><sub>m</sub></th></tr></thead><tbody><tr><td>Mean local trait-based originality</td><td>4.7 10<sup>-1</sup> ***</td><td>7.7 10<sup>-4</sup> ***</td><td>9.9 10<sup>-4</sup> ***</td><td>-8.3 10<sup>-4</sup> **</td><td>-2.4 10<sup>-5</sup> ***</td><td>0.44</td><td>0.14</td></tr><tr><td>Mean regional trait-based originality</td><td>5.2 10<sup>-1</sup> ***</td><td>2.8 10<sup>-4</sup> ***</td><td>-2.1 10<sup>-5</sup> NS</td><td>-1.3 10<sup>-4</sup> NS</td><td>-9.8 10<sup>-6</sup> ***</td><td>0.64</td><td>0.10</td></tr><tr><td>Mean local phylogenetic originality</td><td>2.3 10<sup>2</sup> ***</td><td>-3.4 10<sup>-2</sup> NS</td><td>9.6 10<sup>-2</sup> NS</td><td>-3.9 10<sup>-1</sup> NS</td><td>-4.9 10<sup>-3</sup> NS</td><td>0.27</td><td>0.01</td></tr><tr><td>Mean regional phylogenetic originality</td><td>2.4 10<sup>2</sup> ***</td><td>-8.6 10<sup>-2</sup> ***</td><td>-2.4 10<sup>-1</sup> ***</td><td>1.3 10<sup>-1</sup> **</td><td>2.2 10<sup>-3</sup> *</td><td>0.63</td><td>0.10</td></tr><tr><td>Skewness of local trait-based originality</td><td>9.2 10<sup>-2</sup> ***</td><td>7.5 10<sup>-4</sup> NS</td><td>2.6 10<sup>-2</sup> ***</td><td>-2.3 10<sup>-3</sup> NS</td><td>excluded</td><td>0.22</td><td>0.14</td></tr><tr><td>Skewness of regional trait-based originality</td><td>-2.1 10<sup>-2</sup> NS</td><td>1.5 10<sup>-3</sup> ***</td><td>3.4 10<sup>-2</sup> ***</td><td>-2.7 10<sup>-3</sup> NS</td><td>excluded</td><td>0.34</td><td>0.20</td></tr><tr><td>Skewness of local phylogenetic originality</td><td>4.0 10<sup>-1</sup> ***</td><td>6.8 10<sup>-4</sup> NS</td><td>6.5 10<sup>-3</sup> **</td><td>-4.9 10<sup>-3</sup> NS</td><td>-2.3 10<sup>-4</sup> ***</td><td>0.15</td><td>0.01</td></tr><tr><td>Skewness of regional phylogenetic originality</td><td>-4.5 10<sup>-2</sup> NS</td><td>9.2 10<sup>-4</sup> NS</td><td>1.5 10<sup>-2</sup> ***</td><td>2.2 10<sup>-3</sup> NS</td><td>excluded</td><td>0.28</td><td>0.05</td></tr><tr><td>Species richness</td><td>1.0 10<sup>-1</sup> NS</td><td>4.5 10<sup>-3</sup> ***</td><td>omitted</td><td>-4.1 10<sup>-1</sup> NS</td><td>omitted</td><td>3.7 10<sup>-2</sup></td><td>2.3 10<sup>-3</sup></td></tr><tr><td>Trait-based originality</td><td>4.8 10<sup>-1</sup> ***</td><td>3.8 10<sup>-4</sup> ***</td><td>9.1 10<sup>-4</sup> ***</td><td>-5.6 10<sup>-4</sup> ***</td><td>-1.1 10<sup>-5</sup> ***</td><td>0.68</td><td>0.03</td></tr><tr><td>Phylogenetic originality</td><td>9.4 10<sup>-3</sup> NS</td><td>8.0 10<sup>-4</sup> NS</td><td>8.4 10<sup>-3</sup> ***</td><td>-6.9 10<sup>-3</sup> **</td><td>-1.6 10<sup>-4</sup> **</td><td>0.46</td><td>3.8 10<sup>-2</sup></td></tr><tr><td>Local/regional trait-based originality</td><td>9.1 10<sup>-1</sup> ***</td><td>7.4 10<sup>-4</sup> ***</td><td>1.8 10<sup>-3</sup> ***</td><td>excluded</td><td>-2.0 10<sup>-5</sup> ***</td><td>0.46</td><td>0.06</td></tr><tr><td>Local/regional phylogenetic originality</td><td>9.5 10<sup>-1</sup> ***</td><td>1.4 10<sup>-4</sup> NS</td><td>1.2 10<sup>-3</sup> ***</td><td>-9.5 10<sup>-4</sup> *</td><td>-2.4 10<sup>-5</sup> ***</td><td>0.54</td><td>2.9 10<sup>-2</sup></td></tr></tbody></table>
<figure><img src="image_16.png" /><figcaption>Figures</figcaption></figure>

**Figure 1** Land cover and spatial distribution of the sampling plots. The map shows the Île-de-France region, including its largest city Paris and approximately 1300 other cities and towns, which is represented by urbanized areas in red (including individual housing (8%), collective housing (2%), industrial and business areas (2%), facilities (1%), quarries, dumps and worksites (0.5%) and transportation infrastructure (2.5%)). The urbanization cover and other land cover types (spatial resolution of 12.5 meters) that correspond to forests (dark green; 24% of total area), seminatural areas (bright green, 2%), agricultural areas (beige, 50%), artificial open areas (violet, 6%) and rivers and water points (blue, %) were extracted from data from the Institut d'Aménagement et d'Urbanisme (IAU, 2017). The blue dots represent the 2362 sampling plots of Vigie-flore from 2009 to 2017 (many of them are superimposed).
<figure><img src="image_17.png" /><figcaption>**Figure 2** Conceptual framework for measuring species trait-based and phylogenetic originality and diversity at multiple spatial scales. We illustrate this framework with a theoretical set of five plant species (Sp). First, the pairwise distances among species are calculated to form a dissimilarity matrix based on a set of species trait values using Gower's metric for several types of traits (1.1, where Tr T is a species trait) or using species phylogenetic distances represented by branch lengths in millions of years (1.2). Second, species originality (trait-based or phylogenetic) is calculated at the regional scale considering all species from the regional pool (2.1, rAVᵢ) and at the local scale considering only species from a local plot P (2.1, LAVᵢP). The fewer trait values that species Sp shares with the other species (in a region or in a local plot), or the fewer species Sp are related to the other species, the more original it is. Finally, community metrics are measured for each plot P (3): the mean of regionally measured originality values (*MrO*<sub>p</sub>) and their skewness, the mean of locally measured originality values (*MlO*<sub>p</sub>) and their skewness, and species richness. *MrO*<sub>p</sub> represents the regional originality of the local plot, and MlOP represents a measure of local species diversity.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>**Figure 3** Variation in (A) mean regional trait-based originality, (B) mean local trait-based originality (local trait-based diversity) and (C) mean regional phylogenetic originality according to the percentage of urbanization within a 1 km² grid cell (grid cell corresponds to the Vigie-flore protocol) and species richness with a complete species dataset (natives and non-natives). One data point represents one species plot. The colors represent a scale of species richness (SR) going from blue for low SR (minimum of 3 species) to red for high SR (maximum of 50 species). Colored curves represent the estimated trends of an interaction between urbanization and SR for 5 (blue), 25 (salmon) and 40 (red) species retrieved from the mean originality GLMM models.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>**Figure 4** The skewness of the (A) regional and (B) local trait-based originality and the (C) regional and (D) local phylogenetic originality according to the species richness (SR) in each species plot of the complete dataset. One data point represents one plot. In (A, B, C), the black curve represents the estimated trend retrieved from the skewness GLMM models. In (D), the colors represent a scale of urbanization gradient calculated as the percentage of urbanized area cover; the colored lines represent the estimated trends in an interaction between SR and urbanization for 0%, 50% and 100%, respectively, within a 1 km² grid cell, retrieved from the skewness GLMM model.</figcaption></figure>
<table><caption>**Supplementary Table 1.** A list of traits and their states (for traits with multiple categories), trait description according to LEDA (Kleyer et al., 2008) and BiolFlor (Klotz et al., 2002) databases, sources data bases, type of trait variable and the proportion of total species number with missing data for each trait.</caption><thead><tr><th>Trait</th><th>Trait states (species number)*</th><th>Description</th><th>Source data base</th><th>Variable type</th><th>Proportion (%) of total species number with missing data</th></tr></thead><tbody><tr><td>Begin of flowering</td><td>annual (228)</td><td>Start of flowering period (month)</td><td>BiolFlor; Catminat; Multiple online sources</td><td>circular</td><td>0</td></tr><tr><td>Duration of flowering</td><td>annual (228)<br>biannual (73)<br>perennial (350)</td><td>Duration of flowering period (month)</td><td>BiolFlor; Catminat; Multiple online sources</td><td>quantitative</td><td>0</td></tr><tr><td>Life span</td><td>annual (228)<br>biannual (73)<br>perennial (350)</td><td>The individual cycle lasts for a maximum of one year (12 months)<br>The plant has a vegetative growth for approx. one year before reaching the generative phase after which it completes its life cycle<br>The plant has more than one generative phase in its life</td><td>BiolFlor; Ecoflora</td><td>binary</td><td>0</td></tr></tbody></table>
<table><tr><td></td><td>pluriannual (33)</td><td>The plant has a vegetative growth longer than one year (up to 5 years) before completing its life cycle after the first and only generative reproduction stage</td><td></td><td></td></tr><tr><td rowspan="6">Pollination vector</td><td>Apomixis (1)</td><td>Asexual reproduction, no fertilization</td><td rowspan="6">BiolFlor; Ecoflora; Catminat</td><td rowspan="6">binary</td><td rowspan="6">0</td></tr><tr><td>Geintonogamy (13)</td><td>Pollination by a neighboring flower</td></tr><tr><td>Insects (442)</td><td>Pollination by insects</td></tr><tr><td>Self spontaneous (366)</td><td>Spontaneous pollination within a flower</td></tr><tr><td>Water (1)</td><td>Pollination on or below water</td></tr><tr><td>Wind (156)</td><td>Pollination by wind</td></tr><tr><td rowspan="5">Seed dispersal mode</td><td>Self dispersal (227)</td><td>Seed dispersal by an explosive mechanism, by gravity, by autonomous placement of seeds or daughter plant away from mother plant</td><td rowspan="5">BiolFlor; Catminat</td><td rowspan="5">binary</td><td rowspan="5">0.1</td></tr><tr><td>Dispersal by wind (338)</td><td>Seed dispersal by wind and by rolling over soil with wind</td></tr><tr><td>Dispersal by animals (505)</td><td>Seed dispersal by hoarding by animals, dispersed after digestion or by adhesion on animals</td></tr><tr><td>Dispersal by human activity (357)</td><td>Seed dispersal by trading of seeds/plants and with agricultural seeds</td></tr><tr><td>Dispersal by water flows (383)</td><td>Seed dispersal by surface currents and by raindrops</td></tr></table>
<table><tr><td></td><td>s (297)</td><td>Reproduction only by seed or by spore</td><td></td><td></td></tr><tr><td></td><td>ssv (39)</td><td>Reproduction is mostly by seed, rarely vegetative</td><td></td><td></td></tr><tr><td rowspan="2">Method of reproduction</td><td>sv (205)</td><td>Reproduction can be by seed and vegetative equally</td><td>BiolFlor; Ecoflora</td><td>ordinal</td><td>0.1</td></tr><tr><td>vvs (22)</td><td>Reproduction is mostly vegetative, rarely by seed</td><td></td><td></td><td></td></tr><tr><td></td><td>v (2)</td><td>Reproduction is only vegetative</td><td></td><td></td><td></td></tr><tr><td>Seed weight</td><td></td><td>Mean of seed weight (mg)</td><td>LEDA; TRY; Multiple online sources</td><td>quantitative</td><td>2.29</td></tr><tr><td>LDMC (mg/g)</td><td></td><td>Leaf Dry Mass Content is a measure of tissue density, is the ratio dry leaf mass to fresh leaf mass</td><td>LEDA; TRY</td><td>quantitative</td><td>14</td></tr><tr><td>Leaf size</td><td></td><td>Leaf size is the one-sided projected surface area of an individual leaf or lamina (mm2)</td><td>LEDA; TRY; Multiple online sources</td><td>quantitative</td><td>19.8</td></tr><tr><td>SLA</td><td></td><td>Specific Leaf Area is a ratio of fresh leaf area to leaf dry mass (mm2/mg)</td><td>LEDA; TRY</td><td>quantitative</td><td>9.65</td></tr><tr><td>Plant height</td><td></td><td>Mean distance between ground and the highest photosynthetic tissue of plant (meters)</td><td>BiolFlor; Multiple online sources</td><td>quantitative</td><td>0</td></tr></table>

