species whose individuals have the rarest biological characteristics (Cadotte and Davies, 2010; Mouillot et al., 2013b; Pavoine et al., 2017). An increase through time of the abundance-based rarity of a species might indicate that profound changes in the ecosystem functioning are ongoing. Some even included extinction risks in phylogenetic originality measures, where extinction risk is partly determined by a decrease in population size, i.e. in a number of individuals (Isaac et al., 2007; Steel et al, 2007). These measures allow to detect species that are both original and threatened (Isaac et al., 2007) as well as species that are original and belong to phylogenetic clades where many species have a high risk of extinction (Steel et al., 2007). If conservation actions are based on such measures, they could prevent the loss of species with unique biological characteristics.

### 1.1.2 On the benefits of trait-based analysis of diversity and originality

Biological characteristics of species can be linked to environmental variables, illustrating species adaptations to environmental conditions (Diaz and Cabido, 2001). Trait-based (functional) diversity and originality inform us on species' ecological dissimilarities, related ecological niche breadth and ecosystem functions and services (Violle and Jiang, 2009; Cadotte et al., 2011, Mouillot et al., 2013a, Hidasi-Neto et al., 2015). Therefore, trait-based approaches have increasingly gained many areas of ecological research going from community assembly (McGill et al., 2006, Cavender-Bares et al., 2009, Mason et al., 2013), ecosystem functioning (Herben and Goldberg, 2014), ecosystem services (Lavorel et al., 2011; Diaz et al., 2007b, 2018) and species' invasions (Rejmanek and Richardson, 1996; Kühn et al., 2017) to conservation biology (Grenié et al., 2018; Kosman et al., 2019), urban ecology (Vallet et al., 2010; Knapp et al., 2008, 2012; Palma et al., 2017) and many other areas. One of the common goals of those studies is to understand how species' traits mediate community assembly and species coexistence and thus to predict the effects of global change on biodiversity.

Today it is still unclear which traits should be defined as functional (Mlambo, 2014). Violle et al. (2007) provided one of the most comprehensive and most cited definitions to date: species functional trait should be measurable on individual level and have the influence on species fitness,
i.e. its ability to survive and reproduce. Even if there is no universal definition of term "function"
itself in ecology (see Bellwood et al., 2019), the use of any trait defined as functional should go with
an explanation of a function related to that trait. Classifying species' traits into response traits
 involved into species' responses to environmental processes, measured by its overall fitness) and
effect traits (involved into mechanisms by which species influence ecosystem processes) is a good
 beginning (Hooper et al., 2005). That is, in plants, such traits as specific leaf area, plant height, and
 seed mass are used as the principal traits to describe three fundamental axes of phenotypic variation
 amongst plants, which are respectively related to the acquisition and use of resources, the
 competitive ability, and the capacity for sexual regeneration (Diaz et al., 2016; Garnier et al., 2016).
 Species' traits without clear functional significance should not be referred as functional (Cadotte et
 al., 2011), as for example Ellenberg values (Ellenberg et al., 1991; Garnier et al., 2017) and Grime
 strategies (Grime et al., 1988) or else life-history traits (González-Suárez and Revilla, 2013).
 However, originality can be measured with any trait data making sense in a specific context. This
 is why in this thesis we decided to use more inclusive terms of "traits" or "biological characters".

The trait-based approach in biodiversity studies turns out to be particularly useful in
establishing links between species performances and ecosystem functioning processes at local scale
(Loreau et al., 2001). Indeed, the range of trait values present in a community was found to be a
good predictor of ecosystem functioning in several studies (Tilman et al., 1997; Petchey & Gaston,
2006). Trait-based or functional ecology offers a promising way to model deterministic processes
of local community assembly and improves our way of understanding local diversity variations
(Diaz and Cabido, 2001) and increases the amount of benefits that humanity could gain from
biodiversity (Diaz et al., 2007b). In line with trait-based diversity, the main assumption in trait-
based originality approach is that the most original species with more unique combinations of traits
would contribute more to the ecosystem functioning or may maintain specific, irreplaceable
functions and processes (Mouillot et al., 2013b; Brandl et al., 2016; Leitão et al., 2016). Recently,
models were developed in functional ecology in order to depict the mechanisms determining
species trait-based differences and to identify traits involved into community assembly processes (Jabot, 2010; Münkemüller and Gallien, 2015; Munoz et al., 2017). That is, mechanistic insights into patterns of community assembly may rely more on the trait-based originality of species than on their abundance alone (Cadotte & Davies, 2016).

Many community ecology studies have investigated a community-weighted mean (CWM) of trait values (a mean of community-level trait values weighted by species abundances) along abiotic gradients to describe species' trait distribution compared to the mean value of community (Garnier et al., 2004; Cornwell et al., 2006; Diaz et al., 2007b). Other researchers related specific trait variations between and within species to particular environmental conditions (Dwyer et al. 2014; Siefert et al., 2014). A recent work on trait-environment relationship proposed a concept of species functional optimality, where species trait values are distributed according to a Gaussian function around a community optimal value of functional traits (Mucarella and Uriarte, 2016). The community-weighted mean (CWM) and variance (CWV) of trait values are thus expected to depict the optimum and the intensity of environmental filtering, respectively (Denelle et al, 2019). Trait-based originality measures could be seen here as a connection between species trait-based diversity and trait distribution of a community. Indeed, the deviation between species trait value and CWM, as a measure based on trait-based distances between species, is similar to originality (Violle et al., 2017a). In addition, the CWM is the weighted mean of the squared deviations between species trait values and CWM and thus a mean of originality values, equal to the variance of trait values and related to a well-known index of diversity – Rao's quadratic entropy (e.g., Pavoine et al. 2004).

When several traits are jointly considered, the use of multidimensional functional trait space may allow visualizing a species position relatively to the other species (Villéger et al., 2008). In multidimensional trait space, trait-based originality can be measured as the distance from a species' position to the community multivariate centroid (the hypothetical average species, Buisson et al., 2013), which actually represents the multivariate CWM of the community (Mucarella and Uriarte, 2016) or else the optimal value of species traits enabling species to persist in the community
(Denelle et al., 2019). Species located closer to the CWM would be less original, and contribute less to the community trait-based diversity (Figure 1). For example, Muscarella and Uriarte (2016) found that 25% of the tree species in Puerto Rico significantly opposed CWM-optimality correlation for at least one trait (by being distant from CWM), thereby being more original and contributing greatly to local trait-based diversity. To the contrary, 75% of species were closer to the community optimal trait value, i.e. they were more redundant in their traits. Therefore, variation in trait distributions of a community influences species originality, as it is relative to other species' trait values. Thus, species trait-based originality may reflect the trait-based structure of communities and help to assess the relative part of each species into the trait-based diversity variation under future global changes. In this manner, species trait-based approach is mostly useful at the local community level for determining how species trait differences shape species originality and their capacity to colonize new communities just as much as their influence on ecosystem functioning. Thus, each study can adapt its choice of traits to the underlying assembly processes of interest.

However, traits are related to phylogeny by the underlying evolutionary processes. Indeed, each species is a product of evolutionary processes, during which the species acquires its biological characteristics (Darwin, 1856; Harvey and Pagel, 1991). Thus, while trait-based (functional) diversity and originality may inform us on the contribution of each species to ecosystem functions and services, phylodiversity and originality could provide information on the evolutionary and biogeographic histories of taxa. Thus, species trait evolution must also be taken into account (see article in Chapter 1, Section IV.1) to bridge community trait-based diversity and phylodiversity to community assembly processes (Losos, 2008).
<figure><img src="image_33.png" /><figcaption>**Figure 1.** Theoretical illustration of species trait-based originality. Nine species presented by geometrical colored figures (a – i) are projected in a two-dimensional trait space (trait 1 = shape and trait 2 = color). The red points represent a community multivariate centroid (the hypothetical average species) or else a Community Weighted Mean of A) a regional species pool (CWM<sub>r</sub>), and of two local species communities B) CWM<sub>11</sub>, C) CWM<sub>12</sub>. Species originality is measured as a distance from a focal species to the centroid (dashed lines). B) Species i is the most original in the region, however its originality varies between communities and is higher in community B) than in community C). *Inspired from Buisson et al., 2013.*</figcaption></figure>

1.1.3 On the benefits of phylo-based analysis of diversity and originality

Today, the large availability of DNA sequences in datasets such as in Genebank together with
efficient computer algorithms facilitates the reconstruction of species evolutionary histories
represented by phylogenetic trees with previously unseen completeness (Diniz-Filho et al., 2013).
Therefore, phylogenetic information in conservation biology and community ecology has become
increasingly reliable and meaningful (see Mouquet et al., 2012).

Although Phylogenetic Diversity measure (PD, Faith, 1992) has been recently included to
the IPBES Biodiversity Assessment (IPBES, 2019, Diaz et al., in press), underpinning its
importance for prioritization of species and areas for conservation, the confusion remains about
which biodiversity measure to use in conservation. Due to their phylogenetic relatedness species
are not independent units of biodiversity (Felsenstein, 1985) and should not be treated otherwise.
Evolutionary-based approaches reveal that species embody different amounts of unique versus
shared evolutionary information, which can be measured by species originality. Indeed, species
phylogenetic originality has been used to identify sites or taxa with particular conservation value
(Barker, 2002; Isaac et al., 2007; Cadotte & Davies, 2010). Yet, current conservation actions often
ignore species phylogenetic originality, targeting areas with high species richness and degree of
endemism (Veron et al., 2017). An exception would be the EDGE program (Evolutionarily
Distinct and Generally Endangered) at the Zoological Society of London. It is probably the only
conservation initiative that really uses species phylogenetic originality measure (Evolutionary
Distinctiveness or ED) weighted by species IUCN threat status as an estimate of its extinction risk
thus focusing conservation efforts specifically on threatened species (Isaac et al., 2007). However,
the EDGE index has been criticized for considering species extinction risks and species'
phylogenetic originality as independent variables, whereas the extinction of a given species would
increase the originality of its sister species (Faith, 2008). The EDGE index was thus improved into
the HEDGE index that integrates the probability of extinction for species and its close relatives
(Steel et al., 2007). Therefore, global broad-scale conservation schemes can be established (Isaac et
al., 2007).

However, most conservation actions focus on the biodiversity within areas and not on the
originality of each species. We must recall that originality is not diversity. Protecting only the most
phylogenetically original species will not necessarily maximize phylodiversity. Indeed, the set of
species maximizing phylodiversity has to be dispersed across the tree, while phylogenetically
original species may in some cases be phylogenetically clustered (Pavoine et al., 2005). Empirical
studies have, however, shown that subsets of species with high phylogenetic originality capture
more phylodiversity than at random (Redding et al., 2008) and that ranking mammal species for
conservation priority with a metric used in the EDGE program, could protect more phylodiversity
than if species selection was based on phylogenetic originality value or on threat status alone
(Redding and Mooers, 2015b).

In the human-centric benefits of evolutionary history for focusing conservation actions on
preserving evolutionary history, Tucker et al. (2019) identified six general arguments (such as "Does
evolutionary history have surrogacy value for phenotypic diversity?" or "Does evolutionary history
or phenotypic diversity provide greater evolutionary potential and future options for humanity?"),
gave a logical support for or against, and future directions for each argument (Table 2 in Tucker et al., 2019). However, some arguments are still poorly empirically supported, although optimizing the total amount of evolutionary history under protection should enhance the protection of important species features useful to humanity, with features defined as the variety of states across homologous traits (Lean and Maclaurin, 2016).

A common assumption is indeed that maximizing the amount of protected evolutionary history should capture the variation in species characteristics that are otherwise unknown, supporting the intrinsic value of biodiversity (Faith, 1992). Phylogenetic trees have thus been widely used with the assumption that shared ancestry provides shared biological traits (Webb et al., 2002; Redding and Mooers, 2015b). In that way, the evolutionary potential of species represents species' intrinsic and option values for the future potential uses by humanity (Faith, 1992). Thus, taking into account phylogenies in conservation schemes means to plan the protection of potential genetic and trait-based diversity (Sarrazin and Lecomte, 2016). Moreover, when there is a lack of understanding of the relationship between species traits and ecosystem functioning or which traits matter the most in a particular environmental conditions, phylodiversity appears to be a useful tool for estimating species feature diversity (Faith, 1992; Webb et al., 2002; Tucker et al., 2018). It therefore appears to be a reasonable assumption that preserving phylogenetically distant and original species supports trait-based diversity (Redding and Mooers, 2015b; Tucker et al., 2019). However, the use of phylodiversity to represent trait-based diversity has been strongly debated and studies showed contradictory results (Flynn et al., 2011; Mazel et al., 2017, 2018, 2019; Owen et al., 2019, see the next section). Such results brought sometimes an uncertainty about the worth of valuing species phylodiversity as a proxy of feature diversity, creating a gap between produced scientific theory and its application in conservation actions (Winter et al., 2013).

Moreover, phylogenetic originality informs on the evolutionary history events such as immigration, speciation, extinctions and divergence of species characters, which are at the basis of species trait-based differences. However, in search for general patterns and rules governing
community assembly based on species' ecological differences, direct use of trait-based approaches
of species' diversity and originality could be more informative (Best et al., 2013, Cadotte et al.,
2013).

