# Measuring inter-specific diversity. Benefits of species originality indices in community ecology and conservation biology

Anna Kondratyeva

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Anna Kondratyeva. Measuring inter-specific diversity. Benefits of species originality indices in community ecology and conservation biology. Biodiversity and Ecology. Muséum National d'Histoire Naturelle Paris, 2019. English. NNT: tel-04066501

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MUSÉUM NATIONAL D'HISTOIRE NATURELLE

École Doctorale 227

Sciences de la Nature et de l'Homme

Année 2019

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N° attribué par la bibliothèque

|_|-|-|-|-|-|-|-|-|-|

THÈSE

pour obtenir le grade de

DOCTEURE DU MUSÉUM NATIONAL D'HISTOIRE NATURELLE

Spécialité : Ecologie

Présentée et soutenue publiquement par
Anna KONDRATYEVA

Le 11 décembre 2019

Measuring inter-specific diversity.
Benefits of species originality indices in community ecology and
conservation biology

Sous la direction de : Sandrine Pavoine et Philippe Grandcolas

Devant le jury :

Mme Sandrine Pavoine

Maitre de conférences au MNHN

Directrice de Thèse

Mr Philippe Grandcolas

Directeur de recherche au CNRS

Directeur de Thèse

Mme Sophie Nadot

Professeure à l'Université Paris-Sud

Rapportrice

Mr Cyrille Violle

Directeur de recherche au CNRS

Rapporteur

Mme Hélène Morlon

Directrice de recherche au CNRS

Examinatrice

Mr Hervé Daniel

Maitre de conférences à l'Agrocampus Ouest

Examinateur
Se lancer dans une thèse de doctorat est une grande décision mais quand c'est fait c'est comme une
porte qui s'ouvre sur un grand monde de la recherche scientifique qui grouille de personnes
curieuses, passionnées par leur travail. Je voudrais garder des liens avec ce monde pour pouvoir
transmettre ses savoirs auprès des plus jeunes, dans les écoles en tant qu'intervenante ou
professeure – on verra...

Je voudrais remercier toutes les personnes qui ont joué un rôle plus au moins important
pour moi pendant ces trois années. Cette aventure ne serait pas possible sans ma directrice et mon
directeur de thèse. **Sandrine**, merci de m'avoir donnée cette chance de m'essayer dans la recherche.
Je te suis reconnaissante d'avoir fait confiance et de m'avoir accompagnée depuis le début, merci
pour tes encouragements, ton calme, ta patience et ta sagesse. **Philippe**, ta justesse des mots et
l'incroyable vision globale des choses m'épateront toujours, j'ai appris beacoup à tes cotés.

J'adresse de chaleureux remerciements aux membres de mon jury – **Sophie Nadot**, **Cyrille Violle**, **Hélène Morlon** et **Hervé Daniel** – pour avoir accepté d'évaluer ce travail.
Je remercie également le **Labex BCDiv** pour le financement de ce projet de thèse.

**Anne-Cécile Haussonne** je te remercie de m'avoir aidé dans toutes ces démarches administratives à se casser la tête, sans toi je serai encore en train d'attendre un ordinateur et serais jamais partie aussi simplement en congrès.

Merci à l'Ecole Doctorale 227 et tout particulièrement à **Nathalie Machon** et **Anaïs Ranera** (même si tu as changé de poste) pour tout ce que vous faites pour la vie des doctorants au Muséum.

Je voudrais remercier vivement l'équipe allemande de Helmholtz Centre for Environmental Research – UFZ, Halle (Saale) pour m'y avoir accueilli pendant deux mois et pour m'avoir aidé avec les données et les analyses. Dankeschön à **Walter Durka** qui connait les espèces végétales sur les bouts des doits, à **Ingolf Kühn** pour les précisions sur les analyses spatiales et à **Sonja Knapp** pour les idées qui ont aidé à structurer notre travail en commun.

Un travail efficace n'est pas imaginable pour moi sans un environnement agréable. Je voudrais donc remercier tout.e.s mes collègues auprès qui j'ai eu plaisir de passer ces trois années. Tout d'abord merci au personnes du bureau coquelicot: **Typhaine R.** pour ton inépuisable bonne humeur, même quand les modèles ne tournent plus et il faut faire une pause smarties ; **Gabrielle M.** prête à aider à tout moment, tu me manquais à la fin pour tenir le club de travaille-tard ; **Alain D.**, pour ton grand sourire réconfortant et ta sérénité d'être; **Margaux A.** pour des conseils déteinte ; **Fabien C.** pour ton humour qui manque maintenant dans le bureau, **Kévin B.** le grand
sportif toujours discret, mais aussi **Olivier B.**, **Mona O.**, **Ana-Cristina T.**, **Tanguy L-L.**, **Laura T.** (merci pour le voyage à Nancy, c'était presque ma seule session de terrain), **Eduardo**, le dernier arrivé qui commence tout juste, **Nélida P.**, qui es partie trop vite, (j'étais trop contente de se retrouver à Rennes !).

En dehors du bureau coquelicot,
**Minh-Xuan T.**, toujours soucieux de bien-être de ces collègues, merci pour tes conseils et ta bienveillance, **Jean-François J.** merci pour ton inépuisable stock de connaissances naturalistes, **François D.** merci pour des conseils en stat et des blagues osées;
Un mot tout particulier à **Adrienne**. Merci d'avoir jamais perdu l'énergie et l'envie de m'écouter, merci pour ces échanges rassurants mais aussi pour des astuces dans le R;

Même si on était séparés entre deux bâtiments, je passais souvent vous faire un coucou et je voudrais particulièrement remercier **Diane** pour ne jamais se prendre la tête, le bureau des **barbus** qui a disparu au compte de goutte et merci **Théo** (le dernier survivant du bureau) d'y m'avoir accueilli à la fin; **Olivier B.**, on t'a perdu de vue ces derniers temps, mais merci pour les pauses discussion; **Grégoire L.**, tu m'étonnerais toujours avec tes bêtises et tes histoires incoyables. Il est compliqué de citer encore toutes ces personnes que j'ai côtoyées pendant ces trois années au sein de ce laboratoire et qui ont eu d'importance à un moment ou à un autre grâce à leurs conseils, connaissances et tout simplement leur bienveillance. Une pensée pour **Jean-Baptiste M.**, **Colin F.**, **Emmanuelle P.**, **Simon B.**, **Romain L.**, **Anne D.**, **Léo B.**, **Emmanuel C.**, **Pauline C.**, **Julie P.**, **Florence D.**, **Denis C.**, **Thibault F.**, **Guillaume T.**, **Benjamin Y.**, **Léo M.**, **Camila A.**, **Julie M.**, **Benoit F.**, **Sébastien T.**, **Marine L.**, **Maud M.**, **Marine R.**, **Anne-Caroline P.** et enfin à **Romain J.** pour m'avoir acceptée dans ce labo.

Au meilleur laboratoire d'écologie que je n'échangerais pour rien au monde - Merci pour l'ambiance, pour la chaleur, pour les histoires, pour les soirées. Merci **CESCO** !

Comment j'aurais fait sans mes amis pour qui j'étais là de façon chaotique certes, mais vous vous y étiez toujours quand il le allait. **Jérôme M.** te rappelles-tu que c'était toi qui a lu le mail de résultats des auditions pour avoir cette thèse. Je n'oublierai jamais l'ascenseur émotionnel que tu m'as fait avec une loooongue pause avant de le lire à haute voix, merci pour tout; **Pilou**, mon voisin préféré, je te remercie d'être là dans les moments compliqués de ma vie, tu sais que ça va mieux maintenant et t'y es pour quelque chose. Il y a des personnes qui s'est sont transformée de collègues en colocs puis en amis, oui je parle de vous **Nicolas D.** et **Simon V.**, même si nous ne sommes plus à la coloc family à part Simon ça nous fait un.e 4ème docteur.e. à la maison 😊. **Lesia**, **Moussa**, **Sabina**, **Nasra**, **Guillaume**, **César** – cette coloc m'a transformé également, alors merci pour les soirées, des repas et plein d'autres folies !
Petite pensée à **Badis**, toujours en vadrouille.

J'ai fait des belles rencontres lors de cette thèse, certaines sont devenues des très bonnes amitiés. Merci **Christian** de m'avoir appris comment fonctionnent des ailes des avions – au moins la formation doctorale a servi à quelque chose !

A part de faire avancer mes recherches, j'ai passé deux ans à gérer une association étudiante « Bureau des Doctorants et Etudiants du Muséum » et ça ne serait pas possible sans une équipe (enfin j'en ai connue deux) au taquet. Je voudrais remercier **Margot B.** pour des heures passées sur des dossiers de subventions, des mails et encore des dossiers - tu fais les meilleurs igloo en carton !; **Adrienne et Typhaine** pour rendre la trésorerie plus facile; **Antoine H.**, **Juliette W.**, ainsi que l'équipe de l'année suivante et tout particulièrement **Charly F.** qui a repris enfin ma place et je sais que ne trouverais pas mieux.

Mes grands remerciements de fond du cœur à **ma mère** qui a semée en moi une graine d'amour de la nature depuis que j'étais toute petite et qui m'a soutenue lors de cette aventure française... depuis déjà dix ans ! Merci à **mon père** pour l'attention et des apparitions bréves mais riches en souvenirs. Je remercie tout particulièrement **mon petit frère** pour avoir relu le manuscrit (même si tu avais du mal à comprendre), **Андрюша** спасибо за проверку английского, даже если ты не всё понял ! Nous ne voyons vraiment pas souvent et un océan nous sépare, mais vous avez su me reconforter même à distance.

Enfin, un dernier mot pour une personne qui était là pour moi tout au long de cette thèse. Merci infiniment chéri, d'avoir cru en moi, de m'avoir soutenu et supporté mes abscences, mes caprises, mes folies. J'espère que tu auras encore de la patience de rester à mes cotés pour nos futurs echanges à refaire le monde toute la nuit.
Summary

<table><tr><td>Remerciements</td><td>3</td></tr><tr><td>Introduction. News from the Anthropocene</td><td>9</td></tr><tr><td>1. Biodiversity crisis of Anthropocene</td><td>9</td></tr><tr><td>1.1 Biodiversity Definition</td><td>11</td></tr><tr><td>1.2 Components of Biodiversity</td><td>13</td></tr><tr><td>1.3. Scales of Biodiversity</td><td>14</td></tr><tr><td>2. Humanity and Biodiversity relations</td><td>14</td></tr><tr><td>2.1. Biodiversity and ecosystem functioning</td><td>15</td></tr><tr><td>2.2. Biodiversity contributions to humanity</td><td>19</td></tr><tr><td>2.3. What threats to biodiversity?</td><td>20</td></tr><tr><td>2.4. Value of biodiversity</td><td>21</td></tr><tr><td>2.5. Tools for biodiversity conservation: from global problems to local solutions</td><td>22</td></tr><tr><td>3. Is Biodiversity measurable?</td><td>24</td></tr><tr><td>3.1. What unit for biodiversity measure?</td><td>25</td></tr><tr><td>3.2. Measurement of Biodiversity: how to proceed?</td><td>27</td></tr><tr><td>4. Objectives of the thesis</td><td>33</td></tr><tr><td>Chapter 1</td><td>35</td></tr><tr><td>Reconciling the concepts and measures of diversity, rarity and originality in ecology and evolution</td><td>35</td></tr><tr><td>Chapter 2</td><td>69</td></tr><tr><td>Diversity in the city: urbanization affects differently trait-based and phylogenetic plant originality at local and regional scales</td><td>69</td></tr><tr><td>Introduction to chapter 2</td><td>70</td></tr><tr><td>Abstract</td><td>73</td></tr><tr><td>Introduction</td><td>74</td></tr><tr><td>Materials & Methods</td><td>77</td></tr><tr><td>Study Area</td><td>77</td></tr><tr><td>Biological Data</td><td>78</td></tr><tr><td>Land Cover Data</td><td>79</td></tr><tr><td>Species Biological Characteristics and Phylogeny</td><td>79</td></tr><tr><td>Data Analyses</td><td>81</td></tr><tr><td>Community and Species Metrics</td><td>81</td></tr><tr><td>Statistical and Spatial Modeling</td><td>83</td></tr><tr><td>Results</td><td>84</td></tr><tr><td>Community Level Metrics</td><td>84</td></tr><tr><td>Species Level Metrics</td><td>85</td></tr><tr><td>Dataset without Non-native Species</td><td>87</td></tr><tr><td>Discussion</td><td>87</td></tr><tr><td>Trait-based Originality of Urban Communities</td><td>88</td></tr><tr><td>Phylogenetic Originality of Urban Communities</td><td>89</td></tr><tr><td>Species Richness and Non-native Species Originality</td><td>90</td></tr><tr><td>Conclusion and Directions for Further Research</td><td>91</td></tr><tr><td>Conflict of Interest Statement</td><td>93</td></tr><tr><td>Author Contributions</td><td>93</td></tr><tr><td>Funding</td><td>93</td></tr><tr><td>Acknowledgements</td><td>93</td></tr><tr><td>Data Availability</td><td>94</td></tr><tr><td>References</td><td>95</td></tr><tr><td>Supplementary results and commentaries to chapter 2</td><td>155</td></tr></table>
<table><tr><td>Variation of local species-specific originality</td><td>155</td></tr><tr><td>Comparing data issues and methodological choices with study of Veron et al.</td><td>157</td></tr><tr><td>General discussion</td><td>163</td></tr><tr><td>All is a matter of choice and availability</td><td>163</td></tr><tr><td>1. Choosing and obtaining the data for originality measures</td><td>166</td></tr><tr><td>1.1 On the benefits and limits of using different aspects of species' biology</td><td>166</td></tr><tr><td>1.2 Issues related to the availability and to the quality of the data</td><td>177</td></tr><tr><td>2. The matter of scales for originality measures</td><td>185</td></tr><tr><td>2.1 Importance of the spatial scale</td><td>186</td></tr><tr><td>2.2 Taxonomical scale</td><td>188</td></tr><tr><td>3. Choosing the mathematical formula to measure originality</td><td>188</td></tr><tr><td>3.1 Relations between species originality measures</td><td>189</td></tr><tr><td>3.2 On the ubiquity of originality measures in general</td><td>192</td></tr><tr><td>Conclusion</td><td>195</td></tr><tr><td>References of the thesis</td><td>199</td></tr><tr><td>Afterword</td><td>219</td></tr><tr><td>List of publications</td><td>220</td></tr><tr><td>List of communications</td><td>220</td></tr><tr><td>Appendix A</td><td>221</td></tr><tr><td>On islands, evolutionary but not functional originality is rare</td><td>221</td></tr><tr><td>Abstract</td><td>223</td></tr><tr><td>Introduction</td><td>224</td></tr><tr><td>Methods</td><td>227</td></tr><tr><td>Occurrence data</td><td>227</td></tr><tr><td>Estimating evolutionary and functional originality</td><td>228</td></tr><tr><td>Are species more original on continents or on islands?</td><td>229</td></tr><tr><td>Relationship between evolutionary and functional originality</td><td>229</td></tr><tr><td>Are insular original species geographically rare?</td><td>230</td></tr><tr><td>Dispersal modes, biogeographic characteristic of islands and species originality</td><td>230</td></tr><tr><td>Results</td><td>232</td></tr><tr><td>Functional and evolutionary originality are decoupled</td><td>232</td></tr><tr><td>Species endemic to islands are more original</td><td>233</td></tr><tr><td>Evolutionary but not functionally original species are unusually rare</td><td>234</td></tr><tr><td>Dispersal abilities of original species</td><td>235</td></tr><tr><td>Possibility of islands to be at reach and distribution of originality</td><td>236</td></tr><tr><td>Discussion</td><td>238</td></tr><tr><td>Decoupling of functional and evolutionary originality</td><td>238</td></tr><tr><td>Higher originality on islands than on continents</td><td>239</td></tr><tr><td>Range-restriction of original species</td><td>240</td></tr><tr><td>Dispersal capacities of evolutionary and functionally original species</td><td>241</td></tr><tr><td>Conservation</td><td>242</td></tr><tr><td>References</td><td>245</td></tr><tr><td>Appendix B</td><td>273</td></tr><tr><td>Vigie-flore protocol</td><td>273</td></tr><tr><td>Appendix C</td><td>281</td></tr><tr><td>Teaching Theory of Evolution at primary school</td><td>281</td></tr></table>
Empty spaces, what are we living for?
Abandoned places, I guess we know the score
On and on, does anybody know what we are looking for?

**The show must go on**

Queen, from album “Innuendo”, 1991
... it takes a peculiar kind of genius and courage of spirit to be able to envisage nature in the innumerable multitude of its productions without losing one's orientation, and to believe oneself capable of understanding and comparing such productions...

Buffon, *First discourse*, « Histoire Naturelle » (1749)

# 1. BIODIVERSITY CRISIS OF ANTHROPOCENE

We live in the end of the bottom of material abundance. In 1992, the Union of Concerned Scientists and many other researchers worldwide edited the first “World Scientists’ Warning to Humanity”. What were those scientists worried about 27 years ago? Essentially, they feared that humanity was about to approach the limits of what natural ecosystems could tolerate from current human practices. Today, in 2019, we can confirm that they did not miss the point. In early 2000’s Paul Crutzen suggested that the Earth had left the Holocene and entered a new epoch, the Anthropocene, demarcated by a footprint of human activities (Crutzen, 2000, 2006). Crutzen did not invent the concept, but he proposed to define the start date of the Anthropocene near the end of the XVIII century, about the time that the industrial revolution began (the invention of the steam engine by James Watt in 1784). Other scientists propose to mark the start of Anthropocene with the agricultural revolution, 15 000 years ago, or else with the Columbian Exchange of Old World and New World species (Waters et al., 2016). Although its beginning date is still under debate, the Anthropocene epoch has certainly established in XXI century.

The ‘Great Acceleration’ graphs usually illustrate the Anthropocene trends of an increasing human imprint on the planet Earth (Figure 1): global rises in temperatures, increasing pollutions, defaunation and intensification of land use. Even though human imprint on Earth continue to rise, there were some signs of improvement in the last decade. For example, the Antarctic ozone hole, extreme poverty, hunger and fertility issues and deforestation of some parts of the Earth have been reduced (Ripple et al., 2017). In the last decades human activity pronounced biotic changes in species assemblages worldwide and accelerated species extinction rate, associated mostly with
<figure><img src="image_3.png" /></figure>

**Figure 1.** "Trends over time for environmental issues identified in the 1992 scientists' warning to humanity. The years before and after the 1992 scientists' warning are shown as gray and black lines, respectively. Panel (a) shows emissions of halogen source gases, which deplete stratospheric ozone, assuming a constant natural emission rate of 0.11 Mt CFC- 11-equivalent per year. In panel (c), marine catch has been going down since the mid-1990s, but at the same time, fishing effort has been going up (supplemental file S1 of Ripple et al., 2017). The vertebrate abundance index in panel (f) has been adjusted for taxonomic and geographic bias but incorporates relatively little data from developing countries, where there are the fewest studies; between 1970 and 2012, vertebrates declined by 58 percent, with freshwater, marine, and terrestrial populations declining by 81, 36, and 35 percent, respectively (file S1). Five-year means are shown in panel (h). In panel (i), ruminant livestock consist of domestic cattle, sheep, goats, and buffaloes. Note that y-axes do not start at zero, and it is important to inspect the data range when interpreting each graph. Percentage change, since 1992, for the variables in each panel are as follows: (a) -68.1%; (b) -26.1%; (c) -6.4%; (d) +75.3%; (e) -2.8%; (f) -28.9%; (g) +62.1%; (h) +167.6%; and (i) humans: +35.5%, ruminant livestock: +20.5%. Additional descriptions of the variables and trends, as well as sources for figure 1, are included in file S1 [of Ripple et al., 2017]."

*Figure and legend are extracted from “Warning to Humanity: A second notice” (Ripple et al., 2017).*
land-use intensification (Pimm et al., 2014; Williams et al., 2015). Specific to Anthropocene, a new branch of ecology developed since several decades, called disturbance ecology (Newman et al., 2016). Disturbance ecology makes historical comparisons of past, current and future disturbance regimes and allows the development of adapted conservation measures.

In 2017, a “Warning to Humanity: A second notice” has been written and signed by over 15 000 researchers all over the world (Ripple et al., 2017). If you are one of those who signed this sort of a scientific petition, you might agree that soon it could be too late to “shift course away from our failing trajectory”. Humanity would certainly need natural resources in the future, and would continue to exploit them. Thus, our existence have to be thought sustainably. However, there are cultural differences between societies (development) and therefore there are differences in their relation to the Nature. There are as many natures as there are cultures. In economically wealthy countries, the relationship of humans to the Nature (and not the reverse) could be described as a relation of a subject to an object (Descola, 2005). In such a society we can imagine a kind of a moral border created between the Humanity with its cultural identities and all the rest assembled under the term of Nature. Seen under human interests perspective, Nature loses its role of a subject and becomes an object of admiration, of exploitation and finally of concern. However, these moral values are not universal and alternatives exist. For example, in Amazonian indigenous cultures, there is no conception of Nature at all (Elands et al., 2015), thus there is no separation of human from Nature (Diaz et al., 2015). However, in a world of scientific research, Nature, denoted as biodiversity, is above all an objects of study. Therefore, I will stick to the scientific definition of biodiversity, which is still often underappreciated in socio-economic and political discourses on sustainable use and conservation of biodiversity.

