disentangling the effects of available area, mid-domain...

ORI GIN AL PA PER

Disentangling the effects of available area, mid-domainconstraints, and species environmental toleranceon the altitudinal distribution of tenebrionid beetlesin a Mediterranean area

Simone Fattorini

Received: 29 September 2013 / Revised: 13 May 2014 / Accepted: 2 June 2014 /Published online: 18 June 2014� Springer Science+Business Media Dordrecht 2014

Abstract Most studies have attempted to identify the major environmental factors

responsible for elevational variations in species richness. Such studies have been mainly

performed in temperate and tropical areas, whereas the mediterranean biome has been

substantially neglected. The aim of this paper was to disentangle the effects of available

area, mid-domain constraints, and the environmental tolerance of species, on the altitudinal

distribution of tenebrionid beetles in a Mediterranean region. A comprehensive faunistic

database was used to assess the elevational distribution of tenebrionids in Latium (Central

Italy). Variations in species richness, beta diversity and nestedness were analysed in

association with variation in species ranges and midpoints. Variation in species richness

was contrasted with patterns expected on the basis of the mid domain effect (MDE) and

available surface area. After correcting for differences in area availability due to the

conical shape of mountains, an unexpected triphasic pattern emerged: (1) at low altitudes,

species richness was higher than expected on the basis of the effect of area and the MDE;

(2) at around 800 m elevation, there is an abrupt change in species assemblages, and

richness values fit those predicted by the MDE; (3) a new dramatic change occurred at

1,700 m, with tenebrionid assemblages composed of a small number of mainly eurytopic

species. The integrated approach used in this study demonstrates that neither MDE nor

monotonic patterns fully explain the observed diversity patterns. Variations in species

Communicated by P. Ponel.

Electronic supplementary material The online version of this article (doi:10.1007/s10531-014-0738-y)contains supplementary material, which is available to authorized users.

S. FattoriniAzorean Biodiversity Group (CITA-A) and Platform for Enhancing Ecological Research andSustainability (PEERS), University of Azores, Angra do Heroısmo, Terceira, Acores, Portugal

S. Fattorini (&)Department of Biotechnology and Biosciences, University of Milano Bicocca, Piazza della Scienza 2,20126 Milan, Italye-mail: [email protected]

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Biodivers Conserv (2014) 23:2545–2560DOI 10.1007/s10531-014-0738-y

http://dx.doi.org/10.1007/s10531-014-0738-y

ranges indicate that the elevational gradient filters species according to their ecological

tolerance.

Keywords Biodiversity � Biogeography � Coleoptera: Tenebrionidae � Insects �Mountains � Rapoport’s rule

Introduction

Many studies have shown a continuous decline of species richness with increasing altitude

(see references in Stevens 1992), but there is also substantial evidence for the existence of

mid-elevation peaks in a broad range of organisms (Lomolino et al. 2010; Sanders and

Rahbek 2012) and this latter is sometimes considered the most common pattern (see

Rahbek 1995; McCain 2010).

The monotonic decrease of species richness with altitude has been thought to be both

the response of various abiotic and biotic factors (Kaspari et al. 2000), and a possible

reflection of a variation in range size of the species, the so-called Rapoport’s rule (Stevens

1992), which is in turn a reflection of variation in species tolerance. Because species at

higher elevations must be able to tolerate more variability, they are expected to also have

larger elevational ranges, whereas lowland species are less tolerant and cannot persist at

higher elevations; as a result, species richness will decrease monotonically with elevation

(Sanders 2002).

However, it has been also postulated that species richness might decrease with elevation

simply as a reflection of a reduction in available area because of the basically conical shape

of mountains (McCain 2010). Thus, before evoking any other biological explanations for a

monotonic decreasing pattern, it is important to assess if this pattern might be a conse-

quence of decreasing available area. In fact, after correcting for differences in area

availability using species-area relationships, many groups showed a hump-shaped pattern

(McCain 2010). This hump-shaped pattern has been frequently interpreted as a result of

purely stochastic processes that generate geometric constraints (the so-called mid domain

effect, MDE) (Colwell et al. 2004).