1.1.4 Are phylogenies good proxies for species traits?

Since two decades, many researchers in ecology and conservation biology have used phylogenies
as a proxy for species ecological similarities (Webb et al., 2002; Cavender-Bares et al., 2009;
Mouquet et al., 2012; Strivastava et al., 2012, Cadotte et al., 2013). Some studies even argued that
phylodiversity and trait-based diversity might be redundant measures (see Tucker et al., 2018; 2019
for the examples). Some even proposed to combine both distances when assessing species
assembly rules (“traitgrams” in Cadotte et al., 2013). The assumption made by these studies is that
since phylogenies represent the accumulation of trait evolution among species, closely related
species should be more redundant in their traits, and thus, phylodiversity could be a proxy for trait-
based diversity (Gerhold et al., 2015).

However, recent studies have challenged the assumed relationship between evolutionary
(phylogenetic) distance and ecological trait-based similarity. Cadotte et al. (2017) presented seven
reasons why the seemingly clear link between species ecological differences and their phylogenetic
distances may actually not always be straightforward. These reasons are related to evolutionary and
ecological processes and events (high evolution rates, species assemblage rules different from
expected) or are due to shortcomings of experimental design (use of only one evolutionary model
of traits, poor species phylogenetic resolution or wrong evolutionary processes tested). More
generally, the phylogeny-traits relationship is inherently complex and can take many forms. It is
thus important to analyze trait evolution rather than assuming that phylogenetic distances among
species are proportional to trait-based distances (Letten and Cornwell, 2015). Models of trait
evolution can help us to understand the so-called phylogenetic signal – an expected covariation
between trait differences and phylogenetic distances – and distinguish it from random trait
distributions (Blomberg et al., 2003). Thus, the strength of phylogenetic signal is a measure of the
relationship between phylogeny and trait values, and many metrics of phylogenetic signal exist varying in their sensitivity to the phylogenetic topologies (number of tips, presence of polytomies and availability of branch length information, Münkemüller et al., 2012). Yet, many different models of trait evolution exist (Felsenstein, 1985; Hansen, 1997; Beaulieu et al., 2012) and there is thus no universal model, especially when multiple traits are in focus. In addition, several models may predict the same trait evolution equally well (Revell et al., 2008).

Pavoine et al. (2013) demonstrated that the existence of phylogenetic signal in traits is not sufficient to ensure a strong correlation between phylogenetic diversity and trait-based diversity. Later, Tucker et al. (2018) found that a positive correlation between phylodiversity and trait-based (functional) diversity of simulated data becomes stronger with increasing trait dimensionality, but varies depending on the form of evolutionary model, and on the evolutionary complexity of studied traits. Regarding originality, Pavoine et al. (2017) used simulated and real data to show that the correlation between phylogenetic and trait-based originality increases with the number of traits considered but is dependent upon the evolutionary model (speciation events at random, close to tips or to root) and originality index used. In their real case study on European Carnivores, phylogenetic and trait-based originalities were not significantly correlated and traits had a weak phylogenetic signals.

In a recent theoretical study, Mazel et al. (2017) argued that phylodiversity (measured by Faith's PD) is not a good surrogate for preserving the diversity of traits. By using biologically plausible scenarios of trait evolution, they found that phylodiversity-based conservation priorities selects less of trait-based diversity than at random. In a related empirical study, i.e. using a real dataset, Mazel et al. (2018) found that prioritizing the most phylodiverse sets of species results in an average gain of only 18% of trait-based diversity relative to a random choice of species sets. However, Mazel et al. (2018) stayed aware that they used a limited set of traits and probably more traits information is needed to test the relevance of Faith's PD for predicting features diversity.

For sure, phylodiversity and trait-based diversity are intimately related, but certainly not on
a linear manner and complex evolutionary mechanisms are behind species characteristics underpinned by environmental influence (Tucker et al., 2019). Thus, rather than searching for the phylogeny-traits duality, one should measure both aspects, phylogenetic and trait-based, for diversity and originality, searching how their joint analysis could benefit to ecological studies. In addition, proving that phylodiversity is correlated with a measurable trait-based diversity (diversity of functional traits or trait richness, Pavoine and Bonsall, 2011) is different from assuming that phylodiversity could represent an unmeasured part of trait-based diversity and maintain future benefits to humanity, both known and unknown (“option values”, Faith, 1992, see Introduction, 2.4).

Therefore, while planning conservation actions, policy makers would like to be reassured that, by using a given measure of species diversity or originality, we encompass the right aspects of biodiversity that we want to protect. However, the concepts of diversity and originality are so multidimensional that a single mathematical formula, relying on a finite set of data, would never encompass all these aspects. In a comment to Mazel et al. (2018), Owen et al. (2019) underlined that further exploration of phylogeny-trait link is needed by using different diversity (and originality) measures on multiple spatial scales. Nevertheless, the most important in community ecology studies is to recognize that we use only a small part of measurable traits, and that the remaining features (which are not only functional traits) could completely change the assumption of phylogenetic surrogacy for trait diversity and originality. Thus, it is more precocious to use trait-based and phylogenetic approaches as complementary in ecological and conservational studies, each at the adapted spatial scale: global for evolutionary history and local for ecological processes operating on community level (Losos, 2008; Saito et al., 2016, Tucker et al., 2019). Therefore, each additional aspect of species originality would provide new insights into species contribution to the diversity of a set of species.

**1.2 Issues related to the availability and to the quality of the data**

Ecology, evolution and conservation science requires an exhaustive knowledge on the distribution
of species and their biological characters. Therefore, standardizing protocols of species inventory
is of primary importance to many ecological studies. Standardized data makes possible to combine
several datasets in one study and to make generalizable assumptions about biodiversity patterns.
Nevertheless, it is not only about collecting data, but also about its representativeness of the
available information including raw and processed data, its storage, managing, processing and
sharing (Merino et al., 2016). A growing availability of ecological data has broadened the scope of
questions in biodiversity assessment. There are several ways to assess data on biodiversity: 1. big
online depositories and networks such as Knowledge Network for Biocomplexity<fnref n="3" /> (KNB), Global
Biodiversity Information Facility<fnref n="4" /> (GBIF) mostly for species occurrence data, TRY<fnref n="5" /> for plant trait
data (Kattge et al., 2011), PanTHERIA for mammal traits (Jones et al., 2009) or Dyad<fnref n="6" /> where
multiple contributors and stakeholders share data; 2. multiple softwares allowing finding, extracting
and cleaning data easily (i.e. R (R Core Team, 2019), Morpho (Higgins et al., 2002), Data Retriever<fnref n="7" />)
and techniques of machine learning allowing to generate more data (Peters et al., 2014; Thessen,
2016); 3. network science approaches assembling researchers to collect data together (LTER<fnref n="8" />,
DataONE<fnref n="9" />). A global objective of scientific community is to share standardized data from different
sources stored at interoperable portals to improve data availability and quality (Hampton et al.,
2013). Thus, the use of ecological data serves to analyze specific hypotheses (by estimating model
parameters and predicting complex processes) and to explore and generate new ideas and models.
However, many ecological datasets are still hold by individual scientists and laboratories, often
available on request, but lacking of specific data policy and peer-reviewing (Roche et al., 2015).
Some other datasets, which could be useful to ecological research, comes from old collections,
museums and herbariums, and were not necessarily designed for modern research use (Pearman et
al., 2006). Finally, in Chapter 2, we used the citizen science program of vascular flora survey, “Vigie-

<footnote n="3">³ knb.ecoinformatics.org</footnote>

<footnote n="4">⁴ gbif.org</footnote>

<footnote n="5">⁵ try-db.org</footnote>

<footnote n="6">⁶ datadryad.org</footnote>

<footnote n="7">⁷ https://www.data-retriever.org/</footnote>

<footnote n="8">⁸ lternet.edu</footnote>

<footnote n="9">⁹ dataone.org</footnote>
flore". Citizen science was created to assist scientists in a hard task of large data collection, where quantity is as important as quality. Citizen science data is increasingly used to answer important ecological questions and possess a strong educational outcome (Bonney et al., 2009; Dickinson et al., 2010; McKinley et al., 2017; Martin et al., 2019).

Any biological dataset, whether it includes species occurrences, trait values, gene sequences and other aspects of biological information can include missing or erroneous information. It is related to several issues in ecology sometimes called the “dark data” (ODE Report, 2011). In fact, the path of data transformation from its raw to published form still includes hidden processes of data transformation, making it sometimes inaccessible and creating time-consuming and unnecessary replications between studies (Hampton et al., 2013). A new challenge for ecologists is how to better aggregate, search, cross-reference, and mine large volumes of data (LaDeau et al., 2017). Thus, a crucial point before analyzing any scientific data is to determine the maximum of possible pitfalls and limits relative to the study. One of the most common bias of biodiversity data is the information on species geographic range ("Wallacean shortfall", Hortal et al., 2015). Usually species are recorded in the most accessible locations depending on spatial and temporal sampling effort and even politico-economic situation of a country (Rodrigues et al., 2010). Along with general problems of data availability and reliability, we will briefly discuss the most relevant problems of the data related to the measurement of originality.

### 1.2.1 Phylogenetic bias

If a fully resolved phylogenetic tree existed, it would still possess some topological uncertainties (Swenson, 2009). Each phylogeny is a hypothesis about the real relationships between species represented by specific tree topology, thus different tree topologies might represent differently species evolutionary history (Knapp et al., 2012). Usually phylogenetic trees have to be rooted for calculation of species phylogenetic originality (Thuillier et al., 2015). Sometimes phylogenies also have to be ultrametric (i.e. the shortest path from root to tip is constant). For example, the originality index Qb would estimate some species' originalities as equal to zero if non-ultrametric
trees were used (Pavoine et al., 2005; Pavoine et al., 2017; see Chapter 1). Moreover, the topology
of phylogenetic trees changes due to updates in scientific knowledge in some taxonomic groups
more than in others because of recent diversifications and extinctions (Morlon et al., 2010; Billaud
et al., 2019), resolution of soft polytomies (Kuhn et al., 2011) and lateral transfers of characters
between species (Gribaldo and Philippe, 2002). For example, recent extinctions would make the
remaining closely related species more isolated on the tree and make them more phylogenetically
original (Chapter 1, Figures 7 and 8).

Phylogenetic soft polytomies reflect unresolved nodes that support more than two
descendent branches in a tree. Such low phylogenetic resolution can affect species phylogenetic
originality (Isaac et al., 2007) by wrongly giving different species the same originality value. This
bias would be greater with polytomies occurring deeper in the tree if the originality index is sensitive
to the information nearer the root (such as the AVerage index), and greater with polytomies in the
terminal nodes for measures that overweight information nearer the tips (such as Equal Splits).
Even though polytomies can be resolved by several methods (Lewis et al., 2005; Davies et al., 2008;
Kuhn et al., 2011), their real impact in conservation priorities is still poorly understood.

Furthermore, if polytomies are present or if molecular information is missing for some species,
terminal branch lengths can be poorly estimated, what would cause wrong representation of species
relatedness (Roquet et al., 2013). Phylogenetic branch unit may have length set to 1, or may have
units of observable changes in traits (or features; Faith, 1992), or genetic distances based on the
molecular markers, or may be inferred from node age in millions of years (Cavender-Bares et al.,
2006) or estimated with molecular clock models calibrated by fossils presence (Tucker et al., 2019).
The differences in branch-length estimations would thus under/overestimate species phylogenetic
originality based on phylogenetic distances.

The whole phylogenetic tree may change depending on the molecular data available
(Sanderson and Shaffer, 2002). Plant phylogenies are especially well known to suffer from frequent
topological changes with species replacements. The definitions of the species, placed as the tips of
phylogenetic trees can also sometimes be very instable. Therefore, a species could be placed
differently on the phylogeny if its definition (limits) change. For example, the split of a species into
two species due to updates in taxonomic knowledge modifies estimations of species phylogenetic
originality (Robuchon et al., 2019). Therefore, one should use the most robust and recent version
of phylogeny available. Some studies have hopefully shown that wrongly placed species did not
affect phylogenetic originality substantially (Collen et al., 2011; Isaac et al., 2012), which is true if
taxonomic errors are spread across phylogeny and not concentrated within close relatives.
However, more studies are necessary to evaluate the effect of phylogenetic quality on species'
phylogenetic originalities.

1.2.2 Trait data bias

Prioritizing species conservation based on their trait-based originality is a promising strategy; however, it remains challenging in practice because we have imperfect knowledge about species traits relevant for conservation and how they should be integrated into conservation actions. By working with species biological traits, researchers explore trait-based diversity of biological assemblages and seek for generalizable predictions of species ecological functions highly constrained by the type and number of traits considered (Petchey and Gaston, 2006, Adler et al., 2014). Information on functional traits and the extent to which they relate to an ecological function is available mostly for plant species, and still remains limited for other taxonomical groups, although other non-functional traits are available (Lavorel and Garnier, 2002; Diaz et al., 2016). Many studies in functional ecology and community ecology are thus dominated by plant diversity studies and it is not well understood whether patterns observed with plants can be generalized to the other taxa.