## 1.1 Biodiversity Definition

Despite being a common object of study of many researches, biodiversity is a complex concept and there is no univocal way to define it. DeLong (1996) in his review listed 85 definitions of biodiversity and still was not able to define the semantic basis of it. A whole book has been edited
lately, all about defining, measuring and conserving biodiversity (Casetta, da Silva and Vecchi, 2019), that is to say, the concept of biological diversity is large and includes many revealing components. The invention of the term biodiversity was motivated by efforts to stop species extinctions and habitat destruction (Mayer, 2006). It comes as a modern contraction of the term “biological diversity”, derived from Greek “bios” which stands for “life”. Since its appearance in mid-1980’s (Lovejoy, 1980, Haila and Kouki, 1994) major changes happened in the definitions and use of the term biodiversity, referred to the variety and variability among living organisms. Today, the most widely cited definition comes from the Convention on Biological diversity at the “Earth Summit”, held in Brazil in 1992 (UNEP, The United Nations Convention on Biological Diversity) and which says that biodiversity is:

“the variability among living organisms from all sources including, inter alia, terrestrial, marine, and other aquatic ecosystems as well as the ecological complexes of which they are part; this includes diversity within species, between species and of ecosystems”.

DeLong (1996) offered a more comprehensive definition:

“Biodiversity is an attribute of an area and specifically refers to the variety within and among living organisms, assemblages of living organisms, biotic communities, and biotic processes, whether naturally occurring or modified by humans. Biodiversity can be measured in terms of genetic diversity and the identity and number of different types of species, assemblages of species, biotic communities, and biotic processes, and the amount (e.g., abundance, biomass, cover, or rate) and structure of each. It can be observed and measured at any spatial scale ranging from microsites and habitat patches to the entire biosphere”.

The definition of DeLong allows for modification according to the context in which it is used, but stays vague about the diversity of abiotic components and ecological processes, mentioned as diversity of ecosystems. It is astonishing that in XXI century, after all efforts of conservation and management made for biodiversity, we are still trying to define a universally agreed definition of it, which probably does not exist (Swingland, 2013). Perceived differently among scientists, policymakers or conservationists, a biodiversity definition has to go with pragmatic objectives targeting
specific biodiversity issues (Casetta, da Silva and Vecchi, 2019).

**1.2 Components of Biodiversity**

The definition of biodiversity is organized by three intimately linked main components, proposed
by Gaston and Spicer (2004):

* genetic diversity, referring to the heritable variation of genetic information within and between species and populations,

* species diversity (or organismal diversity), referring to inter-species diversity or more largely to taxonomic diversity from individuals up towards to species, genera and so on,

* ecosystem diversity, referring to the diversity in species' communities at ecosystem level.

The molecular diversity and genetic richness of life (Campbell, 2003) should be considered as the basis of biodiversity evolution. Besides, Boon (2019) proposed to investigate intra-individual genetic heterogeneity, accessible today with accumulated data on genetic information contained in DNA sequences. Concerning species diversity, there are three main aspects: taxonomic, trait-based and phylogenetic. Taxonomic diversity is usually presented as a number of different taxa (Humphries et al., 1995). It is the simplest way to describe biodiversity at a given location. Trait-based diversity represents the richness of character types of species or else their biological traits (Petchey and Gaston, 2002). I will define species traits in the section 3.2.1. Finally, phylodiversity represents the evolutionary potential of a given set of species, hidden behind the branches of a phylogenetic tree.

Biodiversity studies usually integrate the up-mentioned “holy trinity” of components (genes, species and ecosystems). This entity-based approach in conservation is particularly used in species-centered studies, trying to describe and to protect each of them. However, species are not isolated from other ecosystem components, biotic and abiotic, thus, entity-approach should be integrated to the process-based approach (Vecchi and Mills, 2019). Indeed, species are at the base of many ecosystem processes insuring efficient ecosystem functioning, and linking species to the underlying ecosystem processes can strongly enhance conservation action efficacy. Such
investigations can be made at several scales of biodiversity.

**1.3. Scales of Biodiversity**

Living organisms are organized following a nested hierarchy of units with increasing complexity.
We can distinguish two levels of organization: biological systems, from genome to species
population sharing genetic homogeneity, and ecological systems, which include different
interacting species organized into communities at different spatial and temporal levels (André et
al., 2003). The level of ecological organization is in the focus of this thesis.

The diversity of ecological systems can be approached at a broad range of spatial levels.
Usually researchers separate it on three levels: 1) alpha diversity, which corresponds to intra-habitat
diversity between species composing one community, 2) beta diversity of species composition
between communities and 3) gamma diversity of a global landscape in a region (Whittaker, 1972;
Magurran, 2004). Those three levels of biodiversity are interdependent as gamma diversity is a
function of alpha and beta diversities. Moreover, with changing seasons and habitats, biodiversity
vary not only in space (Escuedro et al., 2003) but also in time (Pachepsky et al., 2001). Actual
biodiversity crisis is a global phenomenon. For example, current practices of species extraction
resulted in quasi total disappearance of some plant species (Prance, 2002). However, conservation
efforts are usually made at the local scale. Moreover, global species extinctions have been rarely
observed, as human impact usually leads to local extinctions and biodiversity changes (Hooper et
al., 2005). To be sustainable, we thus need to consider social, political and economic influences that
shape biodiversity management at local scale (Williams, 2004). This requires more integrative
research on the variation of biodiversity across spatial and temporal scales, guided by local priorities
of conservation.

**2. HUMANITY AND BIODIVERSITY RELATIONS**

There is an evidence for global changes across the world that causes planet's biotic
impoverishment (Reid and Miller, 1989). Nature didn't ask to be protected and it causes
contradictions in conservation priorities and “ecological” objectives (Papaux and Bourg, 2015). However, humanity does have big reasons to take care about biodiversity. According to the Food and Agriculture Organization of United Nations, 40% of the world's economy is based directly and indirectly on the use of biological resources that emanate from biodiverse systems. Taking care of biodiversity turns out to taking care of the well-being of our-selves. Unfortunately, humanity's growing needs surpasses biodiversity capacities to satisfy them and if we want to continue benefiting from natural resources, the question of biodiversity use has to be taken seriously. It can be accomplished by the sustainable use of biodiversity, which implies the consideration of eventual future generations' needs. The sustainable use of biodiversity is one of the three main objectives set by the Convention on Biological Diversity (CBD, 1992¹). In 2011, in Nagoya, Japan, a Strategic Plan for Biodiversity of CBD has been updated with elaboration of 20 Aichi Biodiversity Targets<fnref n="2" />. “*The path to solving this dilemma involves changing patterns of behavior and social norms that influence them; but such norms also typically change only on those longer time scales. Sustainability in the new millennium will depend upon our ability to affect with sufficient dispatch the cultural norms and legal instruments that govern individual behaviors in the global commons*” (Levin, 2000).

That is, as current global changes overwhelms the speed of adaptation of biological systems, we probably should accept current environmental conditions as a new normal and adjust conservation actions to the future trajectories of anthropogenic disturbances (Newman, 2019) that will certainly continue. But first of all, we need to understand how natural ecosystems function and how they depend on biodiversity.

**2.1. Biodiversity and ecosystem functioning**

Major advances have been made in describing the relationship between biodiversity and ecosystem functioning – a broad concept including a variety of phenomena happening in ecosystems and including ecosystem proprieties, goods and services (Hooper et al., 2005). Biodiversity is a result

<footnote n="1">¹ https://www.cbd.int/doc/legal/cbd-en.pdf</footnote>

<footnote n="2">² https://www.cbd.int/sp/targets/</footnote>
of dynamical interactions between its components in space and time, which obey partly different
sets of constraints, and hence partly different laws (Loreau, 2010). When studying effects of
biodiversity changes on ecosystem functioning one should define which component of biodiversity
is in focus. Ecosystem ecologists have often studied species and their biological characters, as a key
element for understanding the link between biodiversity and its role in shaping ecosystems (Lavorel
et al, 2011).

Since 1960's, ecology studies combining theoretic, field and experimental methods succeeded to gradually develop the debate on causes and consequences of biodiversity loss (Hooper et al., 2005; Tilman et al., 2014). Those studies were investigating three fundamental ecosystem processes: primary productivity, stability (and resilience) and invasibility. Some examples are presented in Figure 2 extracted from Tilman et al. (2014). Concerning productivity-diversity relationship, the major result showed that a total biomass of ecosystem linearly increase with a number of species in ecosystem (see Tilman et al., 2014). Living organisms, through their metabolism and everyday life activities generate an adaptive feedback with their abiotic environment, thus regulating abiotic conditions (Kylafis & Loreau 2008). Further, many natural processes that are happening spontaneously in ecosystem rely on direct interactions between species such as predation, competition, mutualism, facilitation or disease (Cardinale et al., 2002) influencing diversity-stability relationship. Ecosystem stability is a multifaceted concept with many definitions but which generally determines ecosystem capacity to return to an equilibrium state in terms of species densities and interactions after a perturbation (Holling, 1973; Hooper et al., 2005; Ives and Carpenter, 2007). Together stability and diversity determine ecosystem functioning fluctuations. A variety of observational studies confirmed the theory that greater species diversity leads to greater ecosystem stability, especially if species are redundant in their functions (Lehman and Tilman, 2000, Gross et al, 2015). Finally, diversity-invasibility relationship was found positive. For example, invader success of exotic species decreased with higher native species diversity (Naeem et al., 2000; Laureau 2010).
Scientific community came up with a broad consensus that many species have generally a positive impact on ecosystem functioning (Hooper et al., 2005, Ives and Carpenter, 2007). However, the majority of studies that investigated links between biodiversity and ecosystem functioning were carried out in terrestrial plant communities, which are also the primary producer at the base of every trophic chain. Moreover, contradictory effects of plant diversity on higher trophic levels were found (Mittelbach et al., 2001; Proulx et al., 2010). Thus, it is unclear whether the results found with terrestrial plant species can be generalized to other taxa, different trophic levels and different habitat types (aquatic, subterranean). It has been shown that, depending on the biodiversity components studied, on environmental constraints and on spatial scale of study diverse biodiversity patterns can be observed (Mittelbach et al., 2001). Indeed, different components of species diversity follow different dynamics (Brum et al., 2017) and may have contrasting effects on multiple ecosystem processes (Tilman et al., 2014). Trait-based component of biodiversity, for example, strongly influences ecosystem properties (Diaz and Cabido, 2001, Hooper et al., 2005, Loreau, 2010). Le Bagousse-Pinguet et al. (2019) found that taxonomic, phylogenetic and trait-based diversity of plant species taken together were better predictors of multifunctionality of 123 dryland ecosystems than if taken separately. Therefore, biodiversity relations with ecosystem functioning cannot be summarized by a simple diversity metric and have to be addressed at multiple scales for several levels of biological organization (Tilman et al., 2014).
<figure><img src="image_4.png" /><figcaption>**Figure 2.** "Relationships among species richness, functional composition, and ecosystem function. Increasing species number (a) increases drought resistance; high resistance is associated with a low rate of biomass decline during drought (Tilman & Downing 1994). Increasing species number also (b) increases vegetation cover (Naeem et al. 1994) and (c) increases biomass in a greenhouse experiment (Naeem et al. 1995). (d) Plant abundances (summed percent covers, solid line) increase, whereas soil nitrate (dashed line) decreases (more is taken up by plants) with increasing plant species numbers (Tilman et al. 1996). Aboveground biomass increases with both (e) the number of species and (f) the number of functional groups (Hector et al. 1999). (g) Aboveground biomass depends on plant community composition (Hooper & Vitousek 1997). Abbreviations: E, early-season annuals; L, late-season annuals; P, perennial bunchgrasses; N, nitrogen fixers. (h,i) Diversity effect is plant productivity at high diversity divided by that of monocultures for ~100 experiments, with diversity effects (h) ranked from largest to smallest effects and (i) graphed against length of study (ln of number of days, where "7" = 3 years) (Cardinale et al. 2007). Error bars indicate 1 SEM (standard error of the mean)."</figcaption></figure>

*Figure and legend are extracted from Tilman et al. (2014)*
## 2.2. Biodiversity contributions to humanity

We saw that biodiversity is involved into many natural ecosystem processes and that understanding of the biodiversity-ecosystem functioning relationships would help to prioritize relevant biodiversity components in conservation management actions (Diaz et al., 2006). Humanity's reliance upon biodiversity for its welfare and survival is enormous, and biodiversity loss has unquestionable socio-economic consequences. Ecological process can be seen through the utilitarian prism making biodiversity of a vital importance to humanity. By influencing their environment different species are involved into primary production, decomposition or biogeochemical cycles of nutrients, water and other elements, soil generation, pest control and climate regulation. A plenitude of ecosystem processes were assembled into subgroups according to the nature of benefits they can offer and called "ecosystem services". The Millennium Ecosystem Assessment (MEA, 2005), that gathered around 1700 researchers all over the world, listed four roles of biodiversity in ecosystem services supply:

1. provisioning role of biodiversity by supplying materials, water, food etc.;

2. regulatory role through the influence of biodiversity on production, stability and resilience of ecosystems;

3. supporting role through structural, compositional and trait-based (functional) diversity

4. cultural role through the non-material benefits (spiritual, educational).

Going further than traditionally described ecosystem services, biodiversity provides an inspiration for new energy sources, materials and technologies, commonly called biomimetism. In practice, however, it is quasi impossible to clearly differentiate interrelated ecosystem services.

MEA founded their framework on two pillars: ecological and economic, referring to the multiple ecological functions that serves humanity. In the meanwhile, all societies value nature in very different ways for their own sake (Wilson, 1984) and MEA recommendations failed to be
operational locally, as the definition of ecosystem services lacks socio-cultural values along with
ecological and economic ones. This intrinsic value of nature is of primary importance in some
societies, especially for indigenous people. Therefore, Intergovernmental Science-Policy Platform
on Biodiversity and Ecosystem Services (IPBES, Diaz et al., in press) scientists proposed more
inclusive, yet not approved to this day, notion of “nature’s contributions to people” instead of
MEA’s ecosystem services framework (Díaz et al., 2018), embracing the complex relationship
between humans and nature.

2.3. What threats to biodiversity?

As described above, our planet's living mantle – biosphere – suffers from human intervention,
unprecedented in Earth's history (Tittensor et al., 2014). Indeed, global changes accelerated by
human activity impose selective pressures on the organisms and therefore influence adaptation and
evolutionary trajectories of taxa (Williams et al., 2015). The most recent classification of direct
causes of biodiversity decline was made by the IPBES (Diaz et al., in press). The list consist of five
causes (with a decreasing level of impact): changes in land and sea use; direct exploitation of
organisms; climate change; pollutions; and invasion of non-native species. There are more precisely
agricultural overexploitation, urbanization and industrial development threatening wild populations
by habitat fragmentation, loss and degradation (Krauss et al., 2010); invasive non-native species
taking over native populations in resource use (Walther et al., 2009); introduced pathogens altering
biochemical processes in the water, air and soil; climate change modifying species habitats and also
their behavior, etc. (Diaz et al., 2015; Williams et al., 2015). Those five direct drivers of biodiversity
change are underpinned by societal values and behaviors that include production and consumption
patterns, human population dynamics and trends, trades, technological innovations and local to
global governance. However, the rate of change in the direct and indirect drivers of biodiversity
loss differs among regions and countries. Moreover, new types of anthropogenic disturbances will
interact with natural disturbance regimes resulting in completely different threats, proper to
Anthropocene (Newman et al., 2019). In order to minimize its negative effect on biodiversity,
humanity established red lists of endangered species, created protected natural areas and is trying
to develop a sustainable way of life. Would that be enough?

One of the most obvious and gravest consequences of biodiversity crisis are species
extinctions. Species extinction is a natural process occurring without human intervention on large
time scales, and all living organisms have its limited geological time of existence (Soulé, 1985).
However, extinction rates caused by humans exceeds natural pace of things. The International
Union for the Conservation of Nature (IUCN) estimated that out of 1.7 million of described
species around 25% are threatened and about 1 million are under threat of extinction (Mittermeier
et al., 2011). As species vanish, so does the benefits they offer to humanity. Therefore, because
species extinctions are irreversible, their irreplaceable value in terms of services should be a
mandatory part of ecosystem assessments.

**2.4. Value of biodiversity**

Based on the ecosystem service notion, many scientists and economists tried to quantify the real
value of biodiversity loss (Costanza et al. 1997; Balmford et al. 2002). However, it is difficult to
establish a meaningful and relevant value of biodiversity. There is no single valuation process of
biodiversity and three main perspectives - economic, societal and ecological - should be accounted
for (Laurila-Pant et al., 2015; Sukhdev et al., 2014). The monetary valuation of biodiversity changes
can still serve as a useful link between environmental problems and political decision-making
processes (Braüer, 2003) but there is no univocal, unambiguous monetary indicator (Nunes and
van der Bergh, 2001). How much would cost the restoration measures compared to the loss of
economic benefits? What does it really costs to replace services provided for free by nature? This
economical valuation has a drawback of not accounting for social visions of nature. Therefore,
socio-cultural way of biodiversity valuation may be more adapted to the cultural and spiritual
relations to the nature (Sukhdev et al., 2014). Unfortunately, many ecosystem services cannot be
bought or sold, thus they have been called "non-use values" of biodiversity (Pagiola et al., 2004).
Furthermore, each component of biodiversity has its unique, intrinsic value or “the value of nature regardless of the services and economic benefits it provides” (Pearson, 2016). Some researchers proposed the idea of biodiversity option value, which represents the unanticipated capacity of biodiversity to provide potential future benefits (Faith, 1992, MEA, 2005). Thus, it is not only about focusing on ecosystem current uses, but also about making provisions for future generations.

Today, socio-ecological processes are increasingly shaping the world's natural ecosystems. Humanity exploits a minor part of the world's biological diversity (Swingland, 2013) and undiscovered option and intrinsic non-use values should support the precautionary principle of biodiversity maintaining. In order to conserve biodiversity and prevent its further loss, there is a need to complement the economic and socio-cultural valuation approaches of biodiversity with another one based on precise scientific knowledge of ecosystem functioning (Laurila-Pant et al., 2015). This signifies determining all possible threats, direct or indirect, hanging over biodiversity and measuring its well-being.

**2.5. Tools for biodiversity conservation: from global problems to local solutions**

Prevention of biodiversity loss is becoming of the paramount importance for all nations, societies and individuals. Biodiversity must be urgently assessed in an operational way in order to facilitate conservation targets setting. Since the United Nations Convention on Biodiversity, multiple intergovernmental assessments, conservation programs and agencies flourished in order to understand and reduce biodiversity loss (e.g., European Environments Agency, IUCN, etc). Many of them adopted IPBES as their science-policy interface (Pimm et al., 2014). One of the simplest ways to assure the protection of individual species and entire ecosystems consists in setting aside entire areas, such as natural reserves and parks (Margules and Pressey, 2000), just by isolating a part of biodiversity from any possible threat (“nature for itself” and “nature despite people” policy of conservation; Mace, 2014). Such approaches attempt to preserve native species and their natural habitats they evolved in, excluding any area that has been in contact with human activity (the
concept of wilderness, Mittermeier et al., 2003). However, there are areas around protected spaces,
where damage to biodiversity could be so big that species would not be able to re-colonize some
parts of their native habitat without human intervention. Conservation translocation and *ex situ*
conservation, are another tool for biodiversity conservation (Sarrazin and Barbauld, 1996; Taylor
et al., 2017). Conservation translocation is an intentional human-mediated movement and release
of species in order to re-establish viable populations within species distribution range (Seddon,
2010). *Ex situ* conservation tries to maintain a viable population in cultivation or captivity (in seed
banks, embryos, sperm and oocyte storages). Such conservation tools often involves a loss of
genetic diversity through inbreeding and founder effect (Milner-Gulland and Mace, 1998).
However, species do not influence ecosystem processes equally (Tilman, 1997), nor do they have
the same evolutionary history and potential. Thus, no single taxon is a good proxy for biodiversity
protection (MEA, 2005).

Poiani et al. (2000) proposed a practical framework of biodiversity conservation intended
to make a transition from single-species conservation of rare and endangered species to ecosystem-
oriented conservation, taking into account four ecosystem attributes: composition and structure of
the focal ecosystems; dominant environmental regimes, including natural disturbance; minimum
dynamic area; and connectivity. They recommend to use this framework several times during
conservation actions for more effectiveness. Such ecological management aims to conserve and
restore historical ecosystems and ensure maintenance of ecosystem services and goods. Therefore,
biodiversity protection often requires human assistance. Moreover, once biodiversity passed the
tipping point of no return because of human intervention, the “nature and people” policy should
be developed linking nature to human well-being (Mace, 2014). Thus, as a final step in human
relation to biodiversity, the idea of wilderness should be abandoned and framework of biodiversity
conservation should concentrate on socio-natural areas, integrating both, natural biodiversity
dimension and socio-economic human dimension as partners (Bourg and Papaux, 2015). Such
policy was recently adapted in ecology of landscapes and urban ecology, making accent on ordinary
biodiversity (nature that can be experienced in every-day life).

