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

RESEARCH ARTICLE

# Effect of environmental and spatial factors on the phylogenetic and functional diversity of the Mediterranean tree communities of Europe

M. C. Albassatneh¹,², J.-Y. Dubuisson¹, M. Escudero³, B. Fady⁴ ID, L. Ponger⁵ & S. Muller¹

1. Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum national d'Histoire naturelle (MNHN), CNRS, Sorbonne Université, EPHE, Université des Antilles, Paris, France

2. Institute of Ecology and Environmental Sciences iEES, Sorbonne University, Paris, France

3. Department of Plant Biology and Ecology, University of Seville, Seville, Spain

4. INRAE, Ecology of Mediterranean Forests, URFM, Avignon, France

5. Structure et Instabilité des Génomes, "Muséum National d'Histoire Naturelle" (MNHN), CNRS, Paris, France

**Keywords**

conservation; forest; functional trait;
Mediterranean Basin; plastid DNA;
phylogenetic analysis; phylogenetic signal of
traits; tree.

**Correspondence**

M. C. Albassatneh, Institut de Systématique,
Evolution, Biodiversité (ISYEB), Muséum
national d'Histoire naturelle (MNHN), CNRS,
Sorbonne Université, EPHE, Université des
Antilles, Paris, France.
E-mail: marwan.sch@gmail.com

**Editor**

C. Lara Romero

Received: 13 November 2022;

Accepted: 10 March 2023

doi:10.1111/plb.13525

ABSTRACT

* The tree flora of the Mediterranean Basin contains an outstanding taxonomic richness and a high proportion of endemic taxa. Contrary to other regions of the Mediterranean biome, a comprehensive phylogenetic analysis of the relationship between phylogenetic diversity, trait diversity and environmental factors in a spatial ecological context is lacking.

* We inferred the first calibrated phylogeny of 203 native tree species occurring in the European Mediterranean Basin based on 12 DNA regions. Using a set of four functional traits, we computed phylogenetic diversity for all 10,042 grid cells of 10 × 10 km spatial resolution to completely cover Mediterranean Europe. Then, we tested the spatial influence of environmental factors on tree diversity.

* Our results suggest that the nature of the relationship between traits and phylogeny varies among the different studied traits and according to the evolutionary distance considered. Phylogenetic diversity and functional diversity of European Mediterranean trees correlated strongly with species richness. High values of these diversity indices were located in the north of the study area, at high altitude, and minimum temperature of the coldest month. In contrast, the two phylogenetic indices that were not correlated with species richness (Mean Phylogenetic Distance, Phylogenetic Species Variability) were located in the south of the study area and were positively correlated with high altitude, soil organic carbon stock and sand soil texture.

* Our study provides support for the use of phylogenies in conservation biology to assess ecosystem functioning, and provides insights for the implementation of sustainable forest ecosystem management.

INTRODUCTION

Biodiversity, the variety and variability among living organisms and the ecological complexes in which they occur, is a central concept in the life sciences; this includes diversity within species, between species, and of ecosystems (Gaston 1996). Biodiversity-ecosystem functioning research has shown that the functioning of a biological community is mediated by the diversity of its component species (Hooper et al. 2005; Cardinale et al. 2011). Most experiments reveal that ecosystem function has a positive relationship with species richness (Cardinale et al. 2011). However, biodiversity is not just species richness; in experimental data, species richness typically accounts for between 30–73% of the variance of a given ecosystem function (Cardinale et al. 2006). This wide range has prompted ecologists to look for measures of diversity that more reliably explain variation in ecosystem function, including estimates of phylogenetic and functional diversity (Reiss et al. 2009; Cadotte et al. 2011; Naeem et al. 2012).

Phylogenetic diversity is the full set of nested clades repre-
senting phylogenetic relationships among organisms at all
levels. The perception of biodiversity patterns becomes more
complete when phylogenetic methods are added to traditional
species-based methods (Forest et al. 2007; Rodrigues et al.
2011). Phylogenetic relationships among species have been
incorporated into diversity analyses since the 1990s (Faith 1992;
Cadotte et al. 2009; Pavoine et al. 2010).

Measures of functional diversity are typically based on a subset of traits of the component species that are known to be important for ecosystem functions (Petchey et al. 2004). In general, such measures require careful a priori consideration of which traits to include, and whether or not traits should receive different weights. Despite these complications, functional diversity measures often better explain variation in ecosystem function than species richness and other taxonomic diversity measures (Petchey et al. 2004; Flynn et al. 2011). Functional diversity or trait diversity refers to the diversity of the value and range of species or organism traits (Diaz & Cabido 2001; Tilman 2001;
Ricotta 2005; Petchey & Gaston 2006, 2007; Swenson 2012; Swenson et al. 2012). Functional diversity is regarded as key to understanding ecosystem processes and their response to environmental change or disturbance (Norberg et al. 2001; Cornelissen et al. 2003; Suding et al. 2008; Cadotte et al. 2009; Flynn et al. 2011). In addition, functional traits represent an evolutionary response to climate, water relations, carbon gain and competition (Wright et al. 2004; Donovan et al. 2011). Consequently, functional traits are key to determining the interactions between plant species and their environment (Satdichanh et al. 2015). Large datasets of functional diversity measures have been developed in the literature (Walker et al. 1999; Petchey & Gaston 2002, 2006; Díaz et al. 2007; Villéger et al. 2008; Poos et al. 2009; Schmera et al. 2009; Laliberté & Legendre 2010; Mouchet et al. 2010; Chiu & Chao 2014).

Functional diversity and phylogenetic diversity are expected to be related, as functional traits are often characterized by polygenic inheritance (Kelly et al. 2014), which generally relies on the hypothesis that closely related species are more functionally similar than distantly related species, and therefore a more phylogenetically diverse community will have greater functional complementarity (Srivastava et al. 2012). However, the links between them are never that simple because of the possibility of convergence in traits between distantly related species or divergent selection between closely related species (Cianciaruso et al. 2012; Kelly et al. 2014).