In the present paper, I tested the effects of available area, the environmental tolerance of

species and the mid-domain constraints, on the elevational distribution of a group of insects

(the tenebrionid beetles) in a Mediterranean region (Latium, Central Italy). In spite of an

increasing interest in the elevational gradients, relatively few studies have used such a

comparative approach, especially for invertebrates (e.g. Sanders 2002; Beck and Chey

2008; Levanoni et al. 2011).

The beetle family Tenebrionidae is one of the largest clades of Coleoptera and currently

includes more than 15,000 described species worldwide. Tenebrionid beetles are particu-

larly suitable for an elevational gradient study in Mediterranean ecosystems because (1)

they are particularly abundant and diversified even in arid and semiarid environments,

where other insect groups are less represented, and (2) they range from sea level to high

altitude habitats.

Studies on elevational gradients have been performed in many temperate and tropical

areas for a number of plant and animal groups (see Lomolino et al. 2010 for examples), but

the mediterranean biome has been substantially neglected. Although the mediterranean

biome forms one of the rarest of the Earth’s thirteen terrestrial biomes, covering a mere

2546 Biodivers Conserv (2014) 23:2545–2560

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2 % of the Earth’s land surface, it hosts an impressive biodiversity, including over 20 % of

Earth’s known vascular plants (Cox and Underwood 2011). The study area is placed in the

centre of the Mediterranean biodiversity hotspot and includes virtually all main biotopes

that can be encountered in the Mediterranean basin, with elevation ranging from sea level

to about 2,500 m. Thus, the present study also contributes to partially fill this gap by

offering a first comprehensive analysis of a wide elevational gradient in a Mediterranean

area.

Materials and methods

Study area

Latium is situated in the central part of the Italian peninsula. It comprises a land area of

nearly 17,200 km2, mostly occupied by flat and hilly landscapes interrupted by mountain

chains that can surpass 2,000 m elevation (maximum elevation: 2,458 m) (Fig. 1). The

coast of Latium is mainly composed of sandy beaches and the central section of the region

is occupied by a vast alluvial plain surrounding the city of Rome (about 3 million

inhabitants). The climate is Mediterranean, but with strong variation according to the

elevation: along the coasts, average temperatures are comprised between 9–10 �C in

January and 24–25 �C in July, whereas in the inner (mountainous) areas temperatures may

be below 0 �C in January (-3 �C on Mount Terminillo) and below 20 �C in July. Annual

rainfall ranges between 700 mm along the coastal rim and 1,200 mm in the internal

mountain areas.

To study relationships between altitude and climatic factors that might be important for

tenebrionids, I considered the following climatic variables: maximum annual temperature,

minimum annual temperature, and average annual temperature using data from the fol-

lowing 29 meteorological stations distributed along the regional elevational gradient:

Alatri, Ardea, Atina, Bolsena, Bracciano Vigna di Valle, Civitavecchia, Santa Marinella,

Fiuggi Fonte, Frosinone, Gaeta, Guidonia, Latina Aeroporto, Latina Centro, Leonessa,

Monte Terminillo, Orte, Pomezia, Posta, Posticciola, Pratica di Mare, Rieti, Roma

Ciampino, Roma Fiumicino, Roma Monte Mario, Roma Urbe, Segni, Subiaco, Santa

Scolastica, Terracina, Tuscania, Viterbo. Data referred to the period 1961–1990 and were

taken from Wikipedia (it.wikipedia.org/). For the same 29 stations I have also considered

the following bioclimatic indices proposed by Mitrakos (1980): winter cold stress (WCS),

year cold stress (YCS), summer drought stress (SDS), and year drought stress (YDS).

Values of Mitrakos’ indices were taken from Blasi (1994).

Data sources

I used a database comprising 3,561 tenebrionid records from Latium, from which 84 native

species and subspecies are currently known (note that one ‘record’ refers to a unique

combination of species (or subspecies), place, altitude, year and source, but may involve

from one to hundreds of specimens). Data originate from museum and private collections,

literature, and unpublished lists, for a total of 26,743 specimens (25,349 specimens directly

examined, plus literature data for 1,394 specimens).

The database included three species which occur in the study area with different sub-

species at different elevations: Asida pirazzolii (A. p. pirazzolii and A. p. sardiniensis),

Colpotus strigosus (C. strigosus strigosus and C. strigosus ganglbaueri), and Opatrum

Biodivers Conserv (2014) 23:2545–2560 2547

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sabulosum (O. s. sabulosum and O. s. sculptum). The current taxonomic division into

species and subspecies, as applied to the tenebrionids of Europe, is arguably arbitrary.