For sure, no single methodology is appropriate for multiple biodiversity loss issues. Effects of global change differ between locations, spatial scales and time periods. It is not possible to protect all biodiversity components simultaneously and some of them have to be prioritized for conservation. Hence, conservation efforts are usually made at the local scale (Pimm et al., 2001). First, because local species extinctions are more common (e.g., large changes in species abundancies) than global ones (Hooper et al., 2005). Second, because people care more about their close environment and because localized actions are easier to implement. Developing biodiversity conservation demands a deep knowledge of its state in a first place. Therefore, biodiversity monitoring has to be aligned with local and national priorities of biodiversity conservation (see Durant, 2014). Organizations such as IUCN, play a key role in facilitating effective communication between actors of biodiversity management, creating tools useful to all sides and ensuring that information provided is credible, and gathered via a legitimate process. It is important that current conservation efforts follow relevant scientific progress and that policy-makers use up-to-date methods to ensure better future for nature and people (Mace, 2014; Newman, 2019). One efficient way to evaluate biodiversity state is to quantify it, in other words to give biodiversity a numerical value (do not confuse with monetary value). To move from assessing to conserving biodiversity, many biodiversity measures were developed (Purvis and Hector, 2000).

### 3. IS BIODIVERSITY MEASURABLE?

Measuring such a complex thing as biological diversity, where each level of biological organization varies in composition, structure and function (Noss, 1990; Mayer, 2006), is a hard but achievable task. In spite of such organizational complexity of biodiversity, one have to choose which biodiversity component to measure. Hence, biodiversity conservation has to focus on the dynamic ecological processes at different spatial and temporal scales (Levin, 2000; Poiani et al., 2000). Nevertheless, components of biodiversity are interdependent and accessing biodiversity gradually through its hierarchical organization complements a view of its state. Local biodiversity variations
may lead to biodiversity generation and maintenance on larger spatial scales and longer time periods (Levin, 2000). Therefore, multiscale conservation effort offers a more comprehensive way for protecting a greater amount of biodiversity (Poiani et al., 2000). In ecological sciences a vast range of biodiversity metrics exist, each approaching one of the facets of biodiversity. The choice for the most adapted biodiversity metric will depend on the underlying ecological question, spatial scale of the study and nature of the ecological data (Magurran, 2004, Mayer, 2006). In the Chapter 1 we will compare several biodiversity measures and discuss their possible uses in ecology and conservation biology.

**3.1. What unit for biodiversity measure?**

> *No one definition has as yet satisfied all naturalists; yet every naturalist knows vaguely what he means when he speaks of a species.*
> 
> Darwin (1859/1964)

Generally, biodiversity measures are made in a way to evaluate a state of a biological entity. Such entities can be categorized in sets by what they have in common. In ecology, those entities can be represented by a chosen level of biological organization, from genes to landscapes passing by species and individuals. In my thesis and more generally in community ecology, an entity would be composed of individual organisms from the same species and a community would be a set of these entities. In conservational biology, species became a common currency for studying biodiversity variance and from species distribution and abundance (Agapow et al., 2004). For example, species are used to define biodiversity hotspots (Myers et al., 2000) selected upon IUCN species' threat classification. Public concern plays a great role in budgetary allocations for conservation programs, and easily recognized, charismatic species are highly valued, regardless of their real ecological significance (Agapow et al., 2004; Pearson, 2016; Albert et al., 2018). Would you prefer to participate into conservation of a puma or of a dangerous insect? And besides, what hides behind the term of "species" and how is it defined?

John Ray (1627-1705), an English naturalist, was the first who introduced the term of "animal
species". Then, Carl Von Linné introduced binominal nomenclature (genera + species), and biologists adopted this naming system for any living organism. For example, Canis lupus stands for the wolf, where "Canis" is a genus and "lupus" is a species belonging to that genus with other Canidae species. Thus, species is the smallest and the most commonly used unit of taxonomical hierarchy starting from a kingdom (which would be animal kingdom for C. lupus). Even though sub-species may be named sometimes, they often remain controversial (Wilson and Brown, 1953). However, there is no universal way to define species, and no other subject in biology received so much controversy as the species concept. The extensive literature on species notion shows controversial definitions of species concept which are still debated by scientists. Over 20 definitions were described, and two integrated frameworks of species concepts have been proposed by Mayden (1999) and by de Queiroz (2005b, 2007). Unfortunately, the problem of species concept definition is often confused with issues of how species is delimited and compartmentalized as a meaningful entity in biology (Andersson, 1990; Sites and Marshall, 2004; Naomi, 2011). Globally, there are four partly overlapping definitions of macroorganism species concept:

* the biological (or reproductive) species concept defining species as populations that cannot interbreed successfully (Mayr, 1942, 1963);

* the evolutionary species concept defining species as lineages of ancestral descendant populations evolving separately with its own roles and tendencies (Simpson, 1961; Wiley, 1978);

* the ecological species concept defining species as lineages evolving in minimally different adaptive zones, depending on species ecological preferences (Van Valen, 1976);

* phylogenetic species concept, delimiting species as a group of organisms that share at least one uniquely derived character, perhaps with a shared pattern of ancestry and descent or monophyly (e.g., Nixon and Wheeler 1990).

Which definition to choose when boundaries of species are confused by horizontal gene transfer, hybridization, and recent evolutionary isolation (Agapow et al., 2004)? Furthermore, very different approaches are used in microbiology to define species concept. Microbial taxa definition is
essentially based on DNA sequencing and hybridization, especially when nothing is known about
natural history and ecology of those organisms (see Green and Bohannan, 2006, Box1. and 2.).
Being fundamentally different from described above “pragmatic” attempts of species definition,
the method of 97% - similarity of ‘operational taxonomic units’, based on ribosomal gene
sequences, would join all primates from humans to lemurs in one species (Staley and Gosnik, 1999).
Inversely, the phylogenetic species concept would split the biological species concept and increase
an extant species number (Agapow et al., 2004). Finally, biological species concept definition cannot
be applied to species with asexual reproduction or to hybrids between species. Therefore, the best
solution is to see species as an operational entity, clearly defined with sufficient data and accepted
in a given area of biology. In this thesis I will use an operational species concept used in ecology,
delimited by molecular information and species biological properties as a unit of biodiversity
measurement (see Sites and Marshall, 2004).

**3.2. Measurement of Biodiversity: how to proceed?**

3.2.1 Criteria to describe species diversity

The key to quantify species diversity is to consider species differences (dissimilarity) that create that
diversity. But which criteria should be used to properly describe species differences? The most
frequently used characteristic is species abundance. Species abundance is usually measured as a
population size, or a number of individuals in a population, although other means to measure
species abundance exist (Rabinowitz, 1982). However, species abundance does not tell us much
about species biological characteristics, what gives them the capacity to exploit ecosystem
resources, reproduce, interact with their environment and consequently influence ecosystem
functioning. Thus, more integrative way to describe differences between species is to use their
biological traits. Under the “trait” term have been listed many different types of species’
characteristics, from individually measured features (biochemical, evolutionary, genetic,
morphologic, physiologic or behavioral) to environmental conditions of species populations. In
order to avoid any confusion and ambiguity Violle et al. (2007) proposed to define traits at the
individual level only. A species trait is a character measurable on every individual of one species
and that influence the performance of species (and the ecosystem functioning) and is usually used
to compare species between them (McGill et al., 2006; Violle, 2007). As species trait is measured
on a sample of species individuals, there can be inter-specific variability. Indeed, species trait values
will be different depending on the environment where they were measured (e.g., altitude, latitude,
climate or season; e.g., Albert at al., 2012), however mean trait values per species are usually found
in global databases. I would like to emphasize the notion of a functional trait, which is often
employed to describe many kinds of species traits as a surrogate for species performance in
ecosystem allowing them to respond to environmental changes and to influence ecosystem
proprieties. Yet, not all traits are functional, and probably not all of them impact species fitness
and survival. As the substantial meaning of biological trait varies among authors and ecological
disciplines, from here on I will employ more inclusive term of “biological trait” or just “trait”.
Thereby, we can say that every species (or individual) is an assemblage of traits, whose values can
be unique features of that specific species (or individual) or commonly shared by many species.

Another way to describe species is to use its position on a phylogenetic tree representing the
evolutionary relationships between extant species. Phylogenetic tree is the most used principle to
organize the classification of organisms. Important progress has been made in building technics of
more reliable and meaningful phylogenetic trees (Roquet et al, 2013). Today, phylogenies are usually
made with species molecular data, by aligning multiple genetic sequences in the most parsimonious
way (Roquet et al., 2013). Thus, most of the phylogenetic trees used in ecology are molecular,
usually built for a specific taxonomic level, and then are assembled into a supertree composed of
several molecular phylogenies. Figure 3 represents a structure of a phylogenetic tree (extracted from
Vellend et al., 2011).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** The components of a phylogenetic tree. Tips (or leaves) of a tree represent species, nodes represent the most recent common ancestor of all species descending from it (branches split), the root node (or just a root) represent a single point from which all species descend; branches are characterized by branch length, representing the accumulation of evolutionary history of species changes; polytomies are the nodes with more than 2 descending branches. *Extracted and modified from Vellend et al., 2008, Box 14.1.*</figcaption></figure>

A largely assumed hypothesis says that shared phylogenetic information between species captures on average (and it is important to underline) shared species features, which for the most of them remains, however, unmeasured (Faith, 1992). We will never be able to measure all species biological traits, thus, molecular phylogenies could be a good surrogate for the conservation of both, known (or easily observed) and unknown (hardly observed) characters of organisms. Species unmeasured features are referred as "future options" and represents a potential unanticipated future benefits from features of species in response to future needs (Faith, 1992; Faith, 2017). A recent contradictory debate about phylogenies capturing badly species trait diversity shows that all depends on the traits measured and on a spatial scale considered (see Mazel et al., 2018, 2019 VS Owen et al., 2019). For example, convergent traits, which are independent evolutionary events experienced by species under the same constraints, makes an exception, and convergent trait values are not captured by the phylogenies (Faith and Walker, 1996b). However, an increased use of
phylogenies to describe species in ecological studies reflect the shared recognition of the utility of phylogenetic information for biodiversity representation and conservation.

### 3.2.2 Indices of species diversity – mathematics can save the planet

The eternal problem of “what to protect” could be resolved if conservation efforts were more inclusive using multiple aspects of species biological traits: genetic, morphological, physiological and behavioral. Indeed, species are not equivalent nor exchangeable in their biological characteristics and magnitude of their differences varies across the set of species observed (Vellend et al., 2011). Thus, the choice of appropriate tool to measure biodiversity will depend on the biological discipline, its methods and data availability on species. In general, a biodiversity measure is a mathematical formula, created to give a value to a specific entity. Before being operational, it has to be accepted by scientific community, to be reusable and understood by all actors of biodiversity conservation (Poiani et al., 2000; Balmford, 2005).

We defined that in this thesis the unit of biodiversity measures will be species, studied within the same taxonomic group (e.g., plants, birds, butterflies) at different spatial scales. Measures that determine the amount of biological diversity represented by a set of species are naturally called species diversity measures. Species diversity is the most commonly used measure of biodiversity, mainly because of the ease of working at species level of organization and due to species' recognized roles in ecosystem functioning (Sullivan and Swingland, 2006). In changing environmental conditions, conservation priorities are set upon specific localities and their specific composition with a common goal to minimize the extinction risk by maintaining the greatest number of species and with them, greater amount of ecosystem services. The simplest diversity measure is species richness, which reflects a number of species in a set (Magurran, 2004) but gives no information on species abundances. To overcome this shortcoming other diversity measures have been developed, such as Shannon index (Shannon, 1948), based on the respective proportions of species abundances or Gini-Simpson index combining species richness and evenness (Gini, 1912; Simpson, 1949). However, these commonly used measures also have several drawbacks, as
they do not allow including species biological differences described above. Thus, in recent decades
phylodiversity (Faith, 1992) and trait-based diversity (Petchey and Gaston, 2002) measures have
been developed, using accordingly species phylogenetic and trait information. The greater species
dispersion on a phylogenetic tree would represent a greater phylodiversity diversity of species.
Furthermore, a location with many different higher taxa than species (genus, order, etc.) would be
more phylogenetically diverse than a site with higher species richness belonging to the same
taxonomical level. For example, two New Zealand's endemic species of Tuatara (genus *Sphendon*)
is the only surviving members of its lineage (*Rhynchocephalia*, Hay et al., 2010), dates to the time of
the dinosaurs and looks like lizards (but they are not). These species represents more phylodiversity
than some species-rich taxa with a recent common ancestor. Phylodiversity measures, created to
capture evolutionary relationships between species were successfully adopted in community
ecology to describe community phylogenetic structure (Webb, 2000; 2002; Cadotte et al, 2010).
Finally, more recently, trait-based (or functional) diversity has been used to reveal links between
species traits and species response to global changes and ecosystem functioning (Petchey and
Gaston, 2002).

But what about species response to global change and the amount of diversity this species
support? A measure that provides a value for every species in a set would complement a complex
picture of biological diversity. In the Chapter 1 we will discuss the most largely used measures for
estimating the contribution of each species to biodiversity. Several studies argued that ecological
role of species is proportional to their abundance, which corresponds to the “mass ratio”
hypothesis of Grime (1998) where the most abundant dominant species represent the most of the
biomass and have the most important role in ecosystem functioning (Lavorel et al., 2008; Diaz et
al., 2007b; Pakeman et al., 2011). Yet, it is not always true if differences in species' biological
characters are considered.
### 3.2.3. Species originality measures based on species' biological characters

Forty years ago, a particular group of measures appeared, in order to characterize each species in a set, and not just the species set (May, 1990; Vane-Wright, 1991; Nixon and Wheeler, 1992). Those measures use inter-species differences to evaluate their specific contribution to the diversity of a finite set of species. A large variety of such indices, even if they measure the same concept, have many different names. In my thesis I refer to them as originality measures, simply because the general principle of all those measures is to evaluate how unusual or original each species is, compared to the others in a set (Pavoine et al., 2005). As for diversity measures, species abundance alone is not always a good predictor of the contribution of species into ecosystem functioning and even relatively rare species can harbor important ecosystem functions and can be even irreplaceable (Mouillot et al., 2013a, Brandl et al., 2016). Thus, species biological characters would allow to measure species originality as a rarity of species character states (Pavoine et al., 2017).

Originality measures can be separated into three large groups, depending on the data they use to give each species a value: species taxonomical relations, species phylogenetic position and species trait values (see Appendix 1 in Chapter 1). From there, to be operational, an originality index needs a species pairwise differences structure, which can be rather a tree structure (for taxonomic and phylogenetic data) or a matrix of differences or dissimilarities between species (usually adopted for trait-based data but could be used for the others too). Originality measures will thus compare species through the shared amount of characteristics (see Figure 2 in General Discussion). Species originality concept can help in our understanding of biodiversity response to global changes which is a step towards better prediction of communities change under future scenarios. I will describe species originality concept and associated methods of its quantification in the Chapter 1.
4. OBJECTIVES OF THE THESIS

The main objective of this research work is to bring the light on the benefits of species' originality concept and measures to different biological disciplines, particularly to conservation biology and community ecology. In Chapter 1, we start by reviewing the state of the art of three concepts primordial to any biodiversity study: species diversity, species rarity and species originality. We disentangle various terms, approaches and measures associated with each of the three concepts. We also discuss analytical links between measures of diversity, rarity and originality and discuss some examples of their joint use. Above, we described two main types of species biological characters, phylogenetic and trait-based. Thus, in this thesis we consider both of them, as it allows having a more integrated vision of species diversity and originality aspects.

After a vast theoretical review, we demonstrate which new applications of originality measures are possible using a real dataset and ecological context. Indeed, in Chapter 2 we investigate plant species originality variation along an urbanization gradient. Especially, we propose a conceptual framework of species originality measuring at two spatial scales that can help to identify the most relevant spatial scale for trait-based diversity and phylodiversity evaluation in cities and which species contribute the most to such a variation. We also discuss some of methodological and data problems met in Chapter 2 and compare them with another research work (Veron et al., in prep., Appendix A) that proposed several solutions.

In the end, General Discussion makes an overview of the precedent chapters. We discuss possible drawbacks and advantages of the originality measures use in ecology. Indeed, the data needed for the calculation of originality is not always available but when it does some shortcomings exist.
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Mindmap of this thesis</figcaption></figure>
# RECONCILING THE CONCEPTS AND MEASURES OF DIVERSITY, RARITY AND ORIGINALITY IN ECOLOGY AND EVOLUTION
The concept of biological diversity, or biodiversity, is at the core of many evolutionary and ecological studies. One of the main aims of such studies is to identify the processes that shape biodiversity patterns in space and time. To reach that aim, a myriad of biodiversity measures have been developed especially in the last four decades. Species is one of the central units of these measures. Often species diversity has been quantified not only in terms of species number but also considering species abundance distribution. The abundance of a species is inversely related to an aspect of its rarity, so that traditional diversity measures were shown to be functions of abundance-based rarities. Then, in the last decade, there has been an exponential increase in the number of research studies aimed at determining other aspects of the contribution of each species to diversity. This contribution expresses how original a focal species is compared to all others in an assemblage as regards its position on a phylogenetic tree and the values of its functional traits. Despite their fundamental links, these three concepts of biodiversity, rarity and originality have been mostly independently treated in the past in ecological and evolutionary studies, with only few attempts to show how different they are and how they complement each other. Moreover, there are numerous terms, used currently in the literature, that are synonymous with originality. Those synonyms especially obscure the concept of originality.

In this chapter, presented as a systematic review published in Biological Review in March 2019, we bring attention to the fact that a conceptual explanation of the links between the three concepts of diversity, rarity and originality is still missing. We thus provide a semantic and historical overview of the definitions of the diversity of an assemblage of species and of the rarity and originality of a species. Furthermore, we clarify some aspects of the complementarities between these concepts and their measurements bringing together ideas developed in evolutionary biology and community and functional ecology.

Regarding how the concepts of diversity, rarity and originality are measured, our aim here was not to review all mathematical formulas developed so far. Instead, we analysed a small set of representative biodiversity measures and demonstrate their links with rarity and originality
measures. We prove, for example, that numerous biodiversity measures can be written as functions
of species rarity and originality.

Finally, synthesizing the results of many published studies, we demonstrated why
biodiversity, rarity and originality measures should be used together in evolutionary biology,
ecology and conservation biology. At a large scale, the joint use of these concepts could help clarify
general patterns of evolutionary events such as trait evolution, extinction, speciation, and adaptive
radiation of species. At a local scale, it could aid in understanding community assembly and
ecosystem functioning. At any scale, it could refine conservation strategies.
# Reconciling the concepts and measures of diversity, rarity and originality in ecology and evolution

Anna Kondratyeva<fnref n="1" />,<fnref n="2" />,* , Philippe Grandcolas<fnref n="2" /> and Sandrine Pavoine<fnref n="1" />

## ABSTRACT

The concept of biological diversity, or biodiversity, is at the core of evolutionary and ecological studies. Many indices of biodiversity have been developed in the last four decades, with species being one of the central units of these indices. However, evolutionary and ecological studies need a precise description of species' characteristics to best quantify inter-species diversity, as species are not equivalent and exchangeable. One of the first concepts characterizing species in biodiversity studies was abundance-based rarity. Abundance-based rarity was then complemented by trait- and phylo-based rarity, called species' trait-based and phylogenetic originalities, respectively. Originality, which is a property of an individual species, represents a species' contribution to the overall diversity of a reference set of species. Originality can also be defined as the rarity of a species' characteristics such as the state of a functional trait, which is often assumed to be represented by the position of the species on a phylogenetic tree. We review and compare various approaches for measuring originality, rarity and diversity and demonstrate that (i) even if attempts to bridge these concepts do exist, only a few ecological and evolutionary studies have tried to combine them all in the past two decades; (ii) phylo- and trait-based diversity indices can be written as a function of species rarity and originality measures in several ways; and (iii) there is a need for the joint use of these three types of indices to understand community assembly processes and species' roles in ecosystem functioning in order to protect biodiversity efficiently.

**Key words:** biodiversity measure, community assembly, conservation biology, distinctiveness, extinction risk, functional diversity, originality, phylodiversity, species abundance, trait-based diversity.