Plants are the most studied taxonomic group, because it is the most documented one. Most
plant trait databases include mean trait values per species with no information on trait intraspecific
variability (Diaz et al., 2016). Thus, it seems sometimes difficult to establish relationships between
the performance of a species and a particular habitat if species' traits have been averaged across a
range of different environmental conditions. For example, in Chapter 2 we used trait data from
several countries of northern-western Europe, but excluded those from the other European
countries, where same species occurred (available in TRY database). Moreover, the majority of
species trait values are available for the most common species and for the most easily measurable
traits (Violle et al., 2015), which bias species originality to the most abundant species. Therefore,
values of trait-based originality are relying on the choice of traits, their number and type, how they
were measured and on the way these traits are used to represent dissimilarities between species.

In particular, species traits can be of different statistical types. In Chapter 2, we used quantitative, categorical, ordinal, circular, binary and nominative traits. The type of data will have an influence on how species' originality is perceived. For example, categorical traits lead to binary distances between species: two species are similar (distance=0) if they belong to the same category or they are maximally distant (distance=1) if they belong to distant categories. In that case, species belonging to the less frequent category will be considered highly original compared to others, as they are maximally distant from other species. Quantitative traits lead to more continuous differences between species and thus to more continuous and symmetric distributions of species originalities. Discrete traits could thus lead to the highest distances between species and thus to an overestimation of their trait-based originality (Maire et al., 2015). When working with several species traits, one should also test for collinearity between trait values and exclude highly correlated ones. Then, Gower's measure of species distances (Gower, 1971) allows taking into account multiple trait types and handles missing trait data when calculating trait-based dissimilarities between species. A generalization of Gower's metric into a mixed-variable coefficient of dissimilarity (Pavoine et al., 2009; see Chapter 2 for use) improved the consideration of traits of different types. Although, if the amount of missing data is relatively small to the size of the dataset, then leaving out the few samples with missing features may be the best strategy in order not to bias the analysis, computational bias is still inevitable.

Species trait-based originality measures are sensitive to the missing data, some more than
the others, as found in the case study of Veron et al., (In prep, see Appendix A). Similarly, in our
case study in Chapter 2 species with the most of missing trait data had the highest trait-based
originality scores, even if in reality they may not be that different from the other species. For sure
the best solution to missing trait data would be not having any. Experimentally controlled studies
could probably get nearly complete datasets. However, most ecological datasets combine many
data sources and thus many potential sources of missing data. The common habit is to delete units
with missing observation, which likely increases estimation bias particularly if data is not missing
completely at random (Nakagawa and Freckleton, 2008; Penone et al., 2014). For example, Penone
et al. (2014) found that removing species with missing trait values (especially with >30%) created
more bias than trying to estimate (impute) missing traits.

To avoid inconsistent results, one could try to improve the effort of data collection, for
example by combining multiple databases as we did by combining four plant trait databases in
urban originality study, and completing it with data from literature, museums and fieldwork. If an
important amount of missing data persists, imputation strategies exist (see Chapter 2), replacing
missing data points with an estimated value relative to the given data input (Soley-Bori, 2013).
Penone et al. (2014) compared the power of four imputation methods of missing trait values by
artificially removing an increasing amount of trait data from a complete dataset of Carnivora
species. Penone et al. (2014) also included phylogenetic information into the imputation algorithm
thus improving trait values estimation; however, this could introduce circularity if we calculated
trait-based originality from phylogenetically constrained data and then compared it to the
phylogenetic originality. Penone et al. (2014) found that up to 60% of missing data would be an
acceptable threshold for a meaningful imputation with four methods they tested. However to date,
there were no studies evaluating species originality sensitivity to the missing trait data and to the
mentioned imputation methods (but see Veron et al., in prep in Appendix A).

Another source of trait-based bias is the way of representation of species differences. There
are three main approaches for representing trait-based differences between species: tree-based,
distance-based and trait-value ordinations in multidimensional space (Chao et al., 2014). Because originality indices were first developed for the phylogenetic tree structure, the most used method to measure trait-based originality is the tree-based approach, by constructing functional dendrograms with hierarchical clustering methods (Figure 2.b; Mouchet et al., 2008). Trait-based dendrograms consider all possible interrelations between species of a set (Chao et al., 2014). Yet, there is no rationale for representing trait-based relatedness between species with a dendrogram (Villéger et al., 2017) and it could misrepresent species' raw distances and bias the estimates of species trait-based originality (Pavoine et al., 2017). Nevertheless, there is a lack of research investigating how trait-based clustering approaches influence trait-based originality values (but see Pavoine et al., 2017; Veron et al., in prep in Appendix A.). We would thus recommend to test the quality of several approaches for trait-based distances representation compared to raw trait differences and to choose to most appropriate one. For example, Veron et al. (in prep, Appendix A) found a low Pearson's correlation between species originality computed on trait and phylogeny data.

In a study case, Maire et al. (2015) found that the use of a tree-based approach could artificially increase species trait-based distances and that the use of multidimensional space with axes derived from principal component analysis (PCoA), seems to better represent species trait-based distances. Species originality, calculated in the multidimensional space as a distance from focal species to the centroid of all species, would avoid tree-based approach problems; but, if there are too many traits, the visualization of functional space can somehow become tricky and difficult to interpret (Saito et al., 2016). It is still not clear, however, how big is the difference between tree-based and ordination approaches for traits-based distances representation (Weinstein et al., 2014; Cianciaruso et al., 2017).

As species trait-based originality is drastically dependent on the accuracy of clustering algorithm (Podani and Schimera, 2007) and the quality of multidimensional space (see Maire et al., 2015), yet it could be difficult to choose between the two approaches. An alternative is to apply
originality measures directly to a distance matrix (Ricotta and Szeidl, 2009) as we did in urban plant
originality in Chapter 2. Using the distance matrices for trait-based species originality measuring
avoids us the arbitrariness of choice of clustering and ordination methods and simplifies the
interpretation of multiple trait-based differences between species. Such an approach also makes it
easier to compare trait-based originality and phylogenetic originality as phylogenetic distances can
easily be extracted from a phylogenetic tree (Figure 2.a).

<figure><img src="image_34.png" /><figcaption>**Figure 2.** Illustration of interchangeable use of tree-based and distance-based approaches of species biological data representation for originality measuring: **a.** methods of pairwise phylogenetic distances calculation from phylogenetic trees, **b.** methods of hierarchical clustering from trait distance matrix to trait-based dendrogram. Methods presented in a. and b. can also be reversed. Sp represents five different species in a set.</figcaption></figure>

## 2. THE MATTER OF SCALES FOR ORIGINALITY MEASURES

Levin (1992) noted that “the problem of relating phenomena across scales is the central problem
in biology and in all of science.” Hence, the problem of scale at which ecological processes should
be considered is critical to generate general predictions in ecology (Chave, 2013). As species
originality is relative to a reference set of species, its value depends on the scale at which this species
set is defined. Most studies on species originality have concerned large clades, like birds or
mammals, and have been done at the global, word scale (Safi et al., 2013; Jetz et al., 2014; Thullier
et al., 2015). Such studies allow responding the questions like: which are the most original species
on Earth?; Are original species threatened by extinction? In Chapter 2, however, we analyzed
originality from local to regional scales and not on global scale. When originality is measured in a
local site, it depends on the species composition of this local site, which itself strongly influenced
by the regional source pool of species.

**2.1 Importance of the spatial scale**

As written above, the global world scale has been the most studied scale for calculating species
originality (see e.g., Veron et al. 2017 and references therein). This is because most studies focused
on conservation biology asking which species are globally original and how many globally original
and endangered species are supported by a given region (Safi et al., 2013; Jetz et al., 2014). These
studies focus on patterns of the distribution of global originality and use the concept of originality
as a criterion for the conservation of biodiversity. However, originality concept could help
ecological studies to go beyond describing biodiversity patterns to determination of key processes
underlying these patterns.

Ecological and evolutionary processes that act at different scales of biodiversity
organization are interdependent. Thus, species ecological properties shape processes of species'
evolution that in return influence the distribution and abundance of species in communities
(Ackerly et al., 2003; Cavender-Bares et al., 2004). Local patterns of species originalities are
determined by species ability to disperse, install and survive in local communities constrained by
local community assembly processes (Chase and Myers, 2011). At the same time, local originality
patterns are also constrained by macro-evolutionary processes (Ricklefs, 1987). Moreover, species'
local originality is likely to vary across different specific compositions of communities, constrained
by environmental gradients, and different evolutionary histories of coexisting species.

As a result, in Chapter 2, we chose to explore the concept of originality at two restricted
scales: local and regional. It is indeed possible to calculate the originality of each species in reference
to the set of species present in a local area. If a species is present in several local areas, then its
local originality will change depending on the local area considered (see discussion of Chapter 2).
In addition, it is also possible to calculate the originality of each species in reference to all species
present in a region and to make the mean of these regional originality values (*Mr*<sub>O<sub>p</sub></sub>) in each local
community as we did for urban plant species. This measure (*Mr*<sub>O<sub>p</sub></sub>) represents the amount of the
regional diversity expressed by species in the local community. If the regional diversity is well
represented at the local level, then a local conservation effort could be enough to protect a local
hotspot of regional biodiversity (Margules and Pressey, 2000; Veron et al., 2016). Multi-level
originality approaches from local to global scales could allow determining the scale at which
environmental factors have the strongest effect and where conservation should be prior. Such a
multi-scale approach however questions on the definitions of regional and local species pools.

The regional pool of species was subject to controversial definitions since nearly 80 years
(Graves and Rahbek, 2005). Usually, the composition of observed regional pool is limited to a list
of species known to occur in a given biogeographic area, yet often without the reference to habitat
or distance from the local assemblage (Graves and Rahbek, 2005). With such a definition, some
species would appear highly original within the regional pool. In urban originality study (Chapter
2) for example, some of non-native species introduced by humans from other biogeographical
zones were top ranked by trait-based originality measures. In another study, introduced Carnivore
species were characterized by high phylogenetic and trait-based originality scores in Europe
(Pavoine et al., 2017). Therefore, a regional pool should represent a reference set of species
susceptible to colonize and compete for resource use in local communities (Lortie et al. 2004;
Lessard et al, 2012; Cornell and Harrison 2014). The regional pool composition thus reflects
distinct assembling processes acting hierarchically at local and regional scales and greatly influences
local communities' formation (Zobel, 1997; Lessard et al., 2012). Hence, depending on how
regional pool and local community are defined in terms of the taxa included, originality measures
would give different lists of the most original species and hence, will reveal signatures of different
ecological processes and indicate different priority areas for conservation.

## 2.2 Taxonomical scale

The decision about which taxonomic group to include into a study is based on a number of theoretical and practical considerations. Usually community ecology studies are interested in a finite set of traits and consider species from a delimited taxonomical group that occur in a given place. More fundamental evolutionary studies are searching for global patterns of species diversity and usually work with entire clades of species. Use of an inappropriate taxonomic scale can alter patterns detected during analysis (Cavender-Bares et al., 2006). Decisions on which species are included into the regional pool can fundamentally change the interpretation of species originality patterns. For example in Chapter 2 we decided to exclude species from Bryophytes (mosses) and Pteridophytes (ferns) from plant communities, as they might have very different trait values and evolutionary history compared to Angiosperm species retained. However, this choice was arbitrary and not easily justified, which is often the case (Cadotte et al., 2017). If there is no indications on which taxa to include, one could start with the largest possible species pool and test whether patterns of species originality distribution change within smaller species pools.

## 3. CHOOSING THE MATHEMATICAL FORMULA TO MEASURE ORIGINALITY

A significant part of the efforts made by evolutionary and ecology scientists is guided by a common aim to preserve biodiversity and the benefits it provides to humanity. This ambitious intention demands not only a deep knowledge and understanding of what we want to protect but also an adequate tool helping humanity to make the decision. As seen above, the difficult task of selecting among different facets of biodiversity (species, sets of species as communities) as targets for prioritization depends upon the choice of data type and that of the study scale. It also depends on the measure selected. In this thesis we wanted to emphasize species originality measures that come to complete well-known species diversity measures. Species originality can be seen as a rarity of species characteristics (Pavoine et al., 2005, Chapter 1), completing species abundance-based rarity
with information on species' biological differences. Originality indices are numerous, some of them
are redundant, some are using different types of data and differ in the way they weight species.
These differences, sometimes subtle, can still substantially influence the patterns of originality
scores observed. In Chapter 1, we already presented the relationship between species originality
and diversity measures. Here we overview some methodological aspects that one should be aware
of before choosing the right originality index.

