Diversity and Distributions, (Diversity Distrib.) (2008) 14, 95–105
Blackwell Publishing Ltd
BIODIVERSITY
RESEARCH
Species richness patterns of obligate
subterranean beetles (Insecta: Coleoptera)
in a global biodiversity hotspot – effect
of scale and sampling intensity
Maja Zagmajster1*, David C. Culver2, Boris Sket1
1
Department of Biology, Biotechnical Faculty,
University of Ljubljana, Vecna pot 111,
PO Box 2995, SI-1000 Ljubljana, Slovenia,
2
Department of Biology, American University,
4400 Massachusetts Avenue, NW Washington
DC 20016, USA
*Correspondence: Maja Zagmajster,
Department of Biology, Biotechnical
Faculty, University of Ljubljana,
Veçna pot 111, PO Box 2995,
SI-1000 Ljubljana, Slovenia.
Tel.: +386-1-4233388; Fax: +386-1-2573390;
E-mail: [email protected]
ABSTRACT
We studied species richness patterns of obligate subterranean (troglobiotic) beetles
in the Dinaric karst of the western Balkans, using five grid sizes with cells of 80 × 80,
40 × 40, 20 × 20, 10 × 10, and 5 × 5 km. The same two hotspots could be recognized
at all scales, although details differed. Differences in sampling intensity were not sufficient
to explain these patterns. Correlations between number of species and number of
sampled localities increased with increasing cell size. Additional species are expected
to be found in the region, as indicated by jackknife 1, jackknife 2, Chao2, bootstrap,
and incidence-based coverage (ICE) species richness estimators. All estimates increased
with increasing cell size, except Chao2, with the lowest prediction at the intermediate
20 × 20 km cell size. Jackknife 2 and ICE gave highest estimates and jackknife 1 and
bootstrap the lowest. Jackknife 1 and bootstrap estimates changed least with cell size,
while the number of single cell species increased. In the highly endemic subterranean
fauna with many rare species, bootstrap may be most appropriate to consider. Positive
autocorrelation of species numbers was highest at 20 × 20 km scale, so we used this
cell size for further analyses. At this scale we added 137 localities with less positional
accuracy to 1572 previously considered, and increased 254 troglobiotic species considered
to 276. Previously discovered hotspots and their positions did not change, except for
a new species-rich cell which appeared in the south-eastern region. There are two
centres of troglobiotic species richness in the Dinaric karst. The one in the north-west
exhibited high species richness of Trechinae (Carabidae), while in the south-east, the
Leptodirinae (Cholevidae) were much more diverse. These centres of species richness
should serve as the starting point for establishing a conservation network of important
subterranean areas in Dinaric karst.
Keywords
Biodiversity, Dinaric karst, optimal cell size, sampling intensity, scale, subterranean beetles.
The Dinaric karst in the western Balkan Peninsula is a global
hotspot of subterranean biodiversity, with more than 900 aquatic
and terrestrial obligate subterranean (troglobiotic) species
recorded (Sket, 2005a). However, spatial patterns of species richness within the region are poorly known. Sket et al. (2004) indicate that the western Balkans are the richest area of the Balkans,
within which the north-west part (Slovenia) is especially rich.
Diversity studies were mostly based on species lists for the countries or larger geographical subunits within them, and areas of
equivalent size have not been compared (Guéorguiev, 1977; Sket,
1999a,b, 2005a; Sket et al., 2004).
The same applies to the knowledge on diversity of separate
troglobiotic groups – none has been studied in detail. Troglobiotic
beetles are considered the most important contributors to terrestrial
subterranean biodiversity in most temperate karst regions,
including the Dinaric karst, where they present about 42% of the
terrestrial troglobionts (Sket et al., 2004). They make an ideal
group for such analyses, with relatively many published references
on their taxonomy and distribution.
When mapping species richness, the problem of finding suitable
scale for analyses needs to be addressed (Stoms, 1994). Individual
caves may seem appropriate as the basic sample unit for comparisons.
However, they vary widely in size, and, more importantly, when
considered individually, each one harbours only a small part of
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd
DOI: 10.1111/j.1472-4642.2007.00423.x
www.blackwellpublishing.com/ddi
INTRODUCTION
95
M. Zagmajster et al.
Figure 1 Map of the study area and
demonstration of subsequent divisions of
larger grid cells into smaller ones. Line delimits
the border of Dinarides; points present
sampled subterranean localities with beetles.