Use of phylogenetic diversity (PD) indices as a proxy for functional diversity (FD) implies the existence of a relationship between the phylogeny and traits (Flynn et al. 2011; Srivastava et al. 2012; Swenson et al. 2012; Pavoine et al. 2013). This relationship has made the use of phylogenetic diversity very promising in biodiversity of ecosystem function science, because high-quality phylogenies with complete taxon sampling are now relatively easy to obtain (Joly et al. 2014). While good-quality trait data are costly and time-consuming to gather and still scarce and patchy at best, or even nonexistent for some species, ecosystems, or traits (Winter et al. 2013). Nevertheless, the relationship between phylogenetic and functional diversity is not that simple because trait evolution may not adjust the phylogenetic structure (trait homoplasy from convergence or reversion). Thus, the studies that investigated the phylogenetic signal contained in traits at a community level found divergent results: while there are studies that provide little phylogenetic information in traits studied in temperate and tropical tree communities (Swenson et al. 2012), investigation of plant grassland communities showed a much stronger influence of phylogeny on traits in one instance (Kembel & Cahill Jr 2011), but see Perronne et al. (2014).

The tree flora of the Mediterranean Basin is particularly remarkable and far more species-rich than that of temperate Europe (Quézel & Médail 2003). This region stands out due to its high topographic, edaphic and climatic heterogeneity that has allowed the evolution and long-term persistence of a diversity of conifer and angiosperm tree taxa, both sclerophyllous and deciduous (Quézel & Médail 2003; Aurelle et al. 2022). Their current distribution reflects the complex palaeogeography of the region (Thompson 2005). Surprisingly, comprehensive phylogenetic and functional trait datasets are scarce for Mediterranean tree species. The only available large-scale, dated phylogeny was produced very recently and was done at the genus level (Cheikh Albassatneh et al. 2021). And the first

species dataset of traits and occurrences has only recently been assembled (Monnet et al. 2021). Both concern the tree species of the European part of the Mediterranean Basin. Our study is the first that investigates how functional traits co-vary with the phylogeny at different phylogenetic distances in the European Mediterranean forest tree species community.

This study is organized around four main goals: (i) to provide the first calibrated phylogeny of the 203 tree species native to Mediterranean Europe; (ii) to elucidate the relationship between functional traits and phylogenetic patterns in order to provide stronger arguments for the use of PD as a proxy for FD; (iii) to understand the strength and nature of the correlation between the phylogeny and functional traits, at all phylogenetic levels of tree species that have a significant impact on the functioning of Mediterranean European forests; and (iv) to investigate how a range of likely structured environmental and spatial factors drive taxonomic, functional and phylogenetic diversity at distance scale.

# MATERIAL AND METHODS

## Selection of tree taxa

The woody vegetation of the Mediterranean is made up of diverse types of shrublands (maquis, garrigue, phrygana) and forests, where tree morphology varies widely with environmental constraints (climate, geology, geomorphology, soil type), habitat types and anthropogenic activities. We adopted the criteria elaborated by Médail et al. (2019) concerning tree definition and used the checklist of 203 native tree species and their spatial distribution provided in Monnet et al. (2021). For distribution, we used a total of 10,042 grid cells of 10 × 10 km spatial resolution to completely cover the European Mediterranean Basin (Monnet et al. 2021).

## Phylogenetic inference

In this study, we wanted to infer a calibrated phylogenetic tree for 203 species, with a well-defined topology and adequate for the standard phylogeny of vascular plants. Since the phylogenetic tree of 203 Mediterranean species will not provide a complete topology of vascular plants (absence of several clades and several orders, such as Nymphaeales, Magnoliales, ferns, etc.), inferring the phylogenetic tree was carried out by completing the list of 203 species using additional taxa from the main clades and orders of gymnosperms, angiosperms and ferns to infer an extended tree that contains the main clades and orders (Appendix S1).

## Acquisition of DNA sequence data

We used data from 12 DNA regions commonly used for phylogenetic and taxonomic barcoding purposes: (i) plastid genes (*matK*, *rbcL*, *ndhF*, *atpB*, *psbB*, *rpl16*, *trnQ-rps16*), (ii) plastid intergenic spacers (*trnL-trnF*, *psbA-trnH*), (iii) mitochondrial genes (*atp1*, *matR*), and (iv) ribosomal DNA (Internal transcribed spacer – ITS). In a first step, we gathered all available sequence data for our target taxa from NCBI (https://www.ncbi.nlm.nih.gov/nucleotide/; accessed July 2020) and from the datasets of Monnet et al. (2021) using *matrixmaker* (Freyman & Thornhill 2016) (Appendix S2). The only species that did not have any sequence available was *Salix appendiculata* Vill. and
was thus absent in our DNA sampling. According to data avail-
ability, we finally obtain sequences for 277 species.

Sequence alignment

For each DNA region separately, multiple sequence alignments
were built using *MAFFT* (Katoh *et al.* 2002) and parsed using
*Gblocks* (Castresana 2000) to exclude the segments character-
ized by several variable positions or gaps from the final align-
ment. A phylogenetic tree was inferred for each gene to detect
abnormal species location in the single DNA region trees.

Supermatrix data assembly

We used a supermatrix approach (McMahon & Sander-
son 2006; Sanderson et al. 2007) to combine our large datasets.
We concatenated the DNA region clusters into a combined
matrix, making sure there was species overlap between each
cluster and at least one other cluster. In total, the supermatrix
has 277 species and 11,722 sites (Appendix S3). This step was
performed using the Biopython package (Cock et al. 2009)
implemented in python (Van Rossum & Drake 2009).

Phylogenetic analysis

Phylogenetic analyses using maximum likelihood (Felsen-
stein 1973) were conducted using the Randomized Accelerated
Maximum Likelihood program (RAxML version 8; Stamata-
kis 2014). We conducted analyses on all individual genes using
the supermatrix dataset.

An appropriate substitution model of sequence evolution
was selected for each of the 12 DNA regions using the Akaike
Information Criterion (AIC) as implemented in the JModeltest
2 program (Darriba et al. 2012). The optimal substitution
model identified was for all 12 DNA regions sequences: GTR +
G. The GTRGAMMA nucleotide substitution model was used
for all partitions, with the default settings for the optimization
of individual per-site substitution rates. We applied the con-
straint tree option with a backbone tree using edge null tree of
277 species from V. phyloMaker, scenario 3 (Jin & Qian 2019)
(Appendix S4 and S5).