**3.1 Relations between species originality measures**

The idea that certain species contribute disproportionately to phylodiversity and trait-based
diversity of species set is based on the assumption that those species possess an unusual set of traits
unshared by the rest of species set (see Chapter 1). Studying species originality may reveal whether
or not species with high originality values also maintain important ecosystem functions and large
amounts of evolutionary history and are therefore worth to be protected in priority. In contrast to
widely used diversity metrics, measured at the level of species set (local, regional, or global set),
originality gives a value to each species in a set (Chapter 1). It is important to remember that
originality is a relative measure, thus it can be described as a gradient (Chapter 1, Figure 2), with
two extreme yet probably impossible cases: unique species on one side, not sharing any biological
characteristic with others (100% of unique contribution to the diversity of a set) and redundant
species on another side, sharing all its characters with other species (0% of unique contribution to
the diversity of a set). Such a gradient encompasses all the possibilities of species differences in a
set and its contribution to the diversity. An ideal originality measure would thus be able to position
species along the whole gradient. Some originality measures however are biased along this gradient,
being more sensitive to species uniqueness than to species redundancy (Figure 3).
<figure><img src="image_35.png" /><figcaption>**Figure 3.** Illustration of differences between originality indices in their sensitivity to uniqueness versus redundancy. Here six originality indices are considered. We considered a theoretical rooted phylogenetic tree with 19 species on tips (capital letters). The root is on the left. The distance from tip to root is equal to 5 units. We calculated for each species the NN index (nearest neighbor = pendant edge (PE)), the Equal-Split (ES) and Evolutionary-Distinctiveness (ED) indices (see Chapter 1), the indices derived from the quadratic entropy (Rb and Qb, see Chapter 1), and AV index (average distance to all other species). Then species were ranked from the highest (rank = 1) to the lowest (rank = 19) value (variables NNrank, ESrank, EDrank, Qbrank, Rbrank and AVrank). Species C is considered as the most original according to NN and ES. These indices overweight uniqueness (the branch that leads to a single species) in comparison to redundancy (shared branches) (NN even discards any redundancy). Species C is considered the third most original species by indices ED and Qb, which acknowledges the phylogenetic isolation of species A and B. In contrast, species C is considered the 11th most original species (so among the least original species) according to Rb and AV.</figcaption></figure>
We have already presented a large variety of originality measures in Chapter 1. Redding et al. (2014) showed that some indices were always very closely related (see Table 1 in Redding et al., 2014). As described in the Appendix 1 of Chapter 1 (Table S1.1), there are basically two types of originality measures, nested inside of the species characters type used – phylogenetic or trait-based. We showed in Chapter 1 that many indices independently developed to measure either phylogenetic originality or trait-based originality are actually similar. This is the case for the pendant edge measure of phylogenetic originality (PE, Redding et al., 2014) and the nearest neighbor (NN) used to measure trait-based originality (Pavoine et al., 2017). The PE index does not include the root of phylogenetic tree in the calculation of phylogenetic originality. Therefore, PE reflects a recent dimension of species evolution and is sensible only to changes in the lengths of terminal branches on a phylogenetic tree (Redding et al., 2015a). PE overweighs species uniqueness because it fails to compare the amount of unique evolution with the amount of shared evolution between species, the latter being discarded. Same arguments are valuable for the NN measure. Hence, according to PE and NN indices, a species may appear more original than it actually is (Figure 3).

Among indices that were inferred from phylogenetic trees, the most used ones (Fair Proportion FP, Isaac et al., 2007, and Equal Splits ES, Redding et al., 2006) include the root of the phylogenetic tree in originality calculation and partition the total branch length between all species from the set (Figure 1 from Pavoine et al., 2017). In contrast to PE, they include all branches of the phylogenetic tree from tips to root while calculating species originality. Despite that, it has been shown that these measures are strongly correlated with PE and thus also overweigh species uniqueness over species redundancy (e.g., Jono and Pavoine, 2012).

In Chapter 2, we used an alternative index that averages dissimilarity between the target species and all others in a set. When applied to phylogenetic distances, this index (named AV for Average, Pavoine et al. 2017; or Average Pairwise Distance (APD), Redding et al., 2015a) represents more ancient dimensions of species evolution. Hence, it is sensible to deeper phylogenetic structure changes and errors in branch lengths (particularly the terminal branch) will provide only a limited
amount of incorrect information to a final originality value (Redding et al., 2015a). Redding et al. (2015a) also found that APD, working with phylogenetic information, was strongly correlated with trait-based measures of originality and thus could also capture well the ecosystem trait-based diversity. Thus, APD could represent an analytical link between phylogenetic and ecological conservation studies (Redding et al., 2015a). Pavoine et al. (2017) showed, however, that indices like AV and APD tend to give close values to all species in a set. Thus, if we were interested in distinguishing better between species redundancy and uniqueness of species, another related index named Rb would be more appropriate (Figure 3; Pavoine et al., 2017). This index, derived from Rao's quadratic entropy index (Rao, 1982), is strongly correlated to AV, but leads to higher variances in the values of species originality (Pavoine et al., 2017). Because of their various sensitivity to species uniqueness versus redundancy, the choice of an originality index is crucial to better represent and understand phylogenetic and trait-based structure of species community. For example, applying 11 different indices of phylogenetic originality to world's phylogenies of Mammals and Amphibians created different lists of the top 100 most original species, which also differed from the top species list created by EDGE program (Table 2 in Redding et al., 2014). However, the metrics that were closely related to the ED measure composing the EDGE formula (Isaac et al., 2007) produced very similar lists of top original species (Redding et al., 2014). Therefore, the more originality metrics are different in their calculation manner, the more varies species originality score.

**3.2 On the ubiquity of originality measures in general**

We have seen in section 2.1 that continuous indices of species originality could be measured at any
spatial scale from local to global. However, should the mathematical formula for originality change
depending on the scale of a study? For example, in Violle et al. (2017a) functional rarity framework,
the term of species “distinctiveness” was defined only at the local level and designated the mean
distance to the other species in a set; in contrast, the term of species “uniqueness” was defined only
at the regional level and designated the distance to the *k* nearest neighbors within a regional species
pool. Further research is however needed to determine whether the choice of originality indices
should be dependent on the spatial scale of a study and more generally to determine which index
is most appropriate to respond to which ecological questions (see Carmona et al., 2017; Violle et
al., 2017b).

More generally, originality measures are flexible tools and can be applied in many different
circumstances of various areas of ecological and conservational research. Originality measures
require a set of entities that can be described by a common set of characteristics. In this thesis, we
used species as an entity and brought attention to the most available and used species characters –
traits and phylogenies. Using species as a unit constrains researchers to stick up to one of species'
concept definitions. In this thesis, we used a mix of several definitions of species concept (see
Introduction, section 3.1). Moreover, species could be replaced with individuals characterized by
traits or DNA sequences (already indirectly used with molecular phylogenies) thus representing
intra-specific originality. Species could also be replaced by other taxonomic levels (genera, families,
clades; Cornwell et al., 2014) or trait functional groups assembling multiple species (Brandl et al.,
2016). More broadly, the originality (and rarity) concepts could be extended to any scale of
biological organization from genes to ecosystems (Carmona et al., 2017). As proposed by Pavoine
et al. (2017):

"The concept of originality could then be adapted for establishing conservation priorities across multiple scales. For example, dissimilarities among communities, even when phylogenetic information is used (e.g., Ives and Helmus, 2010; Chiu et al., 2014; Pavoine, 2016), are rarely derived from trees. At the plot and regional scales, our methodology can also be extended to measure the environmental originality of plots within regions, and of regions. This can be done simply by replacing biological with environmental data when calculating dissimilarities between plots and between regions. Our study thus opens the way to new directions of research where the biological originalities of areas will be compared to their environmental originalities."
Within a biogeographic zone, some habitats or communities can have a unique composition, as well as a restricted area. Consideration of originality at any organizational scale of biodiversity would broaden the application of originality framework to study differences between habitats within landscapes or regions, regions within countries and biogeographic domains within the world (Carmona et al., 2017). More research is thus needed to determine which originality indices could allow us to integrate nested scales of originality by measuring the originality of an individual within a species, of a species within a site, of a site within a region, etc.
One of global goals of conservation biology, ecology and other disciplines interested in biological diversity is to "address the underlying causes of biodiversity loss [...] to improve the status of biodiversity by safeguarding ecosystems, species and genetic diversity [...] and mainstreaming biodiversity across government and society" (Aichi Biodiversity Targets, Strategic Plan for Biodiversity 2011–2020, CBD, 2011).

Feasibility of projects considering biodiversity is constrained by available amount of knowledge and by budgetary capacities. At the same time, biodiversity prioritization is guided by nature's benefits to people, reflected in phylodiversity and trait-based diversity of species. Unfortunately, there is still a gap between the scales where biological diversity and ecosystem functioning are studied, scales where conservation actions are taken, and scales where direct benefits to humanity exist. To overpass this gap, an important role of biological research is to bridge theoretical and practical approaches, sometimes by making simplifications and by according theory a practical use.

The main objective of this thesis was to evaluate the benefits that can bring the concept and measures of species' originality to different biological disciplines, particularly to conservation biology and community ecology. Originality indices allow treating species within multiple facets of biological diversity. Combining theoretical and empirical evaluations this thesis has contributed to the following key points:

* **Ø** Common meaning of words used to designate a concept in Science can differ between researchers, which can be misleading for general understanding. Therefore, we first provided a semantic and historical overview of three commonly used concepts – diversity, rarity and originality – at the level of species assemblage. We propose that studies investigating species contribution to the diversity of a set use a unifying term of originality, so that the comparison between studies and key-words research could be more efficient.

* **Ø** We made an explicit differentiation between species diversity, which is a property of a set of species and species originality, which is a property of individual species composing the
set. From there, we were able to describe that originality is a species contribution to the diversity of a reference set of species based on two main attributes used to describe species: traits and phylogeny. Therefore, the concept of species originality can be seen as species rarity based on other attributes than species abundance.

Ø We then showed that mathematical links exist between associated indices of rarity, originality and diversity. Rarity, which is historically the oldest way to describe differences between species, was explicitly integrated into diversity measures by means of species abundance. Then, based on species' biological differences, diversity indices integrated species' phylogenetic and trait characters. We showed that phylodiversity and trait-based diversity can thus be written as a function of species rarity and originality measures in several ways (notably, as a mean, sum or a weighted mean).

Ø Following theoretical outlines of originality-diversity concepts we proposed a practical application of a two-step analytical framework to real plant species data, by first measuring species-specific originality and then inferring species diversity with real plant species data. We demonstrated how regional and local species originality contributed to variations in community diversity along an urbanization gradient. The influence of urbanization gradient on community-level diversity differed between trait-based and phylo-based measures and between local and regional scales, which were the area over which originality was calculated (originality relative to the rest of the community or to the rest of the region).

Ø Our originality framework indicated the most relevant spatial scale for studying trait-based and phylo-based originality. Moreover, we determined which species, original or redundant, are responsible for variations in trait-based diversity and phylodiversity. We notably showed that urbanization causes some species to have higher trait-based originality at the local community scale than at the regional scale. Therefore, variation of species originality across local communities is not random but dependent on regional species pool and community assemblage rules.
Ø We brought attention to possible pitfalls that can be encountered during species originality analysis. Measured by various approaches based on trees (phylogenetic tree or trait-based dendrogram), dissimilarity matrices or multidimensional space, species originality depends on data completeness and accuracy. This issue has been encountered by many studies, however, its influence on species originality measures has not been addressed in literature. We showed with the very first results that originality dependency on trait missing data requires a deeper consideration and analysis in future studies.

Ø We underlined that species originality can vary across spatial and taxonomic scales according to different ecological and evolutionary processes acting at these scales. In addition, we argued that including species abundance-based rarity into originality measuring can drastically change species scores and conclusions about ecosystem functioning, where a high number of individuals supporting the same trait can ensure greater stability. However, we showed that it is possible to develop originality measures that are regulating the relative importance given to rare vs. abundant species.

Ø As it is not possible (and not meaningful) to include all species' attributes into one metric, we stressed the need for a joint use not only of different species' biological characters but also of different originality and diversity measures in order to understand mechanisms shaping species communities at different spatial and temporal scales. Different aspects of species characters would reflect different patterns of species evolution and implications into ecosystem functioning.

Ø Overall, although these methodological considerations will have to be treated in future research, this thesis has demonstrated that the originality concept and its measures could be useful to community ecology, notably to identify the evolutionary events and ecological processes leading to the coexistence of species with different levels of originality. As these processes can include human activity, the originality concept and its measure could be also useful to conservation studies, especially if original species are threatened.
Ø Finally, we underlined the ubiquity of originality measures as they could give a value to any entity in focus compared to other entities of the same type. The focal entity can be a species compared to other species in a set as treated in this thesis. In future research a focal entity could also be a community of species compared to other communities in different regions or habitats, characterized by species composition. Few conservation actions have already shifted from conserving rare threatened species to conserving threatened species with original biological characteristics. As a step further, future actions could shift from conserving more diverse and threatened areas (or hotspots of biodiversity) to conserving areas composed of more original species.
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Extra projects and other doings, which are indirectly related to a research activity of PhD students and researches in general are often forgotten. These usually voluntary work represents, however, an important investment of time and energy. I would like to briefly present here some main activities, which I was happy to be a part of. All of them provided me a great personal experience, whether it was by a popularization of evolution and ecology to a primary school class, by giving botany classes to people passionate by nature and biology during public lectures of MNHN or by leading an association of Master and PhD students (BDEM) during two past years. It was a great pleasure to have a possibility to participate to all those projects, which also opened me different worlds of research, teaching, administration, event organization and many others. As I am planning to continue my past activities before I started to work on this thesis project, which excites me particularly, that is to spread the scientific knowledge to kids, I also participated to a meeting of EvoKE (Evolutionary Knowledge for Everyone) association in Croatia.

Therefore, I would like to thank all the people who I was very glad to meet during these three past years, people which contributed to my personal development in any form.