## CONTENTS

<table><tr><td>I. Introduction</td><td>1318</td></tr><tr><td>II. Rarity and Originality: Two Entangled Concepts</td><td>1319</td></tr><tr><td>(1) The concept of species rarity in ecology and evolution</td><td>1319</td></tr><tr><td>(2) The concept of originality or phylo- and trait-based rarity</td><td>1320</td></tr><tr><td>(3) The measurement of originality</td><td>1321</td></tr><tr><td>(4) How should the originality measure be chosen?</td><td>1323</td></tr><tr><td>(5) Spatial scale matters</td><td>1323</td></tr><tr><td>(6) Directions for future research on species' originality</td><td>1324</td></tr><tr><td>III. Links Between Measures of Diversity, Abundance-, Phylo- and Trait-Based Rarity</td><td>1324</td></tr><tr><td>(1) Biodiversity as a mean of abundance-based rarities</td><td>1325</td></tr><tr><td>(2) Explicit link between originality and diversity indices</td><td>1325</td></tr><tr><td>(3) Most diversity indices only implicitly depend on species' originalities</td><td>1326</td></tr></table>

<footnote n="1">¹Centre d'Ecologie et des Sciences de la Conservation (CESCO), Département Homme et Environnement, Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, 57 Rue Cuvier, CP 135, 75005, Paris, France</footnote>

<footnote n="2">²Institut Systématique Evolution Biodiversité (ISYEB), Département Origines et Evolution, Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université EPHE, 57 Rue Cuvier, CP 50, 75005, Paris, France</footnote>

<footnote>* Author for correspondence (Tel: +33661150410; Fax: +33140793835; E-mail: anna.kondratyeva@edu.mnhn.fr)</footnote>
<table><tr><td>(a) When diversity equals the sum or mean of originalities</td><td>1326</td></tr><tr><td>(b) When diversity equals the sum or mean of originalities weighted by abundance</td><td>1326</td></tr><tr><td>(c) When species' abundances maximize diversity</td><td>1327</td></tr><tr><td>(d) When diversity and originality depend on a multidimensional space</td><td>1327</td></tr><tr><td>IV. Reconciling the Diversity, Rarity and Originality Concepts for Their Useful Applications</td><td>1330</td></tr><tr><td>(1) Understanding the evolutionary emergence of species' originality</td><td>1330</td></tr><tr><td>(2) Analysing the dynamics of community assembly with rarity, originality and diversity</td><td>1332</td></tr><tr><td>(3) Role of originality in ecosystem functioning</td><td>1333</td></tr><tr><td>(4) Guiding conservation actions</td><td>1333</td></tr><tr><td>V. Conclusions</td><td>1334</td></tr><tr><td>VI. Acknowledgements</td><td>1335</td></tr><tr><td>VII. References</td><td>1335</td></tr><tr><td>VIII. Supporting Information</td><td>1337</td></tr></table>

# I. INTRODUCTION

One of the goals of the biological sciences is to identify ecological and evolutionary mechanisms driving community assembly that vary in space and time (Walker, 1992; Myers et al., 2000; Pavoine & Bonsall, 2011). Indices of biological diversity (or biodiversity) are commonly used with this aim. Biodiversity has been defined in many ways. According to the Convention on Biological Diversity published in 1992, biodiversity means ‘the variability among living organisms from all sources including, inter alia, terrestrial, marine and other aquatic ecosystems and the ecological complexes of which they are part: this includes diversity within species, between species and of ecosystems’. We will focus here on ecological and evolutionary aspects of inter-species diversity.

Historically, species diversity was measured as a function of species number and abundance, and the concept of diversity was thus connected to those of species abundance, commonness and rarity (Patil & Taillie, 1982). The concept of rarity has generally been used in the last few decades in ecology to describe the low abundance, restricted range size or habitat specificity of a species (Rabinowitz, 1981). However, many researchers have claimed the need also to describe variety in species' attributes since species are not equivalent and exchangeable (Findley, 1973, 1976; Vane-Wright, Humphries, & Williams, 1991; Faith, 1992). For example, we can expect that a set of species with a crocodile, an emu and a lion has higher diversity than does a set with a lion, a cheetah and a domestic cat because the species of the first set are more distant in the tree of life than are the species of the second set and have very contrasting biologies. Indices of phylodiversity and trait diversity have been produced (e.g. see Faith, 1992; Petchey & Gaston, 2002; Webb et al., 2002; Hardy & Senterre, 2007; Helmus et al., 2007; Villéger, Mason, & Mouillot, 2008; Pavoine & Bonsall, 2011). These measures of biological diversity synthesize the variety of species in terms of their different attributes: their phylogenetic relations (Vellend et al., 2011; Tucker et al., 2017) and their traits, including those that are qualified as functional (Petchey & Gaston, 2002).

Functional traits have been defined in the literature in different ways, notably as the traits that influence species' responses to environmental conditions (response traits) or that influence ecosystem properties (effect traits) (Lavorel & Garnier, 2002) and as 'the traits that are associated with species' ability to gain resources, disperse, reproduce, respond to loss and generally persist' (Weiher et al., 2011, p. 2403). The diversity in species' functional traits was naturally named 'functional diversity' (Petchey & Gaston, 2002). As many studies used the term 'functional trait' to describe any measurable character of a species, we will consider here the diversity in species traits more generally, referred to as 'trait-based diversity' (Pavoine & Bonsall, 2011). The expression 'phylogenetic diversity' is used to describe both a concept of diversity in the evolutionary histories and relatedness among any set of taxa and a measure developed by Faith (1992) for conservation purposes. To avoid any confusion, we use 'phylodiversity' to describe this concept.

In parallel, a few measures described the degree of isolation of a species in a phylogeny (May, 1990; Vane-Wright et al., 1991; Nixon & Wheeler, 1992). More recent studies developed many alternative measures accounting for branch lengths leading to the focal species in dated phylogenetic trees (Redding, 2003; Pavoine, Ollier, & Dufour, 2005; Redding & Mooers, 2006; Isaac et al., 2007; Redding et al., 2008; Huang, Mi, & Ma, 2011; Redding, Mazel, & Mooers, 2014; Pavoine et al., 2017). All these measures evaluate the originality of each species in a phylogenetic tree.

An issue of many general concepts used in science is that the word used to designate the concept also has a common use for a general audience and thus could have different meanings. Rarity and originality are no exception. In the most common sense, the words 'rarity' and 'rare' are usually associated with a low probability of encountering some specific entity. The term 'original' is also used to designate an unusual entity. When used in science, these words may refer to different definitions. In ecology, species rarity usually represents the low probability of encountering the species. Species' originality represents the low probability of encountering the species' biological characteristics (phylogenetic position or traits).
Several studies thus used the term ‘originality’ to describe a concept, a measure or both (e.g. Pavoine *et al.*, 2005; Buisson *et al.*, 2013; Redding *et al.*, 2014) to quantify the degree to which species may harbour or actually harbour rare characters. Since Webb *et al.* (2002), phylodiversity and originality have largely been used in ecological studies as proxies for trait-based diversity and originality, respectively. Recent approaches now recommend considering phylo- and trait-based measures as complementary: the former to reveal historical and evolutionary processes, and the latter to reveal ecological processes (e.g. Grandcolas, 1998; Pavoine & Bonsall, 2011; Kelly, Grenyer, & Scotland, 2014; Gerhold *et al.*, 2015; Mazel *et al.*, 2017).

Indeed, as ecology and evolution share a wide range of scientific questions about biodiversity, there is an increasing interest in inter-disciplinary studies. Ecologists try to understand how species interact with each other and with their environment as well as how these interactions can influence the assembly patterns of multispecies communities (e.g. Weiher, Clarke, & Keddy, 1998; Emerson & Gillespie, 2008; Cavender-Bares *et al.*, 2009). Evolutionary biologists work at different time scales and seek to understand the origins and history of biodiversity and its variation (e.g. Mergeay & Santamaria, 2012). At the frontier between these disciplines, studies in evolutionary ecology are developing frameworks to explain biodiversity patterns and dynamics, combining ecological causes of evolution and evolutionary implications in community assembly and ecosystem processes (e.g. Mouquet *et al.*, 2012). Such developments are now also needed to explain originality patterns. The use of phylogenies in community ecology, macroecology, and conservation biology reflects the shared recognition that accumulated evolutionary differences may explain trait variation and thus predict biological and ecological processes. Phylogenetic approaches have revolutionized these disciplines (Mouquet *et al.*, 2012; Tucker *et al.*, 2017).

Although the concepts of diversity, originality and rarity are fundamentally connected, they have often been treated independently in the past two decades in ecological and evolutionary studies. Even if attempts to bridge two of these concepts do exist (e.g. Pavoine *et al.*, 2005; Redding & Mooers, 2006), very few studies have tried to combine them all (e.g. Rosauer *et al.*, 2009; Cadotte & Davies, 2010). As a result, our three main objectives are: (i) to bring attention to the fact that a conceptual explanation of the links between the three concepts is still missing; (ii) to highlight the need to clarify some aspects of the complementarities between these concepts and their measurement; and (iii) to demonstrate why these three concepts should be used together in evolutionary, ecological and conservation studies.

## II. RARITY AND ORIGINALITY: TWO ENTANGLED CONCEPTS

Since the first ecological studies, one of the main challenges has been to determine the contribution of each species to

biodiversity. Those contributions are measured *via* species characteristics that can be expressed by species rarity or originality. Here, we provide an overview of the definitions of the rarity and the originality of a species. We highlight that these two words refer to the same core concept of being unusual and point out the importance of spatial scale for their definitions.

### (1) The concept of species rarity in ecology and evolution

The measurement of species rarity is relative, as its definition and units depend on the context, nature, quality and quantity of data, constraining every study to define what they mean by ‘rare species’ (Magurran, 2004; Hessen & Walseng, 2008). Many different definitions and viewpoints on rarity exist in the literature with biological aspects (e.g. abundance), threat aspects (e.g. extinction risk) and value aspects (e.g. ‘how special species are’) (Gaston, 1997). In a seminal paper, Rabinowitz (1981) proposed a typology of rare plant species by crossing three characteristics: local population size (high or low), geographic range (large or small) and habitat specificity (wide or narrow). She proposed seven forms of rarity by combining these three dichotomized criteria, excluding the case where a species has high local population size, large geographic range and wide habitat specificity. If local population size, geographic range and habitat specificity are all scarce (the most drastic form of rarity), then a species will be prone to be the most endangered and to extinction.

Because Rabinowitz’s (1981) classification requires a considerable amount of information for a given taxon that is often not available, many studies use only one criterion or a combination of two to determine species rarity (Kunin & Gaston, 1993). Most past studies have favoured a definition of rarity relying on abundance and range size (Gaston, 1997). However, even if a broad consensus has been reached on these two aspects of rarity, abundance and range size may be measured by many different approaches (Gaston, 1997). For example, the geographic range may be analysed in terms of extent of occurrence (EOO, total range extent even if unevenly occupied: Gaston, 1991; Kunin & Gaston, 1993), area of occupancy (AOO, amount of sites or grid squares inhabited: Gaston, 1991; Kunin & Gaston, 1993), or both. This distinction allows the identification of species that occur only in a restricted, localized area (low EOO) from species occupying a low proportion of the area within their otherwise large range boundaries (high EOO but low AOO) (Hartley & Kunin, 2003).

Regarding species abundance, Rabinowitz (1981) considered local abundance in terms of population size, which can be understood as the number of individuals in a population. In the context of biodiversity measurement, the number of individuals is also currently the most frequently used aspect of species abundance. Abundance, however, can be measured by different means (e.g. absolute and relative density, biomass, per cent cover), and these could also be included in species diversity analyses (e.g. Lyons, 1981).
In addition, the relevance of considering a single aspect of rarity as an indicator of all others is likely dependent on the phylogenetic and spatial scales considered. For example, although a positive correlation between species' range size and mean local population size at occupied sites has been reported by many studies across many different taxa and habitats (see Brown, 1984; Gaston, 1996), this correlation was sometimes found to be rather weak or even negative, particularly when the studied species were phylogenetically distant (Brown, 1984; Gaston, 1997; Johnson, 1998). Population size also varies depending on where in the range of the species it is measured, introducing a spatial contingency into defining rarity (Brown, 1984). Finally, part of this correlation may be due to sampling artefacts, as species with low local abundance are likely to be recorded from fewer sites than the number at which they actually occur (Gaston, 1997; Hessen & Walseng, 2008). Rarity is thus a multifaceted concept; and it is reasonable to argue that when data are available, several facets should be considered and compared (Gaston, 1997).

More recently, the concept of rarity has been extended to the functional and phylogenetic characteristics of species (Pavoine et al., 2005). In his paper on the definition(s) of rarity, Gaston (1997) reported that taxonomic distinctness had been considered to define rare species. Pavoine et al. (2005) highlighted that phylogenetic distinctness may represent how rare a species' traits are, and Pavoine et al. (2017) reported that many recent studies measured distinctness directly from a finite set of traits (e.g. Mouillot et al., 2013). Violle et al. (2017) subsequently proposed an expanded framework for rarity to study what they called 'functional trait rarity': a species is rare if it has a low abundance and distinct traits relative to the other species of an assemblage. They also stressed the need to study functional rarity from an evolutionary perspective by examining the phylogenetic signal of trait rarity. Overall, species rarity can thus be based on a variety of species attributes. Species' originality works with the phylogenetic and trait-based aspects of rarity.

## (2) The concept of originality or phylo- and trait-based rarity

Rarity is a concept widely used to determine a species' contribution to the diversity of a finite set of species. Nevertheless, fully to evaluate a species' contribution to phylo- and trait-based diversity, an additional measure of species characters is needed that can be made by means of species' originality. Here, we discuss the definition of species' originality, different types of originality measures and their link to abundance-based rarity.

The vocabulary employed to describe species' characters is continuously evolving and differs between evolutionary and ecological studies, leading to potential confusion when a term is employed without a clear definition or reference (Pavoine & Bonsall, 2011). Since the 1990s, various terms, such as 'originality', 'distinctness', 'distinctiveness', 'uniqueness' and 'isolation', have sometimes been used to refer to the same concepts but sometimes not. Thus, there is an inconsistency

in the literature in the terms used to describe measures and concepts of species rarity and in how they should be used.

The term originality describes species' general rarity using characteristics linked to traits and phylogeny. Pavoine et al. (2017) defined originality as the rarity of species' characteristics in a given set of species, where a characteristic can be a position on a phylogenetic tree or the state of a functional trait. As recommended by Pavoine et al. (2017), we define 'phylogenetic originality' as synonymous with the terms 'evolutionary distinctiveness' (Isaac et al., 2007), 'evolutionary isolation' (Redding et al., 2014), 'phylogenetic rarity' (Winter, Devictor, & Schweiger, 2013) and 'phylo-based rarity'. A species without close sister species in a phylogenetic tree is likely to have high phylogenetic originality and thus a high contribution to phylodiversity (Redding et al., 2008). In recent decades, particular attention has also been paid to species' functional traits that indicate species' roles in ecosystem functioning (e.g. Lavorel & Garnier, 2002; Mouillot et al., 2008; Schmera, Erős & Podani, 2009; Buisson et al., 2013; Mouillot et al., 2013; Rosatti, Silva, & Batalha, 2015; Brandl et al., 2016). The assumption that phylogenies indirectly comprise the evolutionary changes in species' characters may explain why the first originality approaches were inferred from phylogenetic trees.

These approaches were adapted to the analysis of species' functional traits, to measure what we hereafter refer to as 'functional originality'. We consider 'functional originality' a synonym of 'functional rarity' and 'rarity of functional traits'. More generally, we consider below 'trait-based originality' a synonym of 'trait-based rarity' and 'rarity of traits'. Hence, a species with very distinct trait values compared to those of other species is expected to have a higher contribution to trait-based diversity (Jarzyna & Jetz, 2016). Similar to abundance-based rarity, species' originality is relative to the values of the other species in a set. Following Pavoine et al. (2017), we define the uniqueness of a species as an unshared part of the species characteristics in a set. Strict uniqueness is thus an extreme case of originality in which a species does not share any of its characteristics. Finally, redundancy is anonymous with uniqueness and measures the number of shared characteristics of species. Therefore, originality is the full contribution of a species to the diversity of the set composed of species' unshared (uniqueness) and shared (redundancy) characteristics.

Commonness and rarity traditionally have been presented as the extremes of a gradient of the abundance-based rarity of species (Kunin & Gaston, 1993). Redundancy and uniqueness are the extremes of a gradient of species' originality, measured in terms of species' phylogenies and traits (Redding et al., 2014). Some studies have analysed potential correlations between the two gradients, asking whether original species are rare in terms of abundance at different spatial scales (Mi et al., 2012; Pigot et al., 2016). Researchers found variable results showing that abundance-based rarity and phylo- and trait-based originality are not always correlated depending on the
<figure><img src="image_7.png" /><figcaption>**Fig. 1.** Theoretical illustration of the concept of abundance-weighted functional originality. Here, we consider an assemblage of butterflies. The drawings represent butterfly species of different shapes, for which colours are used to represent a functional trait: two species with similar colours have similar values for the trait. Species *a*, *d*, *e*, *g*, *i* and *j* are each represented by one individual; species *b* by 30 individuals; species *c* by three individuals; and species *f* and *h* by two individuals each. The species *a* and *b* are original because they are the sole species with shades of orange. The other eight species have different shades of blue and green. However, given that species *b* is very abundant (being represented by 30 individuals), the abundance-weighted originality of species *a* can be considered low, and if originality is measured at the individual level, instead of at the species level, then the originality of any individual of species *b* can be considered low.</figcaption></figure>

spatial resolution of a study. Violle *et al.* (2017) combined both gradients to define what they called the ‘functional rarity’ of species (see Fig. 1 in Violle *et al.*, 2017): a species is functionally rare if it is both original and scarce. Their concept of functional rarity is not equal to our concept of functional rarity, which expresses the scarcity of species’ traits rather than the scarcity of the species themselves.

The measure of originality indeed can be weighted by
abundance, leading to a third gradient of rarity where
originality and abundance-based rarity are entangled. Along
this gradient, a species is original in an assemblage if
its characteristics are rare among the individuals of the
assemblage (Fig. 1). Similarly, Violle *et al.* (2017) suggested
an abundance-weighted measurement of functional trait
distinctiveness, making it dependent on species scarcity
(Box 2 in Violle *et al.*, 2017). Hence, originality (trait-based
and phylo-based rarities) and abundance-based rarity are
closely related concepts but use different types of data for
characterizing species based on their abundance, traits or
phylogeny (Fig. 2). More generally, other aspects of species

rarity could also be added to this framework, as, for example,
the link between originality and range size has also been
studied (Mouillot et al., 2013; Grenié et al., 2018).

(3) The measurement of originality

The concept of species' originality can be evaluated by
different measures that rely on various types of data (see
online Supporting information, Appendix S1). The first
originality indices were developed on a tree structure and
thus measured phylogenetic originality. However, they were
applied to undated phylogenies ignoring the evolutionary
time of change in taxa (May, 1990; Vane-Wright *et al.*, 1991;
Nixon & Wheeler, 1992). Those indices used the number of
internal nodes or the number of branches descending from
each node to compute species' originality. Unfortunately,
they can hardly distinguish the originality between species
from the same clade (Huang *et al.*, 2011; Redding *et al.*,
2014). These indices are useful when the branch lengths on
a phylogenetic tree cannot be estimated.

With the improved access to dated phylogenetic trees,
measures of phylogenetic originality have been developed
that account for branch lengths. Several of these measures
are derived from the Phylogenetic Diversity (PD) index of
Faith (1992): the sum of branch lengths on a phylogenetic
tree. Some of them are partly redundant, and their formulae
are very similar [the indices of fair proportion (FP) (Redding,
2003), equal splits (ES) (Redding & Mooers, 2006) and
evolutionary distinctiveness (ED) (Isaac *et al.*, 2007)]. Species
with few relatives and deep terminal branches would be more
original than species with many close relatives (Frishkoff
et al., 2014). Those indices can thus be used when one wants
to know which species subtends most of the evolutionary
history in a given set of species. According to Redding *et al.*
(2014), technically, all tree-based indices of species' originality
combine two main aspects of originality: the average distance
to another species on the tree [average phylogenetic distance
(APD) (Redding et al., 2014)] and the terminal branch lengths
that connect the species to the rest of the tree [pendant edge
(PE) (Redding et al., 2008)]. Another framework of tree-based
measures of species' originality has been proposed that uses
the genome evolution model of species characters called
'character rarity' (CHR; Huang et al., 2011). Its advantage
over other indices is that it incorporates models of dynamic
processes, such as character changes and distribution along
lineages during evolution. However, applications of this
framework are still scarce.

With the accumulation of species' trait information in large
databases, a new type of study appeared that considered
a limited number of traits (Violle *et al.*, 2007; Mouillot
*et al.*, 2008; Hidasi-Neto, Loyola, & Cianciaruso, 2015;
Ricotta *et al.*, 2016). As measures of originality already
existed for phylogenetic trees, trait data were transformed
into dendrograms (trees) to measure trait-based originality
(Buisson *et al.*, 2013; Pavoine *et al.*, 2017). A new challenge
appeared: how best to construct trait-based dendrograms
from a finite set of traits and how to avoid the distortion of
the original trait data (Petchey & Gaston, 2002; Mouchet
<figure><img src="image_8.png" /></figure>

**Fig. 2.** Diagram illustrating the three types of rarity discussed herein. Each black circle represents one type of rarity. The areas where the circles cross designate studies where two or more aspects of originality are compared or combined. We illustrate each core concept with a theoretical data set, represented here by seven theoretical butterfly species. (A) Abundance-based rarity (AR) (as an illustration, it is here inversely related to the number of individuals of each species); (B) phylo-based rarity (PR) representing species evolutionary histories (here exemplified by a theoretical phylogenetic tree); (C) trait-based rarity (TR) reflecting species' trait dissimilarities. In C, butterfly colours represent species' traits. We illustrate the trait-based rarity (or originality) of species *a* by the dissimilarities *d* between this species and all the others. The gradient above the top circle represents the two extremes of AR: commonness and rarity. The gradients below the bottom left and right circles represent phylogenetic and trait-based originality, respectively, from redundancy to strict uniqueness. (D, E) PR and TR can be compared with AR using two-dimensional graphs. The phylogenetic and trait data can also be weighted by species' abundances, leading to abundance-weighted trait-based rarity (ATR) and abundance-weighted phylo-based rarity (APR) (see, e.g. Fig. 1). (F) Studies that analyse both TR and PR thus far have compared the two aspects by two-dimensional graphs. No mathematical measures have been developed that combine these two aspects, which could lead to phylo- and trait-based rarity (PTR). (G) The area where the three circles cross would permit identification of the rarest species. Depending on whether different aspects of species rarity are treated independently or are combined, such rare species could be defined as having high AR, TR and PR (identified by the three-dimensional plot), high ATR and APR (identified by the two-dimensional plot), or high abundance-weighted PTR (identified by the diamond). The red circles and question marks show where further research is critically needed to determine how and why phylogenetically and functionally original species emerged in the course of evolution and how this emergence has influenced species abundance.