One point can present more localities, if their
position was determined by the same centroid
(Lambert Conformal Conical Projection).
the subterranean fauna of a karstic region (Gibert & Culver,
2005). Even the world richest cave, Postojna Planina Cave System
(Culver & Sket, 2000), where 84 species were known in 2005
(Sket, 2005a), represented only 9% of the troglobiotic species in
the whole Dinarides, and around 32% of the species of the Slovenian
portion (Sket et al., 2004; Sket, 2005a). Individual species-rich
caves are also insufficient for establishing spatial correlation
patterns, because there are relatively few of them.
A few studies of subterranean fauna have used equally sized
cells to standardize the area of comparison (Krow & Culver, 2001;
Culver et al., 2004a,b; Christman et al., 2005; Deharveng et al.,
in press; Ferreira et al., 2007). But only rarely has the effect of
different sampling unit size on the spatial pattern been considered
(Krow & Culver, 2001; Christman et al., 2005), and sampling
unit size was not the focus of these studies. If the basic sample
cells are too large, the regional patterns and details are lost, but if
the cells are too small, the data are over-scattered (Noonan, 1999;
Hausdorf & Hennig, 2003; Fortin & Dale, 2005).
Sampling adequacy is a problem in biodiversity studies, especially
evident in subterranean habitats. Even in the comparatively well
researched area such as the Dinaric karst in Slovenia, less than
10% of known caves had been sampled faunistically by 2000
(Culver et al., 2004a). New caves are being discovered every year,
and they are mainly not faunistically investigated. Even when
nearly all caves have been sampled, as was the case in a 5 km2 area
in West Virginia (Schneider & Culver, 2004), sampling within
caves was apparently not complete – species accumulation curves
did not reach an asymptote. Still, locations of hotspots of subterranean species richness in Slovenian Dinaric karst remained
the same as new data in subsequent time periods were added
(Culver et al., 2004a).
96
In this study we investigated several aspects of biodiversity
patterns of troglobiotic beetles in Dinaric karst. First, we looked
for optimal sample cell size, by comparing patterns of species
richness and estimated species numbers at five different scales,
ranging from 25 km2 to 6400 km2. We initially used the subset of
records that met the criteria of minimal positional accuracy, to
ensure highest probability that they were delimited to correct
cells also at smallest scale. Second, we mapped numbers of
sampled localities to investigate the sampling intensity patterns
at all scales. Third, we determined the optimal quadrat size by
comparing spatial autocorrelations at different sizes, and find the
scale with the largest spatial autocorrelation. Fourth, we investigated the impact of adding more data to the patterns observed,
by including records with less accurately positioned localities at
the optimal quadrat size. Finally, we identified hotspots of species
richness and described their taxonomic nature and the effect of
sampling intensity.
METHODS
Study area and preparation of data
The Dinarides or Dinaric Alps, situated in the western Balkan
Peninsula, are about a 600 km long and 150 km wide set of
mountain ridges of more than 56,000 km2, near the Adriatic sea
(Sket, 2005a). Our research area covers the whole Dinaric area,
with small portions excluded (Fig. 1). Only in the north-western
part, where Dinarides neighbour the eastern part of Southern
Calcareous Alps, is the boundary for the Dinarides ambiguous.
This is sometimes differently assessed, as there are also patches of
isolated karst in between. We followed the delimitation in Slovenia
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
Hotspots of obligate subterranean beetles diversity
according to the recent map of karst regions by Gams (2003),
and in the rest of the study area according to Oppitz & Mardesic
(1961).
Dinardes are built mainly from carbonate rocks (mostly limestone, also dolomite), which enabled the formation of numerous
underground caves and voids. Just in the Slovenian Dinaric
portion of about 7000 km2, more than 5700 caves were registered
in 2005 (Slovenian Cave Cadastre, Institute for Karst Research,
Postojna). In Croatia, more than 7500 (Gottstein Matoçec et al., 2002)
and in Bosnia and Herzegovina more than 4000 (Mulaomerovic
et al., 2006) have been registered. The total number of caves for
the whole Dinarides is not known, but it is likely to be more than
20,000.