<footnote>* multiple states are possible for a single species.</footnote>
## Supplementary material for DaPhnE phylogeny manipulation

**Supplementary Table 2.** Species names replaced in DaPhnE by their synonyms as found in Vigie-flore database:

<table><thead><tr><th>Species name available in DaPhnE</th><th>Synonym from Vigie-flore (*name available in Daphne but changed to match with trait databases)</th></tr></thead><tbody><tr><td>Amaranthus chlorostachys</td><td>Amaranthus hybridus*</td></tr><tr><td>Centunculus minimus</td><td>Anagallis minima</td></tr><tr><td>Halimione pedunculata</td><td>Atriplex pedunculata</td></tr><tr><td>Helictotrichon pubescens</td><td>Avenula pubescens</td></tr><tr><td>Carex otrubae</td><td>Carex cuprina</td></tr><tr><td>Carex ovalis</td><td>Carex leporina</td></tr><tr><td>Microrrhinum minus</td><td>Chaenorrhinum minus</td></tr><tr><td>Calamintha menthifolia</td><td>Clinopodium menthifolium</td></tr><tr><td>Virga pilosa</td><td>Dipsacus pilosus</td></tr><tr><td>Roegneria canina</td><td>Elymus caninus</td></tr><tr><td>Elytrigia repens</td><td>Elymus repens</td></tr><tr><td>Conyza sumatrensis</td><td>Erigeron sumatrensis</td></tr><tr><td>Ranunculus ficaria</td><td>Ficaria verna</td></tr><tr><td>Picris echioides</td><td>Helminthotheca echioides</td></tr><tr><td>Inula conyzae</td><td>Inula conyza</td></tr><tr><td>Galeobdolon luteum</td><td>Lamiastrum galeobdolon</td></tr><tr><td>Coronopus didymus</td><td>Lepidium didymum</td></tr><tr><td>Cardaria draba</td><td>Lepidium draba*</td></tr><tr><td>Peplis portula</td><td>Lythrum portula</td></tr></tbody></table>
<table><tr><td>Medicago x varia</td><td>Medicago sativa*</td></tr><tr><td>Listera ovata</td><td>Neottia ovata</td></tr><tr><td>Oxalis fontana</td><td>Oxalis stricta</td></tr><tr><td>Hieracium pilosella</td><td>Pilosella officinarum</td></tr><tr><td>Persicaria dubia</td><td>Polygonum mite</td></tr><tr><td>Fallopia japonica</td><td>Reynoutria japonica*</td></tr><tr><td>Tripleurospermum perforatum</td><td>Tripleurospermum inodorum</td></tr><tr><td>Pseudolysimachion spicatum</td><td>Veronica spicata</td></tr><tr><td>Taraxacum sect. Ruderalia</td><td>Taraxacum ruderale</td></tr><tr><td>Arabis glabra</td><td>Turritis glabra*</td></tr><tr><td>Elytrigia x laxa</td><td>Elymus pungens</td></tr></table>

Vigie-flore species not found in Daphne phylogeny and added manually:

Almost all species from Vigie-flore were present in Daphne, except 15 species which were placed to the phylogeny by hand, using existing trees from the literature. We used published molecular phylogenies (see below) to place the missing species on the DaPhnE phylogeny according to their closest relatives using functions of packages phytools (Revell 2012) and ape (Popescu) in R (R Core Team 2019). Taxa were either placed as polytomies or halfway between existing nodes, depending on tree topologies.

Species added with the reference used:

*Antinoria agrostidea* (Inda et al., 2008), *Bellis annua* (Brouillet et al., 2009), *Bromus rubens* (Fortune et al., 2008), *Bryonia cretica* (Volz and Renner, 2008), *Carex mairei* (Escudero et al., 2010), *Centaurea debeauxii* (López-Alvarado et al., 2014), *Crepis pursifolia* (Enke and Gemeinholzer, 2008), *Helictotricon sempervirens* (Rodrigues et al., 2017), *Hypericum perfoliatum* (Meseguer et al., 2013), *Impatiens balsamina* (Janssens et al., 2006), *Potentilla montana* (Kechaykin and Shmakov, 2016), *Silene laeta* (Frajman et al., 2009), *Trifolium pallidum* (Ellison et al., 2006), *Verbena bonariensis* (Marx et al., 2010), *Vulpia muralis*
(Inda et al., 2008).

**References for phylogeny manipulations:**

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Ellison, N. W., Liston, A., Steiner, J. J., Williams, W. M., and Taylor, N. L. (2006). Molecular phylogenetics of the clover genus (Trifolium-Leguminosae). Mol. Phylogenet. Evol. 39, 688–705.
doi:10.1016/j.ympev.2006.01.004.

Enke, N., and Gemeinholzer, B. (2008). Babcock revisited: new insights into generic delimitation and character evolution in Crepis L. (Compositae: Cichorieae) from ITS and matK sequence data.
Taxon 57, 756–768.

Escudero, M., Hipp, A. L., and Luceño, M. (2010). Karyotype stability and predictors of chromosome number variation in sedges: A study in Carex section Spirostachyae (Cyperaceae).
Mol. Phylogenet. Evol. 57, 353–363. doi:10.1016/j.ympev.2010.07.009.

Fortune, P. M., Pourtau, N., Viron, N., and Ainouche, M. L. (2008). Molecular phylogeny and reticulate origins of the polyploid Bromus species from section Genea (Poaceae). Am. J. Bot. 95, 454–464. doi:10.3732/ajb.95.4.454.

Frajman, B., Heidari, N., and Oxelman, B. (2009). Phylogenetic relationships of Atocion and Viscaria (Sileneae, Caryophyllaceae) inferred from chloroplast, nuclear ribosomal, and low-copy gene DNA sequences. Taxon 58, 811–824.

Inda, L. A., Segarra-Moragues, J. G., Müller, J., Peterson, P. M., and Catalán, P. (2008). Dated historical biogeography of the temperate Loliinae (Poaceae, Pooideae) grasses in the northern and southern hemispheres. Mol. Phylogenet. Evol. 46, 932–957. doi:10.1016/j.ympev.2007.11.022.

Janssens, S., Geuten, K., Yuan, Y.-M., Song, Y., Küpfer, P., and Smets, E. (2006). Phylogenetics of Impatiens and Hydrocera (Balsaminaceae) Using Chloroplast atpB-rbcL Spacer Sequences. Syst.
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Kechaykin, A. A., and Shmakov, A. I. (2016). A system of subtribe Potentillinae J. Presl (Rosaceae Juss.). Turczaninowia 19, 114–128. doi:10.14258/turczaninowia.19.4.16.

López-Alvarado, J., Sáez, L., Filigheddu, R., Garcia-Jacas, N., and Susanna, A. (2014). The limitations of molecular markers in phylogenetic reconstruction: The case of Centaurea sect. Phrygia (Compositae). Taxon 63, 1079–1091. doi:10.12705/635.6.

Marx, H. E., O'Leary, N., Yuan, Y. W., Lu-Irving, P., Tank, D. C., Múlgura, M. E., et al. (2010). A molecular phylogeny and classification of Verbenaceae. Am. J. Bot. 97, 1647–1663. doi:10.3732/ajb.1000144.

Meseguer, A. S., Aldasoro, J. J., and Sanmartín, I. (2013). Bayesian inference of phylogeny, morphology and range evolution reveals a complex evolutionary history in St. John's wort (Hypericum). Mol. Phylogenet. Evol. 67, 379–403. doi:10.1016/j.ympev.2013.02.007

Popescu, A.-A., K. T. Huber and E. Paradis (2012). "ape 3.0: new tools for distance based phylogenetics and evolutionary analysis in R." Bioinformatics 28, 1536–1537, DOI: 10.1093/bioinformatics/bts184.

Revell, L. J. (2012). "phytools: an R package for phylogenetic comparative biology (and other things)." Methods Ecol. Evol. 3(2): 217-223 DOI: 10.1111/j.2041-210X.2011.00169.x.

Rodrigues, J., Viegas, W., and Silva, M. (2017). 45S rDNA external transcribed spacer organization reveals new phylogenetic relationships in Avena genus. PLoS One 12, 1–17. doi:10.1371/journal.pone.0176170.

