# A higher taxonomic richness does not ensure the functional resilience of saproxylic beetle communities in evergreen Quercus forests

Diana Pérez-sánchez, Norman W.H. Mason, Hervé Brustel, Eduardo Galante, Estefanía Micó

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## To cite this version:

Diana Pérez-sánchez, Norman W.H. Mason, Hervé Brustel, Eduardo Galante, Estefanía Micó. A higher taxonomic richness does not ensure the functional resilience of saproxylic beetle communities in evergreen Quercus forests. Ecological Entomology, 2021, 46 (5), pp.1215-1229. 10.1111/een.13069. hal-03461651

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# A higher taxonomic richness does not ensure the functional resilience of saproxylic beetle communities in evergreen *Quercus* forests

DIANA PÉREZ-SÁNCHEZ,¹◎ NORMAN W. H. MASON,²
HERVÉ BRUSTEL,³ EDUARDO GALANTE¹,⁴

and ESTEFANÍA MICÓ¹,⁴ ¹Centro Iberoamericano de la Biodiversidad (CIBIO), Universidad de Alicante, Alicante, Spain, ²Landcare Research, Hamilton, New Zealand, ³Ecole d'Ingénieurs de Purpan, Université de Toulouse, UMR INP INRAE, Toulouse, France and ⁴Unidad Asociada IPAB (UA-CSIC), Instituto Universitario de Investigación CIBIO, University of Alicante, Alicante, Spain

**Abstract.** 1. Despite the fact that insects are suffering a global decline, little is known about the extent to which species loss affects functional diversity. Thus, to understand the relationship between taxonomic and functional diversity metrics, we focused on saproxylic beetles, which perform vital functions in forest ecosystems.

2. Beetles were collected monthly for a year using flight interception traps placed in three protected areas of mature evergreen Mediterranean *Quercus* forests. Five morphological traits, one phenological trait, and one physiological surrogate trait were measured, and trophic guild identities were determined. Two taxonomic and six functional metrics were assessed to determine their relationships.

3. Taxonomic and functional diversity patterns, as well as trait patterns, differed depending on the protected areas, as they were influenced by habitat and microhabitat characteristics. Generally, a high number of species tended to support high values of functional richness, but did not ensure high values of functional redundancy, thus affecting the resilience of communities.

4. A 5% of species loss strongly affected the functional redundancy of communities, making them more vulnerable. Functional richness, however, was not affected, since it was associated with a high number of redundant species. Thus, the way species loss affected communities is highly dependent on the presence of rare or redundant species.

5. Taxonomic and functional diversity metrics should be used as complementary tools for conservation purposes. In this way, the comparative analysis of taxonomic and functional patterns may help to predict the vulnerability of saproxylic assemblages to changes or disturbances.

**Key words.** Forest conservation, functional diversity, functional redundancy, functional traits, management strategies, Mediterranean region.

## Introduction

Insects are facing a significant decline at a planetary level (Homburg *et al.*, 2019; Sánchez-Bayo & Wyckhuys, 2019). The ecological consequences of this drop, however, are still

unknown. Since species have different roles within the ecosystems, changes in the taxonomic richness, abundance, or composition can have an impact on a range of ecosystem-level processes and therefore on ecosystem functioning (Naeem & Wright, 2003; Petchey & Gaston, 2006). Traditionally, a high number of species have been associated with higher functional diversity values (Tilman, 2001). Yet other aspects, such as the functional roles of species or their rarity within communities, may play a bigger part in the functioning of the community (Santini *et al.*, 2017). This makes it necessary to ascertain how

Correspondence: Diana Pérez-Sánchez, Centro Iberoamericano de la Biodiversidad (CIBIO), Universidad de Alicante, Carretera San Vicente del Raspeig s/n. 03690, San Vicente del Raspeig, Alicante, Spain. E-mail: diana.perez@ua.es
different facets of insect diversity, such as taxonomic and func-
tional dimensions, are related (Santini *et al.*, 2017).

Many studies about functional diversity in plant communities
have linked specific functional traits to ecosystem functions
and services, thus allowing improvement management and con-
servation practices (see references in Drenovsky et al., 2012;
Lozanovska et al., 2018; van der Plas, 2019). Conversely, the
current state of knowledge of the functional diversity of insects
seems to be still insufficient to fulfil such purposes. This has
resulted in a poor understanding of the relationship between
taxonomic diversity and functional diversity, which may be key
to defining the consequences of species loss for ecosystems, an
issue that is yet to be resolved (Cadotte et al., 2011). Numerous
studies on insects have found that perturbations or habitat frag-
mentation can alter both ecosystem functioning and taxonomic
diversity equally, because coexisting organisms have different
functional characteristics (Audino et al., 2014; Luiza-Andrade
et al., 2017; González et al., 2018; Martello et al., 2018).
However, other studies have found different trends in taxo-
nomic diversity and functional diversity, linking this decoupling
to the functional identity of the species (Correa et al., 2019;
Santoandré et al., 2019). That is, the stability and resilience of
ecosystem services in the face of loss or changes of species may
be more influenced by the species’ function than their numbers
(Tilman et al., 1997; Oliver et al., 2015). Moreover, species
loss in natural communities is not random; it can depend on a
range of factors such as population size or sensitivity to envi-
ronmental stress (Mouillot et al., 2013; Engel et al., 2017). All
this makes it necessary first to analyse the relationship between
different biodiversity metrics, such as taxonomic and functional
measurements, so as to help calibrate the extent to which a
large number of species may ensure ecosystem functions or
a small number may jeopardise them. And second, we need
to understand how species loss (both random or directed) can
affect functional metrics, in order to determine how ecosystem
functioning may react to future perturbations or changes (Engel
et al., 2017; Leclerc et al., 2020).