We focused our research on troglobiotic beetles from two families:
Carabidae (subfamily Trechinae) and Cholevidae (subfamily
Leptodirinae or Bathysciinae). Some authors (e.g. Perreau, 2000)
classify the latter as the tribe Leptodirini, subfamily Cholevinae,
of the family Leiodidae. The majority of the troglobiotic beetles are
members of these two groups, while of the rest only the Pselaphidae
with about 20 troglobiotic species are numerically important
(Sket et al., 2004; Sket, 2005a). Defining whether a beetle species
is troglobiotic or not is often not unambiguous, as morphological
features, typical for subterranean environments (troglomorphies),
are not always present in species known only from caves. In the
case that a species is known mostly from caves, or if it was recognized
as obligate subterranean in published reference, we considered it a
troglobiont. That is, we used an ecological rather than a morphological criterion. We followed the delineation of species of recent
catalogues for Trechinae (Moravec et al., 2003) and Leptodirinae
(Perreau, 2000), and added some recently described species.
There are several major works on the distribution of subterranean beetles in the Dinarides, with extensive reviews prepared by
Pretner for Croatia (Pretner, 1973; Jalzic & Pretner, 1977), Montenegro
(Pretner, 1977), Slovenia (E. Pretner, unpublished manuscript),
and Bosnia and Hercegovina (E. Pretner, unpublished data
cards). Additional data on troglobiotic species distribution were
collected from more than 85 literature sources for Dinaric karst,
resulting in more than 4300 records organized in the form of a
relational database in Microsoft Access©.
Localities were identified according to the descriptions in the
literature sources, where they were given with different level of
precision. We used different database and map sources (a complete
list is given in Zagmajster et al., 2006) to get geographical coordinates
of the localities. Positional accuracy of the localities was determined
according to the accuracy of its description in the literature as
well as of the map where the coordinates were taken from.
When the exact position of the locality could not be determined,
coordinates of the centroid of the wider circular area, where the
locality occurs, were determined. Localities were associated with
the grid cell that contained the centroid. To increase the probability
of correct classification into cells, we set the maximum radius for
a locality to be included according to the smallest grid size with
the following rules – (1) the radius was not greater than the side
of the cell, and (2) the probability that the locality was actually
present in the grid cell with the centroid was always higher than
the probability of its presence in any of the neighbouring cells.
In the first data set for our analyses, where we used smallest
cells of 5 × 5 km, we included only localities for which the position was determined with the centroid of the circle radius of
3 km or less. As a consequence of limiting our data set with such
criterion, some troglobiotic beetle species had to be discarded.
In the second data set, which was used in larger grid cells, we
included localities with positional accuracy of 6 km or less. This
allowed inclusion of additional species, but still five troglobiotic
beetle species present in the area had to be discarded, due to
poorly defined localities in original literature sources.
The study region of the Dinaric karst was presented in the
Lambert Conformal Conical Projection (central meridian 18°,
standard parallels 42° and 46°), which presents the territory with
least distortion (Zagmajster et al., 2006). We covered the research
area by equal cell size quadratic networks, getting grids of five
different sizes: 80 × 80 km, 40 × 40 km, 20 × 20 km, 10 × 10 km,
and 5 × 5 km (Fig. 1).
Data analysis
In the first part of the analysis, we prepared maps of species
numbers per cell at all five cell sizes. We defined a cell as a hotspot,
if it contained at least 85% of the richest cell species number.
We chose this class limit, because we wanted to observe the highest
peaks of species richness, relative to the characteristics of species
numbers distribution within the region observed. Second class
contained at least 60% and the third at least 30% of the richest
cell, fourth class contained more than one, and the last exactly
one species. We used the applications of the program package
arcgis, version 9.1 (ESRI©, Redlands, CA, USA).
We calculated three measures of species number distribution, by
including four neighbouring cells that share the same side (referred
to as ‘rook’, Fortin & Dale, 2005). We calculated ‘join-count’ statistics
to test whether occurrence of species in cells is random. If the
observed joins of cells with the records and cell without (referred
to as black-white joins) are smaller than would be expected, the
pattern is not random (Fortin & Dale, 2005). We used package
‘spdep’ for the statistical software r (http://www.r-project.org).