*et al.*, 2008), as most traits do not possess any structured hierarchy. A common approach is to first calculate pairwise trait-based dissimilarities between species and then construct the dendrogram from these dissimilarities with a clustering algorithm. Numerous mathematical equations exist to calculate the dissimilarity between two species using various traits (Pavoine *et al.* 2009). Additionally, the choice of the clustering method influences the shape of the dendrogram and eventually influences trait-based originality values (Mouchet *et al.*, 2008; Maire *et al.*, 2015). Transforming trait values into dendrograms introduces the risk of distorting the information on traits (Pavoine *et al.*, 2017) but could be justified as a way to compare trait-based originalities to phylogenetic originalities for the same set of species. Thus, in theory, any of the phylogenetic tree-based originality measures can be applied to trait-based dendrograms.

The problem raised by deforming trait data when building functional dendrograms can be bypassed by using trait-based dissimilarity matrices. Originality measures have therefore been adapted to work with dissimilarity matrices. Indeed, the average (*AV*) and nearest neighbour (*NN*) indices, working directly with trait-based dissimilarities between species, are the alternatives of the tree-based *APD* and *PE* indices (Pavoine *et al.*, 2017; Violle *et al.*, 2017; see also Appendix S1). *AV* is the average dissimilarity to all other species, and *NN* is the lowest dissimilarity to all other species. By extension, these dissimilarity-based indices can, in turn, incorporate not only trait-based but also phylogenetic dissimilarities calculated, for example, as the sum of branch lengths in the smallest path that connects the two species on the phylogenetic tree or as the time since their most recent common ancestor (Pavoine *et al.*, 2017). Computing phylogenetic dissimilarities is useful for comparing equally the trait-based and phylogenetic originalities of the same set of species without taking the risk of distorting trait data.

Clustering methods have also been criticized in favour of the use of multidimensional space (Maire *et al.*, 2015). A multidimensional trait space is a geometrical space representing the distribution of species according to their trait values (Mouillot *et al.*, 2013). It can be constructed in several ways (see Appendix S1). The coordinates of the species projected as points along the axes of such a space could be used for measuring originality. For example, originality has been measured as the distance from a species to the centroid (mean position) of all species (Magnuson-Ford *et al.*, 2009; Buisson *et al.*, 2013). In theory, the multidimensional approach was created for the trait-based context but could also be applied to a phylogenetic space obtained, for example, with a principle coordinate analysis applied to phylogenetic dissimilarities between species (e.g. Sobral, Lees, & Cianciaruso, 2016).

Pavoine *et al.* (2005) introduced another family of originality measures by analysing the composition of theoretical species assemblages that would maximize an index of diversity. The first index of this family, the QE-based index (*Qb*), relies on Rao's quadratic entropy (*Q*) (1982). *Q* is an index of diversity equal, in our context, to the phylogenetic
or trait-based dissimilarity between two randomly selected species in an assemblage. *Qb* uses the abundance values that species should have to maximize the phylogenetic or trait-based dissimilarity between two randomly selected species (Huang *et al.*, 2011). Up to a certain degree, the more abundant the original species are, the more the diversity of a set is increased (Pavoine *et al.*, 2017). The hypothetical abundances of species that maximize *Q* thus reflect species' originality. Using the same approach, Pavoine *et al.* (2017) developed the *Rb* index using another index of entropy (*R* index) closely related to *Q*. *Rb* turned out to be similar, although not equal, to the *AV* index (the mean trait-based or phylogenetic distance to all other species), but it better discriminates original from redundant species. The *Qb* and *Rb* indices are discussed in detail in Section III.3.

Finally, a measure of originality based solely on species' phylogeny or traits does not account for other aspects of rarity such as population size or species range size. As shown in Fig. 2, species originality can be weighted by species abundance. Regarding phylogenetic data, Cadotte *et al.* (2010), for example, modified the *FP* index (see Appendix S1) to account for species abundance when species' originalities are calculated within a local community. To do so, at each tip (species) of a phylogenetic tree, they artificially added as many branches with a length of zero as there were individuals from the corresponding species in the local community. In other words, they weighted each terminal branch by the number of individuals it subtends. They then calculated originality using the *FP* index but replaced species with individuals, leading to their abundance-weighted evolutionary distinctiveness (*AED*). Later, they modified their index, replacing individuals with populations or sites (Cadotte & Davies, 2010), leading to the biogeographically weighted evolutionary distinctiveness (*BED*) index.

By doing so, Cadotte & Davies (2010) evaluated the originality of an individual or a population of a species. Other approaches have been proposed using different types of data. For example, Ricotta *et al.* (2016) proposed measuring the originality of a species as the abundance-weighted mean of the trait-based difference between this focal species and all other species in a set (weighted version of *AV* index). Laliberté & Legendre (2010) measured the distance in a functional trait space between a species point and the abundance-weighted centroid of all species points, which can be viewed as a measure of abundance-weighted originality. Other indices were also developed that weight originality by species' extinction risk (e.g. Steel, Mimoto, & Mooers, 2007).

### (4) How should the originality measure be chosen?

Measures of species' originality are expressed by formulae, and their mathematical properties can influence the results and conclusions of a study. Several studies have compared some of these measures, seeking to establish the relations between them (Redding *et al.*, 2008, 2015; Huang *et al.*, 2011; Vellend *et al.*, 2011; Redding *et al.*, 2014; Pavoine *et al.*, 2017). According to these studies, some measures are more redundant than others in capturing species-specific

information and in their contribution to phylodiversity and trait-based diversity. Each type of originality measure has its own advantages and drawbacks. In general, tree-based indices would be influenced by tree topology (terminal and deeper branch lengths, unresolved nodes, root consideration), but trees without branch lengths fail to discriminate individual species.

If one wants to compare species' phylogenetic and trait-based originalities, then dissimilarity-based indices are a better choice than tree-based indices, as the former avoid the potential distortion of trait data. The use of a multidimensional trait space permits potential original species and their traits to be visualized. However, all axes need to be retained. For example, a common practice when measuring trait-based diversity is to apply a principal coordinate analysis to trait dissimilarities and then select the first few axes to calculate diversity, a constraint that may be forced by the mathematical properties of the diversity indices (e.g. Villéger, Mason, & Mouillot, 2008). This dimension reduction can hide some important species if their originalities are explained by traits on the non-visualized axes. Finally, originality indices, such as *Qb* and *Rb* (Appendix S1), predict species' originality expressed as the theoretical abundance of a given species that maximizes the diversity of a set. Note that, as originality is context dependent, its application to a set of two species would give equal originality to both species; the concept of originality is thus useful as soon as there are at least three species in a set.

### (5) Spatial scale matters

Spatial resolution is essential in species' originality analyses that are relative to a reference set of species. Phylogenetic originality is usually measured at the level of an entire clade in an evolutionary context (e.g. Jetz *et al.*, 2014). This allows the identification of the most phylogenetically original species on Earth and the analysis of their biogeography (e.g. Safi *et al.*, 2013) and extinction risks (e.g. Isaac *et al.*, 2007). Such global phylogenetic originalities have been typically summed for all species present in a region to evaluate the worth of the region for conservation: how many globally original species occur in the region (e.g. Pollock, Thullier & Jetz, 2017), and how original are these species (e.g. Safi *et al.*, 2013; Jetz *et al.*, 2014)?

Such an approach has also been applied at local levels. In this case, phylogenetic originality was calculated within the species pool present at a regional (or continental) or global level. Then, these regional or global originalities were summed or averaged for all species within a local site to identify priority sites for conservation (e.g. Veron, Clergeau, & Pavoine, 2016) or to determine variations in the presence of original species among environments (e.g. Morelli *et al.*, 2018). Most of these studies on phylogenetic originality focused on the preservation of evolutionary history.

Another approach, which Redding *et al.* (2015) explored, is to calculate species originalities directly within a local site and to compare these local values with regional originalities of the species. They demonstrated that the correlation between
local and regional originality depends on the originality index used. Analysing Nearctic and Neotropical birds, they found that this correlation is spatially structured and may depend on the habitat, being notably higher in forested habitats. More studies of this type are needed to evaluate how local conservation projects based on local originalities could complement regional and global projects relying on large-scale originalities.

In contrast to phylogenetic originality, trait-based (especially functional) originality is rarely measured at the global level (see, e.g. Grenié *et al.*, 2018). Instead, it is usually evaluated at the regional and local levels, as species' characteristics are directly related to ecosystem functioning and depend mostly on field data (Da Silva, Silva & Batalha, 2012; Mouillot *et al.*, 2013; Rosatti *et al.*, 2015). Several of these studies have underlined the potential vulnerability of functionally original species (Mouillot *et al.*, 2013) and the negative impact of human disturbance on these species, with potential consequences on the sustainability of ecosystems (Rosatti *et al.*, 2015). However, these conclusions may depend on the taxa, traits and geographic areas analysed (Da Silva *et al.*, 2012; Brandl *et al.*, 2016).

Future studies could thus focus on determining and explaining scale-dependent patterns in both phylogenetic and trait-based originalities as local, regional and global originality scores may or may not be correlated depending on the processes that drive diversity patterns (Redding *et al.*, 2015).

## (6) Directions for future research on species' originality

We have shown that studies on species rarity recently have been improved by the addition of aspects of trait-based and phylogenetic originalities. Although many originality measures have been developed at the species level, they can be applied to other units of biodiversity, notably, to individual organisms (Pavoine *et al.*, 2017). For example, replacing trait-based dissimilarities between species with trait-based dissimilarities between individuals and replacing species points in multidimensional trait space by individual points allow all originality indices presented above to be transferred from the measure of the originality of a species to the measure of the originality of each individual (de Bello *et al.*, 2011; Violle *et al.*, 2012). For example, if information on estimated among-individual divergence is available on a phylogenetic tree, Cadotte *et al.* (2010, Appendix S2) proposed replacing species with individuals in the *FP* originality index to measure the originality of each individual. Thus, from a mathematical viewpoint, it is possible to apply all originality indices at an individual level and access the intraspecific variability of genes and traits. Applying such detail is limited by the scarcity of relevant data and by the relative cost of obtaining these data; however, this approach could provide more accurate results when trait variation is large within species.

Although we focused our review on inter-species diversity with aspects of phylo- and trait-based diversity, the concept of originality was also treated in genetics. For example, genetic

originalities can be calculated by replacing phylogenetic distances between species with genetic distances based on a genome evolution model (Thaon d'Arnoldi, Foulley, & Ollivier, 1998; Laval *et al.*, 2000; Huang *et al.*, 2011). Connecting these different fields of research could improve biodiversity analyses, allowing more continuous multiscale analyses, better to connect patterns to underlying processes in ecological and evolutionary studies.

In Section III, we demonstrate how the indices of originality relate to indices of diversity. We develop potential applications of originality indices with real case studies in Section IV.

## III. LINKS BETWEEN MEASURES OF DIVERSITY, ABUNDANCE-, PHYLO- AND TRAIT-BASED RARITY

A biodiversity measure is a calculation method expressed by a mathematical formula that allows specific values for the amount of variety in a biological system to be computed; in our case, these values are computed for a set of species. The oldest and most intuitive biodiversity measure is species richness, computed as the number of species (Magurran, 2004). Nevertheless, species richness has several drawbacks. First, it is strongly dependent on sampling effects: in a highly diverse community, the observed number of species may greatly underestimate the real number of species because species with very low abundance frequently will be absent from even very large samples (Lande, 1996). Second, it would give equal diversity to a region dominated by a single species with two rare species and a region with three species having even abundances (e.g. Magurran, 2004). Third, it does not include any information on species' traits and evolutionary histories. Fourth, it depends strongly on the definition of the concept of species (Gaston & Spicer, 2004; Groves *et al.*, 2017) and on taxonomic incompleteness (e.g. Delić *et al.*, 2017). To overcome these drawbacks, alternative diversity indices have been developed that include species' abundances, phylogenies and traits and thus incorporate various facets of species rarity. Diversity is measured at the level of the species set, while rarity and originality indices provide a value for each species that is linked to its contribution to the diversity of the set.

In the last two decades, the development of trait-based diversity and phylodiversity indices has expanded. This variety of indices is partly explained by the fact that one single mathematical formula cannot alone encompass all aspects of biodiversity in a set, especially phylogeny, functionality and abundance (Mason *et al.*, 2005; Mazel *et al.*, 2016). Several previous studies tried to define semantic frameworks for measurements of species' phylo- and trait-based diversities, in which they specified whether each measure of diversity was weighted by abundance data (e.g. Ricotta, 2007; Pavoine & Bonsall, 2011; Vellend *et al.*, 2011; Tucker *et al.*, 2017). Our aim here is not to review all mathematical indices of species' phylo- and trait-based diversities but to analyse a small set of
previous approaches for which we can try to distinguish the
relative importance of abundance-based, phylogenetic and
trait-based rarities. Although most of the diversity indices
we discuss in this part were developed either for phylo-
or trait-based diversity, all our reasoning could easily be
adapted to be applied to both contexts.

(1) Biodiversity as a mean of abundance-based rarities

Indices that relied on species number and species' abundances were named indices of species diversity. A myriad of such indices have been developed in the literature (e.g. Magurran, 2004). The most famous and most commonly used are the Gini–Simpson index (Gini, 1912; Simpson, 1949) and the Shannon index (Shannon, 1948). In Fig. 3, we provide Patil & Taillie's (1982) demonstration that species richness, the Shannon index, and the Gini–Simpson index can be written as the mean of species abundance-based rarity values. The three indices differ in their sensitivities to the presence of rare species. The most sensitive measure is species richness, as it gives equal importance to all species regardless of their abundance. The least sensitive is the Gini–Simpson index, which overweights common species relative to rare species (Lande, 1996). Thus, using abundance in indices of species diversity is actually a way to give a different value to each species. For example, giving the same value to each individual of a species forces us to give different values to the species, as species are represented by different numbers of individuals in a community. These well-known diversity indices illustrate that the first links between diversity measurement and rarity measurement date back to around 40 years ago, but these links concerned only abundance-based rarity. They did not refer to the phylogenetic or functional trait characteristics of the species.

The simplest approach for measuring aspects of phylo-
and trait-based diversity naturally consisted of replacing
species in the traditional diversity indices discussed above
with trait-based or phylogeny-based entities. When the
analysis of species' functional traits as an aspect of diversity
measurement started to emerge in the ecological literature,
the number of functional groups represented by the species
in a community was the main measure of trait-based
diversity (Petchey & Gaston, 2002). A functional group is
a group of species united by their similarities in a given trait
value or set of traits (Brandl et al., 2016). The traditional
diversity measures can be applied to the abundance of
each functional group instead of to the abundance of each
species (Hooper et al., 2002). In that case, trait-based diversity
can be related to the abundance-based rarity of functional
groups. Such an approach could also be applied to clades
in a phylogenetic tree, replacing functional groups with
clades (Pavoine, Love, & Bonsall 2009). Although such
approaches allow phylodiversity and trait-based diversity
indices to be connected to abundance-based rarities, they do
not connect them directly to measures of originality, that is,
with trait-based and phylogenetic rarities.

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

**Fig. 3.** Functions of rarity associated with three traditional indices of diversity. A simple index to measure the diversity of a set is the number of species, or 'species richness'. A related index is the number of species minus one, for which an assemblage with only one species has null diversity. Indeed, the minimum value of many indices of species diversity is zero and is reached when a single species dominates the assemblage (its relative abundance is 1). Let *S* be the num- ber of species in the assemblage; *p*ᵢ, the relative abundance of species *i*; and **p**, the vector of species' relative abun- dances. Patil & Taillie (1982) emphasized that a richness-related (*H*<sub>r</sub>), the Shannon (*H*<sub>S</sub>) and the Gini–Simpson (*H*<sub>GS</sub>) indices can be written as follows: *H*<sub>r</sub>(**p**) = *S* − 1 = *∑*<sub>i=1</sub><sup>*S*</sup> *p*ᵢ*R*<sub>r</sub>(*p*ᵢ), *H*<sub>S</sub>(**p**) = − *∑*<sub>i=1</sub><sup>*S*</sup> *p*ᵢ log (*p*ᵢ) = *∑*<sub>i=1</sub><sup>*S*</sup> *p*ᵢ*R*<sub>S</sub>(*p*ᵢ), and, *H*<sub>GS</sub>(**p**) = 1 − *∑*<sub>i=1</sub><sup>*S*</sup> *p*ᵢ² = *∑*<sub>i=1</sub><sup>*S*</sup> *p*ᵢ*R*<sub>GS</sub>(*p*ᵢ), where *R*<sub>r</sub>(*p*ᵢ) = 1/*p*ᵢ − 1, *R*<sub>S</sub> = −log(*p*ᵢ), and *R*<sub>GS</sub> = 1 − *p*ᵢ. The functions *R*<sub>r</sub>, *R*<sub>S</sub> and *R*<sub>GS</sub> represent how rare a species is. In Patil & Tail- lie's (1982) framework, rarity means low abundance (for example, in terms of biomass or number of biological organisms). *HCDT* entropy generalizes all these indices of diversity (Havrda & Charvat, 1967; Daróczy, 1970; Tsallis, 1988). Its formula is <sup>q</sup>*H*<sub>HCDT</sub>(*p*) = (1 − *∑*<sub>i=1</sub><sup>*S*</sup> *p*ᵢ<sup>q</sup>) / (*q* − 1) = *∑*<sub>i=1</sub><sup>*S*</sup> *p*ᵢ [<sup>q</sup>*R*<sub>HCDT</sub>(*p*ᵢ)]. If *q* = 0, then the *HCDT* index is *H*<sub>r</sub>; *q* tending to 1 gives *H*<sub>S</sub>, and *q* = 2 gives *H*<sub>GS</sub>. The general form of the function of rarity associated with the *HCDT* index is <sup>q</sup>*R*<sub>HCDT</sub>(*p*ᵢ) = (1 − *p*ᵢ<sup>q−1</sup>) / (*q* − 1). The sensitivity to rare species decreases with *q*. The figure shows how the functions of rarity *R*<sub>r</sub>, *R*<sub>S</sub> and *R*<sub>GS</sub> vary if *p*ᵢ increases from zero to one.

(2) Explicit link between originality and diversity indices

Because a myriad of species diversity indices have been
developed since the 1970s, a myriad of phylo- and trait-based
diversity indices could also be imagined by replacing
species abundance in species diversity indices, such as the
Gini–Simpson and Shannon indices, with species originality
or abundance-weighted originality. This approach was
adopted, for example, by Cadotte *et al.* (2010), Scheiner
(2012) and Scheiner *et al.* (2017). With such an approach, the diversity would be high if the number of species was high and if species' originalities were even. If originalities are weighted by abundance (Figs 1 and 2), then the diversity would be high if the distribution of abundance was imbalanced such that the most original species have the highest abundances (Cadotte *et al.*, 2010).

Ricotta (2004) previously proposed a different approach in which abundance and originality were treated as independent information on species (instead of using abundance-weighted originalities) and were combined into one measure of diversity. Further, he used taxonomy instead of a phylogeny, leading to taxonomic originality, where a species is original if there are no or relatively few species from the same genus, family, order, etc. We will see in Section III.3 that many diversity indices can also be considered as functions (e.g. sum, mean or abundance-weighted mean) of originality values, even if the indices were not developed with that goal in mind. The approach in which species' originality values and potential species abundances are combined in a mathematical formula is thus central to the measurement of biodiversity.

### (3) Most diversity indices only implicitly depend on species' originalities

Contrary to the few approaches presented in the previous Section III.2, species' originality, obtained through phylo- and trait-based rarities, was often only implicitly incorporated into measures of phylo- and trait-based diversity. We will show here that many of these measures of diversity can instead be seen as functions of species' originality or are largely linked in their formulae to a measure of species' originality, even if they were not developed explicitly as functions of species' originality.

#### (a) When diversity equals the sum or mean of originalities

Early on, Faith (1992) suggested replacing species in diversity measurements with features (or character states) of species. He then developed, as a proxy for feature richness, the *PD* index, which is the sum of branch lengths on a phylogenetic tree. His approach assumes that branch lengths reflect feature richness. Ten years later, Petchey & Gaston (2002) similarly proposed representing trait diversity as the sum of branch lengths on a trait-based dendrogram, an index named ‘functional diversity’ (*FD*). Two of the most used phylogenetic originality measures, the *FP* and *ES* indices (Appendix S1), can be written as additive decompositions of *PD* (Redding & Mooers, 2006; Isaac *et al.*, 2007). Thus, the sum of the originality values over all species in a set is equal to the *PD* of the set. If these originality indices, *FP* and *ES*, are calculated from a trait-based dendrogram rather than from a phylogenetic tree, then the sum of species’ trait-based originality values over all considered species equals the *FD* index.