* January - June 2018: Teaching Theory of Evolution at primary school "Parmentier", Paris, during science classes (activity report can be found in Appendix C)

* 26 - 29 September 2019: EvoKE 2019 meeting, Split, Croatia

* May 2017 – May 2019: Head of PhDs and Master Students association, “Bureau des Doctorants et Etudiants du Muséum” (BDEM) of MNHN

* General organization and coordination of Young Natural History scientists Meeting (YNHM) in 2018 and 2019: https://ynhm2019.sciencesconf.org/; https://ynhm2018.sciencesconf.org/

* Public lectures for National Museum of Natural History in botany (Angiosperms reproduction, Plant community ecology, Urban plants ecology)

* Participation to the Vigie-Nature stand during “Fête de la Nature” week-ends in 2018 and 2019
LIST OF PUBLICATIONS

**Kondratyeva, A.**, Grandcolas, P., and Pavoine, S. (2019). Reconciling the concepts and measures of diversity, rarity and originality in ecology and evolution. Biol. Rev. 94, 1317–1337.
doi:10.1111/brv.12504

**Kondratyeva, A.**, Knapp, S., Durka, W., Kühn, I., Vallet, J., Machon, N., Martin, G., Motard, E., Grandcolas, P., and Pavoine, S. (2019). Diversity in the city: urbanization affects differently trait-based and phylogenetic plant originality at local and regional scales. In revision for Frontiers Ecology and Evolution, section Urban Ecology.

S. Veron, R. Pellens, **A. Kondratyeva**, S. Diaz, W. Illes, P. Grandcolas, S. Pavoine, M. Robuchon and M. Mouchet. (2019). On islands, evolutionary but not functional originality is rare. In prep.

LIST OF COMMUNICATIONS

**Chaire Modélisation mathématique et Biodiversité** (MMB), Aussois, 28 mai – 1 juin 2017, oral « *Mesures d'originalité phylogénétique et fonctionnelle* », Kondratyeva, A., Grandcolas, P., Pavoine, S.

**SFEcologie 2018** – International Conference on Ecological Sciences, Rennes, 22-25 October 2018, poster presentation « *"Species' diversity, rarity and originality. Disentangling concepts and measures in ecology and evolution"* » Anna Kondratyeva, Philippe Grandcolas, Sandrine Pavoine (award of the best young researcher poster); Oral presentation « *"Originalité urbaine des plantes herbacées, région Ile-de-France"* », Kondratyeva, A., Knapp, S., Durka, W., Kühn, I., Vallet, J., Machon, N., Martin, G., Motard, E., Grandcolas, P., et Pavoine, S.

**Vigie-flore program days** (27-28 April 2019), oral presentation « *"Originalité urbaine des plantes herbacées, région Ile-de-France"* », Kondratyeva, A., Knapp, S., Durka, W., Kühn, I., Vallet, J., Machon, N., Martin, G., Motard, E., Grandcolas, P., et Pavoine, S.

**Petit pois déridé**, 4-6 June 2019, oral presentation « *"Originalité urbaine des plantes herbacées, région Ile-de-France"* », Kondratyeva, A., Knapp, S., Durka, W., Kühn, I., Vallet, J., Machon, N., Martin, G., Motard, E., Grandcolas, P., et Pavoine, S.
ON ISLANDS, EVOLUTIONARY BUT
NOT FUNCTIONAL ORIGINALITY IS
RARE
On islands, evolutionary but not functional originality is

rare

Authors: S. Veron<sup>1,2,*</sup>, R. Pellens<sup>1</sup>, A. Kondratyeva<sup>2</sup>, P. Grandcolas<sup>1</sup>, Rafaël Govaerts<sup>3</sup>, M. Robuchon<sup>2</sup>, M. Mouchet<sup>2</sup>

<sup>1</sup>Institut de Systématique Evolution et Biodiversité (ISYEB UMR7205), Muséum National d'histoire naturelle, CNRS, Sorbonne Université, EPHE, Université des Antilles, CP51, 47 rue Buffon, 75005, Paris, France

<sup>2</sup>Centre d'Ecologie et des Sciences de la Conservation (CESCO UMR7204) MNHN, CNRS, Sorbonne Université - CP51, 55-61 rue Buffon, 75005, Paris, France

<sup>3</sup>Royal Botanic Gardens, Kew, Richmond, Surrey TW9 3AE, UK.

*Author for correspondence: e-mail: sveron@edu.mnhn.fr; Tel. +33 1 40 79 57 63; Fax: +33 1 40 79 38 35
ABSTRACT

Functionally and evolutionary original species are those whose traits or evolutionary history are
shared by few others in a given set. These original species promote ecosystem multifunctionality,
the ability to cope with an uncertain future, future benefits to society and therefore have a high
conservation value. A potential signal of their extinction risks is their rarity (stating for geographic
range-restriction in this study). In islands life in isolation conducted to the rise of a multitude of
original forms and functions as well as to high rates of endemism. Not only patterns and processes
of insular originality are unexplained but the relationship between originality and rarity is still
unknown. The aim of this study is to assess how original insular species are, to explore whether
original species are rare or not and to investigate the factors that may explain the rarity of original
species. We first compared the functional and evolutionary originality of monocotyledon species
and whether continental or insular species were more original. We found that species restricted to
islands were more original than continental species and, although functionally and evolutionary
original species were dissimilar, many occurred on similar territories so that regional conservation
strategies may allow to conserve these distinct forms. Yet, evolutionary original species were
significantly more range-restricted than those which were distinct in their traits. Reflecting their
rarity, evolutionary original species had low dispersal abilities and were found on islands where
settlement may have been facilitated. On the opposite, functionally original species could reach a
wider set of islands by being transported on long-distances. While some mechanisms may both
explain rarity and originality such as extinctions, others may be specific to each of these biodiversity
facets, in particular diversification, niche shift and expansion, and dispersal power. Implications for
conservation are huge: original species are range-restricted and mostly found in the most threatened
systems of the world, i.e. islands, endangering the reservoir of features against an uncertain future.
# INTRODUCTION

Originality of a given species in a set is the oddity of its biological characteristics, whatever they are, relatively to all other species of the set (Sandrine Pavoine, Bonsall, Dupaix, Jacob, & Ricotta, 2017). Originality can refer to evolutionary history or to functional traits (e.g., Pavoine, Ollier, & Dufour, 2005; Pavoine et al., 2017; Marc W. Cadotte & Jonathan Davies, 2010; Redding, Mazel, & Mooers, 2014; D. Mouillot, Culioli, Pelletier, & Tomasini, 2008; Violle et al., 2017; Cornwell et al., 2014). Originality has been poorly investigated in community ecology or biogeography (but see David Mouillot, Graham, Villéger, Mason, & Bellwood, 2013) but its value for biodiversity conservation is well supported. Indeed, in addition to capture significant amount of phylogenetic or functional diversity (D. Mouillot et al., 2008; Faith, 1992), evolutionary and functionally original species may insure key ecosystem processes and provide services to humanity. Functional and evolutionary originality may sometimes be decoupled (Faith, 1992; Pollock, Thuiller, & Jetz, 2017) and should be differentiated due to the distinct benefits they may provide. Regarding measures of functional originality, they are based on a reduced number of traits generally related to ecosystem functioning. For example, (Petchey, Hector, & Gaston, 2004) used 12 plant traits and showed that functionally original species may have a large contribution to ecosystem functions by increasing plant biomass production. Loss of functionally original species may then directly impact ecosystem stability, resilience and multifunctionality (Fonseca & Ganade, 2001; Pendleton, Hoeinghaus, Gomes, & Agostinho, 2014; Bracken & Low, 2012; Oliver et al., 2015). Evolutionary original species may also have a leading contribution to ecosystem functioning because phylogenetic isolation may be an indicator of distinct species ecological roles (M. W. Cadotte, Cardinale, & Oakley, 2008; Redding et al., 2008; Marc W. Cadotte & Jonathan Davies, 2010). Most of all, phylogenetic diversity represents a reservoir of yet-to-be-discovered resources to humanity and evolutionary original species may highly contribute to these option-values (Faith, 2017). Using together functional and evolutionary originality may allow to identify species that capture irreplaceable ecosystem functions and services to humanity. However, functional and evolutionary
originality have rarely been studied jointly. Systems in which the study of both biodiversity facets could be highly valuable are islands. Insular systems have a huge importance in biodiversity conservation to preserve the heritage of our planet and they have a leading role of “natural laboratory” for biogeography and evolutionary ecology (Robert J. Whittaker, Fernández-Palacios, Matthews, Borregaard, & Triantis, 2017; Warren et al., 2015). Islands are home of species which are found nowhere else on Earth and which represent a unique evolutionary history as well as a multitude of forms and functions. Original species may have a high contribution to the island biota, but their role in shaping insular diversity just begin to be investigated (Veron, Haevermans, Govaerts, Mouchet, & Pellens, 2019; Veron et al., 2019). In addition, very little is known about how originality arose and is distributed, so that investigations on this topic may bring insights that go beyond the scope of island biogeography.

A pre-requisite to the joint study of functional and evolutionary originality is to assess how these
biodiversity facets are related. As stated above, findings about this issue are contradictory and it
has rarely been studied in an insular context. On islands, the relationship between phylogenetic and
functional originality may be weak as early diversification and simultaneous cladogenesis resulted
in the lack of phylogenetic signal in insular species traits (Losos, 2008). A famous example is the
radiation of *Bidens* in Hawaii: closely-related species have greater diversity of traits (e.g., growth
form, floral morphology) than in the rest of the world probably due to the diversity of habitat types
and to loss of dispersability (Knope, Morden, Funk, & Fukami, 2012). As for Hawaiian *Drosophila*,
sexual selection has resulted in clearly distinguishable traits among closely-related (Gillespie &
Clague, 2009). In addition, phenotypic character changes of insular species do not necessarily
involve speciation or a phylogenetic branching pattern so that variation in evolutionary history do
not reflect the variation of some specific traits (R. J. Whittaker & Fernández-Palacios, 2007). The
first issue we investigated was therefore the extent to which functional and evolutionary originality
are related and we specifically tested the assumption that evolutionary and functional originality are
less correlated than on the mainland.
Another unresolved question is whether originality is higher on islands in comparison to continental areas. On islands, a long evolutionary history in isolation coupled with unfilled niches and oceanic climate may have given rise to many evolutionary and functionally original species. Extinctions on the continents may also have isolated species in the tree of life and in the functional trait space, creating evolutionary and functionally original insular species (Rosemary G. Gillespie & Roderick, 2002; Grandcolas, Nattier, & Trewick, 2014)). However, whether insular species are more original than continental has been poorly studied so far and maybe highly taxa specific (Jetz et al., 2014). We therefore explored whether insular species are more original than continental ones, which may add a new line of argument for the preservation of insular biodiversity.

Some original species may be at risk due to their rarity (here stating for geographic range-restriction). Range-restriction is indeed a key factor of extinction risks. Many research found that low geographic range size was the main predictor of these risks and it is also one of the main factors of the threat status of species in the IUCN RedList (Rodrigues, Pilgrim, Lamoreux, Hoffmann, & Brooks, 2006; Bland, Collen, Orme, & Bielby, 2015; Veron et al., 2016). Like original species, range-restricted species provide unique functions in an ecosystem as well as unique services to humanity (David Mouillot, Graham, et al., 2013; Leitão et al., 2016). Species which are both rare and original may then support highly vulnerable and unique functions and option values (Rosauer, Laffan, Crisp, Donnellan, & Cook, 2009; David Mouillot, Graham, et al., 2013; Violle et al., 2017; Faith, 2017). Many of such species may be found on islands. Indeed, a remarkable feature of islands is their high rates of endemism which may outcompete these in the mainland by a factor of 9 (Kier et al., 2009). More generally, many insular species are spatially range-restricted and found only on a few islands. Yet, how range-restricted are original species on islands is still poorly known and, as far as we know, has never been tested in the case of functional originality. To fill this gap, we explored the potential risks of losing the most original species by assessing the relationship between species originality and rarity. Beyond practical conservation implications, assessing the relationship between originality and rarity may allow to shed light on the mechanisms shaping both originality
and range-restriction. For example, past extinctions may have both isolated species on a phylogenetic tree and/or function space and shrunk species area of distribution. Rarity of original species can also be influenced by their dispersal capacities (Flather et al. 2007) which may be lineage specific. In an insular context, factors influencing colonization rates, such as the possibility of an island to have been at reach during its geological history and species dispersal traits have a strong influence on the distribution of biodiversity on islands (Rosemary G. Gillespie et al., 2012; Patrick Weigelt et al., 2015). We therefore investigated how the dispersal capacities of original species and the features of the islands they occur on can give evidence of their rarity.

Originality on islands is still a pristine research field and through our analyses we tackled four main questions that may lay the foundations of the study of originality on islands having implications in both conservation and biogeography 1) Are evolutionary and functionally original species similar on islands? 2) Are insular species more evolutionary and functionally original than mainland species? 3) Are insular original species rare? 4) Can the rarity of original species be attributed to their dispersal capacities and to the biogeographic characteristic of islands? To address these issues we focused on the group of Monocotyledons (Monocots), a morphologically and functionally diverse clade representing a quarter of flowering plant diversity such as all orchids, palms and cereals. The origin of monocot species represents a diversity of evolutionary and ecological processes. Monocots species are also wide-spread across islands and continents, their phylogeny is well-resolved and their traits well-documented. The clade of monocotyledon is therefore a well-suited group for the study of originality on islands.