We added the missing species, *Salix appendiculata*, to the
tree of 277 species by applying the Simulation with Uncertainty
for Phylogenetic Investigating (SUNPLIN) implemented in R,
using the branch-based method as random insertion (Martins
et al. 2013) (Appendix S6 and S7).

We estimated a chronogram of 278 species using the soft-
ware treePL (Smith & O'Meara 2012) with 42 calibration points
from the literature (Magallón et al. 2013; Rothfels et al. 2015)
(Appendix S8 and S9).

Finally, we dropped the supplementary species from the
extended tree of 278 species using the package *ape* (Paradis
et al. 2004) implemented in R (R Core Team 2020)
(Appendix S10) to limit the phylogenetic tree to the 203 Euro-
pean Mediterranean tree species.

Functional traits selection

We assembled a dataset of functional traits for the 203 tree spe-
cies of the European Mediterranean region (Monnet et al. 2021)
and calculated the mean value for each species. Only continuous
traits were used that have demonstrated links to forest produc-
tivity. These were: (i) stem specific density (Swenson &
Enquist 2007; Chave et al. 2009); (ii) seed dry mass (Ben-Hur

et al. 2012) – small-seeded species produce more seeds and the
probability that one seed might reach a suitable new site is
higher, consequently increasing naturalization success (Hamil-
ton et al. 2005), while large seeds might also help in establish-
ment because they have more storage tissue, which allows the
seedling to be independent from external resources for a period
of time (Jensen & Gutekunst 2003); (iii) specific leaf area (SLA)
species with higher SLA take up nitrogen easily and have a high
relative growth rate (Hamilton et al. 2005), SLA also represents
light-capturing area deployed per dry mass allocated, and is
closely correlated with other plant traits, such as photosynthetic
capacity, leaf N content, leaf life-span and, importantly, relative
growth rate (Reich et al. 1997); and (iv) plant maximum height,
which is correlated with competitive ability, improving naturali-
zation success (Bucharova & Van Kleunen 2009), and relates
positively to native distribution size (Lavergne et al. 2004;
Petchey & Gaston 2006; Mokany et al. 2008; Bucharova & Van
Kleunen 2009; Mace et al. 2010; Paquette & Messier 2011).

Topological difference metrics

A functional tree was then inferred using hierarchical clustering
calculated from Euclidean distance and UPGMA on the func-
tional traits (Legendre & Legendre 2012) using the R vegan
package (Oksanen et al. 2022). We performed a Mantel test to
compare distance matrices (Mantel 1967), with pairwise Abou-
heif’s distance for the phylogenetic tree and Euclidean distances
for the traits (Abouheif 1999; Pavoine et al. 2008; Hardy &
Pavoine 2012) using the R adephylo package (Jombart
et al. 2010) and R vegan package (Oksanen et al. 2022). After
which the phylogenetic and functional trees were compared
using the topological difference metric from Penny &
Hendy (1985). The relative topological difference (RTD) was
calculated as the proportion of the topological difference
between the two trees, as:

$$
\text{RTD} = \frac{\text{Topological difference}}{2n-6}.
$$

Ranging from 0 (no difference) to 1 (completely different), where n is the number of species and 2n-6 is the maximum number of topological differences. Trees were also compared using the branch length score (BLS) from Kuhner & Felsenstein (1994) in *ape* (Paradis *et al.* 2004), which takes branch length into account in addition to the number of branches that differ between trees (Steel & Penny 1993). The trees were always considered as unrooted.

Phylogenetic signals

The phylogenetic signal of traits is the tendency of related spe-
cies to resemble each other for that trait more than species
drawn at random from the same tree (Münkemüller
et al. 2012). Concerning the trait data (continuous characters),
the phylogenetic signal of each trait was estimated using
Pagel (1999); essentially, λ is the transformation of the phylog-
eny that ensures the best fit of trait data to a Brownian motion
model. Therefore, when λ = 1, the structure of the phylogeny
alone can explain changes in traits. In this case, traits follow a
pure Brownian motion model of evolution. Alternatively, when
λ = 0, the phylogeny has to become a ‘star phylogeny’ (i.e. lose
all its structure) to be able to explain trait evolution under a Brownian motion model. Therefore, the phylogeny alone does not explain trait evolution. In addition, $\lambda$ can be >1, which would suggest that the rate of evolution of a trait is higher at the root than it is at the tip (Pagel 1999; Freckleton et al. 2002). The P-value likelihood ratio was estimated using the likelihood ratio test (LRT). This method compares the likelihood of an evolutionary model with the observed $\lambda$ against that of a model where $\lambda$ is fixed to zero. These analyses were performed in R using the Geiger package (Harmon et al. 2008), function fitContinuous.

After calculating $\lambda$, the nature of the autocorrelation of traits at different phylogenetic scales (distances) was investigated using I, Moran's autocorrelation coefficient (Moran 1950; Gittleman & Kot 1990). Moran's I is usually known as a measure of spatial autocorrelation in landscape ecology used to describe the spatial structure of a trait. However, in a phylogenetic context, it uses the phylogenetic proximity among species, instead of spatial proximity. This is essentially the same idea as using correlograms in landscape ecology (Legendre & Legendre 2012). The method divides the tree into slices of different distances and evaluates Moran's I only for the species grouping within a given slice at a one time. First, it compares the species that have diverged very recently, then species that have diverged for a slightly longer time, and so on. This allows checking if species are more or less similar than expected at a given distance class (Paquette et al. 2015). However, the use of this form of correlogram could be potently limited for small trees and when tips are not uniformly distributed within the phylogeny. To avoid this potential problem, we used the phylosignal package, which is able to reconstruct the phylogenetic correlogram for which the autocorrelation can be computed continuously (Keck et al. 2016). Moran's I can be seen as an autocorrelation coefficient describing the relation of cross-taxonomic trait variation to phylogeny. Moran's I has a value between -1 and +1. A positive Moran's I value at a given diversification time indicates that species of that diversification time tend to have more similar traits that when all species are considered, while a negative value suggest the opposite (Moran 1950; Gittleman & Kot 1990; Hackathon et al. 2011; Keck et al. 2016). Moran's I and phylogenetic autocorrelograms make the implicit assumptions that traits evolve similarly across the phylogeny. However, Keck et al. (2016) showed that this is rarely the case and that phylogenetic signal is scale-dependent and varies among clades. To test this dependency, Keck et al. (2016) proposed the Local Indicators of Phylogenetic Association (LIPA) index. This index computes local Moran's I for each tip of a tree to detect hotspots of positive and negative autocorrelation.