Two other commonly employed indices of community phylodiversity, the mean pairwise distance (*MPD*) index

and the mean nearest taxon distance (*MNTD*) (Webb *et al.*, 2002), are means of species' originality values. The mean of the *APDs* (average phylogenetic distance to all other species) over all species in a set is equal to the *MPD* or the average phylogenetic distance between any two species in a set (Redding, Mazel, & Mooers, 2014). The minimum phylogenetic distance to another species is an index of strict uniqueness (see indices *NN* and *PE* in Appendix S1). Its mean over all species in a set is equal to the *MNTD* index used during the last two decades to detect the effects of competition in community assembly (Kraft *et al.*, 2007). Such reasoning could also be applied to trait-based dissimilarities between species. Indeed, indices such as *MPD* and *MNTD* were used early on by Findley (1973, 1976) to analyse the morphometrical diversity of bats. These indices do not integrate information on how abundant species are. However, other diversity indices can be seen as functions of abundance-weighted originalities.

#### (b) When diversity equals the sum or mean of originalities weighted by abundance

Regarding phylodiversity, the sum over all individuals of the *AED* value (individual’s phylogenetic originality) of Cadotte *et al.* (2010) is equal to Faith’s *PD* (phylogenetic diversity) of the species pool. Similarly, the sum over all populations of the *BED* value (the phylogenetic originality of each population of a species) of Cadotte & Davies (2010) is also equal to Faith’s *PD* of the species pool. Furthermore, Cadotte & Davies (2010) suggested summing the *BED* values over all species in a site, leading to a measure of the relative originality of each site, and thus to its relative conservative worth within the studied regional area. This *BED* index was inspired by the index of phylogenetic endemism (*PE*) of Rosauer *et al.* (2009), which measures the spatial restriction of the evolutionary history in a site. *PE* is the sum of the ratios branch length/clade range for each branch of the phylogenetic tree pruned to retain only the species of a site. The clade range is the union of the ranges of all species descending from the branch. As such, *PE* combines notions of phylodiversity (if species were ubiquitous, *PE* would be maximal, which means that regional and local phylodiversity would be equal), range-based rarity (each branch length is divided by clade range), and originality (if a species with a small range descends from a long branch, it is likely to have a high contribution to *PE*). Cadotte & Davies (2010) developed *BED* to complement the concept of phylogenetic endemism with a measure that can be calculated at different levels: at the species, population, local-site or regional levels.

Pavoine *et al.* (2009) used clades (groups of all species descending from a specified ancestor) in a phylogenetic tree to obtain measures of phylodiversity. They divided a tree into time periods defined between two consecutive speciation events. They then applied the Havrda-Chavat-Daróczy-Tsallis (*HCDT*) diversity index (a generalization of the well-known species richness, Shannon and Gini-Simpson indices; Fig. 3) at each period, using the number of clades that descend from the period and their
relative abundances (the abundance of a clade is the sum of abundances of all its species). At the most recent period, clades are species. However, from the second period, at least one clade has more than one species. The associated index of diversity is equal to the length of the period (in million years of evolution) times the clade diversity (as measured by the HCDT index applied to clades). Such an approach brings an evolutionary dimension to traditional diversity analysis and has the advantage of considering species abundance when measuring the phylodiversity of a local community.

This approach is a generalization of the HCDT index for phylogenetic studies, which was at the core of the theory of Patil & Taillie (1982) to express diversity in terms of the mean of species' rarities (Fig. 3). In Patil & Taillie's (1982) theory, rarity was expressed as low abundance. In Table 1, we propose a new theory using the generalization of the HCDT index from Pavoine et al. (2009). In this new theory, rarity is measured as an abundance-weighted phylogenetic originality, and the measure of phylodiversity is quantified as the mean of these abundance-weighted originality values. This theory could also apply to trait-based diversity if a dendrogram is available.

Another currently widespread approach to measure biodiversity consists of defining trait-based or phylogenetic dissimilarities between species and taking their sum or their mean (e.g. Rao, 1982; Walker, Kinzig, & Langridge, 1999; Schmera, Erös, & Podani, 2009). The quadratic entropy index ($Q$; Rao, 1982) is a diversity measure that can handle dissimilarities between species and species abundance. Shimatani (2001) demonstrated how to decompose $Q$ into three underlying components: (i) the Gini-Simpson index (an index of species diversity that itself is a combination of species richness and species evenness (Fig. 3); (ii) the non-weighted mean of the (trait-based or phylogenetic) dissimilarities between species; and (iii) a measure of the balance between species' abundances and the (trait-based or phylogenetic) dissimilarities between species (which can be related to a covariance-like measure). Examining this decomposition reveals that $Q$ increases with abundance evenness and with the number of trait-based or phylogenetic dissimilarities between species, and it increases when the most redundant species are rare and the most abundant ones have the highest trait-based or phylogenetic dissimilarities between them (Fig. 4).

We showed in Section III.1 that the first component, the Gini-Simpson index, can be viewed as a mean of abundance-based rarities and, in Section III.3a, that the second component, the non-weighted mean of the trait-based (or phylogenetic) dissimilarities between species, can be viewed as a mean of trait-based or phylo-based rarities. The third component, the balance component, relates abundance to trait-based or phylogenetic differences. More generally, if we measure the originality of a species as the abundance-weighted mean of the (phylogenetic or trait-based) dissimilarity to all species (including the species itself), then the mean of species' abundance-weighted originalities equals Rao's quadratic entropy (Ricotta et al.,

2016). Therefore, the quadratic entropy can be decomposed into independent values of abundance-based rarity and trait-based/phylogenetic originality or into more integrative values of abundance-weighted originalities. In Section III.3c, we also develop a distinct link between quadratic entropy and the notion of originality. We were thus able to extract multiple links between abundance-based rarity, originality and diversity indices from some of the mathematical formulae used to measure these concepts.

(c) When species' abundances maximize diversity

To measure the trait-based diversity and phylodiversity of a species assemblage, Pavoine et al. (2005) and Pavoine et al. (2017) used the quadratic entropy index, $Q$, and a related index, $R$, with the following formulae:

$$Q = \sum_{i=1}^{S} \sum_{j=1}^{S} p_i p_j d_{ij} \qquad (1)$$

$$R = \sum_{i=1}^{S} \sum_{j=1}^{S} \sqrt{p_i} \sqrt{p_j} d_{ij} \qquad (2)$$

where $p_i$ is the relative abundance of species $i$ among $S$ species and $d_{ij}$ is a measure of phylogenetic or trait-based dissimilarity between species [see Pavoine et al., 2005 for the use of $Q$ with a restriction on dissimilarities, i.e. use of ultrametric dissimilarities]. As a measure of species' originality, the authors proposed the abundance that species should have in order to maximize the diversity (according to $Q$ or $R$) of a theoretical assemblage. The associated indices of species' originality, named $Qb$ and $Rb$, thus correspond to the values of $p_i$ that maximize $Q$ and $R$, respectively, given that the trait-based or phylogenetic dissimilarities cannot vary (for details, see Pavoine et al., 2017). The $Qb$ and $Rb$ indices illustrate that, up to a certain degree, trait-based diversity and phylodiversity are high if the most original species in a set (with the highest trait- and phylo-based rarities) have the highest abundance and thus the lowest abundance-based rarity (Fig. 5, Table 2). The indices $Q$ and $R$ thus reveal an important property that abundance-weighted indices of trait-based diversity and phylodiversity should have: a positive correlation between species' abundances and species' originalities tends to increase trait-based diversity and phylodiversity.

(d) When diversity and originality depend on a multidimensional space

The previously discussed diversity indices either used a tree structure or used phylogenetic or trait-based dissimilarities among species directly. However, several diversity indices were also developed specifically for use in a multidimensional space, where axes reflect traits and points are species' positions according to their traits, as described in Section II.3. The distance of a species' point to the centroid of all points (mean position of all species from the reference set
<table><caption>Table 1. Phylodiversity as a mean of phylogenetic rarities</caption><thead><tr><th></th><th>Function of diversity</th><th>Function of rarity<sup>a</sup></th></tr></thead><tbody><tr><td>General formula<sup>b</sup></td><td>I<sub>q</sub> = &sum;<sub>k=1</sub><sup>K</sup> T<sub>k</sub> [1 &minus; &sum;<sub>b &isin; P<sub>k</sub></sub> p<sub>b</sub><sup>q</sup>]<br>= &sum;<sub>i=1</sub><sup>S</sup> p<sub>i</sub> O<sub>q</sub></td><td>O<sub>q</sub> = [&sum;<sub>b &isin; C(i, Root)</sub> L<sub>b</sub> (1 &minus; p<sub>b</sub><sup>q&minus;1</sup>)]</td></tr><tr><td>q = 0<sup>c</sup></td><td>I<sub>0</sub> = PD &minus; T = &sum;<sub>i=1</sub><sup>S</sup> p<sub>i</sub> O<sub>0</sub></td><td>O<sub>0</sub> = &sum;<sub>b &isin; C(i, Root)</sub> L<sub>b</sub> (1 &minus; 1)</td></tr><tr><td>q &rarr; 1</td><td>I<sub>1</sub> = &minus;&sum;<sub>k=1</sub><sup>K</sup> T<sub>k</sub> [&sum;<sub>b &isin; P<sub>k</sub></sub> p<sub>b</sub> ln(p<sub>b</sub>)] = &sum;<sub>i=1</sub><sup>S</sup> p<sub>i</sub> O<sub>1</sub></td><td>O<sub>1</sub> = &sum;<sub>b &isin; C(i, Root)</sub> L<sub>b</sub> ln(1/p<sub>b</sub>)</td></tr><tr><td>q = 2</td><td>I<sub>2</sub> = &sum;<sub>k=1</sub><sup>K</sup> T<sub>k</sub> (1 &minus; &sum;<sub>b &isin; P<sub>k</sub></sub> p<sub>b</sub><sup>2</sup>) = &sum;<sub>i=1</sub><sup>S</sup> p<sub>i</sub> O<sub>2</sub></td><td>O<sub>2</sub> = &sum;<sub>b &isin; C(i, Root)</sub> L<sub>b</sub> (1 &minus; p<sub>b</sub>)</td></tr></tbody></table>

<sup>a</sup>The functions $O_q$, $O_0$, $O_1$ and $O_2$ represent the abundance-weighted originality of a species.

<sup>b</sup>q is the parameter of the diversity index. It controls the importance given to species lineage abundances (from presence/absence if $q = 0$ to the overweighting of the most abundant lineage if $q \to \infty$). $p_i$ is the relative abundance of species i. b is a branch of the phylogenetic tree. k is a period in the phylogeny, $T_k$ is its length (time elapsed between two speciation events) and $P_k$ is the set of branches that cross period k. K is the number of periods. $C(i, Root)$ is the shortest path from species i (tip) to the root of the tree. $L_b$ is the length of branch b. $p_b$ is the summed relative abundance of all species descending from branch b. S is the number of species. See Appendix S2 for a proof of this.

<sup>c</sup>For $q = 0$, PD is the sum of branch lengths on the phylogenetic tree, and T is the height of the tree. The function of rarity is linked to the AED index of abundance-weighted phylogenetic originality in Cadotte et al. (2010): if $p_i = N_i/N$, with $N_i$ being the number of individuals of species i, and N is the number of individuals across all species, then $O_0 = AED \times N - T$.

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

**Fig. 4.** The quadratic entropy ($Q$) and its main components according to Shimatani (2001). Here, we considered nine theoretical examples formed by crossing three phylogenetic trees with species as tips and three vectors of species abundance. In each example, species are named a, b, c and d. We defined the dissimilarity $d_{ij}$ between any two species i and j as the time since their first common ancestor. For example, in the top tree, $d_{ab} = 1$ million years. The vectors of abundance give the number of individuals of each species in a community (the $N_i$ values). We calculated the relative abundance of any species i as $p_i = N_i / \sum_{i \in \{a, b, c, d\}} N_i$. According to Shimatani (2001), the quadratic entropy ($Q = \sum_{i,j \in \{a, b, c, d\}} p_i p_j d_{ij}$) is a function of the Gini-Simpson index of species diversity ($H_{GS} = 1 - \sum_{i \in \{a,b,c,d\}} p_i^2$), the mean dissimilarity between two species ($MPD = (\sum_{i,j \in \{a,b,c,d\}} d_{ij}) / (4 \times 3)$) and a balance component measuring a link between species' dissimilarities and species' abundances ($B = \frac{1}{2} \sum_{i,j \in \{a,b,c,d\}} (d_{ij} - MPD) / [p_i p_j - H_{GS} / (4 \times 3)]$: $Q = H_{GS} * MPD + B$. The figure shows that B is negative if the most abundant species are closely related and that it is positive if the most abundant species are also the most dissimilar. It shows that B is zero in two cases: when the abundances are even and when the dissimilarities between species are even. The top and middle trees have similar topologies, but we multiplied branch lengths by 10 in the middle tree, which allowed us to emphasize that $Q$, MPD and B are multiplied by any factor X (here, 10) if the dissimilarities between species are all multiplied by X.

in the space) is the main originality measure derived from this method (e.g. Magnuson-Ford et al., 2009). Laliberté & Legendre (2010) proposed using the average distance to the centroid over all species as a measure of trait-based diversity (functional dispersion index, FDis). They defined two versions of this index: one unweighted (presence/absence data) and

the other weighted by species' relative abundances. In the unweighted version, FDis values are high if species are all very original, i.e. have the highest distances to the centroid. In the weighted version, they used abundance-weighted distance to the centroid and thus abundance-weighted originality values. In this weighted version, FDis will be high if the most original
<figure><img src="image_11.png" /><figcaption>**Fig. 5.** Theoretical illustration of the *Qb* and *Rb* originality measures. Up to a certain degree, increasing the abundance of the most original species increases diversity. As in Fig. 1, we use different drawings of butterflies as theoretical species and differences in colours as indicators of functional/phylogenetic dissimilarities between species. Let us first consider a community in which each species is represented by three individuals (A). This butterfly community appears mostly blue. However, if we increase the abundance (in terms of number of individuals) of orange species, which are more original, then the colour diversity of the new community appears much higher (B). If we increase the abundance of orange species too much, then the butterfly community appears mostly orange, and the diversity decreases (C). The relative originalities of the species do not change from A to C if abundance data are discarded, but the relative abundance-weighted originalities of the orange species decrease, and those of the blue species increase from A to C. In other words, an orange species in C is original (species level), but an orange individual is not (organism level). The indices *Qb* and *Rb* determine precisely the abundance that species should have to maximize diversity (see Table 2 for an example with the index *Rb*).</figcaption></figure>

<table><caption>Table 2. Illustration of the link between species’ originality (*Rb*) and the index of diversity named *R* in Pavoine et al. (2017)</caption><thead><tr><th>Species<sup>a</sup></th><th>Mass<sup>a</sup>(kg)</th><th>S1 (%)<sup>b</sup></th><th>S2 (%)<sup>b</sup></th><th>S3 (%)<sup>b</sup></th><th>S4 (%)<sup>b</sup></th><th>S5 (%)<sup>b</sup></th></tr></thead><tbody><tr><td>U. arctos</td><td>266.50</td><td>10</td><td>25</td><td>49.60</td><td>70</td><td>85</td></tr><tr><td>V. vulpes</td><td>5.60</td><td>30</td><td>25</td><td>16.61</td><td>10</td><td>5</td></tr><tr><td>M. erminea</td><td>0.90</td><td>30</td><td>25</td><td>16.89</td><td>10</td><td>5</td></tr><tr><td>M. nivalis</td><td>0.04</td><td>30</td><td>25</td><td>16.90</td><td>10</td><td>5</td></tr><tr><td></td><td></td><td>↓</td><td>↓</td><td>↓</td><td>↓</td><td>↓</td></tr><tr><td>Body-mass diversity (R)<sup>c</sup></td><td></td><td>282</td><td>402</td><td>462</td><td>422</td><td>328</td></tr></tbody></table>

species have the greatest abundance, again highlighting that an inverse correlation between abundance-based rarity and originality increases diversity. *FDiS* is closely related to quadratic entropy (their mathematical formulae are very similar, as shown in Pavoine & Bonsall, 2011).

Villéger, Mason, & Mouillot (2008) developed indices to describe three facets of trait-based diversity: functional richness, functional evenness, and functional divergence. None of these indices can be divided into continuous values of species' originality. However, they also implicitly depend on the notion of originality. The first one, functional richness (*FRic*), is the volume of the convex hull of species' points (minimal space that includes all species' points) in the trait-based multidimensional space. From a certain

viewpoint, one can consider that the most original species support the convex hull, with less-original species lying inside the convex hull. The second index, functional evenness (*FEve*), increases if species and their abundances are evenly distributed in the multidimensional space delimited by the most original ones (those at the vertices of the convex hull). The third index, functional divergence (*FDiv*), is linked to the mean distances from species' points to a centroid, but this centroid is measured differently than in the *FDiS* index developed by Laliberté & Legendre (2010). Villéger, Mason, & Mouillot (2008) considered only the species at the vertices of the convex hull, i.e. from a certain viewpoint, the most original species when finding the centroid coordinates. Their *FDiv* index relies on the difference between the

<footnote><sup>a</sup>We considered a theoretical set of four New World terrestrial carnivora species (*Ursus arctos*, *Vulpes vulpes*, *Mustela erminea* and *M. nivalis*) with estimates of each species' body mass obtained from Diniz-Filho & Tôrres (2002).</footnote>

<footnote><sup>b</sup>We considered five case studies corresponding to five scenarios of species' relative abundances, from S1 to S5. S3 corresponds to the species' relative abundances that lead to the highest possible value for *R* and thus to the values of species' relative originalities (*Rb*). This table shows that, up to a certain threshold (S3), increasing the abundance of the most different (original) species (here, *U. arctos*) more than that of others increases diversity.</footnote>

<footnote><sup>c</sup>We consider the following formula for the index *R* applied to this data set: $R = \sum_{i=1}^{4} \sum_{j=1}^{4} \sqrt{p_i p_j} |M_i - M_j|$, where $p_i$ is the relative abundance given to species *i* in the set (for example, in terms of number of individuals), and $M_i$ is the body mass of species *i* (in kg).</footnote>
unweighted mean of the distance to the centroid and the abundance-weighted mean of the distance to the centroid. In this index, Villéger, Mason, & Mouillot (2008) did not use species abundance to define the centroid – originality and abundance-based rarity are thus treated separately – while Laliberté & Legendre (2010) did use species abundance to define the centroid of all points, thus obtaining an abundance-weighted originality.

$FDiv$ increases if the most peripheral species have the highest abundances (Fig. 6). Here, we can again observe an inverse link between abundance-based rarity and originality, if we consider the most peripheral species as the most original. However, $FDiv$ is not an index of biodiversity: we identified that $FDiv$ can be close to its maximum when a single species dominates in abundance, with the remaining species having residual abundance; for this to happen, this dominant species must be at the largest distance to the centroid of the convex hull (Fig. 6B). Diversity means variety. If intraspecific variations are omitted and if an assemblage is composed of a single species, then any measure of the trait diversity of the assemblage must attain its minimum. Similarly, if intraspecific variations are omitted and if a single species represents almost all the abundance of an assemblage, then, whatever its trait values, the trait diversity of the assemblage is bound to be close to the minimum, whereas in contrast, $FDiv$ may be close to its maximum. $FDiv$ thus describes a particular pattern of community functional compositions but is not an index of trait-based diversity.

## IV. RECONCILING THE DIVERSITY, RARITY AND ORIGINALITY CONCEPTS FOR THEIR USEFUL APPLICATIONS

In the previous sections, we have discussed the definitions of the concepts of rarity and originality. We have also demonstrated that fundamental links exist in the definition and in the measurements of the concepts of rarity, originality and diversity. Now we highlight, through many examples, how these concepts can be explored conjointly for the benefits of ecology and evolution.

Every organism is a product of its own individual evolutionary history and is shaped by the environmental conditions and interactions experienced by its ancestors (Cadotte & Davies, 2016). We observed that originality and rarity indices are designed to capture species-specific features, while diversity indices are applied to sets of species. Diversity, rarity and originality thus complement each other in describing the amount of biological variability in species assemblages. Here, we briefly develop some examples of fields where diversity, rarity and originality indices can be applied and underline the importance of their joint analysis.

### (1) Understanding the evolutionary emergence of species' originality

No approaches have been developed thus far to analyse how trait-based and phylogenetic aspects of originality

<figure><img src="image_12.png" /><figcaption>**Fig. 6.** A key property of the functional divergence ($FDiv$) index of Villéger *et al.* (2008) is that it tends to the maximum (equal to 1) when a single original species dominates in abundance. Here, we use the theoretical example of Villéger *et al.* (2008) including a set of nine species (points) characterized by two traits (axes). The traits (positions in the functional space) of the nine species are similar in A and B. Each species is represented by a point, the size of which is proportional to the species' abundance. In A, species have even abundances: the abundance of a species equals 1/9. In B, one of the species dominates in abundance: it has a relative abundance of 0.99, while other species have relative abundances equal to 0.00125 (given that 0.99 + 0.00125 × 8 = 1). The convex hull of the set of points is represented by the grey square in the two panels, and the centroid of its vertices is simply its centre. As noted in the two panels, the $FDiv$ value in case A is lower than that in case B, where the value of $FDiv$ is close to the maximum (1). These relations hold because the dominant species is one of the original species located on the convex hull of all points, and this dominant species is one with the largest distance to the gravity centre of this convex hull.</figcaption></figure>

are entangled. Often researchers have measured the two aspects independently and have then searched for statistical correlations between them (two-dimensional graph in Fig. 2F). A given species could have evolved, for example, a specific set of characteristics that are individually rare or rare in their combination. Such a species could be both phylogenetically original and original according to its traits, in which case the overall originality reflects the history of a species in terms of trait evolution and species relations [the phylo-trait rarity (PTR) case in Fig. 2F]. The correlation between phylogenetic and trait-based originality is expected if traits have a phylogenetic signal, which means that closely related species tend to share similar values of traits, while distantly related species tend to have different traits.