Volz, S. M., and Renner, S. S. (2008). Hybridization, polyploidy, and evolutionary transitions between monoecy and dioecy in Bryonia (Cucurbitaceae). Am. J. Bot. 95, 1297–1306. doi:10.3732/ajb.0800187.
<table><caption>**Supplementary Table 3.** Lists of top 5% (30 out of 586 species) original species using regional scores and mean local scores for each species across all the plots it occurred in. Trait-based originality unit relies on Gower distances calculated for species' pairwise trait differences, bounded between 0 and 1, and phylogenetic originality unit represents Million years of evolution.</caption><thead><tr><th>Regional trait-based originality<br>(min=0.44, median =0.52, max=0.67)</th><th>Mean of all local trait-based originalities<br>(min=0.36, median=0.48, max=0.71)</th><th>Regional phylogenetic originality<br>(min=218.51, median=237.20, max=287.28)</th><th>Mean of all local phylogenetic originalities<br>(min=90.41, median=233.56, max=297.20)</th></tr></thead><tbody><tr><td>Exaculum pusillum</td><td>0.67 Helictotrichon sempervirens* (1)</td><td>0.71 Lemna minor</td><td>287.28 Vulpia muralis (1)</td><td>297.20</td></tr><tr><td>Antinoria agrostidea</td><td>0.67 Vulpia muralis (1)</td><td>0.67 Alisma lanceolatum</td><td>287.02 Antinoria agrostidea (1)</td><td>297.20</td></tr><tr><td>Minuartia viscosa</td><td>0.65 Exaculum pusillum (1)</td><td>0.67 Alisma plantago-aquatica</td><td>287.02 Triticum monococcum (1)</td><td>295.60</td></tr><tr><td>Bellis annua*</td><td>0.65 Trifolium pallidum* (1)</td><td>0.67 Arum italicum</td><td>286.97 Carex pseudocyperus (1)</td><td>295.60</td></tr><tr><td>Erigeron sumatrensis*</td><td>0.64 Arctium nemorosum (1)</td><td>0.65 Arum maculatum</td><td>286.97 Platanthera bifolia (2)</td><td>290.44</td></tr><tr><td>Potentilla montana</td><td>0.64 Antinoria agrostidea (1)</td><td>0.65 Tamus communis</td><td>284.20 Muscari neglectum (2)</td><td>290.03</td></tr><tr><td>Phragmites australis</td><td>0.63 Veronica acinifolia (1)</td><td>0.65 Colchicum autumnale</td><td>283.74 Arum italicum (1)</td><td>288.87</td></tr><tr><td>Carex mairei</td><td>0.63 Minuartia viscosa (1)</td><td>0.64 Iris pseudacorus</td><td>282.28 Dactylorhiza maculata (1)</td><td>288.23</td></tr><tr><td>Arctium nemorosum</td><td>0.63 Erigeron sumatrensis* (76)</td><td>0.64 Allium cepa*</td><td>281.44 Alisma plantago-aquatica (2)</td><td>286.27</td></tr><tr><td>Anagallis minima</td><td>0.63 Bellis annua (2)</td><td>0.64 Allium sphaerocephalon</td><td>281.34 Arum maculatum (24)</td><td>285.85</td></tr><tr><td>Stellaria media</td><td>0.63 Triticum monococcum* (1)</td><td>0.62 Allium vineale</td><td>281.34 Allium cepa* (1)</td><td>285.28</td></tr><tr><td>Utricularia australis</td><td>0.63 Orobanche picridis (11)</td><td>0.62 Goodyera repens*</td><td>281.12 Sorghum halepense* (1)</td><td>285.28</td></tr><tr><td>Trifolium pallidum*</td><td>0.62 Heracleum mantegazzianum* (1)</td><td>0.62 Cephalanthera damasonium</td><td>281.01 Anthericum ramosum (1)</td><td>285.28</td></tr><tr><td>Triticum monococcum*</td><td>0.62 Petroselinum crispum* (1)</td><td>0.62 Neottia ovata</td><td>280.96 Setaria viridis (3)</td><td>285.24</td></tr><tr><td>Helictotrichon sempervirens*</td><td>0.62 Centaurea debeauxii (1)</td><td>0.61 Epipactis atrorubens</td><td>280.92 Alisma lanceolatum (1)</td><td>285.15</td></tr><tr><td>Amaranthus hybridus*</td><td>0.62 Artemisia verlotiorum* (2)</td><td>0.61 Epipactis belleborine</td><td>280.92 Carex pendula (1)</td><td>283.45</td></tr><tr><td>Vulpia muralis</td><td>0.62 Anagallis minima (1)</td><td>0.61 Himantoglossum hircinum</td><td>280.63 Iris pseudacorus (2)</td><td>282.95</td></tr><tr><td>Poa annua</td><td>0.62 Impatiens balfourii* (2)</td><td>0.61 Ophrys apifera</td><td>280.63 Anemone sylvestris (1)</td><td>282.80</td></tr><tr><td>Veronica acinifolia</td><td>0.62 Potentilla montana (5)</td><td>0.61 Dactylorhiza maculata</td><td>280.61 Scilla autumnalis (1)</td><td>282.70</td></tr><tr><td>Centaurea debeauxii</td><td>0.61 Beta vulgaris* (2)</td><td>0.61 Anthericum ramosum</td><td>280.60 Goodyera repens* (1)</td><td>282.70</td></tr><tr><td>Veronica agrestis</td><td>0.61 Catapodium rigidum (8)</td><td>0.60 Platanthera bifolia</td><td>280.59 Himantoglossum hircinum (7)</td><td>282.24</td></tr><tr><td>Heracleum mantegazzianum*</td><td>0.61 Sison anomum (2)</td><td>0.60 Platanthera chlorantha</td><td>280.59 Allium vineale (5)</td><td>282.08</td></tr></tbody></table>
<table><tr><td>Orobanche picridis</td><td>0.61</td><td>Bromus catharticus* (4)</td><td>0.60</td><td>Convallaria majalis</td><td>280.46</td><td>Tamus communis (29)</td><td>281.52</td></tr><tr><td>Bromus diandrus</td><td>0.61</td><td>Crepis bursifolia* (3)</td><td>0.59</td><td>Polygonatum multiflorum</td><td>280.41</td><td>Anemone nemorosa (40)</td><td>280.84</td></tr><tr><td>Arum italicum</td><td>0.60</td><td>Arum italicum* (1)</td><td>0.59</td><td>Polygonatum odoratum</td><td>280.41</td><td>Colchicum autumnale (1)</td><td>279.06</td></tr><tr><td>Lemna minor</td><td>0.60</td><td>Centaurea microptilon (2)</td><td>0.59</td><td>Ornithogalum pyrenaicum</td><td>280.36</td><td>Helictotrichon sempervirens (1)</td><td>278.54</td></tr><tr><td>Amaranthus blitoides*</td><td>0.60</td><td>Persicaria amphibia (6)</td><td>0.58</td><td>Hyacinthoides non-scripta</td><td>280.28</td><td>Ranunculus flammula (1)</td><td>278.53</td></tr><tr><td>Sison amomum</td><td>0.60</td><td>Phragmites australis (2)</td><td>0.58</td><td>Scilla autumnalis</td><td>280.26</td><td>Ranunculus bulbosus (10)</td><td>278.33</td></tr><tr><td>Poa bulbosa</td><td>0.60</td><td>Poa annua (319)</td><td>0.58</td><td>Muscari comosum</td><td>280.24</td><td>Lemna minor (1)</td><td>275.44</td></tr><tr><td>Taraxacum sect. ruderalia</td><td>0.60</td><td>Clinopodium menthifolium (5)</td><td>0.58</td><td>Muscari neglectum</td><td>280.24</td><td>Papaver rhoeas (184)</td><td>275.31</td></tr></table>

*non-native species

(n) species frequency across plots where it occurred in 10 years of sampling.
**Supplementary Table 4.** Results for the generalized linear mixed models (GLMM) ran on the dataset without non-native species including nine community level metrics (means and skewness of local and regional trait-based and phylogenetic originalities and species richness) and four species-specific measures (trait-based and phylogenetic originality and local to regional ratios)

Estimated coefficients and their significance are shown for the explanatory variables that remained after backward selection of variables based on Akaike information criterion (AIC) values including urbanization percentage, species richness, interaction (×) between urbanization and species richness and year of sampling. The mention "excluded" means that the explanatory variable was not retained in the final model according to the Akaike criterion. Conditional and marginal R² describes the proportion of variance explained by fixed (R²_c) and random+fixed (R²_m) covariables. P-values: NS = non-significant (P>0.050); * = 0.010 < P ≤ 0.050; ** = 0.001 < P ≤ 0.010; *** = P ≤ 0.001
<table><thead><tr><th>Dependent variable</th><th>Intercept</th><th>Urbanization (U)</th><th>Species richness (SR)</th><th>Year of sampling</th><th>U×SR</th><th>R<sup>2</sup><sub>c</sub></th><th>R<sup>2</sup><sub>m</sub></th></tr></thead><tbody><tr><td>Mean local trait-based originality</td><td>4.693 10<sup>-1</sup> ***</td><td>6.894 10<sup>-4</sup> ***</td><td>9.077 10<sup>-4</sup> ***</td><td>-5.827 10<sup>-4</sup> *</td><td>-2.341 10<sup>-5</sup> ***</td><td>0.435</td><td>0.119</td></tr><tr><td>Mean regional trait-based originality</td><td>5.150 10<sup>-1</sup> ***</td><td>2.430 10<sup>-4</sup> ***</td><td>-5.223 10<sup>-7</sup> NS</td><td>-1.708 10<sup>-4</sup> *</td><td>-1.042 10<sup>-5</sup> ***</td><td>0.644</td><td>0.083</td></tr><tr><td>Mean local phylogenetic originality</td><td>2.322 10<sup>2</sup> ***</td><td>-3.589 10<sup>-3</sup> NS</td><td>-1.028 10<sup>-1</sup> NS</td><td>-1.514 10<sup>-1</sup> NS</td><td>-4.291 10<sup>-3</sup> NS</td><td>0.242</td><td>0.004</td></tr><tr><td>Mean regional phylogenetic originality</td><td>2.428 10<sup>2</sup> ***</td><td>-6.832 10<sup>-2</sup> ***</td><td>-2.252 10<sup>-1</sup> ***</td><td>excluded</td><td>1.541 10<sup>-3</sup> NS</td><td>0.619</td><td>0.077</td></tr><tr><td>Skewness of local trait-based originality</td><td>1.110 10<sup>-2</sup> ***</td><td>3.706 10<sup>-5</sup> NS</td><td>2.525 10<sup>-2</sup> ***</td><td>-1.522 10<sup>-3</sup> NS</td><td>excluded</td><td>0.194</td><td>0.120</td></tr><tr><td>Skewness of regional trait-based originality</td><td>-3.719 10<sup>-2</sup> NS</td><td>1.566 10<sup>-3</sup> ***</td><td>3.141 10<sup>-2</sup> ***</td><td>-9.724 10<sup>-4</sup> NS</td><td>excluded</td><td>0.332</td><td>0.175</td></tr><tr><td>Skewness of local phylogenetic originality</td><td>4.187 10<sup>-1</sup> ***</td><td>1.691 10<sup>-4</sup> NS</td><td>3.903 10<sup>-3</sup> NS</td><td>-4.360 10<sup>-3</sup> NS</td><td>-1.894 10<sup>-4</sup> **</td><td>0.149</td><td>0.012</td></tr><tr><td>Skewness of regional phylogenetic originality</td><td>-5.189 10<sup>-2</sup> NS</td><td>6.392 10<sup>-5</sup> NS</td><td>1.583 10<sup>-2</sup> ***</td><td>3.086 10<sup>-3</sup> NS</td><td>excluded</td><td>0.28</td><td>0.05</td></tr><tr><td>Species Richness</td><td>1.929 ***</td><td>3.959 10<sup>-3</sup> ***</td><td>omitted</td><td>-4.032 10<sup>-3</sup> NS</td><td>omitted</td><td>3.456 10<sup>-2</sup></td><td>1.659 10<sup>-3</sup></td></tr><tr><td>Trait-based originality</td><td>4.755 10<sup>-1</sup> ***</td><td>3.826 10<sup>-4</sup> ***</td><td>8.754 10<sup>-4</sup> ***</td><td>-5.202 10<sup>-4</sup> ***</td><td>-1.066 10<sup>-5</sup> ***</td><td>0.655</td><td>0.034</td></tr><tr><td>Phylogenetic originality</td><td>1.818 10<sup>-2</sup> NS</td><td>6.550 10<sup>-4</sup> NS</td><td>7.694 10<sup>-3</sup> ***</td><td>-6.562 10<sup>-3</sup> *</td><td>-1.474 10<sup>-4</sup> **</td><td>0.428</td><td>0.003</td></tr><tr><td>Local/regional trait-based originality</td><td>9.091 10<sup>-1</sup> ***</td><td>7.388 10<sup>-4</sup> ***</td><td>1.733 10<sup>-3</sup> ***</td><td>excluded</td><td>-1.992 10<sup>-5</sup> ***</td><td>0.450</td><td>0.056</td></tr><tr><td>Local/regional phylogenetic originality</td><td>9.564 10<sup>-1</sup> ***</td><td>1.125 10<sup>-4</sup> NS</td><td>1.064 10<sup>-3</sup> ***</td><td>-8.562 10<sup>-4</sup> *</td><td>-2.076 10<sup>-5</sup> **</td><td>0.512</td><td>0.002</td></tr></tbody></table>
A)

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

B)

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

C)

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

Moran's I for mean regional phylogenetic originality

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

**Supplementary Figure 1.** Correlograms of the residuals of the GLMM models with complete dataset (natives and non-natives): (A) mean local and (B) regional trait-based originality models and (C) mean local and (D) regional phylogenetic originality model. Local Moran's correlation was tested on the residuals of the GLMM models. Significance of the correlation was assessed based on 999 randomizations at the 500 meters increment for neighborhood definition. First ten increment points are visualized, with blue points representing non-significance and the red points a significant spatial autocorrelation. Only the points at zero meters of distance were significant, reflecting the data structure with a systematic sampling performed each year at the same place for many species plots. No spatial autocorrelation was detected in models' residuals at any other distance. Thus, no correction for spatial autocorrelation was needed.
<figure><img src="image_24.png" /><figcaption>**Supplementary Figure 2.** Variation of species-specific (A) trait-based and (B) phylogenetic originalities of native and non-native species according to the percentage of urbanization within 1x1km grid cell (grid cell corresponds to the Vigie-flore protocol) (in (A)) and species richness (SR) (in (B)). One data point represents one species score, including originality values of native and non-native species from all plots across all the years, thus the same species can occur multiple times in different plots. In (A), colors represent a scale of species richness (SR) going from blue as a lower SR (minimum of 3 species) to red as a higher SR (maximum of 50 species). Colored curves represent the estimated trends of an interaction between urbanization and SR for 5 (blue), 25 (salmon) and 40 (red) species retrieved from the mean originality GLMM models. In (B), colors represent a scale of urbanization gradient calculated as the percentage of urbanized area cover; colored lines represent the estimated trends of an interaction between SR and urbanization for 0%, 50% and 100% respectively, within 1x1km grid cell, retrieved from the skewness GLMM model.</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>**Supplementary Figure 3.** Variation of species (A) trait-based and (B) phylogenetic originality ratio *LAV*<sub>ip</sub> / *rAV*<sub>i</sub> according to the percentage of urbanization within 1x1km grid cell (grid cell corresponds to the Vigie-flore protocol) (in (A)) and species richness (SR) (in (B)). One data point represents one species score, including originality values of native and non-native species from all plots across all the years, thus the same species can occur multiple times in different plots. Originality ratio superior to one indicate species that became original at the local scale at least once although they were not at the regional scale. In (A), colors represent a scale of species richness (SR) going from blue as a lower SR (minimum of 3 species) to red as a higher SR (maximum of 50 species). Colored curves represent the estimated trends of an interaction between urbanization and SR for 5 (blue), 25 (salmon) and 40 (red) species retrieved from the mean originality GLMM models. In (B), colors represent a scale of urbanization gradient calculated as the percentage of urbanized area cover; colored lines represent the estimated trends of an interaction between SR and urbanization for 0%, 50% and 100% respectively, within 1x1km grid cell, retrieved from the skewness GLMM model.</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>**Supplementary Figure 4.** Variation of species (A) trait-based and (B) phylogenetic originality ratio *LAV*<sub>iP</sub> / *rAV*<sub>i</sub> according to the percentage of urbanization within 1x1km grid cell (grid cell corresponds to the Vigie-flore protocol). One data point represents one species score, including originality values of native and non-native species from all plots across all the years, thus the same species can occur multiple times in different plots. Originality ratio superior to one indicate species that became original at the local scale at least once although they were not at the regional scale. Colors represent a scale of species urbanity going from blue for urbanophobic species to green for urbanophilic species. Species urbanity was calculated as the mean percentage of urbanized area in all grid cells where a species occurred.</figcaption></figure>
<figure><img src="image_27.png" /></figure>