Comparative analyses were performed in R (Core Team 2020) using the ape (Paradis et al. 2004) and phytools packages (Revell 2012). We used phylobase (Hackathon et al. 2011), adephylo (Jombart et al. 2010), and gtools packages (Warnes et al. 2015) to plot the results.

## Functional and phylogenetic alpha diversity and environmental factors

Using the calibrated phylogenetic tree and the matrix of species occurrence (10 × 10 km), we computed phylogenetic diversity metrics that were mathematically independent of species richness (SR). The phylogenetic diversity index corresponds to the sum of the branch lengths of a phylogenetic tree connecting all species in a community (PD; Faith 1992). The phylogenetic

species variability index (PSV; Helmus et al. 2007; Winter et al. 2013) is based on species presence/absence and their phylogenetic relationships. For a sample of n species,

$$PSV = \frac{ntrC - \sum C}{n(n-1)},$$

where C is the n × n sample phylogenetic covariance matrix, trC is the sum of diagonal elements of C, and ΣC is the sum of all elements of C. As species in a sample become more closely related, PSV decreases toward zero; on the other hand, as species become less closely related, PSV increases toward 1. The extreme value of PSV = 1 occurs if all species in the community are completely unrelated to each other. We can see that the statistical expectation of PSV is independent of species richness, thus any change in PSV due to disturbance is not a statistical artefact of varying species richness (Helmus et al. 2007).

We also calculated MPD (mean phylogenetic distance) between each sampled taxon and every other tip in the phylogeny, (it is used as a tool for deciding if the species of a given group or communities are closely related), and MNTD (mean nearest taxon distance: mean distance between each sampled taxon and its own most closely related taxon in the phylogeny) indices (Webb et al. 2002).

Functional diversity was measured using the FD index (Petchey & Gaston 2006, 2007), which is defined as the total branch lengths of a functional dendrogram in which there are as many branch tips as species. We computed the dendrogram for all 203 species, and calculated FD from the total branch lengths connecting the subset of species present at a given grid. To produce the functional dendrogram, we used a combination of the extended Gower distance measure (Gower 1971; Legendre & Legendre 1998; Podani 1999) and an unweighted pair-group arithmetic average (UPGMA) clustering (Petchey & Gaston 2006, 2007). Then, to make FD unaffected by species richness, we computed the Functional dispersion (FDIs) index, which corresponds to the mean distance of individual species to the centroid of all species in the multidimensional trait space (Laliberté & Legendre 2010),

## Observed and expected functional diversity within habitats

To test whether the observed congruence between phylogenetic and functional diversity reflected ecological processes or were simply due to sampling effects, and to assess whether habitats exhibited a higher or lower level of phylogenetic or functional diversity than expected by chance, we calculated the standardized effect size (Gotelli & McCabe 2002; Manly & Sanderson 2002). The standardized effect size is calculated by standardizing the difference between the observed index ($I_{obs}$) to the mean of the 999 indices from the simulated communities (null distribution) ($I_{sim}$), which is “the pattern expected in the absence of species interactions” divided by the standard deviation of the 999 indices from the simulated communities ($\sigma_{sim}$). The standardized effect size (SES) were estimated as:

$$SES = \frac{(I_{obs} - I_{sim})}{\sigma_{sim}}.$$

Negative values indicate phylogenetic or functional clustering, and positive values indicate phylogenetic or functional
overdispersion, with a significance threshold (α = 0.05, two-
sided) at -1.96 and +1.96, respectively.

All alpha diversity indices and SES computations were car-
ried out within the R environment (R Core Team 2020) using
the FD and *picante* libraries (Kembel *et al.* 2010; Laliberté *et al*
2014).

Environmental and spatial factors explaining alpha biodiversity indices

We computed the alpha diversity indices (Richness, FD, FDis,
PD and PSVs), community phylogenetic indices (MPD and
MNTD) and standardized for species richness effects. This
standardization was used to remove implicit interdependence
between species richness and phylogenetic diversity (SES.PD,
SES.MPD and SES.MNTD). For each grid cell at a resolution
of 10 × 10 km, we modelled each diversity index as the response
variable of predictor variables: (i) Climate variables from
WorldClim, resolution 1 km (Fick & Hijmans 2017) (http://
www.worldclim.org/): “Total annual precipitation” (Bio12, in
mm), which approximates average water availability; “Precipi-
tation of the driest month” (Bio14, in mm), which describes
the extremes associated with drought events and stress due to
low water availability; “Temperature seasonality” (Bio4, in T
standard deviation ×100), which describes the variability of
temperature during the year; and “Minimum temperature of
the coldest month” (Bio06, in °C), which quantifies potentially
lethal frost events and more generally, stress due to low tem-
peratures. (ii) Soil variables from SoilGrids 250 m, resolution
250 m, from (Hengl et al. 2017; Shangguan et al. 2017) (http://
soilgrids.org): “Texture class” (USDA system) at a depth 0.30
m, “Soil organic carbon stock” in tons per ha for depth interval
0.00–0.30 m. Soil variables from https://zenodo.org/record/
4139912#.YOHzeej7500 (Hájek et al. 2021) at a resolution 250
m: pH in water, Ca⁺ in water. We extracted the data for each
cell using the R package raster (Hijmans et al. 2016), adjusted it
to resolution 10 × 10 km, then calculated the mean value of all
raster pixels contained in the each cell. All description and
acronyms of variables are provided in Appendix S11. All vari-
ables were standardized (mean = 0 and SD = 1) to make the
regression coefficients comparable.

Relationships between diversity indices and associated vari-
ables were then estimated using ordinary least squares (OLS)
models. To model spatial effects as well as incorporate predic-
tors into the analysis, we used the spatial autoregressive error
model (SAErr) (Kissling & Carl 2008).