One target group that allows to analyse the relationship
between functional and taxonomic diversity metrics is saprox-
ylic beetles, which depend on dead wood during some part of
their life cycle (Speight, 1989; Alexander, 2008). Not only do
they make up about one-third of the world's insect diversity
(Ulyshen & Šobotník, 2018), but they also are a highly diverse,
complex, and functionally important group for forest ecosys-
tems. In this way, saproxylic beetles contribute to several key
ecosystem functions in forests, as they play a significant role in
the maintenance of nutrient cycling and trophic complexity. In
addition, saproxylic predators naturally control forest pests and
some adults are pollinators (Speight, 1989; Micó et al., 2011;
Sánchez-Galván et al., 2014). Due to the significance of saprox-
ylic insects, and particularly of their ecosystem services, many
attempts have been made to understand the environmental
factors that affect their biodiversity. Most studies have focused
on taxonomic diversity (e.g. Müller & Bütler, 2010; Bouget
et al., 2012; Quinto et al., 2014; García-López et al., 2016),
whereas little is known about the relationship between the
taxonomic and functional diversity of saproxylic beetles or how
species loss can affect functional metrics.

Within this framework, we used the data of saproxylic beetle
communities of three protected areas in the Iberian Peninsula
dominated by mature evergreen *Quercus* species. The objective
was two-fold: on the one hand, to assess the relationship between
the taxonomic and functional diversity patterns, and on the other,
to test the sensitivity of saproxylic beetle functional diversity
to possible species loss (Petchey & Gaston, 2002; Cadotte
et al., 2011; Parisi et al., 2018).

Taxonomic diversity may not necessarily be a reliable surro-
gate for the functional diversity of saproxylic groups, so both
approaches should be used as complementary tools in conser-
vation studies. In fact, since saproxylic beetles are strongly
affected by environmental filters relating to habitat and micro-
habitat (e.g. Okland, 1996; Gibb et al., 2006; Micó et al., 2015;
García-López et al., 2016; Kissick et al., 2018; Parmain &
Bouget, 2018), we believe that the various taxonomic and func-
tional diversity metrics would not necessarily change in the same
direction. For example, communities in a preserved area, with a
small number of species, may be able to maintain their func-
tional diversity, while at the same time, other metrics, such as
functional redundancy or rarity, may reflect the vulnerability
of these communities to future changes (Scheffer et al., 2015;
Kissick et al., 2018; Martello et al., 2018; Thorn et al., 2018).
Moreover, we expect that the loss of species (whether random or
directed) would affect the functional richness and resilience of
communities in different ways, since species can have different
functional roles in communities (Mason et al., 2005; Carmona
et al., 2017; Violle et al., 2017). In addition, we anticipate that
the analysis of trait patterns would help to better understand the
diversity of each assemblage and to identify potential vulnera-
bilities regarding preservation functions (Watts & Mason, 2015;
Micó et al., 2020).

Achieving our objectives would allow us not only to highlight
the importance of using both taxonomic diversity and functional
diversity when proposing appropriate conservation measures,
but also to detect the vulnerability of functionally relevant
communities, such as saproxylic beetles, even in protected areas,
despite the number of species.

**Materials and methods**

Study area

Evergreen *Quercus* forests were surveyed in three protected
areas in the Spanish Mediterranean region separated by a dis-
tance of between 200 and 480 km (Fig. 1): Cabañeros National
Park (hereinafter, Cabañeros), Sierra Espadán Natural Park
(hereinafter, Sierra Espadán), and Font Roja Natural Park (here-
inafter, Font Roja). The dominant trees in all three parks are
evergreen mature *Quercus* species; a description of the charac-
teristics of each park is given in Table 1.

Sampling strategy

Fifteen sites were selected within the three protected areas,
which best represented the vegetation's structural variability
within each area (see Table S1 for details).
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Study areas and study sites. (a) Map showing the distribution of the protected areas in the Iberian Peninsula sampled in the study. Sampling sites in (b) *Quercus suber* forest in Sierra Espadán Natural Park; (c) *Q. rotundifolia* forest in Font Roja Natural Park; (d) *Q. suber* forest in Cabañeros National Park; (e.1) typical Mediterranean *Q. rotundifolia* forest with undergrowth in Cabañeros National Park, and (e.2) 'la Raña', a highly open *Q. rotundifolia* forest in Cabañeros National Park</figcaption></figure>

In Sierra Espadán, three sites (separated by a maximum distance of 6 km) in *Quercus suber* forests were selected: two of the sites were located in a valley and had a highly complex undergrowth; and in the third, the undergrowth was sparser, and the cork oaks undergo occasional management.

In Font Roja, the three selected sites in *Quercus rotundifolia* forests were located on an altitudinal gradient (from 1087 to 1275 m) and separated by a maximum of 1 km. They presented the same forest composition with homogeneous tree size, shallow, and sun exposure, and little undergrowth.

Cabañeros presented the highest woodland heterogeneity because of variations in tree species composition and had both open and closed landscape structures. So, we selected nine sites, separated by a maximum distance of 24 km: three in a *Q. suber* forest with the trees of similar size, undergrowth, and deadwood in soil; and six in a *Q. rotundifolia* forest, of which four were in 'la Raña', typified by savanna-type open vegetation with sparse undergrowth, and two were on a private land with denser vegetation.

Since the samples were collected in protected areas, three flight interception traps were placed on each site, which ensures the minimum number of replications. Thus, nine traps were installed in both Sierra Espadán and Font Roja, and 27 traps were set up in Cabañeros. All the traps were kept in the field

for one year, from April 2015 to June 2016. The collector bottle in each trap was replaced monthly, using propylene glycol as a preservative. The beetle specimens were stored in alcohol until their species were identified, with the collaboration of several taxonomists (see Acknowledgements). The nomenclature was based on the Fauna Europaea (http://www.faunaeur.org/), the study by Bouchard et al. (2011), and the Catalogue of Palaearctic Coleoptera (Löbl & Smetana, 2004, 2006, 2007, 2008, 2010). The specimens were added to the Entomological Collection of Alicante University (CEUA) and deposited at the CIBIO Research Institute (University of Alicante, Spain).