Spatial autocorrelation of species abundance data is dependent
on the sample cell size (Fortin, 1999), which can be used in
choosing the suitable basic unit for analyses. We tested autocorrelation of data on species numbers with Moran’s I and Geary’s
c indices. Positive autocorrelation means that the data are clustered,
and if it is negative, the data have dispersed pattern (Fortin &
Dale, 2005). The size at which the autocorrelation is strongest is
the most suitable for analyses (Fortin & Dale, 2005), which was
already implemented in the set of subterranean data (Zagmajster
et al., 2006). Significance was tested according to the values, calculated
from 1000 Monte Carlo permutations. We used the freeware
Add-In application for Excel (Microsoft) Rookcase.xla (Sawada,
1999) and ArcGIS (ESRI©).
We calculated species accumulation curves based on Mau Tau
values to compare sampling completeness at all different cell sizes
(Colwell, 2005). Local richness can be estimated by using nonparametric techniques based on distribution of species among
samples (Colwell & Coddington, 1994). We employed five estimators:
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
97
M. Zagmajster et al.
jackknife 1 and 2 (Burnham & Overton, 1978, 1979; Heltshe &
Forrester, 1983; Smith & van Belle, 1984), Chao2 (Chao, 1984,
1987), bootstrap (Smith & van Belle, 1984), and ICE (incidence-based
coverage estimator) (Lee & Chao, 1994; Chazdon et al., 1998;
Chao et al., 2000). Calculating procedures were made with 100
randomizations of sampling order, using the freeware program
EstimateS, version 7.52 (Colwell, 2005).
We evaluated the effect of sampling intensity in searching for
troglobiotic beetles by considering the number of localities,
where subterranean beetles were sampled. We also included cave
localities, where only non-troglobiotic beetle species were
reported because this indicated that the cave had been sampled
for beetles. There may be differences among localities as a result
of different numbers of times they were visited or different collectors’
efforts, but this information is rarely reported and could not be
used.
To investigate the effect of sampling intensity on the species
richness patterns observed, we mapped the distribution of
number of sampled localities at all cell sizes and used the same
percentage delineation of classes as in case of species richness.
We investigated the relationship between number of species and
localities per sample cell by calculating the Spearman’s correlation coefficients. We used statistical software spss, version 14
(SPSS Inc., Chicago, IL, USA).
Finally, we mapped data for troglobiotic beetles from the
families together and separately at the optimal cell size. The
dependence of number of species from number of sampled
localities was investigated with linear regression analysis. We
mapped also subset of localities, where at least one troglobiotic
beetle was sampled, to compare this pattern to the species richness
pattern of troglobiotic beetle subfamilies.
RESULTS
Number of species
In the Dinaric karst area, we identified 282 troglobiotic beetle
species from the two subfamilies we studied (Table 1). Seventy
percent of them belong to the Leptodirinae and 30% to the
Trechinae. According to the criteria of positional accuracy, 254
troglobiotic species were considered in the first data set, and 276
species in the second. Five species with corresponding five poorly
defined localities and one species with dubious locality were not
included in the analyses.
Trechinae
(Carabidae)
No. of all troglobiotic species
First data set
No. of species
No. of localities
Second data set
No. of species
No. of localities
98
Leptodirinae
(Cholevidae)
More than 84% of all sampled localities contained at least one
troglobiotic beetle of the studied subfamilies (Table 1). Less
than one third of localities with troglobiotic beetles considered
contained representatives of both subfamilies (383 within the
first and 404 within the second data set).
Observed and estimated species richness at different
grid cell sizes
Distribution of richness of troglobiotic beetles shows roughly the
same general pattern at all different grid cell sizes (Fig. 2). Cells
with highest species numbers are always situated close to the
extreme north-west and south-east of Dinarides. Positions of the
richer cells identified at the smallest size are for the most part
included in the larger richer cells, even though this is not always
the case. The discrepancy is especially evident if patterns at
10 × 10 and 20 × 20 km are compared. Cells in the middle
Dinaric area that are ranked into second class at 10 × 10 km size
are merged into cells of lower class at 20 × 20 km. This shows that
other merged cells contained either the same species or none at
all. The opposite effect, when new cells of high species richness
appeared with increase in the cell size, can be observed. Merging
10 × 10 km cells of low species richness in extreme north-western
Dinarides into a 20 × 20 km cell resulted in a species-rich cell. A
similar result was obtained when 20 × 20 km cells in north-eastern
Bosnia and Herzegovina are merged into a 40 × 40 km cell. In
these cases, merged cells contained different species, and their
sum presented higher portion of species number of the richest
cell at larger scale.