We constructed the SARerr model using the package spdep
(Bivand et al. 2006) implemented (R Core Team 2020) R ver-
sion 4.0.3 for all analysis. We specified the neighbourhood dis-
tance based on the coordinates (longitude and latitude) of
10042 centroid grids and specified a maximum distance of 50
km, as outlined in Kissling & Carl (2008) using the function
dnearneigh. Then, using the function nb2listw, we calculated
the spatial weight matrix by weighting the neighbours with
coding of the variance-stabilizing coding scheme (S) (Tiefels-
dorf et al. 1999), and used maximum neighbour distance 50
km (Kissling & Carl 2008). The neighbourhood weighted was
then defined as 1/x where x equals 50 km. Finally, the SARerr
model was specified by the function errorsarlm. As recom-
mended in Kissling & Carl (2008), to minimize the Akaike
information criterion (AIC), the environmental predictors
were then added to this spatial model using a forward selection

strategy. All descriptor variables were examined for multicollo-
nearity using the function *vif* in the package *car* (Fox & Weis-
berg 2019). To illustrate the relationship between all SARerr
coefficients of alpha diversity indices, we prepared a table with
diversity indices as rows and environmental variables as col-
umns. We then standardized SARerr coefficients using Z-scores
across the rows (indices) and computed a Principal Compo-
nents Analysis (PCA) using the package FactoMineR (Lê
et al. 2008) implemented in R. All R functions were run using
R version (R Core Team 2020).

RESULTS

There was ample variation and spatial patterns in all four traits
examined across tree species in Mediterranean Europe (Fig. 1).

Mismatch between phylogeny and function

The Mantel test revealed a weak, significant relationship
between the distance matrices of phylogeny and functional dis-
tances (r = 0.11, P < 0.05), meaning that the phylogenetic and
functional pairwise distances between the 203 Mediterranean
trees species were correlated. The tree topologies based on the
distance matrices for the phylogenetic and the trait data
showed strong differences (RTD = 0.964; BLS = 4720.87;
Appendix S12).

Phylogenetic signal of traits

Pagel's λ showed that species functional traits are linked to the
evolutionary history of the species (Table 1, Appendix S13).
Functionally similar species tend to be closely related, as is the
case, for example, of the small-seeded Betulaceae and Ulmaceae
and the large-seeded *Aesculus hippocastanum*, *Juglans regia* and
*Quercus* species (Fagaceae). Additionally, stem specific density
tended to be phylogenetically conserved, as is the case of all Sal-
icaceae (*Salix*), *Sambucus*, and *Lonicera*, species that have a low
density. Conversely, stem specific density was high in such
related genera as *Buxus*, *Pistacia*, *Ostrya*, *Carpinus*, *Sorbus*, *Olea*
and *Erica*. Concerning specific leaf area (SLA), the species were
divided into two main groups. The first group was the gymno-
sperms, and genera *Spartium*, *Genista*, *Cytisus* from Fabaceae
and from Ericaceae, which have a low SLA. The second group
with a high SLA included the majority of the angiosperms,
especially *Corylus*, *Ostrya* and *Fagus* (Appendix S13). Maxi-
mum plant height was clustered in the Pinaceae as well as in
*Fagus* and some *Quercus* (*Q. canariensis*, *Q. petraea*, *Q. frai-
neto*) from the Fagaceae, and genera *Aesculus*, *Fraxinus*, *Ulmus*,
*Acer*, *Tilia* and *Populus* from other plant families
(Appendix S13). Whereas stem specific density had a compara-
tively lower Pagel's λ, suggesting less dependence on the phy-
logeny, and all traits were found to display a high and
significant phylogenetic signal (Table 1).

All traits had a significant positive Moran's I at the most
recent time scale, suggesting that trait values tend to be con-
served in closely related species (tip of the phylogeny) (Fig. 2).
However, at ancient time scale, all traits showed significant
negative correlations, as illustrated in the diversification time
(Fig. 2). For instance, seed dry mass and stem specific density
showed significant positive correlations for the most recent
times (Fig. 2), and this correlation decreased sharply for
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Spatial distribution of trait values in Mediterranean Europe (five level scale). A: Plant maximum height, B: Seed dry mass, C: Specific leaf area, D: Stem specific density. Area outside of Mediterranean Europe is coloured light grey.</figcaption></figure>
<table><caption>**Table 1.** Phylogenetic information in functional traits.</caption><thead><tr><th>Functional trait</th><th>Unit</th><th>Trait code</th><th>Pagel's λ</th><th>Ln lik</th><th>Ln lik (λ = 0)</th><th>P value</th><th>delta.AICc</th></tr></thead><tbody><tr><td>Plant maximum height</td><td>m</td><td>Height</td><td>0.89681801</td><td>-684.4811</td><td>-728.5308</td><td>&lt;0.001</td><td>86.04</td></tr><tr><td>Seed dry mass</td><td>mg</td><td>SeedMass</td><td>0.99280659</td><td>-1600.2879</td><td>-1704.0228</td><td>&lt;0.001</td><td>205.41</td></tr><tr><td>Leaf area per leaf dry mass</td><td>mm²·mg⁻¹</td><td>SLA</td><td>0.94177967</td><td>-520.5775</td><td>-602.3925</td><td>&lt;0.001</td><td>161.57</td></tr><tr><td>Stem specific density</td><td>g·cm⁻³</td><td>StemSpecDens</td><td>0.78254468</td><td>163.5066</td><td>135.2829</td><td>&lt;0.001</td><td>54.39</td></tr></tbody></table>

When values of Pagel's λ are close to 1 and significantly different from 0, the structure of the phylogeny alone can explain a high proportion of changes in traits. The P-value likelihood ratio was calculated using the likelihood ratio test (LRT) and indicates that Pagel's λ is significantly different from 0, indicating that the four functional traits evaluated are linked to the evolutionary history of species.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Phylogenetic correlogram of four functional traits (maximum plant height, specific leaf area, seed dry mass, stem specific density) for 198 Euro-Mediterranean trees (excluding five species with missing trait data). The phylogenetic signal increased rapidly toward the tips in seed dry mass and stem specific density. However, the phylogenetic signal increased progressively toward the tips in maximum plant height and specific leaf area. The figure shows the mean phylogenetic signal (solid bold black line represents Moran's I index of autocorrelation) with a 95% confidence interval resulting from 100 bootstraps (dashed black lines represent both lower and upper bounds of confidence interval). The coloured horizontal bars show whether the autocorrelation is significant: red is a significantly positive autocorrelation, blue is a significantly negative autocorrelation, and black is a non-significant autocorrelation.</figcaption></figure>

ancient times (100 Mya). On the other hand, SLA and maximum plant height showed significant positive correlations at recent times and these correlations started to decrease progressively over time (Fig. 2), suggesting a strong difference between gymnosperms and angiosperms for these two traits.