## Functional trait selection

Four different types of functional traits (morphological, trophic, phenological, and a surrogate for physiological) were chosen to characterise the assemblages (Table 2).

Of the five morphological traits, three were directly measured for each specimen (body length, antenna length, and eye surface), whereas two (robustness and elytra ratio) were calculated based on a combination of other morphological measures (Table 2, Fig. 2). The morphological measurements were taken from up to 11 individuals per species, when possible, to capture
<table><caption>**Table 1.** Description of the protected areas (and coordinates) where saproxylic beetles were sampled.</caption><thead><tr><th>Protected area (coordinates)</th><th>Area (ha)</th><th>Altitude (m)</th><th>Annual temperature (mean; min; max) (°C)</th><th>Annual precipitation range (mm)</th><th>Main arboreal formations</th><th>Traditional management</th></tr></thead><tbody><tr><td>Cabañeros National Park (39°23.47 N, 4°29.14 W)</td><td>40 865</td><td>620–1500</td><td>(13–16); –12; 40</td><td>500–750</td><td><ul><li>'la Raña': open landscape of Quercus rotundifolia Lam and Q. suber L.</li><li>Q. suber (in the slopes), and Q. pyrenaica Willd. and mixed forest of Q. pyrenaica and Q. faginea Lam. (in the valleys)</li></ul></td><td>Before its designation as a national park (1995): agricultural management of 'la Raña', cork harvesting and hunting activities</td></tr><tr><td>Sierra Espadán Natural Park (39°52 N, 0°17.30 W)</td><td>31 180</td><td>300–1100</td><td>16; –2; 39</td><td>800</td><td><ul><li>Q. suber with a complex undergrowth of ferns (several species), holly (Ilex aquifolium L.), heather (Erica scoparia L.), juniper (Juniperus communis L.) and strawberry trees (Arbutus unedo L.)</li><li>Pine trees (Pinus pinaster Ait. and P. halepensis Mill.) in the surroundings, as well as Q. rotundifolia and Q. faginea.</li></ul></td><td>Traditional cork harvesting in the more accessible areas</td></tr><tr><td>Font Roja Natural Park (38°38.51 N, 0°32.46 W)</td><td>3000</td><td>800–1356</td><td>(15–20); 5; 25</td><td>402–479</td><td><ul><li>Mainly Q. rotundifolia with some Q. faginea and Acer granatense Boiss.</li><li>Pine trees (P. halepensis) in the surroundings</li></ul></td><td>Important tourist destination<br>Fire prevention methods<br>(shredding of deadwood in some parts of the park)</td></tr></tbody></table>

The altitude, annual temperature (mean, minimum, and maximum), and annual precipitation (range or mean), as well as the main arboreal formation and its current or previous management are provided for each area. See references for Cabañeros in Balasch I Blanch & Ruiz (1998), for Sierra Espadán in Aneto Publicaciones (2010) and for Font Roja in Bonet & Terrones (2016).

within-species variations (Fountain-Jones et al., 2015). A stereomicroscope (Leica M205C) and LEICA Application Suite software version 4.8 were used for the measurements. After calculating each trait's mean per species, it was standardised using body size, following the study by Barton et al. (2011).

The single phenological trait (the beetles' active period or active months) was calculated as the number of months that the adult beetles of each species were captured; it was thus used as a surrogate for the number of months that the beetles were active (Moretti et al., 2017). The trophic trait was the abundance of each species in the feeding guild (Watts & Mason, 2015; Moretti et al., 2017; Table 2).

Finally, regarding the surrogates for the physiological traits, we measured the parks' air temperature (Table 2). We installed one data logger (HOBO U23 Pro v2 Temperature/RH Data Loggers with U23-001 sensor) in each site in Font Roja and Sierra Espadán. In Cabañeros, the temperature data were obtained from the 'Alcornoquera' meteorological station located inside the park, which belongs to the Global change Monitoring Network in National Parks of Spain's Ministry for Agriculture, Food and Environment (MAPAMA, 2017).

In order to simplify our analytical model, we performed three successive principal component analyses (PCAs), removing the previously discarded traits at each new analysis. The first was performed using only the residuals of the morphological traits. Its first two axes explained nearly 99% of the variation, body length having the main coefficient, whereas the second axis captured the rest of the morphological traits (Table S2). For

the second PCA, to which we added the surrogates for the physiological traits, the first three axes explained nearly 90% of the variance and, therefore, we decided to keep TRangeAct and TmeanAct (see Table 2 for the definitions of the abbreviations) as independent physiological traits (Table S2). In the last PCA, which included the added phenological trait, the first three axes explained nearly 96% of the variance. Since TRangeAct and active months were closely correlated, we decided to remove TRangeAct (Table S2). The final traits that we used for the rest of the analysis were body length, robustness, elytra ratio, both sensory traits (body length-corrected eye area and antenna length as residuals), TmeanAct, active months, and all the trophic guilds (Table 2).

## Data analysis

To verify the inventory completeness of our sampling, both for each park and for all the parks together, we calculated the sample coverage estimator, which measures the proportion of the total number of individuals in a community that belongs to the species represented in the sample (Chao & Jost, 2012). We used the 'SPADE' package in R version 3.4.3 (Chao & Shen, 2010; Chao et al., 2016; R Core Team, 2017).