Observed frequency of black-white joins was smaller than
expected by chance at all grid cell sizes (Table 2), so data are clustering (Christman et al., 2005; Fortin & Dale, 2005). The difference
between expected and observed frequency was largest at the
20 × 20 km cell. The statistics could not be calculated for the
largest grid network, as all cells had a species and no black-white
joins could be observed.
Moran’s I and Geary’s c indices of autocorrelation expressed
the same trends at different sizes of grid cells (Table 3, see Appendix
S1 in Supplementary Material). At the smallest cell size of
5 × 5 km, values of autocorrelation are lowest (Fortin & Dale,
2005). The strength of positive autocorrelation is gradually
increasing, reaching its maximum value at the cell size of
20 × 20 km. As sample cells become larger than 20 × 20 km, the
strength of autocorrelation decreases.
Both groups
All sampled
localities
83
199
282
77
733
177
977
254
1329
1572
80
791
196
1058
276
1445
1709
Table 1 Number of troglobiotic beetle
species of two species-rich subfamilies and
number of sampled localities with beetles in
Dinaric karst. The first data set includes
records with localities of positional accuracy of
3 km or less, and the second with 6 km or less.
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
Hotspots of obligate subterranean beetles diversity
Figure 2 Distribution of troglobiotic beetle species number in Dinaric karst at different grid cell sizes: (a) 80 × 80, (b) 40 × 40, (c) 20 × 20,
(d) 10 × 10, (e) 5 × 5 km. Included are records with localities of positional accuracy of 3 km or less, including 254 species (Lambert Conformal
Conical Projection).
Table 2 Frequency of black-white joins of troglobiotic beetles
presence in the Dinaric karst per sample cells of different size.
Considered are records with localities of positional accuracy of 3 km
or less, including 254 species. All the values are statistically significant
(P < 0.001).
Table 3 Moran’s I and Geary’s c indices for number of troglobiotic
beetles per sample cells of different size. Considered are records with
localities of positional accuracy of 3 km or less, including 254 species.
All values are statistically significant (P < 0.05 at 80 × 80 km and
P < 0.001 at the rest).
Cell size (km × km)
Observed frequency
Expected frequency
Cell size (km × km)
Moran’s I
Geary’s c
80 × 80
40 × 40
20 × 20
10 × 10
05 × 05
0
0.192
0.286
0.233
0.120
/
0.397
0.499
0.357
0.169
80 × 80
40 × 40
20 × 20
10 × 10
05 × 05
0.233
0.472
0.527
0.442
0.366
0.638
0.539
0.511
0.584
0.649
Species accumulation curves, calculated from presence of
individual species in the sample cells of different sizes, did not
reach an asymptote at any cell size (Fig. 3). The slope of the
curves did get less steep, as the cells got smaller.
Estimations of species richness differed among predictors, as
well as at different cell sizes (Table 4). The lowest predicted values
are given by bootstrap estimator, which is also least dependent on
the sample size used. Values of jackknife 1 and jackknife 2 rise
gradually until 20 × 20 km cell size, after which the values of
jackknife 2 get much higher. Change in the ICE estimator is most
apparent, with large jump in predicted values at cells larger than
20 × 20 km. At smaller cell sizes, the highest estimates are given
by jackknife 2, while at larger sizes the highest estimates are from
ICE (see Appendix S2 in Supplementary Material). Only Chao2
species estimator exhibits a different pattern with increase in cell
size (see Appendix S3 in Supplementary Material). At the cell size
of 20 × 20 km, it reaches lower value than at the 10 × 10 km cell.
The number of single cell species is very high already at the
smallest scale (representing 109 or 42.9% of all species) and
increases substantially with cell size (up to 179 or 70.4% of all
species studied) (Fig. 4). The number of species, represented in
exactly two cells (duplicates), is highest at the cell size of
20 × 20 km (reaching 56 species).