Recent morphological (Trichas 2008; Condamine et al. 2011; Ferrer 2008, 2011) and

molecular (Pons et al. 2004; Soldati and Soldati 2006; Stroscio et al. 2011) analyses

showed that populations traditionally classified as subspecies are really ‘evolutionarily

significant units’ (sensu Ryder 1986), usually demanding a species status. Thus, I con-

sidered the aforementioned subspecies as species, and the term species will be used for

simplicity. At any, excluding subspecies led to virtually identical results (not shown).

Sample sites were geo-referenced with the maximum precision allowed by the original

datum using digital topographic maps. The high density of place names in Latium, and the

great attention paid by entomologists to be as accurate as possible in compiling specimen

labels, suggest that true collecting places were really close to the locality reported on the

label (see Ruffo and Stoch 2006).

Data spanned from 1860 to 2011 and included 615 sampling stations (Fig. 1). The very

large sampling effort made through more than a century by hundreds of collectors not

Fig. 1 Study area. a Elevation map of Latium (25 m contour lines taken from http://opendem.info/index.html) with distribution of locality records (dots). The inset shows the position of the study area within Italy.b Diagrammatic representation of the elevational gradient in Central Italy, with indication of main vege-tational and soil settings


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http://opendem.info/index.html

http://opendem.info/index.html

directly interested in tenebrionids but in other insect groups and who used any kind of

collecting method (hand searching, pitfall traps, aerial traps, soil examination, etc.) ensures

that these data collectively form a ‘random’ sampling, not affected by biases due to

collector preferences for certain biotopes, sites or species. Also, because these beetles are

not considered particularly attractive by most amateur entomologists, and are largely

collected as by-product of generalized collecting activities performed by entomologists

mostly interested in other groups, it is unlikely that entomologists under-collected common

species and over-collected rare species. Further details on database records and data quality

can be found in Fattorini (2013).

When available from the label data of museum specimens or explicitly given in liter-

ature records, elevation data were included in the database as recorded by the collector.

When elevation was not reported among label data or not given in the literature sources, it

was retrieved by overlapping point records with topographic maps at the best available

resolution. Species minimum, maximum and midpoint elevations are given in Supporting

Information.

Because some specimens were decades old, it is possible that, given climate change

effects, some species ranges may have shifted over time (Menendez et al. 2014) and older

specimens mixed with more recent specimens may lead to incorrect results. However, a

recent study has demonstrated that only two species had a significant change in their

elevational distribution as a possible consequence of climate change and that, in general,

no overall change was detected when the entire fauna was considered (Fattorini and Salvati

2014). In general, mean elevational shifts did not extend far enough to reach the next belt

(see below) and hence to influence the overall patterns. Moreover, these changes consisted

in an increased mean elevation, which should have, if any, an opposite effect on the

monotonic decrease in species richness found in this study. Thus, the effect of climatic

change on the overall pattern is negligible.

Elevational subdivision

In order to explore the basic relationship between elevation and richness, the gradient was

divided into 24 belts of 100 m (0–100, 101–200, 201–300 m, etc.). To calculate species

richness per belt I used two approaches. In a first set of analyses, I considered only

documented species presences in each belt, instead of interpolating species presences

between maximum and minimum elevation. This poses, however, the problem of false

absences, which may cause serious problems especially for beta diversity calculation (see,

for example, Beck et al. 2013). Thus, in a second set of analyses, I assumed that the

elevational ranges of species were continuous, as currently done in most research on

elevational gradients, because gaps in recorded distribution at this scale are more likely

attributable to sampling incompleteness rather than to true gaps in the distribution of

species (e.g. Grytnes and Vetaas 2002; Mena and Vazquez-Domınguez 2005; Rowe 2009).

Only for A. pirazzoli I maintained gaps relative to certain belts because of the peculiar

altitudinal distribution of the two subspecies of this species. The lack of the subspecies

pirazzolii in a certain belt might be due to replacement with the ssp. sardiniensis and, as

found for other tenebrionid populations taxonomically recognized as subspecies, they

might represent distinct species. Values of observed species richness were strictly corre-

lated with those obtained from interpolated presences (Pearson r = 0.944, P \ 0.0001).