Inferring that a species is original both in the phylogeny and according to its traits requires observing a particular pattern on a phylogenetic tree (Fig. 7A). However, we must remember that past species extinction could blur the phylogenetic history of a trait state by suppressing many species displaying it (Fig. 7B). This inference also depends on the relevance of our present-day taxonomic sampling (Grandcolas, Nattier, & Trewick, 2014). Rare, distinct traits can occur either on old, persisting branches (Fig. 7C) or on rapidly and recently evolved branches of a given phylogenetic tree (Fig. 7D). The combination of phylogenetic and trait-based originalities of a species is then
<figure><img src="image_13.png" /></figure>

**Fig. 7.** Theoretical examples where a species is original according to both its traits and phylogenetic position. We considered a theoretical phylogenetic tree with 11 extant and up to four extinct species as tips and branch lengths expressed in time of evolution. A shows the tree with only extant species, and B, C and D represent potential scenarios of complete trees with extinct species and trait values for all species and their theoretical ancestors (interior nodes). Circle colours express different values of a theoretical trait (or a complex of traits). (A) Among all extant species, the current species with the blue trait is both the most phylogenetically original, because it is the most isolated, and the most trait-based original, because it is very different in its trait (colour) value from the other extant species. (B) A scenario where past extinction events have suppressed many species displaying the blue trait. (C) A scenario where the species with the blue trait is a relictual species that has kept an ancestral value of the trait (that at the root of the tree). (D) The case where the species with the blue trait has become original among extant species, although its trait value is a recent autapomorphy. In B to D, crosses indicate extinct species.

not explained only by the species' relictness if it alone remains from a group that is mainly extinct (Grandcolas, Nattier, & Trewick, 2014). Species' originality can emerge from evolutionary autapomorphy, that is, from the appearance in a given taxon of a distinctive derived value of a trait that is unique to this taxon (Fig. 7D).

Species originality may also be used to replace intuitive characterizations that are still too often employed in evolutionary biology, such as ‘evolved’ or ‘primitive’ (Crisp & Cook, 2005; Grandcolas & Trewick, 2016). The comparison of a species to a reference species group could benefit from being carried out while considering speciation or extinction rates. For example, adaptive radiations are associated with both an increase in speciation rates and the adaptation of constituent species to a diversity of ecological niches (Gavrilets & Losos, 2009). This adaptation usually leads to high levels of diversity in the trait(s) on which selection for local adaptation acts. As a consequence, adaptation leads to species being original for the trait(s), relative to each other and to the remaining species of the clade. For example, the

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

**Fig. 8.** Theoretical examples of the potential effects of speciation and extinction events on species' originality and diversity. (A) Adaptive radiation. (B) Phylogenetically structured extinctions. As in Fig. 7, we considered theoretical phylogenetic trees with species as tips and with branch lengths expressed in time of evolution. Circle colours express different values of a theoretical trait (or a complex of traits). Each species and each of their hypothetical ancestors (interior nodes) has a defined value for the trait(s). In A, the ancestor at the root of the phylogeny has a grey colour. All of its descendants kept this trait value up to a given period when adaptive radiation yielded speciation events with rapid trait evolution. In B, the crosses indicate that four species are extinct. Extant species are named A to H. The colours in the circles represent the assumption that some extant species might have acquired traits (pink, orange and green) that render them either tolerant or adapted to new environmental conditions. We used different colours for the acquired traits as there may be multiple ways of being either tolerant or adapted to some specific environmental conditions. This shows that species A, which is phylogenetically distant from the other extant species, is also likely to be functionally original (here by remaining with the ancestral character state), but this may depend on the considered traits.

adaptive radiation in Darwin's finches led to an increase in the diversity of beak size and shape, making these species more original than their relatives (Grant & Grant, 2008). If speciation events occur in a short time period during such a radiation, then trait-based and species diversities are likely to increase by a much larger extent than phylodiversity. In such a case, trait-based originality is not always accompanied by phylogenetic originality (Fig. 8A).

Inversely, high extinction rates in a particular lineage could lead to the phylogenetic isolation of a relict species and thus to its phylogenetic originality among extant species. The trait-based originality of the relict species is likely dependent on the traits considered and on their evolution
(Fig. 8B). An example of a relict species is the present-day ginkgo (Ginkgo biloba) (Crisp & Cook, 2011). Phylogenetically clumped extinctions could thus decrease the diversity of a regional pool in terms of species number and phylogeny. At the same time, they could lead to a skewed distribution of phylogenetic originality across the study region, with a few very original species belonging to the species-poor lineages where extinctions occurred. Future research could thus concentrate on searching for how and why phylogenetically and functionally original species emerge in the course of evolution and at which spatial and taxonomic scales they emerge. Additionally, future studies could evaluate how the emergence of original species has influenced current patterns of diversity and distributions of species' abundances.

(2) **Analysing the dynamics of community assembly with rarity, originality and diversity**

Species evolve in continuously changing communities governed by numerous ecological, evolutionary and stochastic processes. The ecological processes that have been most studied to explain species diversity are competition, in which biotic interactions between species regulate species' abundances; environmental filtering, where abiotic forces act to constrain certain species' traits within limits; and density dependence, where abundant species have lower individual performance than do rare species (e.g. Hubbell, 2001; Holyoak, Leibold, & Holt, 2005). Mathematical models have been developed in recent decades to synthesize knowledge on community assembly and diversity patterns (Hubbell, 2001; Holyoak, Leibold, & Holt, 2005; Munoz & Huneman, 2016). So far, most of them have focused on measuring and/or predicting patterns of species abundance (number of individuals) and patterns of species richness in communities (MacArthur & Wilson, 1967; Magurran, 2004; McGill et al., 2007). These models have thus rarely focused on patterns of phylo- and trait-based diversity and originality, despite copious numbers of empirical and conceptual studies on community assembly in terms of functional and phylodiversity. A few recent models have, however, started to give insights into functional and phylodiversity (e.g. Münkemüller & Gallien, 2015; Munoz et al., 2018).

There is a broad consensus now on the fact that several distinct processes, including niche-based and neutral ones, interact in species assemblages (e.g. Chase & Myers, 2011). However, despite this consensus, it was also shown that community assembly models based on contrasting underlying processes (e.g. niche-based *versus* neutral processes) may predict the same patterns of species abundance and diversity equally well (e.g. McGill *et al.*, 2007). Such equivalence hampers the direct inference of assembly processes from observed patterns of species abundance and diversity. In fact, mechanistic insights into patterns of community assembly may rely more on the originality of species rather than on their abundance alone (Cadotte & Davies, 2016). One of the oldest identified patterns in ecology is the hollow curve of the distribution of species abundance, with few dominant and many rare species (McGill *et al.*,

2007). The shape of this curve is usually described in terms of skewness. The skewness of an abundance curve is thus inversely related to species diversity, with the latter increasing with an increase in abundance evenness. A hollow curve can also describe the distribution of species' originality in the case of many redundant and few original species (Da Silva *et al.*, 2012). Both species abundance and originality could thus be described by the same tool: a hollow curve.

In parallel, since Webb *et al.* (2002), ecologists often use statistical approaches to associate the trait and/or phylogenetic structure of a community with scenarios of community assembly. This association can be accomplished by comparing observed patterns of trait-based diversity and phylodiversity with those expected by chance using a null model of randomly assembled communities from a regional species pool (Emerson & Gillespie, 2008; Hardy, 2008; Cavender-Bares *et al.*, 2009). Trait-based clustered communities, with many redundant species and low average originality, would reveal environmental filtering, while trait-based overdispersed communities, with the presence of highly original species, would indicate limiting similarity and interspecific competition (Webb *et al.*, 2002). These paradigms may, however, be an oversimplification as competition can sometimes lead to a reduction in functional diversity, particularly when traits are linked to species fitness (Mayfield & Levine, 2010).

The same statements were made for phylodiversity patterns under the assumption of the phylogenetic signal mentioned in Section IV.1 (Webb *et al.*, 2002; see also Saito *et al.*, 2018). Phylogenetic signal tends to decrease at local spatial scales in communities and for narrow taxonomic levels (Cavender-Bares, Keen, & Miles, 2006). Differences between trait-based diversity and phylodiversity patterns for the same community would indicate labile, convergent traits, character displacement between lineages (Gerhold *et al.*, 2015), or environmentally determined traits that vary more than others (Cadotte & Davies, 2016). Species' trait evolution must thus be quantified (see Section IV.1) to connect community trait-based diversity and phylodiversity patterns to the assembly processes (Losos, 2008).

Therefore, to date, the processes that shape community assembly have mostly been analysed based on the concepts of diversity, abundance and thus abundance-based rarity. The roles of trait-based (functional) and phylo-based rarities and thus of species' originality in community assembly have been far less studied. For example, during a colonization event from a regional pool to a local community, the local originality of a colonizing species relative to resident species could be decisive for the successful colonization of the species. The local originality could enhance the colonization success of those species if they can avoid competition with natives for resources due to their high originality ('Darwin's naturalization hypothesis'; Strauss, Webb, & Salamin, 2006; Pearson, Ortega, & Sears, 2012). Inversely, the species' local originality could hamper its colonization success if its high originality makes it less adapted to the local environmental conditions than are the native species (Strayer, 2012).
The local coexistence of species could influence the lineage diversification of the species (Gerhold *et al.*, 2015) and the distribution of the species' originalities. Overall, analyses of community species, phylo- and trait-based diversity, rarity and originality patterns at various spatial scales could therefore better indicate underlying ecological and evolutionary processes that influence species coexistence than analyses of species abundance could alone. A given process could impact only part of the species of an assemblage, which could result in patterns in the distributions of species' originalities. The joint use of rarity, originality and diversity indices could thus provide insights into which interactions among stochastic, ecological, biogeographical and evolutionary processes shape local communities and their dynamics.

### (3) Role of originality in ecosystem functioning

Many studies of biodiversity patterns are focused on common species because they consider common species to represent the largest part of the biomass of a community and thus to be essential for ecosystem functioning (Gaston, 2012). By contrast, rare species may be difficult to include in community-level analyses due to sampling limits. In cases in which knowledge of these species is poor, rare species can be even more difficult to include in trait-based and phylogenetic originality analyses. However, originality and abundance-based rarity were found to be correlated in some cases (e.g. Mi *et al.*, 2012). If low-density species possess the most distinct functional traits, then they could support vulnerable functions in ecosystems and be of particular importance to the community (Gaston, 1998; Mouillot *et al.*, 2013; Leitão *et al.*, 2016). The removal of such species from a community could result in a considerable reduction in ecosystem functioning (Mouillot *et al.*, 2008; Bender *et al.*, 2017), with no possibility for other species to compensate for their loss (Leitão *et al.*, 2016). For example, using species extinction simulations, Rosatti *et al.* (2015) showed that, under extinction scenarios based on abundance and fire tolerance, the probability of losing the most functionally original woody cerrado species was higher than that expected by chance, while the loss of phylogenetically original species was random. In this case study, trait-based functional originality could thus be an indicator of species vulnerability defined based on species rarity and fire sensitivity.

By contrast, two species are functionally redundant if they overlap in their functional niches, i.e. they maintain similar ecosystem functions (Brandl & Bellwood, 2014; Carmona *et al.*, 2016). However, any two species in a set are unlikely to be perfectly redundant but likely to be complementary, i.e. sharing parts of their functional niches and their roles in the ecosystem (Rosenfeld, 2002; Loreau, 2004). Contrary to trait-based originality, trait-based redundancy does not increase trait-based diversity, but it may increase the stability and resilience of communities of species and ecosystem functions. Determining redundant species can thus guide conservation measures. Indeed, local species extinctions caused by perturbations could be compensated for by the

persistence of species that are functionally similar to the lost species but that differ from them in their responses to changes in environmental factors or disturbances (Walker, 1992; Pillar *et al.*, 2013). For example, an increase in trait-based functional originality has been found in coral reef communities after a cyclone disturbance due to the local extinction of redundant species that had trait values similar to those of the surviving species (Brandl *et al.*, 2016). Ecosystem functioning, stability and resilience are dependent on the composition of species and those species' abundance-, phylo- and trait-based rarities. Inversely, species' characters, abundances and distribution patterns are shaped by many ecological processes. We should continue to consider this mutuality in future studies to better comprehend the complexity of ecosystem functioning.

### (4) Guiding conservation actions

Species are among the key units of biodiversity measurement. Their conservation is at the core of many national and international programmes that request effective methods for habitat prioritization and species preservation. Criteria related to ecosystem services evaluated as economic costs, aesthetic value, contribution to well-being, and species richness and rarity are often parts of these programmes. Should such programmes also include phylo- and trait-based diversity and originality? Even if conservation planning alternates between preserving particular units of biodiversity and preserving the processes that shaped those units, the most commonly employed method to design conservational priorities uses species richness ranked by species' abundance-based rarity and endemism (Mace, 2003). Conservation values are thus often given to geographical units, or biodiversity hotspots, that are not chosen with regard to species' traits and evolutionary histories (Veron *et al.*, 2017).

For example, Brum *et al.* (2017) reviewed currently protected areas determined by the International Union for Conservation of Nature (IUCN) risk classification system. They demonstrated that those areas do not harbour more phylo- or trait-based diversities of threatened mammals than would be expected if they were randomly selected. As shown in the previous sections, species are not equivalent, and the phylo- and trait-based diversities of an area are the products of numerous stochastic, evolutionary and ecological processes. Conservation planning would thus benefit from considering biodiversity as multidimensional by including the phylogenetic and trait-based originalities of species (Rodrigues & Gaston, 2002; Pellens, Faith, & Grandcolas, 2016). For example, Pollock, Thuiller, & Jetz (2017) showed that a 5% expansion of protected areas could more than triple the protected range of species or trait-based or phylo-based units.

Originality indices could be useful as a complement for conservation actions that target species rather than areas (Pavoine *et al.*, 2005; Isaac *et al.*, 2007; Jetz *et al.*, 2014; Laity *et al.*, 2015; Grenié *et al.*, 2018). Species are often ranked for conservation attention by their patrimonial,
abundance-based rarity and threat status, which is not sufficient to determine priorities (Mouillot *et al.*, 2008). These conservation attentions could nevertheless indirectly (non-intentionally) preserve original species. For example, Thévenin *et al.* (2018) showed that, although evolutionary considerations are unlikely to have driven explicitly the allocation of reintroduction efforts, reintroduced birds and mammals in Europe, North America and Central America tend to be more phylogenetically original than expected by chance.

Originality indices allow treating species within multiple facets of biological diversity. The explicit inclusion of such indices in conservation actions would be useful to evaluate each species' contribution to local and global phylo- and trait-based diversity (Jensen *et al.*, 2016; Pavoine *et al.*, 2017). The recommendation to safeguard species that are both original and threatened inspired some authors to extend pre-existing indices of originality to include additional species attributes: abundance, range size and probability of extinction (Isaac *et al.*, 2007; Rosauer *et al.*, 2009; Cadotte & Davies, 2010; Hidasi-Neto *et al.*, 2015). Although alternatives exist (Steel *et al.*, 2007; Jensen *et al.*, 2016), the most widely cited originality measure that also integrates species extinction risk is the evolutionary distinct and globally endangered (EDGE) species index (Isaac *et al.*, 2007). The EDGE index combines the phylogenetic originality of a focal species with its IUCN threat status. Given that it was designed for large-scale analyses, the EDGE index does not consider species' traits. However, conservation actions are often developed locally. Hidasi-Neto *et al.* (2015) thus modified the EDGE index and proposed the EcoEDGE index, which combines phylogenetic and functional components of species' originalities with those species' extinction risks.

Some studies found that low abundance and narrow range size are the characteristics of functionally original species that are threatened to extinction at the local scale in French breeding birds (Calba, Maris, & Devictor, 2014) and freshwater bivalve molluscs (Burlakova *et al.*, 2011) and at both local and regional scales in coral reef fishes, alpine plants and tropical trees (Mouillot *et al.*, 2013). At a global scale, phylogenetically original primate species were found to be more threatened with extinction than were other primates (Verde Arregoitia, Blomberg & Fisher, 2013; but see Redding, deWolff, & Mooers, 2010), but this was not true for the whole class of mammals (Verde Arregoitia *et al.*, 2013). The endangerment of original species could, however, lead to a drastic decrease in phylo- and trait-based diversity if it were also associated with the phylogenetic clustering of species extinction (Parhar & Mooers, 2011).

Therefore, the spatial distribution of original species may provide information on whether they are concentrated in low-diversity areas that are not targeted by current conservation actions (e.g. Jetz *et al.*, 2014; Veron, Clergeau & Pavoine, 2016). Additionally, using simulations, Redding *et al.* (2008) showed that prioritizing species by different originality indices measured globally tends to safeguard more local phylodiversity than is expected by the selection

of random species. Similarly, prioritizing Neotropical and Nearctic bird species with high average global phylogenetic originality scores could allow local phylodiversity to be safeguarded (Redding *et al.*, 2015). Clearly, species' originality identifies 'key' species for preservation priority that could be overlooked by classical abundance-based rarity and diversity methods.

V. CONCLUSIONS

(1) Due to the emergence of myriad terms related to biodiversity, rarity, and originality in the ecological literature, it has become difficult to determine whether terms used by different studies refer to the same concepts and measures. Each concept encompasses different aspects of species assemblages and can be measured by several mathematical indices. We provided a semantic and historical overview of these three concepts – biodiversity, rarity and originality – at the level of an assemblage of species. We showed that mathematical links exist between their associated indices.

(2) Historically, rarity was explicitly integrated into diversity measures by means of species abundance. Later, diversity measures incorporated species' biological (trait or phylogenetic) differences, sometimes weighted by the species' abundances. Species identities can be interchanged without affecting diversity measurement based on the number and abundances of species. Phylo- and trait-based diversities have thus gone far beyond the vision of species diversity.

(3) The contribution of individual species to the phylo- and trait-based diversities of a reference species set is captured by the concept of originality. This concept brings together ecologists and evolutionary biologists because it can combine species' evolutionary histories, traits and abundances. It can be measured by various approaches based on phylogenetic trees and trait-based dendrograms, dissimilarity matrices and multidimensional space. Focused at the individual species level, originality complements the diversity measures that can then sometimes be written as simple functions of species' trait-, phylogeny-, and abundance-based rarities.

(4) The joint use of the concepts of diversity, rarity, and originality could aid in the understanding of the multiple mechanisms shaping communities at different spatial and temporal scales. At a large scale, the joint use of these concepts could help clarify general patterns of evolutionary events such as trait evolution, extinction, speciation, and adaptive radiation of species. At a local scale, it could aid in understanding community assembly and ecosystem functioning. At any scale, it could refine conservation strategies.

(5) It is widely accepted that no single mathematical formula could alone encompass all aspects of biodiversity. Here, we have shown that the joint use of diversity, rarity and originality measures has the potential to recompose accurately the complex picture of the diversity of a species assemblage.
## VI. ACKNOWLEDGEMENTS

A.K. and S.P. conceived of the overarching ideas and wrote the manuscript; P.G. contributed to idea development and manuscript editing. A.K., P.G., and S.P. acknowledge the support of the French State through the Agence Nationale de la Recherche under the reference project 10-LABX-0003, within the framework of the programme 'Investing for the future' (11-IDEX-0004), and thank the anonymous referees for their very constructive comments and Simon Veron for his useful comments on an earlier version of the paper.

## VII. REFERENCES

References marked with asterisk have been cited within the supporting information.

*ALTSCHUL, S. F. & LIPMAN, D. J. (1990). Equal Animals. *Nature* **348**, 493–494.

BENDER, I. M. A., KISSLING, W. D., BÖHNING-GAESE, K., HENSEN, I., KÜHN, I., WIEGAND, T., DEHLING, D. M. & SCHLEUNING, M. (2017). Functionally specialised birds respond flexibly to seasonal changes in fruit availability. *Journal of Animal Ecology* **86**, 800–811.

BRANDL, S. J. & BELLWOOD, D. R. (2014). Individual-based analyses reveal limited functional overlap in a coral reef fish community. *Journal of Animal Ecology* **83**, 661–670.

BRANDL, S. J., EMSLIE, M. J., CECCARELLI, D. M. & RICHARDS, Z. T. (2016). Habitat degradation increases functional originality in highly diverse coral reef fish assemblages. *Ecosphere* **7**, e01557.

BROWN, J. H. (1984). On the relationship between abundance and distribution of species. *The American Naturalist* **124**, 255–279.

BRUM, F. T., GRAHAM, C. H., COSTA, G. C., HEDGES, S. B., PENONE, C., RADELOFF, V. C., RONDININI, C., LOYOLA, R. & DAVIDSON, A. D. (2017). Global priorities for conservation across multiple dimensions of mammalian diversity. *Proceedings of the National Academy of Sciences* **114**, 7641–7646.

BUISSON, L., GRENOUILLET, G., VILLÉGER, S., CANAL, J. & LAFFAILLE, P. (2013). Toward a loss of functional diversity in stream fish assemblages under climate change. *Global Change Biology* **19**, 387–400.