To analyse the differences, between the parks, in the patterns of diversity metrics for taxonomic diversity and functional diversity, we calculated two and six metrics for each park, respectively. Due to the data's non-normality, we looked for
<table><caption>**Table 2.** Definitions and justification of use (with references) of functional traits of saproxylie beetles, as well as the functional groups to which the traits belonged, and how they were measured.</caption><thead><tr><th>Functional trait group</th><th>Trait</th><th>Definition and measurement</th><th>Justification for use (and references)</th></tr></thead><tbody><tr><td rowspan="10">Morphological traits (mm)</td><td>Body length*</td><td>Total lateral length from the anterior of head to the apex of the abdomen</td><td>One of the most highly informative traits for beetles. It may provide information about<ul><li>Microhabitat use</li><li>Dispersal ability</li><li>Foraging behaviour and</li><li>Movement through complex environments (Forsythe, 1987; Ribera &amp; Nilsson, 1995; Cunningham &amp; Murray, 2007; Barton et al., 2011; Fountain-Jones et al., 2017; Gillespie et al., 2017)</li></ul></td></tr><tr><td>Antenna length*</td><td>Maximum length from the base of the antenna to its apex (residual when body length-corrected)</td><td>Both sensory traits can vary between trophic or taxonomic groups (Fountain-Jones et al., 2015) and they may indicate<ul><li>Microhabitat use and structure</li><li>Lifestyle of species</li><li>Ability to locate new microhabitats or prey (Ribera et al., 1999b; Woodcock et al., 2010; Talarico et al., 2011)</li></ul></td></tr><tr><td>Eye surface (mm²)*</td><td>Area contained within the outline of the eye perimeter (residual when body length-corrected)</td><td>It can be used as<ul><li>An indicator of the microhabitat</li><li>As a protective trait (Barton et al., 2011; Fountain-Jones et al., 2017)</li></ul></td></tr><tr><td>Robustness*</td><td>Combination of the body length-corrected maximum dorsal width of the head and the body length-corrected maximum width, length, and lateral depth of the pronotum (Barton et al., 2011; Micó et al., 2020; Pérez-Sánchez et al., 2020)</td><td>It may provide information about<ul><li>Flight capacity of the beetles (in the Carabidae short elytron length is linked to better dispersal efficiency)</li><li>Microhabitat use (longer elytra may provide protection for the wings in harsh environments; Forsythe, 1987; Ribera et al., 1999a; Barton et al., 2011; Johansson et al., 2012; Fountain-Jones et al., 2015)</li></ul></td></tr><tr><td>Elytra ratio*</td><td>Body length-corrected elytra ratio: ratio of maximum lateral length of the elytra to the measure of maximum lateral length from meso-metathorax to the apex of the abdomen<br>It measures the extent to which the elytra cover the abdomen (lower ratios mean exposed abdomen)</td><td>Since each trophic guild responds differently to pressures and ecosystems, guilds provide information about<ul><li>The resource use of each assemblage, and</li><li>Possible responses of communities to different pressures (Fountain-Jones et al., 2017)</li></ul></td></tr><tr><td>Predator guild*</td><td>Abundance of predator beetles</td><td>It could be related to<ul><li>Species interactions</li><li>Community structure</li><li>Dispersal capacity</li><li>Response to changes in the environment (smaller temporal windows of activity may reduce the resilience to changes in the environment, because of the lack of time for finding proper microhabitats and mates; Gillespie et al., 2017; Moretti et al., 2017)</li></ul></td></tr><tr><td>Saproxylagous guild*</td><td>Abundance of saproxylagous beetles</td><td>Although measured as the air temperature of each protected area, these traits can provide information on<ul><li>The temperature niche of adult activity for each species,</li><li>Tolerance to abiotic conditions</li><li>The extent of the range of each species (helping to understand how climate change can affect species distribution; Fountain-Jones et al., 2015; Moretti et al., 2017)</li></ul></td></tr><tr><td>Saproxylagous guild*</td><td>Abundance of saproxylagous beetles</td><td></td></tr><tr><td>Xylophagous guild*</td><td>Abundance of xylophagous beetles</td><td></td></tr><tr><td>Xylomycetophagous guild*</td><td>Abundance of xylomycetophagous beetles</td><td></td></tr><tr><td rowspan="2">Phenological trait</td><td>Active months*</td><td>Number of months that adult beetles were active</td><td></td></tr><tr><td>TmaxAct</td><td>Environmental maximum temperature of the months when the adult beetle was active (°C)</td><td></td></tr><tr><td rowspan="5">Physiological traits (as a surrogate)</td><td>TminAct</td><td>Environmental minimum temperature of the months when the adult beetle was active (°C)</td><td></td></tr><tr><td>TrangeAct</td><td>Difference between the environmental maximum and minimum temperatures of the months when the adult beetle was active (°C)</td><td></td></tr><tr><td>TmeanAct*</td><td>Environmental mean temperature of the months when the adult beetle was active (°C)</td><td></td></tr><tr><td>WSD</td><td>Weighted mean of the environmental temperature of the months with maximum abundance of adult beetles</td><td></td></tr></tbody></table>

significant differences using the nonparametric Kruskal-Wallis test (Ostertagová et al., 2014; De La Riva et al., 2017).

For taxonomic diversity, we calculated species richness (S) and the number of effective species using Hill numbers, or true diversity of order 1 (¹D), since it weights each species according to its frequency in the sample (Jost, 2006; Scheiner, 2012). True diversity was calculated as the exponent of the Shannon index using the 'SPADE' package in R (Chao & Shen, 2010).

For functional diversity, we calculated functional richness, functional richness after removing the effect of the number of species, functional evenness, functional divergence, functional redundancy (after removing the effect of the number of species), and functional rarity. Functional richness is the amount of trait space occupied by the species present within a community (Mason et al., 2005; Laliberté & Legendre, 2010). Functional evenness is the regularity of the distribution of species abundances in the occupied functional trait space

<footnote>*Final traits used for statistical analysis.</footnote>
<figure><img src="image_5.png" /><figcaption>b. Lateral view</figcaption></figure>

<figure><img src="image_6.png" /></figure>

**Fig. 2.** Schematic representation of the morphological trait measurements in each individual through (a) dorsal view and (b) lateral view. Maximum dorsal width of the head and the body and maximum width, length, and lateral depth of the pronotum were used to create Robustness, whereas Elytra length and meso-metathorax and abdomen length were used to create the Elytra ratio. Information and definition of traits in Table 2. Illustration by Sara Pérez-Sánchez

(Mason *et al.*, 2005; Laliberté & Legendre, 2010). Functional divergence measures the degree to which species abundances in a community are distributed towards the edges of the occupied trait space. Functional redundancy refers to the diversity of species performing similar functions in the ecosystem and represents community resilience (Mason *et al.*, 2005; Villéger *et al.*, 2008; Laliberté & Legendre, 2010; Pillar *et al.*, 2013; Carmona *et al.*, 2016; McPherson *et al.*, 2018). Finally, the functional rarity of species in each park was calculated as the combination of functional distinctiveness (whether a species is more or less functionally close to the rest of the community) and taxon scarcity (a local-scale feature of a species with low relative abundance in the community; Grenié *et al.*, 2017;

Violle *et al.*, 2017). We used 'FD' (Laliberté *et al.*, 2015), 'SYNCSA' (Debastiani & Pillar, 2012), and 'funrar' packages (Grenié *et al.*, 2017) in R (R Core Team, 2017).