# METHODS

## Occurrence data

We used the e-monocot database (emonocot.org) to extract data on both the spatial distribution of monocot species and delimitations of 126 islands (TDWG 3rd level). E-monocot compiles records from several botanical institutions for all 70,000 monocotyledons. We kept only native and terrestrial species from this database.
Estimating evolutionary and functional originality

We followed results from (Sandrine Pavoine et al., 2017) to choose the metrics of evolutionary
and functional originality (EO and FO, respectively). We followed the recommendations of the
authors to use a distance-based metric over dendrogram metrics because the latter may be biased
by clustering methods, the use of a low number of traits and of the use of non-ultra-metric trees
(Mouchet et al., 2008; Pavoine et al., 2017). We first estimated functional and phylogenetic distances
thanks to the Gower's distance (Legendre & Legendre, 2012) and derived an originality score
calculated with the distinctDis function of the package advi (S. Pavoine, 2019). EO is estimated in
million years of evolution and FO has no units and ranges between 0 and 1.

We estimated monocot FO with six traits compiled by (Díaz et al., 2015), adult plant height, stem specific density, leaf size expressed as leaf area, leaf mass per area, leaf nitrogen content per unit mass, and diaspore mass (see Díaz et al., 2015 for a description). The traits we chose have been widely used and recognized as fundamentally representatives of plant ecological strategies (Díaz et al., 2015). Missing values were imputed by performing random forest algorithm a thousand times, and by then estimating the mean of the 1000 imputation. We performed a sensitivity analysis (Appendix 1) to i. assess the extent to which missing values may have influenced our results ii. estimate the correlation between traits iii. Assess the contribution of each trait to FO scores. We did not include dispersal mode in the measure of functional originality because it was used later as an explanatory factor of the originality-rarity relationship. By doing so we assumed that dispersal mode was more related to the geographic extent of a species than to its function in an ecosystem or its response to environmental conditions.

To estimate EO thanks to the distinctDis function, the Monocot phylogeny we used was built by
extracting monocotyledon species from a larger supertree (Qian & Jin, 2016) which is an updated
version of a mega-phylogeny of plant species (Zanne et al., 2013). EO was estimated in million
years of evolution and FO has no units and ranges from 0 (lowest originality possible) to 1 (highest
originality possible). Due to differences in data availability, and in order to estimate originality on
the largest sets of species possible, evolutionary originality was estimated on a set of 6.682 species
and FO on a set of 2.281species.

Are species more original on continents or on islands?

We distributed species among three non-overlapping categories a) insular endemics, i.e. present only on islands; b) continental endemics, i.e. present only on continents; c) insular non-endemic species, i.e. present on both continents and islands. We then estimated the distribution and the average of originality scores among the three categories. We tested whether the average originality was significantly different between the three categories: we randomized species among categories 1000 times, estimated the new average originalities per category in the randomized set and compared them to the observed originalities in each category. A correction under phylogenetic constraint of the average originality score and its significance per category is provided in Appendix 2 but did not influence the results.

Relationship between evolutionary and functional originality

For each category of species, we performed correlation tests between functional and evolutionary
originality and measured the phylogenetic signal of each of the six traits individually (Kstar test).
To perform these particular analyses, we excluded species that did not have both phylogenetic and
functional information. As EO and FO scores are relative to the set of species used to calculate
originality, estimating their correlation required to re-calculate a second EO score from the similar
set used to estimate FO (i.e. 2.281 species).

Considering again the original set of species (i.e. 6.682 species for EO and 2.281 species for FO),
we then only focused on species present in islands (insular endemic and non-endemic species) and
drew maps of the number of top 5% original species, i.e. the 5% most original species, in islands
both for FO and EO. This included 110 non-endemic species and 40 insular endemics for EO and
38 insular non-endemic and 15 insular endemics for FO.
**Are insular original species geographically rare?**

Focusing on species present on islands, i.e. both insular endemics and non-endemics, we tested the
correlation (Pearson's test) between the originality score and geographic rarity, measured as the
number of islands each species occur on. The fewer islands a species occur on the rarer it is. We
also used linear regressions corrected for phylogenetic signal in model residuals (Revell, 2010),
when necessary. We then specifically tested whether the most original species occurred on fewer
islands than expected. We classified species with "top originality", "high originality", "moderate
originality", "low originality", "very low originality" (ranked in the 5%, 25%, 50%, 75% and >75%
of the originality score distribution, respectively) and calculated for each class the mean number of
islands each species occurred on. We randomized species among classes 1000 times and estimated
whether the observed mean number of islands species occurred on per originality class was lower
than in the randomized set (see also Appendix 2). Finally, we identified islands where species were
both among the top original species and were single endemics (found on a single island). We did
not use indices incorporating both originality and geographic rarity as they may sometimes be less
relevant for practitioners (Rosauer et al., 2009) and may be difficult to interpret at the functional
level.

Dispersal modes, biogeographic characteristic of islands and species originality

Coupling dispersal features of original insular species and the characteristics of the islands where
they occur may help to understand the distribution of original species and possibly their rarity (e.g.,
Veron, Haevermans, et al., 2019). First, we compiled information on insular species dispersal mode
from (Carvajal-Endara, Hendry, Emery, & Davies, 2017) as well as from the TRY, SID, FRUBASE,
BROT and LEDA databases. Dispersal modes corresponded to dispersal by animal, wind, water,
unassisted and others (mainly transportation by humans). Species dispersal strategy was then
estimated as either long-distance dispersal (animal, wind, water), short distance (unassisted) or
unknown (others). We calculated the mean species originality per dispersal mode and strategy and
assessed their significance by using null models based on the random distribution of dispersal modes among species. In the case of zoochory, we also explored the effect of the identity of the species dispersing seeds (bird [flying/non flying], mammal [flying/non flying], reptile [terrestrial-marine], insects) on originality (Appendix 3, in prep.). However, diaspore mass is a trait used in our measure of FO that may be related to dispersal strategy and their association thus needed to be tested. We found that long-distance dispersal was moderately related to heavy diaspore mass (Welch two-sided test p-val=0.07). Moreover, as “diaspore mass” did not prevail on other traits in the estimation of FO scores (Appendix 1), the relationship between FO and dispersal strategy may have been poorly influenced by the moderate association between “diaspore mass” and “dispersal strategy”.

Following our investigation on dispersal strategies of original species, we investigated how the possibility of an island to be/have been at reach may explain the distribution of original species. In particular, we focused on the biogeographic characteristics of islands that may be linked to past, present and future dispersal events (Table 1) although we acknowledge that some of these island features may also drive species diversification and therefore originality.

<table><caption>**Table 1:** Assumptions about how island features can relate to species geographic rarity</caption><thead><tr><th>Island characteristics</th><th>Assumption about the relation to dispersal potential</th><th>Reference</th></tr></thead><tbody><tr><td>Distance to the continent (km)</td><td>An original species having reached a remote island may be a good disperser</td><td>Global Island Database (GID; (UNEP-WCM, 2013))</td></tr><tr><td>Proportion of surrounding landmass</td><td>Colonization events may be less frequent on/from isolated islands</td><td>(P. Weigelt, Jetz, & Kreft, 2013)</td></tr><tr><td>Elevation (m)</td><td>Elevation may provide a climatic refuge for ancient lineages which may consequently rarely disperse</td><td>(P. Weigelt et al., 2013)</td></tr><tr><td>Area (m²)</td><td>Successful colonization events may be more probable towards/from large islands</td><td>GID (Depraetere & Dall, 2007)</td></tr></tbody></table>
<table><tr><td>Oceanic or Continental</td><td>Species present on oceanic islands may occur following colonization and are likely to be relatively good dispersers</td><td>(P. Weigelt et al., 2013)</td></tr><tr><td>Glaciated or non-Glaciated</td><td>The presence of ancient species on a glaciated island may be due to colonization and tendency to dispersal</td><td>Literature; pers. comm.</td></tr><tr><td>Age (million years)</td><td>An ancient species occurring on a recent island may reflect colonization, and the probability of the species to be a good disperser may be higher</td><td>Literature; pers. comm.</td></tr><tr><td>Latitude (decimal degrees)</td><td>Variable used to highlight a possible spatial effect</td><td>GID (UNEP-WCM, 2013)</td></tr><tr><td>Longitude (decimal degrees)</td><td>Variable used to highlight a possible spatial effect</td><td>GID (UNEP-WCM, 2013)</td></tr></table>

Our aim here was to assess the characteristics of the islands where original species occur, potentially revealing the tendency for species to disperse or not (Table 1). To do so, we estimated for each species the average value of each of the biogeographical features of the islands in which it occurred. Each species was therefore associated to 9 values representing these average island features (Table 1). We then performed generalized linear models and multi-model selection with species originality as the response variable and island average features as the explanatory variables (see also Appendix 2 for models performed under phylogenetic constraint). We identified the strongest interactions thanks to Boosted Regression Trees, which were added in the multi-model selection process. To complement these analyses, we used the originality classification previously described (top, high, moderate, low and very low originality) and estimated which island features had a significant effect in each category of originality (Appendix 4, in prep.).

# RESULTS

## Functional and evolutionary originality are decoupled

The diaspore mass of insular species excepted, none plant functional trait exhibited a phylogenetic
signal (Table 2). Functional and evolutionary originality were weakly correlated, although the correlation coefficient was higher in species occurring on islands (Pearson's correlation test: cor=0.16, 0.13, 0.018 and 0.093 in insular endemics, insular non-endemics, continental endemics and all monocot species, respectively). This result was robust to our sensitivity tests (Appendix 1).

<table><caption>**Table 2:** Phylogenetic signal (Kstar) in 6 ecological plant traits. Black values mean that there is no phylogenetic signal and bold red value indicate a phylogenetic signal.</caption><thead><tr><th></th><th>Insular endemics</th><th>Insular non-endemics</th><th>Continental endemics</th><th>All monocots</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center; font-weight: bold;">Phylogenetic signal (Kstar)</td></tr><tr><td>Leaf area</td><td>0.05***</td><td>0.028***</td><td>0.02**</td><td>0.019***</td></tr><tr><td>Nmass</td><td>0.002</td><td>0.002</td><td>0.009***</td><td>0.002</td></tr><tr><td>LMA</td><td>0.003</td><td>0.006**</td><td>0.008**</td><td>0.006***</td></tr><tr><td>Plant height</td><td>0.06***</td><td>0.04***</td><td>0.05***</td><td>0.04***</td></tr><tr><td>Diaspore mass</td><td>0.59***</td><td>0.03**</td><td>0.003</td><td>0.003</td></tr><tr><td>SSD.combined</td><td>0.002</td><td>0.001</td><td>0.009***</td><td>0.002</td></tr></tbody></table>

**Species endemic to islands are more original**

Evolutionary originality was estimated on a set of 6.682 species and FO on a set of 2.281species. Islands with the highest number of evolutionary original endemic monocotyledons were Borneo (being home of 10 top 5% original species), the Philippines (9 species), Sumatra (7 species) and Japan (6 species; Figure 1). The maximum concentration of species with top 5% functional original endemic species in a given island was 4 (Philippines), while several other islands were home of 3 top 5% functionally original endemic species (New Guinea, New Zealand, Norfolk Island). Thirteen islands were home of both top 5% functionally original endemic species and top 5% evolutionary original endemic species.

In non-endemic species, the highest numbers of top 5% evolutionary original species were found
in Taiwan (22 species), Hainan (20 species), Japan (17 species), Borneo and Java (16 species).

Regarding FO, Cuba was home of 11 top 5% original species, Java and Dominican republic of 9 species while Hainan, the Bahamas, and Trinidad and Tobago harbored 8 top 5% functionally original species. There were 63 islands where both top 5% evolutionary and functionally non-endemic original species occurred.

By estimating the average originality in function of the endemic or non-endemic status of species we found that mean EO and FO was equal to 236.7 and 0.061 for species endemic to islands, 234.9 and 0.043 for non-endemic insular species and 224.5 and 0.047 for continental species only (Figure 2). Average EO was higher than expected for species endemic to islands and those found only in continents. Using phylogenetic analysis of variance models, EO was the highest for insular endemic species (Appendix 2). Average FO was significantly high for insular species whether they were endemic or not.

**Evolutionary but not functionally original species are unusually rare**

The top 5% original endemic species regarding EO and FO were present on, on average, 1.7 and 3.4 islands, respectively. The original non-endemic species occurred on 5.0 to 5.4 islands, on average. Geographic range had a significant negative effect on originality for EO of insular non-endemic species (Pearson's test: cor = -0.15 p-value = 4.794e-11) and a significant positive effect on FO of insular endemic species (cor = 0.43 p-value = 3.794e-7). In other cases, no significant effect was found (EO endemics: cor = -0.043 p-value = 0.26; FO non-endemics: cor = -0.028 p-value = 0.28).