Thus only results obtained from interpolated occurrences are presented here.


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Elevational gradients in species richness

To investigate a possible monotonic decrease in species richness with elevation (Stevens

1992), I used linear, polynomial, power and exponential functions and then selected the

model that explained most variance (highest R2). Because the species-elevation relation-

ship may be an indirect effect of decreasing available area (McCain 2010), I used here the

procedure recommended by McCain (2007) to control for variation in belt area. I have first

calculated the amount of land surface in each 100 m elevational belt using a digital

elevational model. Then I applied a power function S = cAz (where S is species richness,

A is area and C and z are estimated parameters) in a double-log species-area plot. After the

z value was empirically obtained using areas and species richness for the full array of

available belts, the power model was solved for the constant c (c = S/Az), which was a

measure of species density (i.e. number of species per area unit). To rescale c to similar

values as the empirical diversity, each c estimate was multiplied by a constant (in this case,

100). Further details can be found in McCain (2007).

To test the spatial constraint hypothesis, species richness patterns were compared to

mid-domain effect (MDE) null model predictions obtained using the empirical version of

the constrained range-size model implemented in the software RangeModel 5.0 (Colwell

2006). Species recorded at only a single site may create problems during the randomization

of range midpoints because they have an observed elevational range of zero even after

interpolation. Although these species were only seven within the studied fauna, to surpass

this problem I adopted the strategy used by Cardelus et al. (2006) and assumed that the

actual elevational range of each species extended beyond each end of its recorded range by

10 m elevation, which is the error scale of barometric altimeters commonly used to record

elevation. The only exceptions were species recorded at elevation lower than 10 m, which

were only incremented by 10 m.

Elevational gradients for uncorrected and area-corrected values of species richness were

finally compared with the pattern (95 % confidence intervals) predicted by the MDE (see

Levanoni et al. 2011). A regression of empirical and predicted values (based on the average

of simulations at each 100 m elevational band) was used to have an R2 estimate of MDE

overall fit (see Colwell et al. 2004; McCain 2007). To test the impact of the MDE on

species richness in addition to the effect of area, I conducted a multiple regression analysis

using log-transformed species richness as response variable and log-transformed values of

area and richness predicted by the MDE as independent variables (see Bachman et al.

2004; Herzog et al. 2005; Rowe 2009, for the rationale and details about the procedure). To

estimate the relative influence of each variable, I applied the corrected Akaike information

criterion (AICc) to select the best-fit model.

To test Rapoport’s rule, I calculated species altitudinal ranges as the highest and lowest

sites where the species occurred, and the mid-point as the average of these values, and

correlated the two measures (see Sanders 2002 for the rationale).

In elevational studies, tests of significance are complicated by autocorrelation problems,

which may influence the results of regression analyses, although not necessarily (Diniz-

Filho et al. 2003). Species richness per band, as used in this study, derive from aggregation

of sites that are sparse over the entire region, albeit at the same altitude, which makes it

difficult to correct probability levels for autocorrelation. Moreover, in the framework of

model selection, significance tests are not appropriate and should be not mixed with

information-theoretic approaches (Burnham and Anderson 2002). Thus, regression anal-

yses focused on the proportion of variance explained rather than on probability values

(Herzog et al. 2005; see also Burnham and Anderson 2002).


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Beta diversity

There is a long debate about the most appropriate index to express beta diversity. Koleff

et al. (2003) found that Whittaker’s bw index fulfilled most of the desired characteristics.

This index is calculated as follows:

bw ¼Aþ Bþ Cð Þ

2Aþ Bþ Cð Þ=2� 1

where A is the number of shared species between two compared belts, and B and C are the

species unique to the first and the second belt, respectively.

However another beta diversity measure with appealing properties is Simpson’s bsim

dissimilarity coefficient, which accounts only for spatial turnover (species replacement)

and excludes the effect of dissimilarity due to nestedness (Baselga 2010, 2012). bsim is

calculated as follows:

bsim ¼ min B;Cð Þ= Aþmin B;Cð Þð Þ

where A, B and C have the same meaning as above.

In this paper, I used both indices to express species turnover between all possible pairs

of belts. Pairwise dissimilarity matrices were then subjected to a cluster analysis using the

UPGMA as amalgamation rule (e.g. Kreft and Jetz 2010; Holt et al. 2013). Analyses were

performed using the software PAST 2.17 (Hammer et al. 2001).