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

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

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

**Supplementary Figure 6.** Box plots for (A) local trait-based, (B) local phylogenetic, (C) regional trait-based and (D) regional phylogenetic originality of 586 species. Regional originality values were calculated regarding all species in the dataset; local originality values were calculated for each of 586 species regarding only coexisting species in a particular plot where species occurred. Bold black lines represent medians, boxes are 25-75% interquartile, moustache (vertical lines) are samples with less than 1.5 times of the interquartile range and dots are outliers. Native and Non-native species differed significantly in their median (Wilcoxon rank test, W=5819854, W=11347166, W=18069, p < 0.001) in (A), (B), and (C).
<table><caption>**Supplementary Table 5.** Plant species dataset used in this study and corresponding amount of missing trait data</caption><thead><tr><td>Species name</td><td>Missing trait data (%)</td></tr></thead><tbody><tr><td>Clinopodium menthifolium</td><td>35,80%</td></tr><tr><td>Bromus diandrus</td><td>28,40%</td></tr><tr><td>Hypochaeris radicata</td><td>24,69%</td></tr><tr><td>Verbena bonariensis</td><td>22,22%</td></tr><tr><td>Dactylis glomerata</td><td>20,99%</td></tr><tr><td>Hypericum hirsutum</td><td>20,99%</td></tr><tr><td>Vulpia muralis</td><td>20,99%</td></tr><tr><td>Centaurea microptilon</td><td>16,05%</td></tr><tr><td>Eschscholzia californica</td><td>14,81%</td></tr><tr><td>Helictotrichon sempervirens</td><td>14,81%</td></tr><tr><td>Catapodium rigidum</td><td>13,58%</td></tr><tr><td>Crepis bursifolia</td><td>13,58%</td></tr><tr><td>Duchesnea indica</td><td>13,58%</td></tr><tr><td>Trifolium pallidum</td><td>13,58%</td></tr><tr><td>Antinoria agrostidea</td><td>12,35%</td></tr><tr><td>Carex mairei</td><td>12,35%</td></tr><tr><td>Centaurea debeauxii</td><td>12,35%</td></tr><tr><td>Triticum monococcum</td><td>12,35%</td></tr><tr><td>Exaculum pusillum</td><td>11,11%</td></tr><tr><td>Minuartia viscosa</td><td>11,11%</td></tr><tr><td>Pulmonaria longifolia</td><td>11,11%</td></tr></tbody></table>
<table><tr><td>Solanum melongena</td><td>11,11%</td></tr><tr><td>Bellis annua</td><td>9,88%</td></tr><tr><td>Bromus rubens</td><td>9,88%</td></tr><tr><td>Bryonia cretica</td><td>9,88%</td></tr><tr><td>Centaurea nigra</td><td>9,88%</td></tr><tr><td>Erigeron sumatrensis</td><td>9,88%</td></tr><tr><td>Nigella damascena</td><td>9,88%</td></tr><tr><td>Potentilla montana</td><td>9,88%</td></tr><tr><td>Vicia parviflora</td><td>9,88%</td></tr><tr><td>Allium cepa</td><td>8,64%</td></tr><tr><td>Amaranthus hybridus</td><td>8,64%</td></tr><tr><td>Arum italicum</td><td>8,64%</td></tr><tr><td>Carduus tenuiflorus</td><td>8,64%</td></tr><tr><td>Carum verticillatum</td><td>8,64%</td></tr><tr><td>Ornithogalum pyrenaicum</td><td>8,64%</td></tr><tr><td>Orobanche picridis</td><td>8,64%</td></tr><tr><td>Veronica acinifolia</td><td>8,64%</td></tr><tr><td>Amaranthus cruentus</td><td>7,41%</td></tr><tr><td>Impatiens balfourii</td><td>7,41%</td></tr><tr><td>Neottia ovata</td><td>7,41%</td></tr><tr><td>Petroselinum crispum</td><td>7,41%</td></tr><tr><td>Pilosella officinarum</td><td>7,41%</td></tr><tr><td>Pisum sativum</td><td>7,41%</td></tr></table>
<table><tr><td>Vicia faba</td><td>7,41%</td></tr><tr><td>Amaranthus deflexus</td><td>6,17%</td></tr><tr><td>Artemisia verlotiorum</td><td>6,17%</td></tr><tr><td>Betonica officinalis</td><td>6,17%</td></tr><tr><td>Bromus catharticus</td><td>6,17%</td></tr><tr><td>Calamintha nepeta</td><td>6,17%</td></tr><tr><td>Calendula officinalis</td><td>6,17%</td></tr><tr><td>Campanula patula</td><td>6,17%</td></tr><tr><td>Carex cuprina</td><td>6,17%</td></tr><tr><td>Chaenorrhinum minus</td><td>6,17%</td></tr><tr><td>Crassula tillaea</td><td>6,17%</td></tr><tr><td>Crepis foetida</td><td>6,17%</td></tr><tr><td>Cucubalus baccifer</td><td>6,17%</td></tr><tr><td>Festuca filiformis</td><td>6,17%</td></tr><tr><td>Ficaria verna</td><td>6,17%</td></tr><tr><td>Gnaphalium uliginosum</td><td>6,17%</td></tr><tr><td>Helminthotheca echioides</td><td>6,17%</td></tr><tr><td>Lepidium didymum</td><td>6,17%</td></tr><tr><td>Linaria arvensis</td><td>6,17%</td></tr><tr><td>Lolium rigidum</td><td>6,17%</td></tr><tr><td>Melissa officinalis</td><td>6,17%</td></tr><tr><td>Muscari comosum</td><td>6,17%</td></tr><tr><td>Muscari neglectum</td><td>6,17%</td></tr></table>
<table><tr><td>Scilla autumnalis</td><td>6,17%</td></tr><tr><td>Sedum forsterianum</td><td>6,17%</td></tr><tr><td>Sisymbrium irio</td><td>6,17%</td></tr><tr><td>Taraxacum ruderale</td><td>6,17%</td></tr><tr><td>Tripleurospermum inodorum</td><td>6,17%</td></tr><tr><td>Vicia grandiflora</td><td>6,17%</td></tr><tr><td>Zea mays</td><td>6,17%</td></tr><tr><td>Amaranthus albus</td><td>4,94%</td></tr><tr><td>Arabis turrita</td><td>4,94%</td></tr><tr><td>Arctium nemorosum</td><td>4,94%</td></tr><tr><td>Arenaria serpyllifolia</td><td>4,94%</td></tr><tr><td>Brassica napus</td><td>4,94%</td></tr><tr><td>Carex leporina</td><td>4,94%</td></tr><tr><td>Crepis setosa</td><td>4,94%</td></tr><tr><td>Cynosurus echinatus</td><td>4,94%</td></tr><tr><td>Dipsacus sativus</td><td>4,94%</td></tr><tr><td>Elymus caninus</td><td>4,94%</td></tr><tr><td>Festuca heterophylla</td><td>4,94%</td></tr><tr><td>Glebionis segetum</td><td>4,94%</td></tr><tr><td>Goodyera repens</td><td>4,94%</td></tr><tr><td>Lathyrus aphaca</td><td>4,94%</td></tr><tr><td>Lathyrus hirsutus</td><td>4,94%</td></tr><tr><td>Lepidium draba</td><td>4,94%</td></tr></table>
<table><tr><td>Luzula forsteri</td><td>4,94%</td></tr><tr><td>Matricaria recutita</td><td>4,94%</td></tr><tr><td>Mycelis muralis</td><td>4,94%</td></tr><tr><td>Oxalis stricta</td><td>4,94%</td></tr><tr><td>Persicaria amphibia</td><td>4,94%</td></tr><tr><td>Persicaria hydropiper</td><td>4,94%</td></tr><tr><td>Persicaria lapathifolia</td><td>4,94%</td></tr><tr><td>Persicaria maculosa</td><td>4,94%</td></tr><tr><td>Sedum telephium</td><td>4,94%</td></tr><tr><td>Setaria verticillata</td><td>4,94%</td></tr><tr><td>Sison amomum</td><td>4,94%</td></tr><tr><td>Sorghum halepense</td><td>4,94%</td></tr><tr><td>Stellaria alsine</td><td>4,94%</td></tr><tr><td>Verbascum blattaria</td><td>4,94%</td></tr><tr><td>Viola alba</td><td>4,94%</td></tr><tr><td>Amaranthus blitoides</td><td>3,70%</td></tr><tr><td>Atriplex prostrata</td><td>3,70%</td></tr><tr><td>Cirsium palustre</td><td>3,70%</td></tr><tr><td>Epilobium tetragonum</td><td>3,70%</td></tr><tr><td>Erodium cicutarium</td><td>3,70%</td></tr><tr><td>Euphorbia cyparissias</td><td>3,70%</td></tr><tr><td>Festuca ovina</td><td>3,70%</td></tr><tr><td>Geranium rotundifolium</td><td>3,70%</td></tr></table>
<table><tr><td>Matricaria discoidea</td><td>3,70%</td></tr><tr><td>Odontites vernus</td><td>3,70%</td></tr><tr><td>Ononis spinosa</td><td>3,70%</td></tr><tr><td>Polygonum mite</td><td>3,70%</td></tr><tr><td>Ranunculus reptans</td><td>3,70%</td></tr><tr><td>Rubia peregrina</td><td>3,70%</td></tr><tr><td>Solanum tuberosum</td><td>3,70%</td></tr><tr><td>Vicia villosa</td><td>3,70%</td></tr><tr><td>Anagallis minima</td><td>2,47%</td></tr><tr><td>Anchusa arvensis</td><td>2,47%</td></tr><tr><td>Armeria maritima</td><td>2,47%</td></tr><tr><td>Aster lanceolatus</td><td>2,47%</td></tr><tr><td>Avenula pubescens</td><td>2,47%</td></tr><tr><td>Beta vulgaris</td><td>2,47%</td></tr><tr><td>Brassica oleracea</td><td>2,47%</td></tr><tr><td>Buglossoides arvensis</td><td>2,47%</td></tr><tr><td>Centaurea jacea</td><td>2,47%</td></tr><tr><td>Chaerophyllum hirsutum</td><td>2,47%</td></tr><tr><td>Elymus pungens</td><td>2,47%</td></tr><tr><td>Kickxia spuria</td><td>2,47%</td></tr><tr><td>Lamiastrum galeobdolon</td><td>2,47%</td></tr><tr><td>Limonium vulgare</td><td>2,47%</td></tr><tr><td>Lythrum portula</td><td>2,47%</td></tr></table>
<table><tr><td>Medicago minima</td><td>2,47%</td></tr><tr><td>Medicago polymorpha</td><td>2,47%</td></tr><tr><td>Medicago sativa</td><td>2,47%</td></tr><tr><td>Melica uniflora</td><td>2,47%</td></tr><tr><td>Ranunculus lanuginosus</td><td>2,47%</td></tr><tr><td>Reynoutria japonica</td><td>2,47%</td></tr><tr><td>Securigera varia</td><td>2,47%</td></tr><tr><td>Sedum acre</td><td>2,47%</td></tr><tr><td>Setaria italica</td><td>2,47%</td></tr><tr><td>Tamus communis</td><td>2,47%</td></tr><tr><td>Trifolium aureum</td><td>2,47%</td></tr><tr><td>Turritis glabra</td><td>2,47%</td></tr><tr><td>Utricularia australis</td><td>2,47%</td></tr><tr><td>Veronica spicata</td><td>2,47%</td></tr><tr><td>Agrostemma githago</td><td>1,23%</td></tr><tr><td>Amaranthus blitum</td><td>1,23%</td></tr><tr><td>Anagallis arvensis</td><td>1,23%</td></tr><tr><td>Anemone sylvestris</td><td>1,23%</td></tr><tr><td>Anthoxanthum odoratum</td><td>1,23%</td></tr><tr><td>Antirrhinum majus</td><td>1,23%</td></tr><tr><td>Brachypodium pinnatum</td><td>1,23%</td></tr><tr><td>Brassica rapa</td><td>1,23%</td></tr><tr><td>Bromus erectus</td><td>1,23%</td></tr></table>
<table><tr><td>Bromus inermis</td><td>1,23%</td></tr><tr><td>Bromus japonicus</td><td>1,23%</td></tr><tr><td>Bromus ramosus</td><td>1,23%</td></tr><tr><td>Bromus squarrosus</td><td>1,23%</td></tr><tr><td>Bromus sterilis</td><td>1,23%</td></tr><tr><td>Campanula rapunculoides</td><td>1,23%</td></tr><tr><td>Carex arenaria</td><td>1,23%</td></tr><tr><td>Carex muricata</td><td>1,23%</td></tr><tr><td>Carex vulpina</td><td>1,23%</td></tr><tr><td>Centaurea cyanus</td><td>1,23%</td></tr><tr><td>Centranthus ruber</td><td>1,23%</td></tr><tr><td>Cerastium arvense</td><td>1,23%</td></tr><tr><td>Cerastium semidecandrum</td><td>1,23%</td></tr><tr><td>Chenopodium hybridum</td><td>1,23%</td></tr><tr><td>Chenopodium polyspermum</td><td>1,23%</td></tr><tr><td>Chenopodium rubrum</td><td>1,23%</td></tr><tr><td>Conyza canadensis</td><td>1,23%</td></tr><tr><td>Coronopus squamatus</td><td>1,23%</td></tr><tr><td>Crepis vesicaria</td><td>1,23%</td></tr><tr><td>Daucus carota</td><td>1,23%</td></tr><tr><td>Deschampsia cespitosa</td><td>1,23%</td></tr><tr><td>Deschampsia flexuosa</td><td>1,23%</td></tr><tr><td>Dipsacus pilosus</td><td>1,23%</td></tr></table>