Regarding the classification of species in function of their originality score, the top 5% EO endemic species on fewer islands than expected by chance while top FO species occurred on more islands than expected (Table 3, Appendix 2). For insular non-endemic species, high EO species also occurred on fewer islands than expected by chance (Table 3, Appendix 2). Conversely, species with low or very low EO occurred on more islands than expected by chance. As for non-endemic FO, those with moderate FO occurred on more islands than expected.
<table><caption>**Table 5:** Average geographic range of species, i.e. the number of islands it occurs on, and whether it is lower or larger than expected by chance per category of originality (one-tailed tests). Red color means that species occurs on fewer islands than expected by chance (p-value<0.05). Blue color means that species occurs on more islands than expected by chance (p-value<0.05).</caption><thead><tr><th></th><th>Distribution</th><th>Top original</th><th>Very original</th><th>Moderate</th><th>Low</th><th>Very Low</th></tr></thead><tbody><tr><td rowspan="2">Evolutionary Originality</td><td>Insular endemics</td><td>Range=1.7</td><td>Range=1.7</td><td>Range=2.1</td><td>Range=2.0</td><td>Range=1.8</td></tr><tr><td>Insular non-endemics</td><td>Range=5.0</td><td>Range=3.7</td><td>Range=5.9</td><td>Range=7.4</td><td>Range=6.4</td></tr><tr><td rowspan="2">Functional Originality</td><td>Insular endemics</td><td>Range=3.2</td><td>Range=2.4</td><td>Range=1.7</td><td>Range=1.8</td><td>Range=1.8</td></tr><tr><td>Insular non-endemics</td><td>Range=5.4</td><td>Range=6.4</td><td>Range=7.2</td><td>Range=6.1</td><td>Range=6.0</td></tr></tbody></table>

31 species were both endemic to a single island and among the top 5% most evolutionary original species. Among the areas which concentrated the highest number of these rare and original species, Borneo harbor 7 of them, 5 were found in the Philippines, Japan and Sumatra, and 3 in Madagascar (Figure 3). 6 species were single endemics and top 5% functionally original. They occurred on Madagascar (2 species), Lord Howe island (2 species), Bermuda (1 species) and the Canaries (1 species). Regarding non-endemic species 27 were top evolutionary original species and found on a single island. They were mainly concentrated on Equatorial Guinea islands (7 species), Japan (5 species), Cuba (4 species), Hainan (3 species) and Ceylan (2 species). 11 non-endemic species were top functionally original and had a single insular location, in particular Trinidad (3 species), Bali, Tasmania, Sicily, Japan, Equatorial Guinea, Ceylan, Corea, Taiwan (1 species).

## Dispersal abilities of original species

On average, species that dispersed on small distances were more evolutionary original than species dispersing on long-distances (Figure 4a). Endemic evolutionary original species were mainly transported by wind, animals or their mode of dispersal was unassisted. Non-endemic evolutionary original species had mainly an “unassisted” mode of dispersal. Regarding FO, species dispersing on long distances were more functionally original, especially zoochor and anemochor species
(Figure 4c and d).

## Possibility of islands to be at reach and distribution of originality

One common point to functionally and evolutionary original species is that they were located at low latitudes, indicating a spatial effect on originality (Table 4). The magnitude of the longitude effect was generally higher for FO than for EO. We observed a high number of differences between the effects of island features on FO and EO. Whatever they were endemic to islands or not, evolutionary original species tended to be found on islands that were continental, non-glaciated, flat and close to the mainland. In addition, endemic evolutionary original species occurred mainly on old islands while non-endemic evolutionary original species were found on more recent islands. The effect of the proportion of surrounding landmass was not significant on the evolutionary originality of non-endemic species and was negative in the case of endemic species but with a low significance (Table 4a). Area had little effect on evolutionary original species whether they were endemic or non-endemic to islands. Regarding FO we found important differences between endemic and non-endemic species. Endemic functionally original species tended to occur on islands that were oceanic, glaciated, small, relatively elevated, close to other landmasses but far from the continent (Table 4b). On the opposite, non-endemic functionally original species were present on islands of continental origin, close to the mainland, and which were relatively young. (Table 4b).

<caption>**Table 4:** Effects and importance of island features on Evolutionary and Functional Originality estimated from multi-model selection.</caption>

a)

<table><thead><tr><th rowspan="3">Evolutionary<br>Originality</th><th colspan="3">Species restricted to islands</th><th colspan="3">Species found in both islands<br>and continents</th></tr><tr><th>Estimate</th><th>Pr(&gt;z)</th><th>Importance</th><th>Estimate</th><th>Pr(&gt;z)</th><th>Importance</th></tr></thead><tbody><tr><td>SLMP</td><td>-4.2</td><td>.</td><td>0.62</td><td>-0.34</td><td></td><td>0.29</td></tr><tr><td>Distance</td><td>-2.6</td><td>*</td><td>0.89</td><td>-2.21</td><td>***</td><td>1</td></tr></tbody></table>
<table><tr><td>Area</td><td>0.62</td><td>0.28</td><td>-0.45</td><td>0.32</td></tr><tr><td>Elevation</td><td>-6.3</td><td>***</td><td>1</td><td>-0.31</td><td>0.29</td></tr><tr><td>Age</td><td>5.1</td><td>**</td><td>0.98</td><td>-1.5</td><td>*</td><td>0.88</td></tr><tr><td>GMMC</td><td>7.07</td><td>***</td><td>1</td><td>4.9</td><td>***</td><td>1</td></tr><tr><td>Glaciated</td><td>-5.9</td><td>***</td><td>1</td><td>-3.5</td><td>***</td><td>1</td></tr><tr><td>Latitude</td><td>-6.79</td><td>***</td><td>1</td><td>-2.6</td><td>**</td><td>1</td></tr><tr><td>Longitude</td><td>-4.25</td><td>**</td><td>0.79</td><td>0.58</td><td></td><td>0.35</td></tr><tr><td>Latitude:SLMP</td><td>7.21</td><td>***</td><td>1</td><td></td><td></td><td></td></tr><tr><td>Latitude:Age</td><td>1.53</td><td></td><td>0.29</td><td></td><td></td><td></td></tr><tr><td>Distance:Elevation</td><td></td><td></td><td></td><td>1.5</td><td>.</td><td>0.66</td></tr><tr><td>Glaciated:Distance</td><td></td><td></td><td></td><td>0.21</td><td></td><td>0.24</td></tr></table>

b)

<table><thead><tr><th rowspan="3">Originality</th><th colspan="3">Species restricted to islands</th><th colspan="3">Species found in both islands and continents</th></tr><tr><th>Estimate</th><th>Pr(&gt;z)</th><th>Importance</th><th>Estimate</th><th>Pr(&gt;z)</th><th>Importance</th></tr></thead><tbody><tr><td>SLMP</td><td>0.33</td><td>***</td><td>1</td><td>-0.087</td><td>***</td><td>1</td></tr><tr><td>Near_dist</td><td>0.049</td><td>**</td><td>0.98</td><td>-0.058</td><td>***</td><td>1</td></tr><tr><td>Area</td><td>-0.083</td><td>***</td><td>1</td><td>-0.005</td><td></td><td>0.39</td></tr><tr><td>Elevation</td><td>0.029</td><td></td><td>0.42</td><td>-0.027</td><td>***</td><td>1</td></tr><tr><td>Age</td><td>NA</td><td></td><td></td><td>-0.033</td><td>***</td><td>1</td></tr><tr><td>GMMC</td><td>-0.22</td><td>***</td><td>1</td><td>0.013</td><td>*</td><td>0.85</td></tr><tr><td>Glaciated</td><td>0.081</td><td>***</td><td>1</td><td>-0.04</td><td>***</td><td>1</td></tr><tr><td>Latitude</td><td>-0.26</td><td>***</td><td>1</td><td>-0.005</td><td></td><td>0.27</td></tr><tr><td>Longitude</td><td>0.084</td><td>***</td><td>1</td><td>-0.09</td><td>***</td><td>1</td></tr><tr><td>Latitude:Distance</td><td>0.01</td><td></td><td>0.25</td><td></td><td></td><td></td></tr></tbody></table>
<table><tr><td>SLMP:Latitude</td><td>-0.14</td><td>***</td><td>1</td><td></td></tr><tr><td>SLMP:Longitude</td><td></td><td></td><td>0.001</td><td>0.25</td></tr><tr><td>SLMP:Elev</td><td></td><td></td><td>0.029</td><td>***</td><td>1</td></tr></table>

# DISCUSSION

## Decoupling of functional and evolutionary originality

In this study we drew a first picture of species originality on islands. Original species have a strong contribution to the uniqueness of island biota, to divergence with the mainland and to centers of endemism (Veron, Haevermans, et al., 2019; Veron, Mouchet, et al., 2019). Among the islands where concentrate both functionally and evolutionary original species are Indonesian islands, the Philippines, Taiwan, Hainan, New-Guinea, Japan, Sumatra, Madagascar. However, some islands harbor only EO species and others only FO species. Importantly, the sets of the most functionally and evolutionary original species were not similar and functional traits of plants representing their ecological strategies had weak phylogenetic signals. There are many possible reasons why FO and EO are not congruent (e.g., Cornwell et al., 2014; Véron, Saito, Padilla-García, Forest, & Bertheau, 2019). On islands, species representative of adaptive radiation, and consequently being closely related and having very close evolutionary originality values, may have very distinct traits in order to fill the niche space let vacant by the low species richness or/and to avoid competition. On the opposite, the process of relictualization, i.e. extinctions of close-relatives on the continent, may have artificially isolated species in the tree-of-life, among which some have diversified relatively recently and share several traits with current species (Grandcolas et al., 2014; R. J. Whittaker & Fernández-Palacios, 2007). Another reason for this decoupling is convergent evolution which has been reported in multiple lineages on multiple islands (Robert J. Whittaker et al., 2017). This is the process by which evolutionary unrelated organisms show similar features as a result of natural selection and adaptation under similar environmental constraints (Mazel et al., 2017). Although
eco-evolutionary studies would be needed to better understand the evolution of plant species traits
in islands, this provided a first evidence that ecological strategies of plants are not conserved in the
phylogeny (see also Cornwell et al., 2014).

Higher originality on islands than on continents

A second important finding was that island endemic species were on average more functionally
and evolutionary original than these occurring on continents. A possible explanation is the isolated
nature of islands which allows evolution to take its own course giving birth to species evolutionary
isolated from all others and/or that harbor unique forms and functions. Higher evolutionary
originality on islands compared to the mainland was however unexpected. Indeed, the initial insular
pool is generally a set of the continental pool, therefore one could expect that insular species would
not be more ancient and evolutionary isolated than continental ones. Moreover, high speciation
rates in islands may result in the diversification of many closely-related species which is expected
to decrease the average originality of insular pools. Past history in continents may be one
explanation of our contradictory finding of higher evolutionary originality on islands than on
continents. One main process could be continental extinction: species loss on continents may have
pruned the tree of life so that several insular species lost some close-relatives and consequently
became evolutionary original and sometimes endemic (Gillespie & Roderick, 2002). High climatic
fluctuations and extreme events in the mainland concomitant with a relative stable climate in islands
drove extinctions on the continent and persistence on islands, especially in plants (Cronk, 1997; R.
J. Whittaker & Fernández-Palacios, 2007 but see Jetz et al., 2014). Climate change in continents
may not only have conducted to extinctions but also to diversification resulting from natural
selection of the most adapted forms. The result is similar as for extinctions: the evolutionary
isolation of insular forms. Besides, not only species loss on the continent but the very high number
of past extinctions on islands may have conducted to the evolutionary isolation of insular forms
(Courchamp, Hoffmann, Russell, Leclerc, & Bellard, 2014; Robert J. Whittaker et al., 2017). This
process of isolation depends on the phylogenetic signature of extinctions in insular lineages, which
remains to be explored, but our result suggest that they have been clustered in species-poor clades
which originated in evolutionary original species. A phylogenetic tree incorporating extinct species
coupled to the identification of their past distribution may help to disentangle the roles of
extinctions and ancient diversification on insular originality.

As for functional originality we also showed that it was higher on islands than on the mainland.
Two main mechanisms may have resulted in a higher functional originality on islands: niche
expansion and niche shift. Niche expansion relates to low species richness and high disharmony in
islands which resulted in a diversity of vacant niches to be colonized by functionally diverse species
(R. J. Whittaker & Fernández-Palacios, 2007). For example, (Diamond, 1970) reported that pacific
birds could occupy higher altitudinal ranges, a higher diversity of habitats and a higher diversity of
vertical foraging ranges compared to their continental counterparts after they colonized species-
poor islands in the pacific. Niche shift is the changing biological and ecological characteristics of
insular species compared to the mainland (MacArthur & Wilson, 1967; Simberloff, 1974). The
persistence of insular species and especially plants may have been conditioned to adaptations to
the oceanic climate of islands resulting in the evolution of distinct ecological strategies.

Range-restriction of original species

The few studies exploring the relationship between evolutionary originality and geographic rarity
showed that original species were not more range-restricted than others (Jetz et al., 2014; Thuiller
et al., 2015; Stein et al., 2018). Here, although correlation between rarity and EO was generally low,
we found that the most evolutionary original species were rare. Some mechanisms generating
originality in insular systems also generate endemism, for example adaptation to particular
environmental conditions and the function of refuge of many islands. As stated above, island biota
has also suffered a number of extinctions and it is likely that human-induced extinctions on islands
have both artificially isolated insular species in the Tree of Life and turned multi-endemic species
into single endemics (R. J. Whittaker & Fernández-Palacios, 2007). Extinctions may therefore be
both responsible for higher originality and higher range-restriction on islands than on continents
but, as we will discuss further, it is not the only factor explaining these patterns.