Since both functional richness and redundancy are strongly dependent on the taxonomic richness, we divided the functional redundancy by its upper bound (species richness – 1) (Carmona *et al.*, 2017), while we applied a null model for functional richness (Mason *et al.*, 2013). We generated 999 randomised abundance matrices, through the 'trialswap' randomisation algorithm (Miklós & Podani, 2004). For each park, we calculated the standardised effect size (SES) as the difference between the observed functional richness and the mean of the 999 matrix simulations divided by the standard deviation of the simulated values
(de Bello, 2012); the *P*-value was calculated as the proportion of expected functional richness values that were less or more extreme (i.e. ≥ or ≤) than the observed value (Götzenberger *et al.*, 2016). For the null model, we used the ‘randomizeMa-trix’ function of the ‘picante’ package in *R* (Kembel *et al.*, 2010; *R* Core Team, 2017).

In order to determine the strength and direction of the relation-
ships between the two taxonomic metrics and the five functional
metrics (without considering the functional richness dependent
on taxonomic richness), we performed Spearman rank correla-
tion tests (Vieira *et al.*, 2013; Morelli *et al.*, 2018). They were
conducted for each protected area separately and also for the
three protected areas together (at a regional level).

In addition, to uncover how the loss of species (reduction
in species richness) may influence the functional richness and
redundancy of the communities for each protected area, we
applied three species extinction scenarios, the first with a
random loss of species. However, since extinctions in nature
are not random, we also modelled a directed loss of species
in the other two species extinction scenarios. To do this, we
focused on the fact that rare species (understood as those
represented only by few individuals or restricted to particular
habitats – functionally rare species) are considered to be more
vulnerable to loss and affected by changes or perturbations
(Mouillot et al., 2013; Violle et al., 2017). Therefore, for
one of the directed scenarios, we considered the functional
distinctiveness or rarity of each species (the uncommonness of a
species' traits compared to other species' traits in an assemblage;
Grenié et al., 2017; Violle et al., 2017), while for the second
scenario, we considered the abundance of each species (by
removing the less abundant species). Species extinction was
applied to all scenarios by removing intervals of 5% (from 5
to 70% species loss) from each protected area's total richness.
In addition, to detect at which point significant differences
of functional richness and redundancy appeared between the
loss intervals, we applied the Kruskal–Wallis test between the
species loss intervals.

Finally, to analyse trait patterns between parks, the selected
traits were first weighted according to the number of individuals
per trap (community weighted mean; Watts & Mason, 2015).
Due to the data's non-normality, we then looked for sig-
nificant differences between parks using the nonparametric
Kruskal–Wallis test (Ostertagová et al., 2014; De La Riva
et al., 2017).

Results

The sample coverage estimator for the group of parks showed
that our inventory completeness was 82.2% accurate. It was
also high for each park, with 89.4% for Cabañeros, 82.2% for
Font Roja, and 91.3% for Sierra Espadán. The total number of
specimens collected during the sampling year in the three parks
was 14 127. It spanned 288 species and 44 families (Table S3).
Sierra Espadán presented the highest number of species (189
species), followed by Cabañeros (178 species) and Font Roja (94
species), with the lowest species richness. Of the 288 identified
species, the 242 species (13 616 individuals, belonging to 43

families) for which all traits were measured were included in
the analysis (Table S4).

Sierra Espadán presented a greater taxonomic rich-
ness than Font Roja and Cabañeros (Kruskal–Wallis:
X²₂ = 21.20; *P* < 0.001; Fig. 3). However, when using the
true diversity of order 1 (X²₂ = 5.36; *P* = 0.07), Sierra Espadán
had the lowest values and Cabañeros the highest. Sierra Espadán
was the only protected area to present a different functional
richness (X²₂ = 9.57; *P* = 0.008), with higher values than the
other areas. However, when the effect of the number of species
was removed from functional richness (using ‘trialswap’ ran-
domisation), significant differences were found between Font
Roja and Cabañeros (X²₂ = 12.48; *P* = 0.002), Font Roja pre-
senting the highest values and Cabañeros the lowest (Fig. 3).
Moreover, the results from null models showed that the observed
functional richness of Sierra Espadán was significantly lower
than the expected functional richness (SES = −0.21, *P* = 0.05).
Finally, although functional evenness (X²₂ = 0.89; *P* = 0.64)
did not show variations between parks, the rest of the metrics
showed significant differences between Sierra Espadán and
the other parks (Fig. 3). Sierra Espadán presented the highest
functional divergence (X²₂ = 10.22; *P* = 0.006) and functional
rarity (X²₂ = 15.54; *P* < 0.001), whereas an opposite pattern
was found for functional redundancy (X²₂ = 21.49; *P* < 0.001),
Sierra Espadán having the lowest (Fig. 3).

Results of the relationship between the taxonomic and the
functional metrics showed that at a regional level (consider-
ing all protected areas together), both species richness and true
diversity were positively related to the functional richness and
the SES.Functional Richness (after ‘trialswap’ randomisations),
but negatively related to the functional redundancy (Table 3,
Fig. 4). In addition, true diversity was also negatively related
to functional rarity and functional divergence (Table 3, Fig. 4).
Conversely, when analysing the relationship between the tax-
onomic and functional metrics for each protected park, the
results changed, and only two relationships common to the three
areas were significant: the negative association between the tax-
onomic richness and functional redundancy, and the negative
association between true diversity and functional rarity (Table 3,
Figure S1).