## Results of local indicators of phylogenetic association (LIPA)

The LIPA analyses revealed significant local positive autocorrelation for maximum plant height (Appendix S14, right panel, red dots) in the Pinaceae clade, and all nodes of the

Caryophyllales (Tamaricaceae), with the exception of *T. parvifolia*, clades of Eriaceae, clades of *Tilia* (Malvaceae), *Fagus* (Fagaceae), several clades of Salicaceae, several clades of Fagales (*Fagus*, *Castanea*, *Q. canariensis*, *Q. frainetto*, *Q. petraea*) and several clades of Rosales (*Ulmus*, *Crataegus pycnoloba*). Concerning seed dry mass, LIPA analyses showed local positive autocorrelation expanded in principal clades of Fagales (Fagaceae, *Corylus*, *Myrica* and *Juglans*) and clades of the genera *Myrica* and *Juglans* (Appendix S14, right panel, red dots). LIPA analyses revealed significant local positive autocorrelation for stem density (Appendix S14, right panel, red dots) in some
species of conifer in the Cupressaceae (*Juniperus oxycedrus* and *J. macrocarpa*) and Dipsacales in Adoxaceae (*Sambucus*), is several clades of Oleaceae and Sapindales, and whole clades of the Malpighiales (Salicaceae). Some clades of Fagales, especially *Carpinus* in Betulaceae, and some species of *Quercus* genus and *Sorbus* in Rosaceae (Appendix S14, right panel, red dots). Concerning SLA, LIPA analyses showed local positive autocorrelation in all clades of conifers, *Buxus*, and several clades of Fagales, especially Betulaceae, Juglandaceae and Myricaceae and *Fagus* in the Rosales and Ulmaceae in *Celtis* (Appendix S14, right panel, red dots).

Relationship between phylogenetic and functional diversity

The results showed a significant relationship between PD and
FD, whereas the relationship between the phylogenetic species
variability (PSVs) and the functional dispersion was very weak
(r = 0.03) (Fig. 3).

Species richness (log_SR) was strongly correlated with PD (r = 0.93), FD (r = 0.91) and subsequently with MNTD (r = -0.58). A much weaker correlation was observed between species richness and FDis (r = 0.29) (Fig. 3). Both MPD and MNTD were correlated with their standardized equivalents (MPD and SES.MPD: r = 0.89; MNTD and SES.MNTD: r = 0.59; see Fig. 3). Although neither showed a relationship with richness (PSVs, MPD: r = 0.03; Fig. 3). In Fig. 3, we found a strong correlation between MPD and PSVS (r = 1) (Appendix S15).

Across the whole of the grids cells, the mean of the standard-
ized pairwise distance index (SES-MPD) and of the standard-
ized mean nearest taxon distance index (SES.MNTD) were

both significantly below zero: SES.MPD mean = -0.064, *t* test
*t* = -5.477, *P* < 0.001; SES.MNTD mean = -0.267, *t* = -22.248,
*P* < 0.001, indicating a general tendency of species phylogenetic
clustering within communities. Moreover, we found
SES.MNTD values were significantly lower than SES-MPD
(Fig. 3, paired *t* test *t* = -15.675, *P* < 0.001), indicating that
species clustering was possibly more important near the tips of
the tree.

Factors shaping phylogenetic and functional diversity

The results of the spatial autoregressive error model and the
cluster analysis of PCA (Figs 4 and 5) showed a positive asso-
ciation between PD, FD, FDis and richness and the variables
latitude “South–North gradient” and minimum temperature
of the coldest month. PD was strongly correlated with the lati-
tude “South–North gradient” and minimum temperature of
the coldest month. High values of PD were concentrated in
southern North Macedonia, Mediterranean France, the north
Spanish coast, Corsica, northwest Italy (Province of Toscana),
southeast Slovenia and southern Croatia (Appendix S18).
High values of species richness were concentrated in southeast
and southwest North Macedonia, Mediterranean France and
northern Spain (Appendix S18; Fig. 6). Our results showed a
positive association between MPD and PSVs (Figs 4 and 5)
and low latitude “South”, high altitude, texture class, and soil
organic carbon stock (OMS) (Figs 4 and 5). This pattern was
concentrated in western and central Spain, and located
sparsely in Cyprus, Greece and Italy (Appendix S18; Fig. 6).
On the other hand, MNTD was negatively associated with
altitude, latitude and minimum temperature of the coldest

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** The correlogram shows Pearson correlation coefficients for all pairs of variables (with more intense colours for more extreme correlations), and correlations not significantly different from 0 at *P* = 0.01 are represented by a white and cross box.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Principal components analysis (A: factor map; B: cluster dendrogram) of the standardized coefficient of SARerr result, based on Z-score standardization of row of the SARerr coefficients table, showing: (1) clustering of PD, FD, FDis and Richness, (2) clustering of MPD, PSVs, (3) MNTD, (4) clustering of SES.MPD, SES.PD and SES.MNTD.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** Error bars of diversity indices based on the coefficient of SARerr result (coefficients ± SE).</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 6.** A: Map of Functional dispersion (FDis) of trees in Mediterranean Europe. B: Map of phylogenetic tree species variability PSVs in Mediterranean Europe. Area outside of Mediterranean Europe is coloured light grey.</figcaption></figure>

month, and concentrated in eastern Spain, southern France, Corsica, southeast Sardinia, and North Macedonia in three Region "Southeastern, Southwestern, and Pelagonia". In contrast, the standardized values (SES.PD and SES.MPD) were located in the low value of latitude and longitude "the southern part of the study area" (Fig. 3; Appendix S18). The cluster analysis of PCA results (Fig. 4) showed that the indices PD, FD, FDis and richness clustered together, opposite to the indices MPD, PSVs, MNTD, SES.MPD, SES.PD and SES.MNTD, which grouped together.

DISCUSSION

Is phylogenetic diversity a good surrogate of functional diversity in the Euro-Mediterranean tree community?