Nestedness

Nestedness represents the degree to which small assemblages of species are subsets of

successively larger assemblages. Nestedness can be viewed as the spatial outcome of a

species pool being ‘‘filtered’’ by local (site-specific) environmental constraints (Patterson

and Brown 1991; Wright et al. 1998), with each species’ distribution among sites deter-

mined by its ability to overcome the constraints (Cook et al. 2004). Environmental gra-

dients can generate nested subset patterns if species with the broadest tolerance persist

throughout the gradient, while others with more limited tolerances are restricted to one end

of it (Cook et al. 2004). It has been frequently observed that species found at high altitude

are subsets of low altitude faunas (Patterson and Brown 1991; Wright et al. 1998). This

may happen (1) if most species occur at low altitudes and (2) if, among them, those with

high ecological tolerance are mostly able to survive at higher elevations, whereas high

altitude specialists are a minor component of the faunas. Although this model cannot be

ubiquitous, it might be common among organisms that are negatively affected by a

decrease in temperature associated with increasing elevation, such as most insect groups.

In such a situation, a general species impoverishment along an elevational gradient would

be determined by environmental filters and this process should generate highly nested

distributions of species along elevational bands. This seems particularly applicable to

tenebrionids because they are basically beetles of dry and hot habitats (which are con-

centrated in lowland areas), so that for this group a gradual impoverishment with elevation

might be expected to prevail over a counteracting addition of high altitude specialists. To

assess if species distribution across elevational bands was nested, I compiled a presence/

absence matrix of species (rows) per elevational band (columns). I measured nestedness of

this matrix using the Brualdi and Sanderson Discrepancy index (BR) as recommended by

Ulrich et al. (2009). Significance was assessed by calculating 1,000 null matrices using the


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‘‘proportional row and column totals’’ algorithm to calculate the Z-value. Nestedness

analysis was done using the software NeD (Strona and Fattorini 2012).

To evaluate species habitat specificity I calculated for each species a Shannon index

using species abundances across the 15 main phytoclimatic units occurring in Latium (see

Fattorini et al. 2013 for details). Increasing values of this index indicate increasing eco-

logical tolerance (decreasing habitat specificity) of the species (see Supporting

Information).

Results

Species richness (S) decreased exponentially with elevation (E) (Fig. 2, S = 91.24e-0.0016E,

R2 = 0.912). However, species richness was also dependent on available area (A), strictly

following a power function relationship (S = 0.642A0.564, R2 = 0.949). This species-area

relationship was therefore used to obtain values of richness independent of area. Area-

corrected values of species richness did not show a monotonic decrease with altitude (Fig. 2).

Richness increased from 0 to about 700 m and then decreased; a second, and higher peak, was

detected at about 2,200 m. This pattern also deviated substantially from what can be expected

on the basis of the MDE (Fig. 2).

Regression analyses of lnS against lnA, lnS predicted by the MDE and a combination of

both, revealed that only area is an important predictor of the overall trend of species

richness (Table 1).

A comparison among the observed values of species richness, area-corrected values and

MDE expected values (Fig. 2) indicates that the MDE null model produced values close to

those obtained after area correction only at intermediate elevations, whereas there were

strong deviations at the extremes, which indicates that the species distributions cannot be

simply explained as a result of a stochastic distribution of small- and large-ranged species

along the gradient.

In particular, at lower altitudes (\700 m) the observed species richness was much

higher than expected by the MDE, and this cannot be simply due to an area effect, because

this pattern was obtained also after correcting for available surface. At high altitudes

([2,000 m), the reduced availability of land surface had a strong effect on the observed

species richness, which disappeared after correcting for area. Area corrected values of

species richness at high altitudes were in fact substantially higher than the uncorrected ones

and also higher than those expected on the basis of the MDE. This triphasic pattern is a

reflection of the distribution of single species elevational ranges. Most species which are

found at high altitudes have, in fact, a broad elevation range (Fig. 3). Few species reach

very high altitudes ([2,000 m), but these species have smaller ranges centred on middle

and high altitude belts. The size of the range of altitudes over which species occurred

increased with increasing altitude (see Supporting Information; y = 1.441x ? 97.439,

R2 = 0.778), as predicted by Rapoport’s rule. In general, species have an elevational range

that extends equally above and below the midpoint (elevational range: midpoint ratio = 2).