<table><tr><td>Elymus repens</td><td>1,23%</td></tr><tr><td>Epipactis atrorubens</td><td>1,23%</td></tr><tr><td>Erophila verna</td><td>1,23%</td></tr><tr><td>Euphorbia peplus</td><td>1,23%</td></tr><tr><td>Festuca arundinacea</td><td>1,23%</td></tr><tr><td>Festuca gigantea</td><td>1,23%</td></tr><tr><td>Festuca pratensis</td><td>1,23%</td></tr><tr><td>Festuca rubra</td><td>1,23%</td></tr><tr><td>Galinsoga parviflora</td><td>1,23%</td></tr><tr><td>Galium saxatile</td><td>1,23%</td></tr><tr><td>Hieracium umbellatum</td><td>1,23%</td></tr><tr><td>Hordeum vulgare</td><td>1,23%</td></tr><tr><td>Inula conyza</td><td>1,23%</td></tr><tr><td>Juncus effusus</td><td>1,23%</td></tr><tr><td>Kickxia elatine</td><td>1,23%</td></tr><tr><td>Knautia arvensis</td><td>1,23%</td></tr><tr><td>Lemna minor</td><td>1,23%</td></tr><tr><td>Leontodon autumnalis</td><td>1,23%</td></tr><tr><td>Leontodon hispidus</td><td>1,23%</td></tr><tr><td>Lepidium latifolium</td><td>1,23%</td></tr><tr><td>Leucanthemum vulgare</td><td>1,23%</td></tr><tr><td>Linaria repens</td><td>1,23%</td></tr><tr><td>Linum usitatissimum</td><td>1,23%</td></tr></table>
<table><tr><td>Lolium multiflorum</td><td>1,23%</td></tr><tr><td>Lotus pedunculatus</td><td>1,23%</td></tr><tr><td>Luzula pilosa</td><td>1,23%</td></tr><tr><td>Luzula sylvatica</td><td>1,23%</td></tr><tr><td>Malva sylvestris</td><td>1,23%</td></tr><tr><td>Medicago arabica</td><td>1,23%</td></tr><tr><td>Melica nutans</td><td>1,23%</td></tr><tr><td>Melilotus albus</td><td>1,23%</td></tr><tr><td>Melittis melissophyllum</td><td>1,23%</td></tr><tr><td>Myosotis discolor</td><td>1,23%</td></tr><tr><td>Myosoton aquaticum</td><td>1,23%</td></tr><tr><td>Papaver somniferum</td><td>1,23%</td></tr><tr><td>Phacelia tanacetifolia</td><td>1,23%</td></tr><tr><td>Pimpinella major</td><td>1,23%</td></tr><tr><td>Poa bulbosa</td><td>1,23%</td></tr><tr><td>Poa compressa</td><td>1,23%</td></tr><tr><td>Poa trivialis</td><td>1,23%</td></tr><tr><td>Polygonatum multiflorum</td><td>1,23%</td></tr><tr><td>Potentilla anserina</td><td>1,23%</td></tr><tr><td>Potentilla sterilis</td><td>1,23%</td></tr><tr><td>Pulsatilla vulgaris</td><td>1,23%</td></tr><tr><td>Sagina apetala</td><td>1,23%</td></tr><tr><td>Salvia pratensis</td><td>1,23%</td></tr></table>
<table><tr><td>Sanguisorba minor</td><td>1,23%</td></tr><tr><td>Scrophularia auriculata</td><td>1,23%</td></tr><tr><td>Senecio erucifolius</td><td>1,23%</td></tr><tr><td>Senecio inaequidens</td><td>1,23%</td></tr><tr><td>Senecio jacobaea</td><td>1,23%</td></tr><tr><td>Setaria viridis</td><td>1,23%</td></tr><tr><td>Silene latifolia</td><td>1,23%</td></tr><tr><td>Solanum nigrum</td><td>1,23%</td></tr><tr><td>Solidago canadensis</td><td>1,23%</td></tr><tr><td>Sonchus palustris</td><td>1,23%</td></tr><tr><td>Spergularia rubra</td><td>1,23%</td></tr><tr><td>Stachys palustris</td><td>1,23%</td></tr><tr><td>Stachys recta</td><td>1,23%</td></tr><tr><td>Stellaria nemorum</td><td>1,23%</td></tr><tr><td>Teucrium scordium</td><td>1,23%</td></tr><tr><td>Tordylium maximum</td><td>1,23%</td></tr><tr><td>Torilis nodosa</td><td>1,23%</td></tr><tr><td>Trifolium repens</td><td>1,23%</td></tr><tr><td>Triticum aestivum</td><td>1,23%</td></tr><tr><td>Valerianella rimosa</td><td>1,23%</td></tr><tr><td>Viola tricolor</td><td>1,23%</td></tr><tr><td>Vulpia bromoides</td><td>1,23%</td></tr><tr><td>Achillea millefolium</td><td>0,00%</td></tr></table>
<table><tr><td>Adoxa moschatellina</td><td>0,00%</td></tr><tr><td>Aethusa cynapium</td><td>0,00%</td></tr><tr><td>Agrimonia eupatoria</td><td>0,00%</td></tr><tr><td>Agrostis canina</td><td>0,00%</td></tr><tr><td>Agrostis capillaris</td><td>0,00%</td></tr><tr><td>Agrostis gigantea</td><td>0,00%</td></tr><tr><td>Agrostis stolonifera</td><td>0,00%</td></tr><tr><td>Ajuga genevensis</td><td>0,00%</td></tr><tr><td>Ajuga reptans</td><td>0,00%</td></tr><tr><td>Alisma lanceolatum</td><td>0,00%</td></tr><tr><td>Alisma plantago-aquatica</td><td>0,00%</td></tr><tr><td>Alliaria petiolata</td><td>0,00%</td></tr><tr><td>Allium sphaerocephalon</td><td>0,00%</td></tr><tr><td>Allium vineale</td><td>0,00%</td></tr><tr><td>Alopecurus geniculatus</td><td>0,00%</td></tr><tr><td>Alopecurus myosuroides</td><td>0,00%</td></tr><tr><td>Alopecurus pratensis</td><td>0,00%</td></tr><tr><td>Amaranthus retroflexus</td><td>0,00%</td></tr><tr><td>Ammi majus</td><td>0,00%</td></tr><tr><td>Anemone nemorosa</td><td>0,00%</td></tr><tr><td>Angelica sylvestris</td><td>0,00%</td></tr><tr><td>Anthemis cotula</td><td>0,00%</td></tr><tr><td>Anthericum ramosum</td><td>0,00%</td></tr></table>
<table><tr><td>Anthriscus caucalis</td><td>0,00%</td></tr><tr><td>Anthriscus sylvestris</td><td>0,00%</td></tr><tr><td>Apera spica-venti</td><td>0,00%</td></tr><tr><td>Aphanes arvensis</td><td>0,00%</td></tr><tr><td>Apium nodiflorum</td><td>0,00%</td></tr><tr><td>Aquilegia vulgaris</td><td>0,00%</td></tr><tr><td>Arabidopsis thaliana</td><td>0,00%</td></tr><tr><td>Arctium lappa</td><td>0,00%</td></tr><tr><td>Arctium minus</td><td>0,00%</td></tr><tr><td>Arrhenatherum elatius</td><td>0,00%</td></tr><tr><td>Artemisia vulgaris</td><td>0,00%</td></tr><tr><td>Arum maculatum</td><td>0,00%</td></tr><tr><td>Astragalus glycyphyllos</td><td>0,00%</td></tr><tr><td>Atriplex patula</td><td>0,00%</td></tr><tr><td>Avena fatua</td><td>0,00%</td></tr><tr><td>Avena sativa</td><td>0,00%</td></tr><tr><td>Ballota nigra</td><td>0,00%</td></tr><tr><td>Barbarea vulgaris</td><td>0,00%</td></tr><tr><td>Bellis perennis</td><td>0,00%</td></tr><tr><td>Blackstonia perfoliata</td><td>0,00%</td></tr><tr><td>Brachypodium sylvaticum</td><td>0,00%</td></tr><tr><td>Brassica nigra</td><td>0,00%</td></tr><tr><td>Bromus arvensis</td><td>0,00%</td></tr></table>
<table><tr><td>Bromus commutatus</td><td>0,00%</td></tr><tr><td>Bromus hordeaceus</td><td>0,00%</td></tr><tr><td>Bromus racemosus</td><td>0,00%</td></tr><tr><td>Bromus secalinus</td><td>0,00%</td></tr><tr><td>Calamagrostis epigejos</td><td>0,00%</td></tr><tr><td>Calendula arvensis</td><td>0,00%</td></tr><tr><td>Calystegia sepium</td><td>0,00%</td></tr><tr><td>Campanula rapunculus</td><td>0,00%</td></tr><tr><td>Campanula rotundifolia</td><td>0,00%</td></tr><tr><td>Capsella bursa-pastoris</td><td>0,00%</td></tr><tr><td>Cardamine hirsuta</td><td>0,00%</td></tr><tr><td>Cardamine pratensis</td><td>0,00%</td></tr><tr><td>Carduus acanthoides</td><td>0,00%</td></tr><tr><td>Carduus crispus</td><td>0,00%</td></tr><tr><td>Carduus nutans</td><td>0,00%</td></tr><tr><td>Carex acutiformis</td><td>0,00%</td></tr><tr><td>Carex divulsa</td><td>0,00%</td></tr><tr><td>Carex flacca</td><td>0,00%</td></tr><tr><td>Carex hirta</td><td>0,00%</td></tr><tr><td>Carex pendula</td><td>0,00%</td></tr><tr><td>Carex pilulifera</td><td>0,00%</td></tr><tr><td>Carex pseudocyperus</td><td>0,00%</td></tr><tr><td>Carex remota</td><td>0,00%</td></tr></table>
<table><tr><td>Carex riparia</td><td>0,00%</td></tr><tr><td>Carex spicata</td><td>0,00%</td></tr><tr><td>Carex sylvatica</td><td>0,00%</td></tr><tr><td>Carlina vulgaris</td><td>0,00%</td></tr><tr><td>Centaurea scabiosa</td><td>0,00%</td></tr><tr><td>Centaurium erythraea</td><td>0,00%</td></tr><tr><td>Centaurium pulchellum</td><td>0,00%</td></tr><tr><td>Cephalanthera damasonium</td><td>0,00%</td></tr><tr><td>Cerastium fontanum</td><td>0,00%</td></tr><tr><td>Cerastium glomeratum</td><td>0,00%</td></tr><tr><td>Cerastium pumilum</td><td>0,00%</td></tr><tr><td>Chaerophyllum temulum</td><td>0,00%</td></tr><tr><td>Chelidonium majus</td><td>0,00%</td></tr><tr><td>Chenopodium album</td><td>0,00%</td></tr><tr><td>Chenopodium ficifolium</td><td>0,00%</td></tr><tr><td>Circaea lutetiana</td><td>0,00%</td></tr><tr><td>Cirsium arvense</td><td>0,00%</td></tr><tr><td>Cirsium dissectum</td><td>0,00%</td></tr><tr><td>Cirsium oleraceum</td><td>0,00%</td></tr><tr><td>Cirsium vulgare</td><td>0,00%</td></tr><tr><td>Clinopodium vulgare</td><td>0,00%</td></tr><tr><td>Colchicum autumnale</td><td>0,00%</td></tr><tr><td>Conium maculatum</td><td>0,00%</td></tr></table>
<table><tr><td>Convallaria majalis</td><td>0,00%</td></tr><tr><td>Convolvulus arvensis</td><td>0,00%</td></tr><tr><td>Crepis capillaris</td><td>0,00%</td></tr><tr><td>Cruciata laevipes</td><td>0,00%</td></tr><tr><td>Cymbalaria muralis</td><td>0,00%</td></tr><tr><td>Cynodon dactylon</td><td>0,00%</td></tr><tr><td>Cynosurus cristatus</td><td>0,00%</td></tr><tr><td>Dactylorhiza maculata</td><td>0,00%</td></tr><tr><td>Danthonia decumbens</td><td>0,00%</td></tr><tr><td>Datura stramonium</td><td>0,00%</td></tr><tr><td>Digitalis purpurea</td><td>0,00%</td></tr><tr><td>Digitaria ischaemum</td><td>0,00%</td></tr><tr><td>Digitaria sanguinalis</td><td>0,00%</td></tr><tr><td>Diplotaxis tenuifolia</td><td>0,00%</td></tr><tr><td>Dipsacus fullonum</td><td>0,00%</td></tr><tr><td>Echinochloa crus-galli</td><td>0,00%</td></tr><tr><td>Echium vulgare</td><td>0,00%</td></tr><tr><td>Epilobium angustifolium</td><td>0,00%</td></tr><tr><td>Epilobium ciliatum</td><td>0,00%</td></tr><tr><td>Epilobium hirsutum</td><td>0,00%</td></tr><tr><td>Epilobium montanum</td><td>0,00%</td></tr><tr><td>Epilobium parviflorum</td><td>0,00%</td></tr><tr><td>Epipactis helleborine</td><td>0,00%</td></tr></table>
<table><tr><td>Eragrostis minor</td><td>0,00%</td></tr><tr><td>Erigeron annuus</td><td>0,00%</td></tr><tr><td>Eryngium campestre</td><td>0,00%</td></tr><tr><td>Erysimum cheiri</td><td>0,00%</td></tr><tr><td>Eupatorium cannabinum</td><td>0,00%</td></tr><tr><td>Euphorbia amygdaloides</td><td>0,00%</td></tr><tr><td>Euphorbia exigua</td><td>0,00%</td></tr><tr><td>Euphorbia helioscopia</td><td>0,00%</td></tr><tr><td>Fallopia convolvulus</td><td>0,00%</td></tr><tr><td>Filipendula ulmaria</td><td>0,00%</td></tr><tr><td>Filipendula vulgaris</td><td>0,00%</td></tr><tr><td>Fragaria vesca</td><td>0,00%</td></tr><tr><td>Fragaria viridis</td><td>0,00%</td></tr><tr><td>Fumaria officinalis</td><td>0,00%</td></tr><tr><td>Galega officinalis</td><td>0,00%</td></tr><tr><td>Galeopsis tetrahit</td><td>0,00%</td></tr><tr><td>Galium aparine</td><td>0,00%</td></tr><tr><td>Galium mollugo</td><td>0,00%</td></tr><tr><td>Galium odoratum</td><td>0,00%</td></tr><tr><td>Galium palustre</td><td>0,00%</td></tr><tr><td>Galium parisense</td><td>0,00%</td></tr><tr><td>Galium uliginosum</td><td>0,00%</td></tr><tr><td>Galium verum</td><td>0,00%</td></tr></table>