On average, functionally original species were found only on a small number of islands, which seems to be in accordance with studies showing that rare functions are usually supported by rare species (Kunin & Gaston, 1993; Violle et al., 2017; Grenié et al., 2018). Yet, using null models we found that range-restriction of functionally original species was not different from random distribution of species across islands, i.e. functionally original monocot species were not rarer than less original ones. This contradicts previous findings that “species that have a low functional redundancy (...) are rarer than expected by chance” (David Mouillot, Bellwood, et al., 2013; see also Kunin & Gaston, 1993). Violle et al. (2017) recently defined a multifaceted framework of “functional rarity”, linking species’ functional originality (called distinctiveness or uniqueness) and rarity. In their framework 12 forms of functional rarity are possible at two spatial scales (local and regional). The less frequent case of few common species with original traits is possible and goes with our findings. A possible explanation would be that functionally original species have a low level of competition with other species so that they can more easily settle and occupy a vacant niche when they disperse towards it.

## Dispersal capacities of evolutionary and functionally original species

There are many examples of islands where species distribution is due to dispersal power (R. J. Whittaker & Fernández-Palacios, 2007; Borda-de-Água et al., 2017). For example, the Krakatau island has been colonized by ferns whose spores can be transported over long-distances whereas other lineages are absent due to their lack of “ocean-going” dispersal mechanisms (R. J. Whittaker, Jones, & Partomihardjo, 1997). The modes of dispersal of original species and their association with short and long-distance dispersal strongly support our results on the spatial distribution of these species. Highly evolutionary original species are transported predominantly on short distances and their mode of dispersal is generally “unassisted”, which prevents ocean crossing. More strikingly, the strongest the association between originality and the “unassisted” mode of dispersal, the higher is the correlation between originality and rarity. A possibility explaining the
"unassisted" mode of dispersal of original species may be related to the loss of dispersal of plants occurring on islands due to a reduced herbivory pressure and lower competition. Yet, this loss of dispersal syndrome is generally tied to species having recently diversified (Robert J. Whittaker et al., 2017). Consequently, two assumptions that can be made but need to be tested are, first, that evolutionary original species have low dispersal abilities because they have diversified in recent times but became isolated through relictualization, and, second, that low dispersal ability is a relictual trait rather than a derived one in original species.

As for functionally original species, they were more widespread than non-original ones and this also goes with our findings about their mode of dispersal. Indeed, only few of them had an "unassisted" mode of dispersal. Rather they were transported through zoochory (especially birds) and anemochory and may have been able to colonize several islands isolated from each other. Additional important factors of dispersal include the direction, seasonality, strength of dispersal agents (Gillespie et al., 2012). Yet, our results show that the mode of dispersal and the distance on which seeds can be transported are key determinants of the distribution of original species on islands.

The dispersal strategies of original species can also be seen from the features of the islands where they are found. The most evolutionary original species are often single-endemics and rarely occur on islands where recent and/or long-distance dispersal would be needed to establish: they are uncommon on remote, glaciated, young and oceanic islands. Evolutionary original species also occur on islands surrounded by a relatively low proportion of landmass, decreasing the possibilities of dispersal. On the contrary, functionally original endemic species, which are more widespread, occurred on islands with opposite features and that may only be colonized by species having high dispersal abilities. Together, the dispersal abilities of original species and the characteristics of the islands where they are present are important factors explaining the rarity of plants on islands.

**Conservation**

Originality may have an intrinsic value and its conservation is essential to preserve the breadth of
functions necessary against future uncertainty and to maintain the reservoir of future benefits to people (Faith, 1992); (Violle et al., 2017). If original species go extinct first - and previous considerations shows that this is a very likely assumption - some irreplaceable traits and evolutionary branches would be lost, strongly affecting ecosystem processes and services. Preserving EO species is essential due to their high contribution to “option-values” for humanity and the conservation of FO species is needed to maintain ecosystem functions and processes. This is all the more true on islands as, at least for monocots, the world most original species are insular. Preserving species evolutionary history has sometimes been assumed to be a proxy for the conservation of species traits. However, several reasons such as convergent evolution, the consideration of a low number of traits, a fast pace of diversification, sexual selection etc. may blur this correlation (Gerhold, Cahill, Winter, Bartish, & Prinzing, 2015; Faith, 2018; Véron, Saito, Padilla-García, Forest, & Bertheau, 2019). In our study we emphasized a lack of phylogenetic signal showing that the originality of plant ecological strategies may not be reflected by their evolutionary relationships. Therefore, a ‘silver-bullet” strategy aiming at the protection of both species functions and evolutionary history may be inadequate. However, in spite of being different, EO and FO species tend to co-occur on many similar islands which give the opportunity for combined conservation strategies that may protect both functional and evolutionary originality. Further research could then look at the overlap between original species range on a given island which may help to prioritize areas for conservation.

A highly important consideration for conservation is that, among endemic species, the most evolutionary original ones are also the rarest. This was also true for non-endemic species but to a lesser degree. Rare and original species may cumulate a diversity of threats that make them at high risks of extinctions (Jono & Pavoine, 2012). First, rarity may be a signal of higher extinction probabilities (e.g., Harnik, Simpson, & Payne, 2012), in particular for insular endemic species for which none population may survive on the mainland. Second, species occurring on islands are among the most vulnerable on Earth due to the combined effects of human-induced threat, species
naiveté and low range-size. Third, rare and original insular species may also be highly vulnerable to external threats such as climate or land-use changes because they have low dispersal abilities and no land at proximity to escape (Veron, Mouchet, Govaerts, Haevermans, & Pellens, 2019). Finally, evolutionary original species may be at higher extinction risks due to increased extinction risks with age (Warren et al., 2018). The most functionally original species were not rarer (geographic rarity) than other species, but they were distributed in a low number of islands. In addition, the rarest functionally and evolutionary original species generally did not occur on the same islands and regional or local conservation plans may not allow to protect rare species which are both distinct in their traits and evolutionary history. Understanding the diversity of current threats to original species and how they will respond to these pressures is key to assess their extinction status and to propose actions directed toward the preservation of these unique species (Forest et al., 2018), (Stein et al., 2018).

Finally, as a bridge between the conservation of biodiversity and human cultural heritage, and to conclude with originality, islands are not only places where the most original and threatened species occur but they also concentrate the most original and threatened human languages (Perrault, Farrell, & Davies, 2017).
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<figure><img src="image_36.png" /><figcaption>**Figure 1:** Number of top original species per island for a) Evolutionary Originality (EO) of endemic insular species, b) EO of non-endemic insular species, c) Functional Originality (FO) of endemic insular species, d) FO of non-endemic insular species.</figcaption></figure>
<figure><img src="image_37.png" /><figcaption>b) Functional originality</figcaption></figure>

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

**Figure 2:** Species originality depending on its geographical origin. *** significance of a p-value < 0.001
<figure><img src="image_39.png" /><figcaption>**Figure 4:** Number of top 5% original species found on a single island for a) Evolutionary Originality (EO) of endemic insular species, b) EO of non-endemic insular species, c) Functional Originality (FO) of endemic insular species, d) FO of non-endemic insular species.</figcaption></figure>
<figure><img src="image_40.png" /><figcaption>**Figure 4:** Originality scores depending on the dispersal mode and type. Significance level of p-value: *** <0.001; ** <0.05; * <0.1</figcaption></figure>
# Appendix 1: Sensitivity analysis

## a) Missing trait values

i) *Correlation between functional originality scores calculated when missing values were imputed or not*

<table><caption>**Table 1.** Proportion of missing values per trait</caption><thead><tr><td>Trait</td><td>Proportion of missing values</td></tr></thead><tbody><tr><td>Adult plant height</td><td>46%</td></tr><tr><td>Leaf size</td><td>68%</td></tr><tr><td>Stem specific density</td><td>81%</td></tr><tr><td>Leaf mass per area</td><td>68%</td></tr><tr><td>Leaf nitrogen content</td><td>75%</td></tr><tr><td>Diaspore mass</td><td>23%</td></tr></tbody></table>

Missing values were imputed by performing random forest algorithm species (missForest package, Stekhoven and Buehlmann, 2012) a thousand times, and the mean of the 1000 imputations was estimated. To show how this imputation method may affect our originality results we tested the correlation between functional originality scores computed with trait data when missing values were considered as « Non-Attributed » and functional originality scores computed when missing values were imputed. We found that the correlation was positive but moderate (Spearman correlation coefficient r = 0.65, p-value < 0.001). This result was expected, because random forest method imputes missing values for a species according to the non-missing values of other traits.

ii) *Correlation between the number of missing values and functional originality score*

Second, we tested the correlation between the number of species' missing trait data and its functional originality score. We found that the number of species' missing trait values had a poor
influence on its functional originality score (Spearman's correlation coefficient r = -0.14, p-value < 0.001). This contradicts the expectancy that random forest imputations would smooth trait values and therefore conduct to an under-estimation of the functional originality scores of species with many missing trait values.

iii) *Correlation between functional originality scores calculated with a complete trait data and when missing trait values were created artificially*

Third, we artificially created missing values for species with complete data for all traits (259 species). We then imputed these artificially missing values with a random forest method. We then calculated functional originality scores on the complete set of species and estimated their correlation with the functional originality scores coming from artificially created trait data. First, all values of a given trait were considered as missing. Second, we randomly created 2/3 of missing values among all traits and repeated the randomization a hundred times. Results showed high correlations between functional originality scores computed on a complete dataset and obtained after artificial deletion and imputation of trait values (Table 2).

<table><caption>**Table 2.** Correlation between observed functional originality scores of species with no missing values and functional originality scores obtained after artificially creating and imputing missing values in a particular trait or in all traits at random.</caption><thead><tr><th></th><th>Leaf area</th><th>Leaf Nitrogen content</th><th>Leaf Mass per area</th><th>Plant height</th><th>Diaspore mass</th><th>Stem specific density</th><th>2/3 of missing values randomly distributed</th></tr></thead><tbody><tr><td>Pearson coefficient</td><td>0.999</td><td>0.907</td><td>0.993</td><td>0.941</td><td>0.999</td><td>0.879</td><td>0.41</td></tr></tbody></table>

iv) *Relationship between functional and evolutionary originality scores in the set of species having no missing values*

We calculated functional and evolutionary originality scores on the subset of 259 species for which all trait values were available. We found that the correlation estimated on this particular species subset was low (Spearman's correlation coefficients r = 0.29, p-values < 0.001).
We finally estimated the phylogenetic signal of each trait from complete trait data subset using a Kstar method (Blomberg et al., 2003) in adv package (Pavoine 2019). At the exception of the trait “Diaspore mass” none trait displayed a phylogenetic signal.

<table><caption>**Table 3.** Phylogenetic signal of each trait under study calculated with Kstar method.</caption><thead><tr><th></th><th>Leaf area</th><th>Leaf Nitrogen content</th><th>Leaf Mass per area</th><th>Plant height</th><th>Diaspore mass</th><th>Stem specific density</th></tr></thead><tbody><tr><th>Kstar</th><td>0.093<br>p=0.001</td><td>0.0018<br>p=0.743</td><td>0.003<br>p=0.605</td><td>0.112<br>p=0.006</td><td>2.05<br>p=0.001</td><td>0.0036<br>p=0.396</td></tr></tbody></table>

b) Correlations between traits

<table><caption>**Table 4.** Pearson's correlation coefficient between trait values</caption><thead><tr><th></th><th>Leaf Area</th><th>N mass</th><th>LMA</th><th>Plant.Height</th><th>Diaspore.Mass (mg)</th><th>SSD combined</th></tr></thead><tbody><tr><th>Leaf AREA</th><td>1</td><td>0.034</td><td>0.49</td><td>0.44</td><td>0.09</td><td>0.22</td></tr><tr><th>Nmass</th><td>0.034</td><td>1</td><td>0.47</td><td>0.039</td><td>0.001</td><td>0.65</td></tr><tr><th>LMA</th><td>0.49</td><td>0.47</td><td>1</td><td>0.24</td><td>0.081</td><td>0.43</td></tr><tr><th>Plant.Height</th><td>0.44</td><td>0.039</td><td>0.24</td><td>1</td><td>0.33</td><td>0.51</td></tr><tr><th>Diaspore.Mass (mg)</th><td>0.09</td><td>0.0013</td><td>0.08</td><td>0.33</td><td>1</td><td>0.16</td></tr><tr><th>SSD.combined</th><td>0.22</td><td>0.65</td><td>0.43</td><td>0.51</td><td>0.16</td><td>1</td></tr></tbody></table>

c) Contribution of each trait to the functional originality score

To test the influence of each trait on species originality we performed a sensitivity analysis by excluding each trait separately and recalculating functional originality with the five remaining traits. Pearson's correlation coefficient between functional originality calculated on 6 traits and calculated on 5 traits ranged from 0.91 to 0.99. We concluded that none of traits prevailed over others in the calculation of species functional originality.

<caption>**Table 5:** Correlations between the functional originality score calculated with all traits and obtained when a given trait was excluded. *** significance level for p-value < 0.001.</caption>