Most species occur at low elevations, whereas only a minority range into the highlands.

Regressing elevational range against minimum elevation yielded a non-significant

regression that explained \2 % of variation in range amplitude (y = 0.465x ? 591.370,

R2 = 0.018, P = 0.220). By contrast, regressing range against elevational maximum yielded

a relationship that was statistically significant and highly predictive (y = 0.893x ? 19.570,

R2 = 0.928, P \ 0.00001). Similar results were obtained regressing elevational midpoints

against elevational minimum (y = 1.232x ? 295.680, R2 = 0.344, P \ 0.00001) and


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maximum (y = 0.553x - 9.785, R2 = 0.952, P \ 0.00001). This pattern is however dis-

rupted by a few high altitude species, which have a high midpoint but a small range (i.e.

species with a small vertical distribution concentrated at high altitudes).

Analyses of adjacent belts showed that bw was relatively high at the lowest altitudes and

immediately dropped attaining similar values for all between-belt comparisons between

300 and 1,000 m; after these belts, bw peaked at 1,000 m, 1,700 m, 2,000 m, and especially

at 2,300 m. bsim had a much more irregular pattern. This is however due to the fact that

adjacent belts tend to have always many shared species, so variation is very small when

analysed belt by belt. Moreover, because bsim is highly sensitive to species gains and

losses, no change in species composition (zero values) is found more often than with

Whittaker’s bw index. In general, bsim showed very low values, but two major peaks

(bsim [ 0.1) were apparent: one between 901–1,000 and 1,001–1,100 m, and another

between 1,701–1,800 and 1,801–1,900 m. Thus, elevations at around 1,000 and 1,700 m

act as strong turning points in beta diversity.

A cluster analysis based on bw (Fig. 4a), revealed three main clusters, corresponding to

strong variations in beta diversity. A first cluster included belts ranging from 0 to 800 m

Fig. 2 Diversity patterns of tenebrionid beetles along the elevational gradient in Latium. Species richnesspatterns expressed as observed values are shown as a thick black line, whereas area-corrected values areindicated by a grey line. Expected mid domain effect (MDE) values are indicated by squares. The upper andlower 95 % confidence intervals (thin lines) of the MDE were generated from 5,000 Monte Carlosimulations

Table 1 Regressions of observed species richness against area (A) and values of richness predicted by themid-domain effect (SMDE)

Regression model Radj2 AICc

lnS = -8.235 ? 0.564 lnA 0.948 10.314

lnS = 1.8766 ? 0.3001 ln(SMDE) 0.249 74.622

lnS = -8.268 ? 0.566 lnA - 0.02 ln(SMDE) 0.949 12.848

Radj2 is coefficient of determination adjusted for the number of explanatory variables and AICc is the

corrected Akaike information criterion


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elevation. A second cluster included belts from 801 to 1,600 m elevation. A third cluster

included belts ranging from 1,601 to 2,200. Finally, the two belts over 2,200 m clustered

apart; however, this was due to the fact that they have only one species, thus appearing

very different from all other belts. On the whole, these results indicate important changes

in species composition at around 800 and 1,600 m. Use of bsim produced similar results

(Fig. 4b). A first cluster is represented by the separation of all belts above 1,700 m, which

have faunas very different from those of all lower belts, thus confirming that

1,600–1,700 m are an important turning point for variation in species composition. Within

the bands lower than 1,700 m, bsim allowed the identification of a turning point at 900 m.

However, bsim also identified a relatively abrupt change in faunal composition between

belts from 0 to 300 m and those between 301 and 900 m.

Species distribution across belts was significantly nested (BR = 46.0, Z = -21.61,

P \ 0.001). The nestedness observed among tenebrionid assemblages was strongly asso-

ciated with elevation. For nestedness analyses, the original matrix was rearranged

according to row and column totals in order to obtain a maximally packed species per belt

distribution. In doing this, however, the packing procedure very nearly reproduced the

ordering of assemblages along the elevational gradient (Spearman rank correlation

between elevation and reordered belts: rs = 0.990, P \ 0.00001).