<table><tr><td>Geranium columbinum</td><td>0,00%</td></tr><tr><td>Geranium dissectum</td><td>0,00%</td></tr><tr><td>Geranium molle</td><td>0,00%</td></tr><tr><td>Geranium pusillum</td><td>0,00%</td></tr><tr><td>Geranium pyrenaicum</td><td>0,00%</td></tr><tr><td>Geranium robertianum</td><td>0,00%</td></tr><tr><td>Geranium sanguineum</td><td>0,00%</td></tr><tr><td>Geum urbanum</td><td>0,00%</td></tr><tr><td>Glechoma hederacea</td><td>0,00%</td></tr><tr><td>Helleborus foetidus</td><td>0,00%</td></tr><tr><td>Heracleum mantegazzianum</td><td>0,00%</td></tr><tr><td>Heracleum sphondylium</td><td>0,00%</td></tr><tr><td>Herniaria glabra</td><td>0,00%</td></tr><tr><td>Herniaria hirsuta</td><td>0,00%</td></tr><tr><td>Himantoglossum hircinum</td><td>0,00%</td></tr><tr><td>Hippocrepis comosa</td><td>0,00%</td></tr><tr><td>Hirschfeldia incana</td><td>0,00%</td></tr><tr><td>Holcus lanatus</td><td>0,00%</td></tr><tr><td>Holcus mollis</td><td>0,00%</td></tr><tr><td>Hordeum murinum</td><td>0,00%</td></tr><tr><td>Humulus lupulus</td><td>0,00%</td></tr><tr><td>Hyacinthoides non-scripta</td><td>0,00%</td></tr><tr><td>Hydrocotyle vulgaris</td><td>0,00%</td></tr></table>
<table><tr><td>Hypericum humifusum</td><td>0,00%</td></tr><tr><td>Hypericum maculatum</td><td>0,00%</td></tr><tr><td>Hypericum perforatum</td><td>0,00%</td></tr><tr><td>Hypericum pulchrum</td><td>0,00%</td></tr><tr><td>Hypericum tetrapterum</td><td>0,00%</td></tr><tr><td>Illecebrum verticillatum</td><td>0,00%</td></tr><tr><td>Iris pseudacorus</td><td>0,00%</td></tr><tr><td>Juncus acutiflorus</td><td>0,00%</td></tr><tr><td>Juncus bufonius</td><td>0,00%</td></tr><tr><td>Juncus conglomeratus</td><td>0,00%</td></tr><tr><td>Juncus inflexus</td><td>0,00%</td></tr><tr><td>Juncus tenuis</td><td>0,00%</td></tr><tr><td>Lactuca serriola</td><td>0,00%</td></tr><tr><td>Lactuca virosa</td><td>0,00%</td></tr><tr><td>Lamium album</td><td>0,00%</td></tr><tr><td>Lamium amplexicaule</td><td>0,00%</td></tr><tr><td>Lamium purpureum</td><td>0,00%</td></tr><tr><td>Lapsana communis</td><td>0,00%</td></tr><tr><td>Lathyrus latifolius</td><td>0,00%</td></tr><tr><td>Lathyrus nissolia</td><td>0,00%</td></tr><tr><td>Lathyrus pratensis</td><td>0,00%</td></tr><tr><td>Lathyrus tuberosus</td><td>0,00%</td></tr><tr><td>Legousia speculum-veneris</td><td>0,00%</td></tr></table>
<table><tr><td>Linaria vulgaris</td><td>0,00%</td></tr><tr><td>Linum catharticum</td><td>0,00%</td></tr><tr><td>Lolium perenne</td><td>0,00%</td></tr><tr><td>Lotus corniculatus</td><td>0,00%</td></tr><tr><td>Luzula campestris</td><td>0,00%</td></tr><tr><td>Luzula multiflora</td><td>0,00%</td></tr><tr><td>Lycopus europaeus</td><td>0,00%</td></tr><tr><td>Lysimachia nummularia</td><td>0,00%</td></tr><tr><td>Lysimachia vulgaris</td><td>0,00%</td></tr><tr><td>Lythrum salicaria</td><td>0,00%</td></tr><tr><td>Malva moschata</td><td>0,00%</td></tr><tr><td>Malva neglecta</td><td>0,00%</td></tr><tr><td>Medicago lupulina</td><td>0,00%</td></tr><tr><td>Melampyrum arvense</td><td>0,00%</td></tr><tr><td>Melampyrum pratense</td><td>0,00%</td></tr><tr><td>Melilotus officinalis</td><td>0,00%</td></tr><tr><td>Mentha aquatica</td><td>0,00%</td></tr><tr><td>Mentha arvensis</td><td>0,00%</td></tr><tr><td>Mentha suaveolens</td><td>0,00%</td></tr><tr><td>Mercurialis annua</td><td>0,00%</td></tr><tr><td>Mercurialis perennis</td><td>0,00%</td></tr><tr><td>Milium effusum</td><td>0,00%</td></tr><tr><td>Moehringia trinervia</td><td>0,00%</td></tr></table>
<table><tr><td>Molinia caerulea</td><td>0,00%</td></tr><tr><td>Myosotis arvensis</td><td>0,00%</td></tr><tr><td>Myosotis ramosissima</td><td>0,00%</td></tr><tr><td>Myosotis scorpioides</td><td>0,00%</td></tr><tr><td>Myosotis sylvatica</td><td>0,00%</td></tr><tr><td>Oenanthe lachenalii</td><td>0,00%</td></tr><tr><td>Onobrychis viciifolia</td><td>0,00%</td></tr><tr><td>Onopordum acanthium</td><td>0,00%</td></tr><tr><td>Ophrys apifera</td><td>0,00%</td></tr><tr><td>Origanum vulgare</td><td>0,00%</td></tr><tr><td>Ornithopus perpusillus</td><td>0,00%</td></tr><tr><td>Oxalis corniculata</td><td>0,00%</td></tr><tr><td>Papaver dubium</td><td>0,00%</td></tr><tr><td>Papaver rhoeas</td><td>0,00%</td></tr><tr><td>Parietaria judaica</td><td>0,00%</td></tr><tr><td>Parietaria officinalis</td><td>0,00%</td></tr><tr><td>Pastinaca sativa</td><td>0,00%</td></tr><tr><td>Phalaris arundinacea</td><td>0,00%</td></tr><tr><td>Phleum pratense</td><td>0,00%</td></tr><tr><td>Phragmites australis</td><td>0,00%</td></tr><tr><td>Picris hieracioides</td><td>0,00%</td></tr><tr><td>Pimpinella saxifraga</td><td>0,00%</td></tr><tr><td>Plantago coronopus</td><td>0,00%</td></tr></table>
<table><tr><td>Plantago lanceolata</td><td>0,00%</td></tr><tr><td>Plantago major</td><td>0,00%</td></tr><tr><td>Plantago media</td><td>0,00%</td></tr><tr><td>Platanthera bifolia</td><td>0,00%</td></tr><tr><td>Platanthera chlorantha</td><td>0,00%</td></tr><tr><td>Poa annua</td><td>0,00%</td></tr><tr><td>Poa nemoralis</td><td>0,00%</td></tr><tr><td>Poa pratensis</td><td>0,00%</td></tr><tr><td>Polygonatum odoratum</td><td>0,00%</td></tr><tr><td>Polygonum aviculare</td><td>0,00%</td></tr><tr><td>Portulaca oleracea</td><td>0,00%</td></tr><tr><td>Potentilla erecta</td><td>0,00%</td></tr><tr><td>Potentilla recta</td><td>0,00%</td></tr><tr><td>Potentilla reptans</td><td>0,00%</td></tr><tr><td>Primula elatior</td><td>0,00%</td></tr><tr><td>Primula veris</td><td>0,00%</td></tr><tr><td>Primula vulgaris</td><td>0,00%</td></tr><tr><td>Prunella vulgaris</td><td>0,00%</td></tr><tr><td>Pulicaria dysenterica</td><td>0,00%</td></tr><tr><td>Ranunculus acris</td><td>0,00%</td></tr><tr><td>Ranunculus bulbosus</td><td>0,00%</td></tr><tr><td>Ranunculus flammula</td><td>0,00%</td></tr><tr><td>Ranunculus repens</td><td>0,00%</td></tr></table>
<table><tr><td>Ranunculus sardous</td><td>0,00%</td></tr><tr><td>Raphanus raphanistrum</td><td>0,00%</td></tr><tr><td>Reseda lutea</td><td>0,00%</td></tr><tr><td>Reseda luteola</td><td>0,00%</td></tr><tr><td>Rorippa sylvestris</td><td>0,00%</td></tr><tr><td>Rumex acetosa</td><td>0,00%</td></tr><tr><td>Rumex acetosella</td><td>0,00%</td></tr><tr><td>Rumex conglomeratus</td><td>0,00%</td></tr><tr><td>Rumex crispus</td><td>0,00%</td></tr><tr><td>Rumex obtusifolius</td><td>0,00%</td></tr><tr><td>Rumex sanguineus</td><td>0,00%</td></tr><tr><td>Sagina procumbens</td><td>0,00%</td></tr><tr><td>Sambucus ebulus</td><td>0,00%</td></tr><tr><td>Sanicula europaea</td><td>0,00%</td></tr><tr><td>Saponaria officinalis</td><td>0,00%</td></tr><tr><td>Saxifraga tridactylites</td><td>0,00%</td></tr><tr><td>Scandix pecten-veneris</td><td>0,00%</td></tr><tr><td>Scleranthus annuus</td><td>0,00%</td></tr><tr><td>Scrophularia nodosa</td><td>0,00%</td></tr><tr><td>Sedum album</td><td>0,00%</td></tr><tr><td>Senecio viscosus</td><td>0,00%</td></tr><tr><td>Senecio vulgaris</td><td>0,00%</td></tr><tr><td>Sherardia arvensis</td><td>0,00%</td></tr></table>
<table><tr><td>Silene vulgaris</td><td>0,00%</td></tr><tr><td>Sinapis alba</td><td>0,00%</td></tr><tr><td>Sinapis arvensis</td><td>0,00%</td></tr><tr><td>Sisymbrium officinale</td><td>0,00%</td></tr><tr><td>Solidago virgaurea</td><td>0,00%</td></tr><tr><td>Sonchus arvensis</td><td>0,00%</td></tr><tr><td>Sonchus asper</td><td>0,00%</td></tr><tr><td>Sonchus oleraceus</td><td>0,00%</td></tr><tr><td>Spergula arvensis</td><td>0,00%</td></tr><tr><td>Stachys arvensis</td><td>0,00%</td></tr><tr><td>Stachys sylvatica</td><td>0,00%</td></tr><tr><td>Stellaria graminea</td><td>0,00%</td></tr><tr><td>Stellaria holostea</td><td>0,00%</td></tr><tr><td>Stellaria media</td><td>0,00%</td></tr><tr><td>Symphytum officinale</td><td>0,00%</td></tr><tr><td>Tanacetum vulgare</td><td>0,00%</td></tr><tr><td>Teucrium chamaedrys</td><td>0,00%</td></tr><tr><td>Teucrium scorodonia</td><td>0,00%</td></tr><tr><td>Thlaspi arvense</td><td>0,00%</td></tr><tr><td>Torilis arvensis</td><td>0,00%</td></tr><tr><td>Torilis japonica</td><td>0,00%</td></tr><tr><td>Tragopogon pratensis</td><td>0,00%</td></tr><tr><td>Trifolium arvense</td><td>0,00%</td></tr></table>
<table><tr><td>Trifolium campestre</td><td>0,00%</td></tr><tr><td>Trifolium dubium</td><td>0,00%</td></tr><tr><td>Trifolium fragiferum</td><td>0,00%</td></tr><tr><td>Trifolium hybridum</td><td>0,00%</td></tr><tr><td>Trifolium pratense</td><td>0,00%</td></tr><tr><td>Trisetum flavescens</td><td>0,00%</td></tr><tr><td>Tussilago farfara</td><td>0,00%</td></tr><tr><td>Urtica dioica</td><td>0,00%</td></tr><tr><td>Urtica urens</td><td>0,00%</td></tr><tr><td>Verbascum nigrum</td><td>0,00%</td></tr><tr><td>Verbascum thapsus</td><td>0,00%</td></tr><tr><td>Verbena officinalis</td><td>0,00%</td></tr><tr><td>Veronica agrestis</td><td>0,00%</td></tr><tr><td>Veronica arvensis</td><td>0,00%</td></tr><tr><td>Veronica beccabunga</td><td>0,00%</td></tr><tr><td>Veronica chamaedrys</td><td>0,00%</td></tr><tr><td>Veronica filiformis</td><td>0,00%</td></tr><tr><td>Veronica hederifolia</td><td>0,00%</td></tr><tr><td>Veronica montana</td><td>0,00%</td></tr><tr><td>Veronica officinalis</td><td>0,00%</td></tr><tr><td>Veronica persica</td><td>0,00%</td></tr><tr><td>Veronica polita</td><td>0,00%</td></tr><tr><td>Veronica serpyllifolia</td><td>0,00%</td></tr></table>
<table><tr><td>Vicia cracca</td><td>0,00%</td></tr><tr><td>Vicia hirsuta</td><td>0,00%</td></tr><tr><td>Vicia lathyroides</td><td>0,00%</td></tr><tr><td>Vicia sativa</td><td>0,00%</td></tr><tr><td>Vicia sepium</td><td>0,00%</td></tr><tr><td>Vicia tetrasperma</td><td>0,00%</td></tr><tr><td>Vincetoxicum hirundinaria</td><td>0,00%</td></tr><tr><td>Viola arvensis</td><td>0,00%</td></tr><tr><td>Viola hirta</td><td>0,00%</td></tr><tr><td>Viola odorata</td><td>0,00%</td></tr><tr><td>Viola reichenbachiana</td><td>0,00%</td></tr><tr><td>Viola riviniana</td><td>0,00%</td></tr><tr><td>Vulpia myuros</td><td>0,00%</td></tr></table>
# SUPPLEMENTARY RESULTS AND COMMENTARIES TO CHAPTER 2