The three species loss scenarios at a regional level (across all three parks) showed the functional redundancy pattern, which suffered a strong decline in the first percentage of species loss (5%) (Fig. 5b). On the other hand, functional richness (using the ‘trialswap’ randomisation – SES.Functional Richness) hardly changed in the face of species loss, showing only a very slight increase in the scenarios with a species loss of over 50% (Fig. 5a).

In each park, functional redundancy showed the same pattern
as at a general level, suffering a strong decline in the first
percentage of species loss (5%) and then slightly increasing
(Figures S2–S4). In addition, SES.Functional Richness also
showed the same patterns at a general level (without any abrupt
change or with only a very slight increase when species loss
was over 50%), except in the case of Sierra Espadán, for which
SES.Functional Richness significantly increased when 5% of the
species were lost (Figures S2–S4).
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Boxplots of taxonomic diversity and functional diversity of each park. Richness refers to the taxonomic richness; True diversity is the true diversity index of order 1; and SES.Functional Richness refers to functional richness after removing the effect of the number of species. Different letters indicate significant differences based on the Kruskal–Wallis test (*P* < 0.05). The central rectangle indicates the second and third quartiles and the black line the median value. Red points indicate mean values and whiskers, the lower and upper quartiles. Black points indicate possible outliers</figcaption></figure>

<table><caption>**Table 3.** Spearman rank correlation test results between (a) the taxonomic richness and the functional diversity metrics, and (b) true diversity and the functional diversity metrics, for each park separately and for the three parks together (regional).</caption><thead><tr><th rowspan="2"></th><th colspan="2">Cabañeros</th><th colspan="2">Font Roja</th><th colspan="2">Sierra Espadán</th><th colspan="2">Regional</th></tr><tr><th>Rho</th><th>P</th><th>Rho</th><th>P</th><th>Rho</th><th>P</th><th>Rho</th><th>P</th></tr></thead><tbody><tr><td colspan="9"><strong>(a) Taxonomic richness</strong></td></tr><tr><td>Functional richness</td><td>0.77</td><td><strong>&lt;0.001</strong></td><td>0.61</td><td>0.1</td><td>0.38</td><td>0.34</td><td>0.75</td><td><strong>&lt;0.001</strong></td></tr><tr><td>SES.Functional Richness</td><td>0.83</td><td><strong>&lt;0.001</strong></td><td>0.76</td><td><strong>0.03</strong></td><td>0.54</td><td>0.15</td><td>0.47</td><td><strong>0.001</strong></td></tr><tr><td>Functional evenness</td><td>-0.20</td><td>0.309</td><td>0.2</td><td>0.61</td><td>-0.83</td><td><strong>0.008</strong></td><td>-0.17</td><td>0.26</td></tr><tr><td>Functional divergence</td><td>-0.61</td><td><strong>0.001</strong></td><td>0.56</td><td>0.13</td><td>0.48</td><td>0.213</td><td>0.07</td><td>0.64</td></tr><tr><td>Functional redundancy</td><td>-0.92</td><td><strong>&lt;0.001</strong></td><td>-0.81</td><td><strong>0.011</strong></td><td>-0.67</td><td><strong>0.05</strong></td><td>-0.96</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Functional rarity</td><td>-0.42</td><td><strong>0.031</strong></td><td>0.44</td><td>0.25</td><td>0.68</td><td>0.06</td><td>0.27</td><td>0.07</td></tr><tr><td colspan="9"><strong>(b) True diversity</strong></td></tr><tr><td>Functional richness</td><td>0.77</td><td><strong>&lt;0.001</strong></td><td>0.29</td><td>0.46</td><td>-0.15</td><td>0.68</td><td>0.33</td><td><strong>0.026</strong></td></tr><tr><td>SES.Functional Richness</td><td>0.76</td><td><strong>&lt;0.001</strong></td><td>0.4</td><td>0.29</td><td>-0.14</td><td>0.71</td><td>0.32</td><td><strong>0.035</strong></td></tr><tr><td>Functional evenness</td><td>-0.17</td><td>0.4</td><td>0.42</td><td>0.27</td><td>0.39</td><td>0.31</td><td>0.07</td><td>0.66</td></tr><tr><td>Functional divergence</td><td>-0.69</td><td><strong>&lt;0.001</strong></td><td>-0.06</td><td>0.88</td><td>-0.26</td><td>0.49</td><td>-0.56</td><td><strong>&lt;0.001</strong></td></tr><tr><td>Functional redundancy</td><td>-0.45</td><td><strong>0.019</strong></td><td>0.24</td><td>0.56</td><td>0.90</td><td><strong>0.003</strong></td><td>0.03</td><td>0.86</td></tr><tr><td>Functional rarity</td><td>-0.84</td><td><strong>&lt;0.001</strong></td><td>-0.69</td><td><strong>0.043</strong></td><td>-0.93</td><td><strong>0.001</strong></td><td>-0.82</td><td><strong>&lt;0.001</strong></td></tr></tbody></table>

Significance correlations are in bold.
a. Taxonomic richness

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

b. True diversity

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

**Fig. 4.** Correlation graphs (with the results of the Spearman rank correlation test indicating the correlation's significance and strength) between (a) taxonomic richness and the rest of functional diversity metrics, and (b) true diversity and the rest of functional diversity metrics. Linear trends are drawn only for significant correlations (*P* < 0.05)
<figure><img src="image_10.png" /><figcaption>**Fig. 5.** Boxplot representation of how the three species loss scenarios (one random and two directed) influence: (a) SES.Functional Richness (the functional richness after removing the effect of the number of species) and (b) Functional redundancy metrics. x-Axes represented 5% species loss intervals (from 0 to 70% species loss). Different letters indicate significant differences according to the Kruskal–Wallis test between intervals (*P* < 0.05). The central rectangle indicates the second and third quartiles, and the black line the median value. Red points indicate mean values and whiskers lower and upper quartiles. Black points indicate possible outliers</figcaption></figure>