Habitat specialization decreased with species elevational midpoints (rs = 0.5480,

P \ 0.00001), maximum elevation (rs = 0.576, P \ 0.00001) and elevational range

(rs = 0.653, P \ 0.00001), but not with minimum elevation (rs = -0.150, P = 0.172),

thus indicating that species with broad ecological tolerance tend to have larger elevational

range with higher midpoints.

Fig. 3 Elevational ranges of 84 tenebrionid beetle species that occur in Latium. Tenebrionid speciesnumbers (abscissa) provide a cross reference to TableS1. Dots indicate mid-points


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Fig. 4 Cluster analysis (UPGMA) of elevational assemblages of tenebrionid beetles in Latium based onWhittaker (a) and Simpson (b) indices of beta diversity


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Discussion

Tenebrionid species richness decreased monotonically with elevation. This decline in

species richness was best modelled by an exponential (rather than linear) relationship (see

Patterson et al. 1996 for a similar pattern), but it was mostly a result of decreasing available

area. After correcting for differences in available area, an unexpected triphasic pattern

emerged. From 0 to about 800 m elevation, species richness was higher than expected even

on the basis of the effect of a greater area availability and on the basis of the MDE. This

suggests that tenebrionids find particularly favourable conditions at low altitudes, which

might include high habitat diversity (with presence of coastal biotopes, inhabited by many

sand-dwelling stenotopic species) and suitable climatic conditions for both thermophilic

and mesophilic species (Fattorini 2008). Also, within this elevational range, species

richness decreases sharply (whereas the MDE predicts an increase). This sharp decrease

(not determined by an area effect) is a ‘‘genuine’’ species-elevational impoverishment. An

analysis based on climatic data gathered from 29 meteorological stations distributed along

this elevational gradient, revealed strong correlations between elevation (m) and the fol-

lowing annual temperature measures: maximum temperatures (r = -0.957, P \ 0.001),

minimum temperatures (r = -0.813, P \ 0.001), and average temperatures (r = -0.963,

P \ 0.0001). Similarly, using Mitrakos’ aridity indices, both SDS and YDS decreased with

elevation, although correlations were less strong (r = -0.709 and r = -0.742, respec-

tively; P \ 0.001 in both cases). This decrease in temperatures and aridity with altitude

may act as an important factor driving species impoverishment by filtering thermophilic

and xerophilic species. Using Mitrakos’ indices, WCS and YCS, increased with altitude

(r = 0.832 and r = 0.760, respectively; P \ 0.001 in both cases), which also supports the

possible role of elevation as a filtering factor for termo-xerophilic species.

At around 800 m elevation, there is an abrupt change in the elevational gradient in

species richness, and, between 900 and 1,500 m, species richness still decreases, but

mostly as an effect of area reduction. A final abrupt change occurred at around 1,700 m.

Above this elevation, there is a notable impoverishment, which can be hardly explained by

area reduction and which seems also sharper than expected on the basis of the MDE.

Because of the lack of other studies on Mediterranean beetles over an elevation range as

wide as that used in this study (0–2,400 m), it is difficult to establish how general this

pattern may be. However, in a study on dung beetles, Jay-Robert et al. (1997) found that in

the Iberian Central System, along an elevational range from 1,200 to 2,000 m, there was a

monotonic decrease in species richness, whereas in the elevational range of Sierra Nevada

(which spans from 700 to 2,600 m), after an overall declining patterns (from about 1,000 to

2,000 m), there was an increase in species richness at around 2,200 m, which fits with the

tenebrionid pattern in Latium.

Sharp differences in beta diversity indicate that tenebrionid assemblages have little

variation in species composition within the three main elevational sectors (0–900,

900–1,700, and 1,700–2,400 m), but change dramatically between them.

Previous work has shown that beta diversity tends to vary rather irregularly along

elevational gradients (Mena and Vazquez-Domınguez 2005; Levanoni et al. 2011). In the

tenebrionid beetles, multiple peaks are distinctly associated with the triphasic pattern in

species richness. Also, the three beta diversity peaks fit with the three main vegetational

zones traditionally recognized in the study area: the basal belt (with sclerophyll evergreen

vegetation and heliophilic broadleaves: 0–1,000 m); the montane belt (sciaphilous

broadleaves: 800–1,800 m); and the culminal belt (high altitude pastures: 1,800–2,900 m)

(Giacomini and Fenaroli 1958; cf. Fig. 1).