Sampling intensity at different grid cell sizes
Cells with highest sampling intensity are situated at the northwestern Dinaric karst at all grid sizes except the smallest one
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
99
M. Zagmajster et al.
Table 4 Values of species number estimators for troglobiotic beetle diversity in Dinaric karst per sample cells of different size. Considered are
records with localities of positional accuracy of 3 km or less, including 254 species. Percentage increase of the observed species numbers is given
in parentheses.
Cell size (km × km)
Jackknife 1
Jackknife 2
Chao2
80 × 80
40 × 40
20 × 20
10 × 10
05 × 05
426.12
408.93
383.33
370.70
362.82
551.47
515.69
456.85
444.43
431.67
587.01
500.63
400.34
407.83
397.33
(67.76%)
(61.00%)
(50.92%)
(45.94%)
(42.84%)
(117.11%)
(103.03%)
(79.86%)
(74.97%)
(69.95%)
ICE
(131.11%)
(97.10%)
(57.61%)
(60.56%)
(56.43%)
729.58
582.07
451.56
403.03
381.70
Bootstrap
(187.24%)
(129.16%)
(77.78%)
(58.67%)
(50.28%)
324.84
318.91
310.63
304.72
301.29
(27.89%)
(25.56%)
(22.30%)
(19.97%)
(18.62%)
ICE, incidence-based coverage estimator.
Figure 3 Species accumulation curves for troglobiotic beetles in
Dinaric karst for different grid cell sizes. Considered are records with
localities of positional accuracy of 3 km or less, including 254 species.
(Fig. 5). At the largest 80 × 80 km cells, the richest cell contains
such a large number of localities, that there is no other cell that
would contain more than 60% of this number (none can be classified
into second class). As the cell size decreases, cells with high number
of sampled localities appear at the south-eastern Dinarides.
The pattern demonstrates that north-western parts of the
Dinarides are better sampled than the rest. The number of sampled
localities in the middle Dinarides in Croatia is comparable to
other areas except for Slovenia, but there are unsampled areas
especially at the western borders of Bosnia and Herzegovina
(Fig. 5).
The relationship between number of species and number of
localities changes as the cell size increases. Correlation among
number of species and number of localities increases and begins
levelling off at the largest cells (see Appendix S4 in Supplementary
Material). When the maps of species and localities numbers
distribution are compared (Figs 2 and 5), only some of the highly
sampled cells overlap with species-rich cells. There are many cells
where sampling intensity is at a comparable rate, but differences
in species richness among them are very high. Examples of that
can be found especially in south-western Dinarides. Overall,
sampling intensity cannot explain the species richness pattern.
Species richness patterns of troglobiotic beetles
Mapping of species richness and sampled localities distribution
at different scales shows that the pattern depends on the scale size
100
Figure 4 Number of unique (present in exactly one sample cell)
and duplicate (present in exactly two sample cells) species of
troglobiotic beetles in Dinaric karst per different grid cell sizes.
Considered are records with localities of positional accuracy
of 3 km or less, including 254 species.
we use. Spatial autocorrelation of species numbers among cells is
highest at the cell size of 20 × 20 km, so we used this cell size for
further analyses (Fortin & Dale, 2005; Zagmajster et al., 2006).
This cell size also permitted us to include less precise localities in
the analysis. We mapped the species and sampled localities
numbers using the expanded data set (Fig. 6).
As we included additional species and localities on the map,
this did not change positions of the richest cells, discovered at the
smaller data set (Figs 2 and 6). However, one new species-rich cell
in the south-east Dinarides appeared. Species number estimators
gave higher estimations (Table 5), compared to the estimator
of the limited set of data (Table 4), but percentage increase is
comparable. Higher absolute number of species is expected to be
discovered within the Leptodirinae than Trechinae.
We investigated the nature of the richest cells, and mapped the
two subfamilies separately (Fig. 7). Their pattern of species
richness differ between the two, and the correlation between the
number of species of each subfamily is not high (Spearman’s
correlation coefficient = 0.484, P < 0.01). The subfamilies exhibit
different centres of highest species richness. The hotspots for
Trechinae are situated in the north-western Dinarides, while for
Leptodirinae species they lie in the south-eastern Dinarides.