## *Trait patterns*

Most traits differed between the protected areas (Fig. 6). Regarding the elytra ratio (Kruskal–Wallis: $X_2^2 = 10.10$; $P = 0.006$), robustness ($X_2^2 = 21.65$; $P < 0.001$), and mean temperature ($X_2^2 = 21.59$; $P < 0.001$), Sierra Espadán and Font Roja had the highest and the lowest values, respectively, whereas Cabañeros presented intermediate values. This pattern was reversed for the residual antenna length ($X_2^2 = 22.55$; $P < 0.001$), Sierra Espadán presenting the lowest values and Font Roja the highest. In contrast, only Sierra Espadán, with the lowest values for body length, showed a significant difference regarding this trait ($X_2^2 = 10.39$; $P = 0.006$). Sierra Espadán had the highest values for residual eye surface ($X_2^2 = 13.26$; $P = 0.001$) and active months ($X_2^2 = 6.31$; $P = 0.04$), although these values did not significantly differ from that of Cabañeros. In the case of predators ($X_2^2 = 25.89$; $P < 0.001$) and the xylomycetophagous trophic guild ($X_2^2 = 31.24$; $P < 0.001$), the three parks presented variations: Sierra Espadán had the

lowest abundance of predators but the highest of xylomyce-
tophagous, and Font Roja had the highest abundance of
predators but the lowest of xylomycetophagous. Sierra Espadán
presented the lowest abundance of saprophagous beetles
($X_2^2 = 16.56$; $P < 0.001$), a significant difference with the other
areas, whereas Font Roja had the highest abundance of beetles
in the xylophagous trophic guild ($X_2^2 = 13.67$; $P = 0.001$).
Finally, Cabañeros had the highest abundance of beetles in the
sapроxyphagous trophic guild and Sierra Espadán the lowest,
this difference being significant ($X_2^2 = 11.88$; $P = 0.002$; Fig. 6).

Discussion

It is worth highlighting that although higher taxonomic diver-
sity values generally support higher functional richness values,
they can come with low functional redundancy values. In fact,
our results show that the predominance of functionally rare or
redundant species in a community can play a major role if a loss
<figure><img src="image_11.png" /><figcaption>**Fig. 6.** Boxplot of the distribution of functional traits (using the community weighted mean [CWM]) in the three sampled parks. Different letters indicate significant differences based on the Kruskal–Wallis test (*P* < 0.05). The central rectangle indicates the second and third quartiles, and the black line the median value. Red points indicate mean values and whiskers lower and upper quartiles. Black points indicate possible outliers. References, abbreviations, and trait information are given in Table 2</figcaption></figure>

of species occurs, having an impact on the communities' func-
tional vulnerability to perturbations or changes. In addition, we
found differences between taxonomic and functional diversity
metrics and trait patterns in each protected area, suggesting that
various habitat and microhabitat factors strongly influence both
facets of biodiversity.

The comparison of the taxonomic and functional diversity
patterns of each protected area showed that communities with
small numbers of species (i.e. Font Roja) can present high func-
tional diversity, whereas communities with large numbers of
species (i.e. Espadán) may be less resilient because functionally
rare species predominate. However, when analysing the rela-
tionships between taxonomic and functional metrics for each
park and at a regional level (considering all the protected areas
together), some common relationships were found. An increase
in taxonomic diversity was related to an increase in functional
richness (after randomisations), but to a decrease in both the
functional redundancy and functional rarity. This indicates that
although communities with higher numbers of species are using
a greater portion of the niche space (Mason et al., 2005; Morelli
et al., 2018; Pellissier et al., 2018), they may also be more highly
specialised in the use of resources (Bihn et al., 2010; Farias &

Jaksic, 2011; Morelli *et al.*, 2018), for what the disappearance of
species may have strong effects on the ecosystem functioning,
making communities more vulnerable to changes or perturba-
tions (Pillar *et al.*, 2013; Ricotta *et al.*, 2016; Violle *et al.*, 2017).

Various species loss scenarios showed that although functional
richness seems not to be affected by a species reduction at a
regional level, the functional redundancy is strongly affected
even when only 5% of species are lost, regardless of whether
the species loss is random or directed (Fig. 5). This may be
because functional richness is associated with a high number
of redundant species, ensuring communities against species loss
by exercising buffer functions (Winfree et al., 2015). However,
this may also imply that functional redundancy will be strongly
affected by species loss, causing a considerable decrease in the
communities’ functional resilience (Mouillot et al., 2013; Car-
mona et al., 2016; Engel et al., 2017; Stotz et al., 2021). Con-
versely, when a community is dominated by functionally rare
species with low functional redundancy, as in the case of Sierra
Espadán, despite the fact that the communities may be generally
more vulnerable, those rare species might be sustaining the func-
tional diversity (Figures S2–S4; Mouillot et al., 2013; Kissick
et al., 2018). Thus, when species are lost, a compensation effect
may occur, increasing the community's functional richness and
explaining a lower degree of the functional redundancy change
(Larsen et al., 2005; Mouillot et al., 2013; Winfree et al., 2015).

According to all those results, a community with high val-
ues of taxonomic and functional richness together with high
values of functional redundancy could be the best in maintain-
ing ecosystem functions and buffering perturbations. However,
saproxylic communities are very complex and are composed of
numerous functional groups, which not only allow several key
roles in ecosystems, but also respond differently to environmen-
tal changes (Johansson et al., 2007; Wetherbee et al., 2020).
This makes it difficult to predict how communities are going
to respond (Kozák et al., 2021). In this sense, our approach
has allowed to detect saproxylic communities more fragile to
changes or perturbations, as in the case of Sierra Espadán with
lower values of functional redundancy and higher values of func-
tional rarity. However, and as we have seen, the predominance of
functional rare species may also mean that the functional diver-
sity of that community could be better maintained in the face of
a loss of species, avoiding a sharp drop in its resilience.