123

Regarding the assumption that turnover decreases at higher elevations because of

species with larger elevational ranges (Stevens 1992), highland assemblages were indeed

composed of species with larger, overlapping elevational ranges, and turnover values were

correspondingly low, although Whittaker’s index showed an increase at the end of the

gradient.

Nestedness analyses of species distribution along elevational gradients are still rare (see,

for valuable exceptions, Patterson et al. 1996; Cook et al. 2004), yet they may provide

important information to interpret elevational diversity patterns. Tenebrionid species dis-

tribution across elevation was strongly nested, which indicates that high altitude species

assemblages are mainly subsets of lowland species assemblages. Thus, most species occur

at low elevations, whereas only a minority range into the highlands. Most of the highland

species also extend into the lowlands, whereas the reverse is not true. Thus, the tenebrionid

beetles of Latium offer strong support to Rapoport’s rule: many low-elevation species have

very narrow ranges being associated with coastal environments, whereas tenebrionids

living on mountain tips are eurytopic species (e.g. species having a tolerance to a wide

range of environmental conditions) that extend over most of the gradient. This suggests

that the elevational gradient (or better ecological gradients encapsulated by altitude) filters

tenebrionid species according to their ecological tolerance, allowing only the most eury-

topic ones to be present from lowland to high altitude belts. Hoiss et al. (2012) found that

community assembly of Alpine bees at high altitudes is dominated by environmental

filtering effects, whereas the relative importance of competition increases at low altitudes.

As competition does not seem to have an important role in regulating tenebrionid com-

munities (see Fattorini 2008 for a review), an environmental filtering process, based on

decreasing temperature, appears a more reasonable general explanation for the tenebrionids

of Latium.

In general, turnover analysis, coupled with nestedness analysis and species range

midpoint distribution, offers an explanation for the unusual triphasic pattern: (1) from 0 to

900 m, there is a gradual faunal impoverishment, where low altitude species linked to

coastal biotopes tend to disappear and only eurytopic species remain; (2) at 900–1,700 m

communities are relatively similar in species composition and largely constituted of

eurytopic species, whose vertical distribution mostly reflect geometric constraints; (3) over

1,700 m there is an abrupt change in species composition, because communities lose many

species that cannot tolerate high altitude conditions but gain high altitude specialized

species. This turnover pattern is however mostly due to species loss. As already observed

in a completely different situation (bats of a tropical area: Patterson et al. 1996) as species

drop out with increasing elevation, they are seldom replaced by higher-elevation spe-

cialists. Cluster analysis aggregated belts into groups that match these variations in beta

diversity. In general, all analyses converge towards the same basic interpretation: the

elevational gradient filters species according to their ecological tolerance. Interestingly,

there is a plateau at middle elevation that fits with species richness values predicted by the

MDE. A basic assumption of the MDE is that all species are ecologically equivalent and

only geometric constraints are responsible for their distribution. This assumption is not

supported by the lowland species assemblages (which include lowland specialists) and the

high altitude assemblages (which include high altitude specialists, plus the most tolerant

species) but can be roughly met by the species that form middle elevation communities.

These communities are expected to be almost entirely composed of species with similar

broad ecological tolerance, with no strict specialists, and which are therefore ecologically

‘‘interchangeable’’. This may explain why, in the study system, the MDE null model works

best at intermediate altitudes.


123

Conclusions

Most studies on the elevational gradient in biodiversity have attempted to identify the

major environmental factors (such as climate, productivity, and geometric constraints)

responsible for variations in species richness (see Lomolino et al. 2010 for a review). In

this paper, a very different approach was used. I analysed elevational variations in beta

diversity and nestedness patterns and used results from these analyses to shed light on

variation in species richness along the elevational gradient. This integrated approach made

it possible to understand that the elevational variation in species richness is associated with

the presence of variation in species composition, which is in turn a reflection of some

filtering process that allows only the most eurytopic species and few specialists to reach the

highest altitudes.

Acknowledgments A. Di Giulio, E. Maurizi, A. Sciotti and P. Tratzi digitized and geo-referenced manyrecords. G. Strona realised the map presented in Fig. 1. Thanks are due to two anonymous referees for theiruseful comments.

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