When the larger data set is considered (Fig. 6), once again
overall species pattern cannot be explained by sampling intensity. Only about 54% of variance in species numbers can be
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
Hotspots of obligate subterranean beetles diversity
Figure 5 Distribution of sampling intensity in Dinaric karst per different grid cell size: (a) 80 × 80, (b) 40 × 40, (c) 20 × 20,
(d) 10 × 10, (e) 5 × 5 km. Considered are sampled localities of positional accuracy of 3 km or less (Lambert Conformal Conical Projection).
Table 5 Values of species number estimators for troglobiotic beetle diversity in Dinaric karst at sample cell size of 20 × 20 km. Considered are
records with localities of positional accuracy of 6 km or less, including 276 species. Percentage increase of the observed species numbers is given
in parentheses.
Trechinae (Carabidae)
Leptodirinae (Cholevidae)
Both groups
Jackknife 1
Jackknife 2
Chao2
ICE
Bootstrap
118.72 (48.40%)
297.43 (51.75%)
416.28 (50.83%)
142.48 (78.10%)
354.05 (80.64%)
496.76 (79.99%)
125.98 (57.48%)
307.36 (56.82%)
436.98 (58.33%)
132.75 (65.94%)
350.27 (78.71%)
483.18 (75.07%)
96.74 (20.93%)
240.65 (22.78%)
337.43 (22.26%)
ICE, incidence-based coverage estimator.
described by number of all sampled localities, and even less if
subfamilies are considered separately (Table 6). The overlap
among the richest cell in species and number of localities of
that subfamily remains very low. This is especially evident in
Leptodirinae (Fig. 7), but also in Trechinae.
Table 6 Linear regression coefficients of considered troglobiotic
beetle species numbers, with number of sampled localities as
independent variable. Considered are records with localities
of positional accuracy of 6 km or less, including 276 species.
All coefficients are statistically significant (P < 0.01).
Dependent variable
R2
Intercept
Slope
Trechinae (Carabidae)
Leptodirinae (Cholevidae)
Both groups
0.491
0.402
0.536
0.459
1.195
1.654
0.111
0.165
0.276
DISCUSSION
Pattern of species richness is dependent on scale (Stoms, 1994), a
problem that is not often addressed in diversity studies (Whittaker et al., 2005). We could recognize two large areas, where speciesrich cells appeared, at all grid sizes, but we wanted to locate richest
areas with higher precision. Calculating the strength of autocorrelation among species numbers at all scales proved useful in
this regard (Fortin & Dale, 2005; Zagmajster et al., 2006). A cell
size of 20 × 20 km seemed to be optimal, according to strength of
black-white joins and highest level of autocorrelation.
The number of samples taken from a population affects species
estimates, until at certain number of samples they stabilize
(Chazdon et al., 1998). With change in the sample cell size in our
study, we actually changed also the number of samples from the
same population and area studied. With decrease in sample cell
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
101
M. Zagmajster et al.
Figure 6 Distribution of numbers of troglobiotic beetles considered (a), all sampled localities (b), and localities with troglobiotic beetles (c) in
Dinaric karst per grid cell size 20 × 20 km. Considered are records with localities of positional accuracy of 6 km or less, including 276 species
(Lambert Conformal Conical Projection).
Figure 7 Distribution of number of species
of troglobiotic beetle species (S) and sampled
localities (L) per Trechinae (a) and
Leptodirinae (b) in Dinaric karst per grid cell
size 20 × 20 km. Considered are records with
localities of positional accuracy of 6 km or less,
including 80 Trechinae and 196 Leptodirinae
species (Lambert Conformal Conical
Projection).
size and the resulting increase in number of samples, almost all
species richness estimators show some degree of stabilization
after 20 × 20 km sample cells. At this cell size, ICE and Jack 2
estimators give similar predicted value, while Chao2 gives
lower estimate than at smaller cell. Stabilization of estimators
at certain cell size can give additional support to choose it as
optimal scale size for the analysis. Even though changing spatial
pattern should have insignificant effect on the bias, precision,
and accuracy of estimates at the same grid size (Brose et al.,
2003), it may be different when sample cell sizes change.
In our case, at cell size 20 × 20 km species richness pattern
showed highest positive autocorrelation. Estimators calculated
at different cells can give further support to choosing optimal
cell size for analyses.