In our study, the relationships between phylogeny and func-
tions distances were weak. These results are congruent with the

results of Cheikh Albassatneh *et al.* (2019) for genera of Euro-
pean Mediterranean trees, which also revealed a rather weak
and marginally significant relationship between the distance
matrices of phylogeny and life history traits.

Our results confirm no relationship between the phyloge-
netic species variability index (PSV) and functional dispersion
(FDis). These results are congruent with Pavoine et al. (2013),
who performed a field study in the coastal marsh plain of
Mekhada, Algeria, in which they demonstrated that PD was a
poor surrogate for FD, and also less correlated with environ-
mental variables than FD, and that species richness was a better
surrogate for FD than PD in identifying the ecological pro-
cesses that distribute species along a salinity gradient. They
concluded that FD and PD indices might correlate simply
because variation in species richness and evenness (referred to
as co-factors) influences both FD and PD values. According to
several studies (Cianciaruso et al. 2012; Pavoine et al. 2013),
the relationship between FD and PD is evolutionary and
functionally nonlinear and complex because the relationship is
likely to be specific to the community and function studied,
and traits do not necessarily evolve at the same rate
(Donoghue 2008).

Despite the weak correlation between these two (phyloge-
netic and functional) distance matrices, we have demonstrated
that the relationship between phylogenetic information and
functional traits varies among traits according to the evolution-
ary (or divergence) scale considered. This context is perfect for
investigating the nature of such links. Trait values tended to be
similar for closely related species, demonstrating phylogenetic
conservatism (see Kelly et al. 2014). In another example, Swen-
son & Enquist (2007) examined the evolutionary ecology of
wood density using a large database for seed plants in the
American Neotropics. In particular, they focused on the geo-
graphic and phylogenetic variation in wood density for both
gymnosperms and angiosperms. They demonstrated that wood
density was generally highly conserved across the entire seed
plant phylogeny, yet large divergences were found within the
Rosid clade.

In this study, we found that a pure Brownian motion model
was not sufficient to explain the variation of each trait along
the evolutionary distance gradient (time divergence) when the
whole phylogeny was considered (Table 1). Nevertheless,
applying the lambda statistics over the whole tree, our results
also demonstrated that the phylogenetic signal changes in dif-
ferent time slices. In fact, our study showed that different traits
did not vary similarly along an evolutionary distance gradient.
For example, Moran's I of maximum height, stem specific den-
sity, and seed dry mass were found to be low at the largest
divergence times (phylogenetic distance). For these traits, only
closely related species shared similar values, and the phyloge-
netic signal was lost at larger divergences. For SLA and maxi-
mum plant height, the correlation decreased sharply around
400 Mya, i.e., the divergence time between gymnosperms and
angiosperms (385 ± 72 Mya, according to Zimmer et al.
(2007)).

Our results can also help to define where and why phyloge-
netics could be used as a proxy for functional diversity, as
underlined by Winter *et al.* (2013), as the four functional traits
were shown to be important for forest conservation, and those
traits were also shown to vary at different depths in evolution-
ary history. The phylogeny can be seen as a mixture of the
effect of different traits evolving at different speeds and could
be used as a proxy for functional diversity (Paquette
*et al.* 2015). Phylogenetic information may still be used advan-
tageously, in cases where data on traits are scarce or incom-
plete, to accelerate the investigation of biodiversity-ecosystem
functioning relationships in undocumented ecosystems.
Although specific relationships between functional diversity
and phylogenetic diversity are likely context specific.

What are the environmental predictors of species richness,
phylogenetic diversity and functional diversity?

We found that the several phylogenetic indices (MPD, PSVs, SES.MPD, SES.PD and SES.MNTD) were not correlated with species richness. However, MNTD was negatively correlated with species richness, as also shown by Miller *et al.* (2017). PSVs and MPD were strongly correlated, underlining that PSVs is actually analogous to MPD, and they just take different

routes to the same value (Cadotte & Davies 2016). The SES.MPD and SES.MNTD metrics used to measure within-site phylogenetic divergence yielded results with important differences that can be explained by the fact that these two metrics do not detect phylogenetic patterns at the same depth (Swenson 2011). According to previous studies (Losos 2008; Cavender-Bares et al. 2009), phylogenetic structures within communities vary according to the phylogenetic signal in functional traits and habitat association, which in turn varies with the phylogenetic depth. In our study, the degree of clustering was found to be stronger with SES.MNTD (Fig. 3) which, unlike SES.MPD, is a terminal metric detecting patterns near the tips of the trees. This result is consistent with our knowledge of niche evolution of northeastern American forests, as it has been shown that the phylogenetic signal for many ecological optima is mainly located at lower phylogenetic levels (Paquette et al. 2015).

Our study revealed a highly non-random spatial and envi-
ronmental distribution of phylogenetic diversity (PD) across
tree communities of the Mediterranean area in Europe across
all metrics used, with some areas and environments holding
significantly more (or less) phylogenetic diversity than others
(Appendix S16 and S17). Phylogenetic diversity (PD) and func-
tional diversity (FD, FDis) of Mediterranean forest trees in
Europe correlated strongly with species richness (SR). High
values of these indices were located in the north of the study
area and positively influenced by altitude, and minimum tem-
perature of the coldest month. Conversely, the low values of
these indices were located in the south of the studied area and
at low altitudes.

In contrast, the two phylogenetic indices that were not
correlated with species richness (MPD, PSVs) were positively
correlated with low latitude "south of area", soil organic car-
bon stock (OMS) and sandy soil texture. The highest value
of these two indices was situated at the high altitude of the
study area. These results are incongruent with results of Qian
et al. (2013). They tested two predictions of the phylogenetic
niche conservatism hypothesis for the latitudinal diversity
gradient of angiosperm trees in North America: (1) species in
colder regions tend to show a greater phylogenetic clustering
than those in warmer regions, and (2) clades are younger in
colder regions. They demonstrated that species richness and
mean clade age were negatively correlated with latitude and
positively correlated with minimum temperature. This differ-
ence between the two studies is because the climate of Can-
ada and Alaska is Subarctic and Boreal, with winter "freezing
temperatures" unfavourable for specific richness (Latham &
Ricklefs 1993; Wiens & Donoghue 2004; Qian et al. 2013),
whereas the climate of the northern part of Mediterranean
region is dominated by (i) mountain Mediterranean trees,
where the principal vegetation is the upland coniferous for-
ests with *Pinus nigra*, in addition to Mediterranean firs and
cedars, and (ii) supra-Mediterranean trees, as illustrated by
the deciduous oak forests, dominated in the humid bioclimate
with *Ostrya* and *Carpinus*, and sclerophyllous oaks
(Thompson 2005).