BURLAKOVA, L. E., KARATAYEV, A. Y., KARATAYEV, V. A., MAY, M. E., BENNETT, D. L. & COOK, M. J. (2011). Endemic species: contribution to community uniqueness, effect of habitat alteration, and conservation priorities. *Biological Conservation* **144**, 155–165.

CADOTTE, M. W. & DAVIES, T. J. (2010). Rarest of the rare: advances in combining evolutionary distinctiveness and scarcity to inform conservation at biogeographical scales. *Diversity and Distributions* **16**, 376–385.

CADOTTE, M. W. & DAVIES, T. J. (2016). *Phylogenies in Ecology: A Guide to Concepts and Methods*. Princeton University Press, Princeton.

CADOTTE, M. W., DAVIES, T. J., REGETZ, J., KEMBEL, S. W., CLELAND, E. & OAKLEY, T. H. (2010). Phylogenetic diversity metrics for ecological communities: integrating species richness, abundance and evolutionary history. *Ecology Letters* **13**, 96–105.

CALBA, S., MARIS, V. & DEVICTOR, V. (2014). Measuring and explaining large-scale distribution of functional and phylogenetic diversity in birds: separating ecological drivers from methodological choices. *Global Ecology and Biogeography* **23**, 669–678.

CARMONA, C. P., DE BELLO, F., MASON, N. W. H. & LEPŠ, J. (2016). Traits without borders: integrating functional diversity across scales. *Trends in Ecology and Evolution* **31**, 382–394.

CAVENDER-BARES, J., KEEN, A. & MILES, B. (2006). Phylogenetic structure of Floridian plant communities depends on taxonomic and spatial scale. *Ecology* **87**, S109–S122.

CAVENDER-BARES, J., KOZAK, K., FINE, P. V. A. & KEMBEL, S. W. (2009). The merging of community ecology and phylogenetic biology. *Ecology Letters* **12**, 693–715.

CHASE, J. M. & MYERS, J. A. (2011). Disentangling the importance of ecological niches from stochastic processes across scales. *Philosophical Transactions of the Royal Society B: Biological Sciences* **366**, 2351–2363.

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

CRISP, M. D. & COOK, L. G. (2005). Do early branching lineages signify ancestral traits? *Trends in Ecology & Evolution* **20**, 122–128.

CRISP, M. D. & COOK, L. G. (2011). Cenozoic extinctions account for the low diversity of extant gymnosperms compared with angiosperms. *New Phytologist* **192**, 997–1009.

DA SILVA, D. M., SILVA, I. A. & BATALHA, M. A. (2012). Phylogenetic and phenotypic originality and abundance in a cerrado plant community. *Austral Ecology* **37**, 302–307.

DARÓCZY, Z. (1970). Generalized information functions. *Information and Control* **16**, 36–51.

DE BELLO, F., LAVOREL, S., ALBERT, C. H., THUILLER, W., GRIGULIS, K., DOLEZAL, J., JANEČEK, Š. & LEPŠ, J. (2011). Quantifying the relevance of intraspecific trait variability for functional diversity. *Methods in Ecology and Evolution* **2**, 163–174.

DELIĆ, T., TRONTEJ, P., RENDOŠ, M. & FISER, C. (2017). The importance of naming cryptic species and the conservation of endemic subterranean amphipods. *Scientific Reports* **7**, 3391.

DINIZ-FILHO, J. A. F. & TÖRRES, N. M. (2002). Phylogenetic comparative methods and the geographic range size-body size relationship in new world terrestrial Carnivora. *Evolutionary Ecology* **16**, 351–367.

*EISWERTH, M. E. & HANEY, J. C. (1992). Allocating conservation expenditures: accounting for inter-species genetic distinctiveness. *Ecological Economics* **5**, 235–249.

EMERSON, B. C. & GILLESPIE, R. G. (2008). Phylogenetic analysis of community assembly and structure over space and time. *Trends in Ecology and Evolution* **23**, 619–630.

FAITH, D. P. (1992). Conservation evaluation and phylogenetic diversity. *Biological Conservation* **61**, 1–10.

FINDLEY, J. S. (1973). Phenetic packing as a measure of faunal diversity. *The American Naturalist* **107**, 580–584.

FINDLEY, J. S. (1976). The structure of bat communities. *The American Naturalist* **110**, 129–139.

FRISHKOFF, L. O., KARP, D. S., M'GONIGLE, L. K., MENDENHALL, C. D., ZOOK, J., KREMEN, C., HADLY, E. A. & DAILY, G. C. (2014). Loss of avian phylogenetic diversity in neotropical agricultural systems. *Science* **345**, 1343–1346.

GASTON, K. J. (1991). How large is a species' geographic range? *Oikos* **61**, 434–438.

GASTON, K. J. (1996). The multiple forms of the interspecific abundance-distribution relationship. *Oikos* **76**, 211–220.

GASTON, K. J. (1997). What is rarity? In *The Biology of Rarity* (eds W. E. KUNIN and K. J. GASTON), pp. 30–47. Chapman & Hall, London.

GASTON, K. J. (1998). Rarity as double jeopardy. *Nature* **394**, 229–230.

GASTON, K. J. (2012). The importance of being rare. *Nature* **487**, 46–47.

GASTON, K. J. & SPICER, J. I. (2004). *Biodiversity: An Introduction*. Blackwell Publishing, Oxford.

GAVRILETS, S. & LOSOS, J. B. (2009). Adaptive radiation: contrasting theory with data. *Science* **323**, 732–737.

GERHOLD, P., CAHILL, J. F., WINTER, M., BARTISH, I. V. & PRINZING, A. (2015). Phylogenetic patterns are not proxies of community assembly mechanisms (they are far better). *Functional Ecology* **29**, 600–614.

GINI, C. (1912). *Variabilità e Mutabilità*. Studi economica guridi delle facoltați di giurizprudenza dell, Universitè di Cagliari III, Parte II.

GRANDCOLAS, P. (1998). Phylogenetic analysis and the study of community structure. *Oikos* **82**, 397–400.

GRANDCOLAS, P., NATTIER, R. & TREWICK, S. A. (2014). Relict species: a relict concept? *Trends in Ecology & Evolution* **29**, 655–663.

GRANDCOLAS, P. & TREWICK, S. A. (2016). What is the meaning of extreme phylogenetic diversity? The case of phylogenetic relict species. In *Biodiversity Conservation and Phylogenetic Systematics: Preserving our Evolutionary Heritage in an Extinction Crisis* (eds R. PELLENS and P. GRANDCOLAS), pp. 99–115. Springer Open, Cham, Switzerland.

GRANT, P. R. & GRANT, R. (2008). *How and why Species Multiply? The Radiation of Darwin's Finches*. Princeton University Press, Princeton, NJ.

GRENÉ, M., MOUILLOT, D., VILLÉGER, S., DENELLE, P., TUCKER, C. M., MUNOZ, F. & VIOLE, C. (2018). Functional rarity of coral reef fishes at the global scale: hotspots and challenges for conservation. *Biological Conservation* **226**, 288–299.

GROVES, C., COTTERILL, F. P. D., GIPPOLITI, S., ROBOVSKÝ, J., ROOS, C., TAYLOR, P. J. & ZINNER, D. (2017). Species definitions and conservation: a review and case studies from African mammals. *Conservation Genetics* **18**, 1247–1256.

HARDY, O. J. (2008). Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. *Journal of Ecology* **96**, 914–926.

HARDY, O. J. & SENTERRE, B. (2007). Characterizing the phylogenetic structure of communities by an additive partitioning of phylogenetic diversity. *Journal of Ecology* **95**, 493–506.

HARTLEY, S. & KUNIN, W. E. (2003). Scale dependency of rarity, extinction risk, and conservation priority. *Conservation Biology* **17**, 1559–1570.

HAVRDA, M. & CHARVAT, F. (1967). Quantification method of classification processes: concept of structural a-entropy. *Kybernatica* **3**, 30–35.

HELMUS, M. R., BLAND, T. J., WILLIAMS, C. K. & IVES, A. R. (2007). Phylogenetic measures of biodiversity. *The American Naturalist* **169**, E68–E83.

HESSEN, D. O. & WALSENG, B. (2008). The rarity concept and the commonness of rarity in freshwater zooplankton. *Freshwater Biology* **53**, 2026–2035.

HIDASI-NETO, J., LOYOLA, R. & CIANCIARUSO, M. V. (2015). Global and local evolutionary and ecological distinctiveness of terrestrial mammals: identifying priorities across scales. *Diversity and Distributions* **21**, 548–559.

HOLYOAK, M., LEIBOLD, M. A. & HOLT, R. D. (2005). *Competing Theories for Competitive Metacommunities*. The University of Chicago Press, Chicago.
HOOPER, D. U., SOLAN, M., SYMSTAD, A., DÍAZ, S., GESSNER, M. O., BUCHMANN, N., DEGRANGE, V., GRIME, P., HULOT, F., MERMILLOD-BLONDIN, F., ROY, J., SPEHN, E. & VAN PEER, L. (2002). Species diversity, functional diversity and ecosystem functioning. *In Biodiversity and Ecosystem Functioning: Syntheses and Perspectives* (eds M. LOREAU, S. NAEEM and P. INCHAUSTI), pp. 195–208. Oxford University Press, Oxford.

HUANG, J., MI, X. & MA, K. (2011). A genome evolution-based framework for measures of originality for clades. *Journal of Theoretical Biology* **276**, 99–105.

HUBBELL, S. P. (2001). *The Unified Neutral Theory of Biodiversity and Biogeography*. Princeton University Press, Princeton.

ISAAC, N. J. B., TURVEY, S. T., COLLEN, B., WATERMAN, C. & BAILLIE, J. E. M. (2007). Mammals on the EDGE: conservation priorities based on threat and phylogeny. *PLoS One* **2**, e296.

JARZYNA, M. A. & JETZ, W. (2016). Detecting the multiple facets of biodiversity. *Trends in Ecology and Evolution* **31**, 527–538.

JENSEN, E. L., MOOERS, A. Ø., CACCONE, A. & RUSSELLO, M. A. (2016). I-HEDGE: determining the optimum complementary sets of taxa for conservation using evolutionary isolation. *PeerJ* **4**, e2350.

JETZ, W., THOMAS, G. H., JOY, J. B., REDDING, D. W., HARTMANN, K. & MOOERS, A. O. (2014). Global distribution and conservation of evolutionary distinctness in birds. *Current Biology* **24**, 919–930.

JOHNSON, C. N. (1998). Species extinction and the relationship between distribution and abundance. *Nature* **394**, 272–274.

KELLY, S., GRENYER, R. & SCOTLAND, R. W. (2014). Phylogenetic trees do not reliably predict feature diversity. *Diversity and Distributions* **20**, 600–612.

KRAFT, N., CORNWELL, W., WEBB, C. & ACKERLY, D. (2007). Trait evolution, community assembly, and the phylogenetic structure of ecological communities. *The American Naturalist* **170**, 271–283.

KUNIN, W. E. & GASTON, K. J. (1993). The biology of rarity: patterns, causes and consequences. *Trends in Ecology and Evolution* **8**, 298–301.

LAITY, T., LAFFAN, S. W., GONZÁLEZ-OROZCO, C. E., FAITH, D. P., ROSAUER, D. F., BYRNE, M., MILLER, J. T., CRAYN, D., COSTION, C., MORITZ, C. C. & NEWPORT, K. (2015). Phylodiversity to inform conservation policy: an Australian example. *Science of the Total Environment* **534**, 131–143.

LALIBERTÉ, E. & LEGENDRE, P. (2010). A distance-based framework for measuring functional diversity from multiple traits. *Ecology* **91**, 299–305.

LANDE, R. (1996). Statistics and partitioning of species diversity, and similarity among multiple communities. *Oikos* **76**, 5–13.

LAVAL, G., IANNUCELLI, N., LEGAULT, C., MILAN, D., GROENEN, M. A. M., GUFRA, E., ANDERSSON, L., NISSEN, P. H., JORGENSEN, C. B., BEECKMANN, P., GELDERMANN, H., FOULLEY, J.-L., CHEVALET, C. & OLLIVIER, L. (2000). Genetic diversity of eleven European pig breeds. *Genetics Selection Evolution* **32**, 187–203.

LAVOREL, S. & GARNIER, E. (2002). Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the holy grail. *Functional Ecology* **16**, 545–556.

LEITÃO, R. P., ZUANON, J., VILLÉGER, S., WILLIAMS, S. E., BARALOTO, C., FORTUNEL, C., MENDONÇA, F. P. & MOUILLOT, D. (2016). Rare species contribute disproportionately to the functional structure of species assemblages. *Proceedings of the Royal Society B* **283**, 20160084.

LOREAU, M. (2004). Does functional redundancy exist? *Oikos* **104**, 606–611.

LOSOS, J. B. (2008). Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. *Ecology Letters* **11**, 995–1007.

LYONS, N. I. (1981). Comparing diversity indices based on counts weighted by biomass or other importance values. *American Naturalist* **118**, 438–442.

MACARTHUR, R. H. & WILSON, E. O. (1967). *The Theory of Island Biogeography*. Princeton University Press, Princeton.

MACE, G. M. (2003). Preserving the tree of life. *Science* **300**, 1707–1709.

MAGNUSON-FORD, K., IRHAM, T., REDDING, D. W. & MOOERS, A. Ø. (2009). Rockfish (*Sebastes*) that are evolutionarily isolated are also large, morphologically distinctive and vulnerable to overfishing. *Biological Conservation* **142**, 1787–1796.

MAGURRAN, A. E. (2004). *Measuring Biological Diversity*. Blackwell Publishing, Oxford.

MAIRE, E., GRENOUILLET, G., BROsse, S. & VILLÉGER, S. (2015). How many dimensions are needed to accurately assess functional diversity? A pragmatic approach for assessing the quality of functional spaces. *Global Ecology and Biogeography* **27**, 728–740.

MASON, N. W., MOUILLOT, D., LEE, W. G. & WILSON, J. B. (2005). Functional richness, functional evenness and functional divergence: the primary components of functional diversity. *Oikos* **111**, 112–118.

MAY, R. M. (1990). Taxonomy as destiny. *Nature* **347**, 129–130.

MAYFIELD, M. M. & LEVINE, J. M. (2010). Opposing effects of competitive exclusion on the phylogenetic structure of communities. *Ecology Letters* **13**, 1085–1093.

MAZEL, F., DAVIES, T. J., GALLIEN, L., RENAUD, J., GROUSSIN, M., MÜNKEMÜLLER, T. & THUILLER, W. (2016). Influence of tree shape and evolutionary time-scale on phylogenetic diversity metrics. *Ecography* **39**, 913–920.

MAZEL, F., MOOERS, A., VALENTINO, G., RIVA, D. & PENNELL, M. W. (2017). Conserving phylogenetic diversity can be a poor strategy for conserving functional diversity. *Systematic Biology* **66**, 1019–1027.

MCGILL, B. J., RAMPAL, S. E., GRAY, J. S., ALONSO, D., ANDERSON, M. J., KASSA BENECHA, H., DORNELAS, M., ENQUIST, B. J., GREEN, J. L., HE, F., HURLBERT, A. H., MAGURRAN, A. E., MARQUET, P. A., MAURER, B. A., OSTLING, A., et al. (2007). Species abundance distributions: moving beyond simple prediction theories to integration within ecological framework. *Ecology Letters* **10**, 995–1015.

MERGEAY, J. & SANTAMARIA, L. (2012). Evolution and biodiversity: the evolutionary basis of biodiversity and its potential for adaptation to global change. *Evolutionary Applications* **5**, 103–106.

MI, X., SWENSON, N. G., VALENCIA, R., KRESS, W. J., ERICKSON, D. L., PÉREZ, Á. J., REN, H., SU, S. H., GUNATILLEKE, N., GUNATILLEKE, S., HAO, Z., YE, W., CAO, M., SURESH, H. S., DATTARAJA, H. S., SUKUMAR, R. & MA, K. (2012). The contribution of rare species to community phylogenetic diversity across a global network of forest plots. *The American Naturalist* **180**, E17–E30.

MORELLI, F., BENEDETTI, Y., PERNA, P. & SANTOLINI, R. (2018). Associations among taxonomic diversity, functional diversity and evolutionary distinctiveness vary among environments. *Ecological Indicators* **88**, 8–16.

MOUCHET, M., GUILHAUMON, F., VILLÉGER, S., MASON, N. W., TOMASINI, J. A. & MOUILLOT, D. (2008). Towards a consensus for calculating dendrogram-based functional diversity indices. *Oikos* **117**, 794–800.

MOUILLOT, D., BELLWOOD, D. R., BARALOTO, C., CHAVE, J., GALZIN, R., HARMELIN-VIVIEN, M., KULBICKI, M., LAVERGNE, S., LAVOREL, S., MOUQUET, N., PAINE, C. E. T., RENAUD, J. & THUILLER, W. (2013). Rare species support vulnerable functions in high-diversity ecosystems. *PLoS Biology* **11**, e1001569.

MOUILLOT, D., CULIOLI, J. M., PELLETIER, D. & TOMASINI, J. A. (2008). Do we protect biological originality in protected areas? A new index and an application to the Bonifacio Strait natural reserve. *Biological Conservation* **141**, 1569–1580.

MOUQUET, N., DEVICTOR, V., MEYNARD, C. N., MUNOZ, F., BERSIER, L.-F., CHAVE, J., COUTERON, P., DALECKY, A., FONTAINE, C., GRAVEL, D., HARDY, O. J., JABOT, F., LAVERGNE, S., LEIBOLD, M. S., MOUILLOT, D., et al. (2012). Ecophylogenetics: advances and perspectives. *Biological Reviews* **87**, 769–785.

MÜNKEMÜLLER, T. & GALLIEN, L. (2015). VirtualCom: a simulation model for eco-evolutionary community assembly and invasion. *Methods in Ecology and Evolution* **6**, 735–743.

MUNOZ, F., GRENÉ, M., DENELLE, P., TAUDIÈRE, A., LAROCHE, F., TUCKER, C. & VIOLE, C. (2018). Ecolottery: simulating and assessing community assembly with environmental filtering and neutral dynamics in R. *Methods in Ecology and Evolution* **9**, 693–703.

MUNOZ, F. & HUNEMAN, P. (2016). From neutral theory to a comprehensive and multilevel theory of ecological equivalence. *The Quarterly Review of Biology* **91**, 321–342.

MYERS, N., MITTERMEIER, R. A., MITTERMEIER, C. G., FONSECA, G. A. B. & KENT, J. (2000). Biodiversity hotspots for conservation priorities. *Nature* **403**, 853–858.

NIXON, K. C. & WHEELER, Q. D. (1992). Measures of phylogenetic diversity. In *Extinction and Phylogeny* (eds M. J. NOVACEK and Q. D. WHEELER), pp. 216–234. Columbia University Press, New York.

PHARHAR, R. K. & MOOERS, A. Ø. (2011). Phylogenetically clustered extinction risks do not substantially prune the tree of life. *PLoS One* **6**, e23528.

PATIL, G. P. & TAILLIE, C. (1982). Diversity as a concept and its measurement. *Journal of the American Statistical Association* **77**, 548–561.

PAVOINE, S. & BONSALL, M. B. (2011). Measuring biodiversity to explain community assembly: a unified approach. *Biological Reviews* **86**, 792–812.

PAVOINE, S., BONSALL, M. B., DUPAIX, A., JACOB, U. & RICOTTA, C. (2017). From phylogenetic to functional originality: guide through indices and new developments. *Ecological Indicators* **82**, 196–205.

PAVOINE, S., LOVE, M. & BONSALL, M. (2009). Hierarchical partitioning of evolutionary and ecological patterns in the organization of phylogenetically-structured species assemblages: application to rockfish (genus: *Sebastes*) in the Southern California bight. *Ecology Letters* **12**, 898–908.

PAVOINE, S., OLLIER, S. & DUFOUR, A. B. (2005). Is the originality of a species measurable? *Ecology Letters* **8**, 579–586.

PAVOINE, S., VALLET, J., DUFOUR, A.-B., GACHET, S & DANIEL, H. (2009). On the challenge of treating various types of variables: application for improving the measurement of functional diversity. *Oikos* **118**, 391–402.

PEARSON, D. E., ORTEGA, Y. K. & SEARS, S. J. (2012). Darwin's naturalization hypothesis up-close: intermountain grassland invaders differ morphologically and phenologically from native community dominants. *Biological Invasions* **14**, 901–913.

PELLENS, R., FAITH, D. P. & GRANDCOLAS, P. (2016). The future of phylogenetic systematics in conservation biology: linking biodiversity and society. In *Biodiversity Conservation and Phylogenetic Systematics: Preserving our Evolutionary Heritage in an Extinction Crisis* (eds R. PELLENS and P. GRANDCOLAS), pp. 375–383. Springer Open, Cham, Switzerland.

PETCHET, O. L. & GASTON, K. J. (2002). Functional diversity (FD), species richness and community composition. *Ecology Letters* **5**, 402–411.

PIGOT, A. L., BREGMAN, T., SHEARD, C., DALY, B., ETIENNE, R. S. & TOBIAS, J. A. (2016). Quantifying species contributions to ecosystem processes: a global assessment of functional trait and phylogenetic metrics across avian seed-dispersal networks. *Proceedings of the Royal Society B: Biological Sciences* **283**, 20161597.