Functional trait patterns

Generally, saproxylic assemblages of each protected area
showed different patterns of functional traits, some of which
could be used to better understand what is underlying the
taxonomic and functional metrics. In this sense, some functional
traits can provide information about the main characteristics
of the development of microhabitat species. For example, the
smaller eyes and longer antennae, combined with less robust
bodies found in Font Roja, may indicate the possible dominance
of beetles exploiting confined microhabitats such as those
inside wood or under bark (Micó et al., 2020); larger eyes,
shorter antennae, and robust bodies, as found in Sierra Espadán
(Fig. 6), may indicate that the main microhabitats driving
saproxylic diversity in this park are more open, e.g. tree hollows
(Ribera et al., 1999b; Talarico et al., 2011; Micó et al., 2020).
In addition, some other trait patterns could indicate possible
perturbations within an area or a high sensitivity to change, as
in the case of Font Roja and Sierra Espadán, respectively. Thus,
communities with low dispersal ability (i.e. small body size and
high relative length of elytra) may be more sensitive to changes
or perturbations (Ribera et al., 1999b; Barton et al., 2011;
Talarico et al., 2011; Johansson et al., 2012). However, the role
of these traits in saproxylic beetle communities would need to be
further explored, in order to properly determine their ecosystem
services and to test to what extent they can help to predict
vulnerabilities.

To conclude, both approaches (taxonomic and functional)
should be used as complementary tools when making man-
agement and conservation decisions, since a higher number of
species may not ensure the functional resilience of communities.

ACKNOWLEDGEMENTS

We are grateful for the support and dedication for determining
the saproxylic beetle species by Calmont B., Castro A., Courtin
O., Lencina J.L., Liberti G., Otero J.C., Rose O., Soldati F.,

Stuben P., Verdugo A., Vienna P.P., Viñolas A., and Zapata
J.L. In addition, we would like to thank the translation service of
‘Centro Superior de Idiomas’ of the University of Alicante
for their help with the revision and correction of the lan-
guage. Financial support was provided by the research Project
‘Ministerio de Ciencia e Innovación y fondos EUFEDER’
(CGL201231669), ‘Ministerio de economía, industria y com-
petitividad’ (CGL2016-78181-R), and ‘Generalitat Valenciana’
(PROMETEO/2013/034 and AICO 2020/192). This research is
part of Diana Pérez-Sánchez PhD studies, funded by ‘Ministe-
rio de Educación, Cultura y Deporte’, through the fellowship
FPU14/03721. The authors declare no conflicts of interest.

Author contribution

D.P.-S., E.G., and E.M. conceived the ideas, designed method-
ology, and collected the data; D.P.-S. and N.W.H.M. analysed
the data; and D.P.-S. and E.M. led the writing of the manuscript.
H.B. lead part of the species identification process. All authors
contributed critically to the drafts and gave final approval for
publication.

[Correction added on 30-July, after first online publication:
Author Contribution section has been updated]

Data availability statement

Data available in article supplementary material

Supporting Information

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

**Table S1.** Information about the sites sampling in each protected area, including the name of the site, the tree species sampled in each site, the number of flight interception in each site, and the coordinates of each site.

**Table S2.** Summary of principal component analysis performed on **a)** the residuals of the morphological traits; **b)** the morphological and surrogate of physiological traits; and **c)** the morphological and surrogate of physiological and the phenological traits. High coefficients are shown in bold.

**Table S3.** List of identified species with the families and subfamily they belong, abundances (number of individuals), and trophic group of each one.

**Table S4.** List of species used for the analyses with the families and subfamily they belong and number of measured individuals.

**Figure S1.** Correlation graphs for each park (blue squares represent Sierra Espadán Natural Park; red triangles represent Font Roja Natural Park, and green circles represent Cabañeros National Park) between **a.** taxonomic richness and the rest of functional diversity metrics; and **b.** true diversity and the rest of functional diversity metrics. The results of Spearman rank correlation tests are shown in Table 3, only for significantly correlations (*P* < 0.05) linear trends are drawn.
**Figure S2.** Boxplots representation of SES.Functional Richness (functional richness after removing the effect of the number of species), and functional redundancy as the loss of random species increases by 5 percent (from 0 to 70 % species loss) for each protected area (Cabañeros National Park, Font Roja natural park and Sierra Espadán natural park). Different letters indicate significant differences according to the Kruskal–Wallis test between intervals ($P<0.05$). The central rectangle indicates the second and third quartiles, and the black line the median value. Red points indicate mean values and “whiskers” lower and upper quartiles. Black points indicate possible outliers

**Figure S3.** Boxplot representation for each protected area (Cabañeros National Park, Font Roja Natural Park, and Sierra Espadán Natural Park) of SES.Functional Richness (functional richness after removing the effect of the number of species) and functional redundancy, considering the species loss scenarios of functionally rare species (intervals going from 0 to 70% species loss). Different letters indicate significant differences based on the Kruskal–Wallis test between intervals ($P<0.05$). The central rectangle indicates the second and third quartiles, and the black line the median value. Red points indicate mean values and whiskers lower and upper quartiles. Black points indicate possible outliers.

**Figure S4.** Boxplot representation for each protected area (Cabañeros National Park, Font Roja Natural Park, and Sierra Espadán Natural Park) of SES.Functional Richness (functional richness after removing the effect of the number of species) and functional redundancy, considering the species loss scenarios of the less abundant species (intervals going from 0 to 70% species loss). Different letters indicate significant differences based on the Kruskal–Wallis test between intervals ($P<0.05$). The central rectangle indicates the second and third quartiles, and the black line the median value. Red points indicate mean values and whiskers lower and upper quartiles. Black points indicate possible outliers.

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Accepted 25 June 2021

First published online 19 July 2021

Associate Editor: Dirk Sanders