## Variation of local species-specific originality

In this chapter we investigated species' local and regional originality variation along an urbanization gradient. However, as the originality of a given species is relative to the other species in a set, its value should be different from one set to another. We wondered whether the most original species were always highly original despite different composition of species sets or whether its originality varied. I present below the results obtained with trait-based originality scores (unpublished results).

We noticed that many of the top 5% of the most original species occurred only few times in the whole 10-years dataset, making impossible to follow their originality changes. We therefore selected species only occurring more than 50 times from the top 5% trait-based original species and followed their originality variation across land cover types and urbanization gradient. One of such species was a fleabane *Erigeron sumatrensis*. Its high trait-based originality value (max = 0.72, min = 0.57) in highly urbanized areas could reflect the fact that as a non-native species, with original trait values, *E. sumatrensis* is able to colonize novel ecosystems created by urbanization where species competition is low (but see Dawson et al., 2015). Another highly original species in its traits was the annual bluegrass *Poa annua*, native and highly common species on the Ile-de-France region (appeared within every land cover type). *P. annua* trait-based originality increased with urbanization and varied between 0.50 and 0.68 with the greatest variation observed in more urbanized areas (Figure S7).

Trait values vary across species and underpin differences in their ecological strategies (Westboy et al., 2002). Such information on originality variation per species could shed light on such questions as what biotic and abiotic conditions make species the most original and what could be the consequences of the loss of these species. In this chapter, we found that some plant species became more original with increased urbanization at local level compared to their regional originality (ratio of local over regional originality). This could indicate that some urbanization processes filter species regarding their traits and evolutionary history from regional pool making
them more or less original at local level (see Williams et al., 2009, 2015). Urbanization is often characterized by habitat fragmentation creating a mosaic of ecological niches and thus promoting coexistence of many species with different trait values in the same place, thus increasing species trait-based originality and diversity. However, higher trait-based originality in urbanized areas could be also explained by higher competition and limiting similarity within species communities. These hypotheses could be tested with null models, comparing the observed originality patterns and those created with random species distribution (Emerson and Gillespie, 2008; Cavender-Bares et al., 2009). Our exploratory results described species-specific originality patterns, yet more data would be needed to test the hypothesis explaining possible factors and causes of these patterns. For example, including species abundance into originality calculation would certainly decrease the originality value of the most common and the most original species like *Poa annua* (see Chapter1, Figure 5 and Table 2) depending on the abundances of the other species. Thus, using species abundance could reveal influence of interspecific originality and change our conclusions about causes of species originality variation.

Nevertheless, species trait-based originality in this study was inferred from the mean trait values for each species and its variation only depended on the composition of species plots and land cover type. A step further to investigate species originality would be indeed to measure trait-based originality at the individual level, i.e. giving an originality value to each individual of a species. Indeed, intraspecific originality variation could better quantify the variation of trait-based (functional) diversity patterns in communities and increase our understanding of major questions of community ecology (Violle et al., 2012; Siefert et al., 2015; Zhao et al., 2019).
<figure><img src="image_31.png" /><figcaption>**Figure S7.** Variation of trait-based originality of (A) *Poa annua* (n=319) and (B) *Erigeron sumatrensis* (n=76) according to the percentage of urbanization within 1x1km grid cell (grid cell corresponds to the Vigie-flore protocol). One data point represents one species score. Colors represent the land cover type of a plot where species occurred. Black curves represent the estimated trends retrieved from the mean originality GLMM models.</figcaption></figure>

**Comparing data issues and methodological choices with study of Veron et al.**

Here I would like to briefly compare some common problems found in our study presented in this chapter and Veron's et al. (In prep., see Appendix A) study, where I contributed as a co-author by conceiving ideas at early stages and participating into manuscript editing. Veron et al. explored the originality of insular and continental monocotyledon species by using six quantitative traits (from TRY database). Amongst questions they investigated was the relation of a species' trait-based and phylogenetic originality with its geographical range. Are original species also rare? Veron et al. used the same originality index that we used for urban originality study (the AV index) and same taxonomic group of plants (although in Veron et al. there were only Monocotyledon species). Therefore, I will compare problems concerning the sensitivity of trait-based originality measures to the amount missing trait data, to the method of missing data correction and its relation to species abundance.

*Sensitivity of originality indices to missing trait data*

In this chapter, we did not explicitly investigate the sensitivity of originality metric AV to the
amount of missing trait data. However, we found that the top 5% of the most original plant species had a relatively high amount of "Not-Attributed" trait values (see Table S5). Moreover, the missing trait data could influence not only species originality but also a possible biological interpretation of its distribution. We tested a correlation between trait-based originality and the amount of total missing trait data per species which was positive but moderate (Spearman's correlation coefficient r = 0.47, p<0.001 with mean local originality, r = 0.43, p<0.001 with regional originality, Figure S8). To the contrary, Veron et al. (In prep.) found a negative low correlation between trait-based originality and the amount of total missing trait data per species (r = -0.14, p<0.001, see Appendix 1 in Veron et al.). The difference between the negative correlation between trait-based originality and the percentage of total missing trait data found by Veron et al. and the positive correlation found by Kondratyeva et al. is more likely due to difference in the type of traits considered (Table S6).