**Conclusions and perspectives**

We provide, for the first time, a species-level phylogeny of the
203 tree species of the European Mediterranean region. Despite
limited studies in the field of spatial community phylogenetics, the rapid increase in availability of molecular phylogenies and fine species distribution data make it possible for ecologists to start to estimate a proxy of biodiversity ecosystem functioning effects, their direction, strength and shape. This is important, as we lack functional trait descriptions for many organisms of less studied ecosystems, functions or services, and even more so for Mediterranean ecosystems, especially in the eastern Mediterranean Basin that has undergone human impacts since the ancient past (Rundel & Cowling 2013).

## AUTHOR CONTRIBUTIONS

MCA: methodology, data curation, data analysis, original draft preparation, writing, editing, ME: methodology, original draft preparation, writing, reviewing, BF: original draft preparation, writing, reviewing and correction of English, LP: methodology, reviewing, J-YD: original draft preparation, reviewing, SM: original draft preparation, reviewing. All authors took part in the conceptualization of the work and have read and approved the final version of the manuscript.

## ACKNOWLEDGEMENTS

This study was funded by the *Programme national d'aide à l'Ac-cueil en Urgence des Scientifiques en Exil* (PAUSE) of Collège de France, through the National Museum of Natural History in Paris. The study complies to the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Bene-fits Arising from Their Utilization of the Convention on Bio-logical Diversity.

## SUPPORTING INFORMATION

Additional supporting information may be found online in the Supporting Information section at the end of the article.

**Appendix S1.** List of 203 species using additional taxa from the main clades and orders of gymnosperms, angiosperms and ferns to infer an extended tree that contains the main clades and orders.

**Appendix S2.** DNA BarCode_all Species 277.xlsx. All available sequence data for our target taxa from NCBI and from the datasets of Monnet et al. (2021).

**Appendix S3.** seq_all Species 277.nexus. The supermatrix of 277 species and 11,722 sites.

**Appendix S4.** best_tree_RAxML_277.tre. The best tree of the 277 species using RAXML. The GTRGAMMA nucleotide substitution model was used for all partitions.

**Appendix S5.** edge_null_tree_277_from_v.phylomaka-ker.txt.txt. the constraint tree option using a backbone tree using edge null tree of 277 species from V. phyloMaker, sce-nario 3 (Jin & Qian 2019).

**Appendix S6.** 100 Trees 278 Sunplin.nexus. Simulation with Uncertainty for Phylogenetic Investigating (SUNPLIN) imple-mented in R, using the branch-based method as random inser-tion (Martins et al. 2013).

**Appendix S7.** tree278_10_notnoded.tree. The phylogenetic tree file used to estimate a chronogram of 278 species.

**Appendix S8.** treePL_config.txt. The treePL configuration file for estimating the chronogram of 278 species using the software treePL (Smith & O'Meara 2012) with 42 calibration points from the literature (Magallón et al. 2013; Rothfels et al. 2015).

**Appendix S9.** GC.ultrametric_dated_tree物种_278.-tre.pdf. Ultrametric dated tree.

**Appendix S10.** Dropped_DATED TREE 203 tree.

**Appendix S11.** Variables.doc. Full list of variables consid-ered in the analysis of Mediterranean trees in Europe.

**Appendix S12.** *Tangram.pdf.* Tanglegram computing the phylogenetic tree (left) and the tree for functional traits (right) of the 188 tree species of Mediterranean Europe (row with missing value has been deleted).

**Appendix S13.** *bouble_size198sp.pdf.* Standardized values (bubble size and shade) of the functional traits along the maxi-mum phylogeny of the 203 tree species of Mediterranean Europe.

**Appendix S14.** Local_moran_indexes.pdf. Local Moran's index (I i) values for each species (local indicator of phyloge-netic association, LIPA) for each trait: Plant maximum height "Height", Seed dry mass "SeedMass", Leaf area per leaf dry mass "SLA", and Stem specific density "StemSpecDens". Red bars indicate significant I i values (*P* < 0.05), indicating phylo-genetic signal. The figure demonstrates that significant local autocorrelation is concentrated, for example, for Plant maxi-mum height "Height", within Pinaceae, *Ulmus*, *Fagus* and *Tilia*, and these significant values of I i are positive, meaning that these species are positively autocorrelated (e.g. more simi-lar to each other than would be expected due to random processes).

**Appendix S15.** Spearman_correlation_diversity indices.pdf. Relationship between phylogenetic and functional diversity using Spearman correlation.

**Appendix S16.** SARerr_variables_Histogram.docx. Best spa-tial autoregressive error models (SARerr) for the relationships among FD, Richness, SES.MPD, FDis, PD, SES.FDis, MPD, PSVs, SES.PD, MNTD and SES.MNTD and the predictor vari-ables. Elevation (Alt), Temperature seasonality (T seasonality), Minimum temperature of the coldest month (T min_coldest_m), Total annual precipitation (P_ann), Precipitation of driest month (P Dreist_m), Ca⁺ in water (Ca⁺), pH in water (pH), Soil organic carbon stock (OMS), Texture class (Tex-ture_class). Only the statistically significant variables are shown.

**Appendix S17.** SARerr_variables_table.docx. Best spatial autoregressive error models (SARerr) for the relationships among FD, Richness, SES.MPD, FDis, PD, SES.FDis, MPD, PSVs, SES.PD, MNTD and SES.MNTD and the predictor vari-ables. AIC: Akaike Information criterion. Asterisks denote: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05. Elevation (Alt), Temperature sea-sonality (T seasonality), Minimum temperature of the coldest month (T min_coldest_m), Total annual precipitation (P_ann), Precipitation of driest month (P Dreist_m), Ca⁺ in water (Ca⁺), pH in water (pH), Soil organic carbon stock (OMS), Texture class (Texture_class). Only the statistically sig-nificant variables are shown.

**Appendix S18.** alpha_diversityMaps.docx. Maps of alpha diversity indices.
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