<table><caption>**Table S6.** Comparison of data and methods between 2 case studies, Kondratyeva et al. (in revision) and Veron et al. (in preparation) which could have implications in species originality variation. For more details see respectively Table 1 (above) and Appendix A.</caption><thead><tr><th></th><th>Kondratyeva et al. (in revision for Frontiers Ecol. and Evol.)</th><th>Veron et al. (in preparation)</th></tr></thead><tbody><tr><td>Number of traits</td><td>11</td><td>6</td></tr><tr><td>Type of traits</td><td>multiple (see table S1)</td><td>quantitative (see Diaz et al., 2016)</td></tr><tr><td>Amount of overall missing trait data per species</td><td>min = 0%, max = 35%</td><td>min = 0%, max = 83%</td></tr><tr><td>Amount of overall missing trait data per trait</td><td>min = 0%, max = 19.8%</td><td>min = 23%, max = 81% (see Appendix 1)</td></tr><tr><td>Number of species with missing trait data</td><td>250/586</td><td>2022/2281</td></tr><tr><td>Method of distance matrix creation</td><td>Extended Gower's distance adapted to trait types (Pavoine et al., 2009)</td><td>Gower's distance (Gower, 1971)</td></tr></tbody></table>
There is also a quite possible an effect of the geographical origin of species and trait data. In urban
originality study, we used species common to northern-western region of Europe with well-
established trait data coming from multiple standardized field and experimental sources. Veron et
al. studied species dispersed across the world; therefore, differences in data collecting and
availability are probably high. A frequently encountered sampling condition, where rarer species
are often poorly studied and likely characterized by missing trait information, is particularly true
for some insular endemic species. For example, by creating a growing missing trait information by
deleting trait values from a complete data set, Méjeková et al. (2016) found that some trait-based
diversity indices were more sensitive than others were to missing trait data. They supposed that the
missing trait information in rare species would remove extreme or outlier trait values, thus it could
underestimate species originality. Indeed, because of interdependence of diversity and originality
metrics, findings on trait-based diversity could be tested on the originality measures as well.
<figure><img src="image_32.png" /><figcaption>**Figure S8.** Relation between trait-based mean local originality per species (top), regional originality (bottom) and the percentage of total missing trait data. Each data point corresponds to a plant species.</figcaption></figure>

To deal with the problem of missing data, several strategies can be adopted (Pakeman, 2014; Penone et al., 2014). The first one would be to reinforce trait-sampling effort for studies contributing to global databases and to maximize research effort of potential data sources of trait
data. In this chapter we adopted the approach of maximizing data sources, as our species pool was relatively small (586 species). However, Veron et al. had much larger number of species (2281 species for trait-based originality) with many poorly studied species. Therefore, Veron et al. chose to impute missing trait values. Among the multiple methods of imputation described in the literature, they selected a random forest method, which imputes missing trait values by comparing it to the non-missing values of other species (missForest package, Stekhoven and Buehlmann, 2012). After running 1000 random imputations of this type, they found a significant positive but moderate Spearman's correlation between the trait-based originality computed before and after trait data imputation (r=0.65, p<0.001). To test the influence of imputation method one can also artificially delete at random some amount of trait data and compare it to the complete dataset (Méjecova et al., 2016), however, complete trait datasets are usually not available. In Veron et al. only 259 species had all values of all traits for which they deleted at random 2/3 of trait data and then proceeded an imputation of deleted values. There was a positive moderate correlation between trait-based originality computed on a subset of species with complete trait data and on species with created and then imputed missing data (Pearson's correlation coefficient r=0.41, p<0.001). Hence, the chance of imputing an original trait value for a poorly known species is more likely smaller than the chance of imputing a common trait value. Depending on how missing data are distributed across species (at random, not at random or partially at random), the result of the imputation would therefore differ (Penone et al., 2014). Thus, more research is needed to increase our knowledge on the sensitivity of species originality measures to missing trait data.

## Relation of species abundance-based and trait-based originality

We noticed that the majority of the top 5% of trait-based original species appeared only few times across local communities (Table S3). It could confirm previous findings that more original species are rarer than expected by chance (Mouillot et al., 2013a) and are more likely to become extinct (Gaston 1994). However, we did not have information on species abundances, as abundance data are not required by the Vigie-flore protocol (see Appendix B). The only information we disposed
was species frequency in each of the 1m² sampling quadrats of one plot. This led to species characterized by 10 possible cases of "rarity". Therefore, we did not find any strong pattern in the correlation between this discretized estimation of rarity and species local trait-based originality (the Spearman's correlation coefficient r = -0.06, p<0.001; unpublished results). That is to say, one of the main objectives of Veron's et al. (in prep) study was to compare insular plant species geographical range (or rarity) with its originality. They found that the most phylogenetically original species (especially those in the top 5%) were more range-restricted and occurred on fewer islands than expected by chance, although this originality-abundance relation was not strong (Pearson's correlation coefficient r = -0.15, p < 0.001, see Table 5 in Veron et al, in prep.). However, only endemic (rare) species with moderate trait-based originality had a smaller geographic range than expected, and very original species occurred on more islands than expected by chance (Pearson's correlation coefficient r = 0.43, p < 0.001; see also Table 3 in Veron et al., in prep.). Many causes could explain the differences of restrictedness of trait-based and phylogenetic originality discussed by Veron et al. (in prep.).

More generally, species originality and abundance-based rarity were investigated by several studies (e.g., Jetz et al., 2014; Thullier et al., 2015; Grenié et al., 2018; Stein et al., 2018) and their contradictory results may have been influenced by the same data problems as discussed above. If the most original species are actually under extinction risk or are at least more vulnerable to environmental changes because of their rarity, this information could be crucial to guide conservation priorities.
ALL IS A MATTER OF CHOICE AND
AVAILABILITY
"Just when you think you know something, you have to look at in another way. Even though it may seem silly or wrong, you must try."

John Keating in Dead poets' society.

In this thesis, we have sought to confine the attention on the originality metrics in ecology and conservation biology. We have demonstrated main benefits of originality measures completing our understanding of biodiversity. In 1991 Vane-Wright et al. stated that measures of species diversity at that time were inadequate for biodiversity conservation priorities because they did not use species distinctness treating different species as equally valuable. Vane-Wright et al. developed a measure of taxonomic distinctness, called taxic weight, improved by May (1990), which opened a way to a new group of metrics in ecology – species originality. The “agony of choice” in conservation would push policymakers to unreasonable wish to protect one member of each taxonomic group. According to the originality measures, the priority areas for conservation could be designed by accounting for species differences within species communities, on which they are dependent, and to which they contribute by being different in their traits and evolutionary history. That is, species should not be equally considered. We explored in this thesis, phylogenetic and trait-based differences between species and thus phylogenetic and trait-based originality.

When they appeared in the 90's, originality measures were erroneously classified as diversity measures, which they are not. Hence, their use was abandoned in community ecology and conservation biology for the benefit of alternative diversity measures, such as Faith's (1992) Phylogenetic Diversity index, considered more convenient (Pavoine et al., 2005). However, species originality metrics do not measure an amount of diversity at the species level; instead, they evaluate the contribution of each species to the diversity of a delineated set of species, defined at specific spatial and taxonomic levels. That is, the originality of a given species is dependent on a reference set of species and varies if the composition of the set changes. Even if species originality is not a measure of diversity per se, the diversity of a species set, however, depends implicitly on species originality. The more species are different from each other, the more they are original, thus
increasing the diversity of the species set. We discussed in the Chapter 1 some theoretical examples
of such a dependence. Indeed, many diversity measures can be seen as functions of species
originality (mean or sum of species originalities) even though they were not developed as such. The
link between diversity and originality is thus both conceptual and mathematical, as indices of
species diversity and originality are interdependent and use common concepts and data. Performing
two-steps analysis by first measuring species originality and then evaluating species diversity thanks
to these originalities has the advantage of giving simultaneous information on the level of diversity
of a species set and on the contribution of each species to that level. In other words, we can easily
switch between originality and diversity measures. In Chapter 2, we used such a switch by
calculating the mean and the skewness (asymmetry) of species originality values to evaluate how
urbanization affects the trait-based and phylogenetic compositions of plant communities. In this
case study, the mean of species originalities corresponded to a measure of trait-based or
phylogenetic diversity, depending on whether species were characterized by their traits or by their
phylogeny. The skewness (or asymmetry) of species' originalities informed on the shape of the
distribution of originality values and can be interpreted as the proportion of uniqueness versus
redundancy in a set of species.

This thesis started with the objective of evaluating an impact that the concept of originality
could have in ecological studies. Two chapters that are at the core of this thesis highlighted links
between the concepts and measurements of diversity, originality and rarity and provided a first test
of the joint analysis of originality and diversity in urban ecology. During the last three years, we
were, however, repeatedly confronted to the reality of data analysis, with missing data and plethora
of potential mathematical formulas to evaluate diversity and originality, each formula having its
own interests and drawbacks. In addition, our knowledge on biodiversity, although always growing,
is still incomplete. Our conservation efficiency thus depends upon series of choices made in
scientific studies, notably: the choices made about which species we focus on, which biological
characters we consider, at which spatial scales, and with which measures of species diversity and
originality. We will discuss below how some of these choices affect originality measures and their
interest for ecological and conservation studies.

# 1. CHOOSING AND OBTAINING THE DATA FOR ORIGINALITY MEASURES

Biodiversity is declining across the planet, but it's taxonomic, trait-based, and evolutionary aspects
are declining at different paces. Thus, understanding how these aspects respond to the global
change and what effect they have on ecosystem functioning is crucial to better prevent the
ecological consequences of biodiversity loss. Most current studies of species originality consider
species' phylogenetic relations at large spatial scales for studying evolutionary history of entire
groups of species in a context of conservation biology (Safi et al., 2013; Jetz et al., 2014; Pollock
et al., 2017). In contrast, community ecologists report processes of species co-existence acting at
local scale (Mouillot et al., 2013b; Garnier et al., 2016; see also Chapter 2, section II.5). However,
evolutionary and ecological processes both structure species local communities (Lessard et al.,
2012). There are more and more studies coupling ecological and evolutionary sides of species
biology with the common aim to protect biodiversity at global scale (e.g., Mazel et al., 2014; Brum
et al., 2017; Le Bagousse-Pinguet et al., 2019; Kosman et al., 2019). Besides, multifaceted and
multiscale approaches for biodiversity evaluation have been adapted by Intergovernmental Science-
Policy Platform on Biodiversity and Ecosystem Services (IPBES) in Conceptual Framework for
nature benefits to people (Diaz et al., 2015; Brondizio et al., 2019, in prep.). Indeed, as described
in the Introduction, every species (if we talk about biological concept of species in macro-ecology)
can be described by its abundance, phylogeny and trait values or at least one of those. Below, we
will discuss the benefits and shortcomings of each facet of species originality measuring.

## 1.1 On the benefits and limits of using different aspects of species' biology

### 1.1.1 On the benefits of species abundance analysis

More informative than species occurrence data (presence/absence), abundance can be measured
by total number of individuals, but also by its density (number of individuals within a standard
sampled quadrat), percentage cover or total biomass. Species abundance is the first and simplest step to estimate how much species contribute to biological diversity. We should, however, be aware of sampling biases when estimating species abundance, depending on where, when and how species were observed (Brown, 1984; Gaston, 1997, Buckland et al., 2005, see Chapter 1).

Depending on how it is measured, abundance informs on species' commonness (and rarity), distribution range and habitat preferences. Furthermore, the notions of species' dominance and specificity to habitat type are also based on species abundance. According to the “mass ratio” hypothesis (Grime, 1998), species influence ecosystem functioning in proportion to their biomass and to the extent of their functional trait values. Thus, dominant species would have the most of the influence on ecosystem functioning independently of the richness of subordinate species. All information about community change is therefore contained within the patterns of species abundance distributions (Buckland et al., 2005).

Species abundance is determined by local environmental conditions and regional species pool (Shipley et al., 2006). In return, species' relative abundances would determine the number and strength of interactions between species (Vázquez et al., 2007). Hence, abundance-based rarity of species is tightly involved into relationship between species diversity and ecosystem functioning. The reasons why species are dominant or rare in their abundance is related to their biological characters and their ecological performance, constrained by community assembly mechanisms (e.g., Adler et al., 2014). Thus, the distribution of species in a certain environment depends not only on their abundance but also on the degree of dissimilarity with other species in their biological characters (Shipley et al., 2006), i.e. their originality. Hence, species affects ecosystem functioning via phylogenetic and trait-based originalities. Analyzing both abundance and species' biological characteristics may thus inform on the processes underlying variations in biodiversity.

As shown in Chapter 1, originality measures can integrate abundance data, leading to species abundance-based originality. The originality value of a given species can change if weighted by species abundance (see Figure 5 in Chapter 1) and thus represents a tool to emphasize the rarest