102
Five estimators expressed different height of expected species
richness. Small differences in uniques and duplicates among
different grid cells were observed in the study of Guralnick &
van Cleve (2005), who therefore recorded very slight changes
of individual estimators at different grid cell sizes. In our data
set, unique species present very high part of all species observed,
and increased with sample cell size, presenting 42.9% of
observed species at smallest sample cells and 70.4% at largest
cells. High number of unique species in data set on subterranean
fauna can only partly be attributed to inadequacy in sampling
(Longino et al., 2002). Cave animals exhibit high levels of
endemism (Guéorguiev, 1977; Sket, 1999b; Christman et al.,
2005) being known from one site only or having small distribution ranges compared to surface counterparts (Guéorguiev,
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
Hotspots of obligate subterranean beetles diversity
1977; Trontelj et al., in press). It is therefore highly unlikely that
the number of unique species would decrease to near zero with
additional sampling effort (Longino et al., 2002). It is actually
expected that the number would stabilize at a certain value.
Decisions on the choice of the most proper estimator in the
analysis can differ according to the data sets used (overview
given in Magurran, 2003). For studies of cave fauna, estimators
less dependent on rare species may be considered more appropriate. Jackknife 2, Chao2, and ICE included two measures of
rarity (numbers of both unique and duplicate species or unique
and species present in less than 10 samples) and expressed greatest
changes at different sample cell sizes. Similarly, Chazdon et al.
(1998) by using the abundance data discovered that Chao2 and
ICE performed well at moderate degrees of patchiness of
individual species’ distribution (when the number of unique
species was low) and less well at higher patchiness (and the
number of unique species was higher). Poulin (1998) used computer-simulated parasite communities and discovered that
Chao and jackknife estimators were less precise when there were
many rare species and overestimated the true richness value. In
our study, jackknife 1 increased at a slower pace with increasing
sample cell size than jackknife 2, and may be considered more
appropriate than the latter. The least dependence on scale and
hence the number of rare species in our study was exhibited by
the bootstrap estimator. Poulin (1998) has also shown that
bootstrapping gave the best estimate when there were rare
species present. Because rare species were very numerous in our
study, bootstrap may be the most accurate.
Sket et al. (2004) reported that the area of highest species richness
of terrestrial fauna is situated in the south-eastern Dinarides.
This more detailed analysis of troglobiotic beetles only partly
supports that statement. Even though the hotspots of beetle
diversity do lie in the south-east, there are two larger centres of
high troglobiotic beetle richness in Dinaric karst. Contribution of
individual subfamilies to these centres is different. Troglobiotic
Trechinae are richest in the north-west, while troglobiotic Leptodirinae
beetles are most diverse in south-eastern parts. The latter was proposed already by Sket (2005a) and in preliminary spatial analysis by
Zagmajster et al. (2006). Correlation between the species numbers
of both subfamilies is not strong, and reasons for species richness
within both are different. Within Trechinae there is an intense
speciation within small amount of genera, while in the Leptodirinae
diversification already at the level of genera is much higher.
When investigating centres of biodiversity, sampling intensity
should be accounted for. If more sampling is done at a certain
area, it is expected that more species can be found there. We confirmed that north-western parts in Slovenia are sampled better
than others (Culver et al., 2004a). The rest of the Dinarides are
sampled at a comparable rate, but there are also some larger
unsampled areas. Efforts in sampling cave beetles should focus
on less well-sampled areas. But, new species in the area can be
discovered not only by sampling new caves, but also by additional sampling of already investigated caves.
When patterns of species richness and sampling intensity are
compared, they differ. Sampled localities in Croatia and southeastern Dinarides reach comparable numbers, but species richest
cells can be found only in the latter. Variation in species numbers
can only partly be explained by variation in sampling intensity,
and when the two subfamilies are treated separately, variance of
their species richness explained by variance in sampling intensity
is even smaller. Further on, additional sampling had little effect
on hotspots discovered. We simulated new discoveries in the
Dinaric karst by adding species and localities with less positional
accuracy in the analysis at the optimal cell size. Even though
there were some cells, where changes in species richness were
substantial, positions of hotspots remained the same. Comparisons of species richness at different time snapshots in the past
revealed the same pattern when the number of localities increased
(Culver et al., 2004a).
There are other influences that could explain species patterns
discovered. Palaeogeographical and palaeoclimatic events that
influenced dispersal and migrations of ancestors to subterranean
realm influence present richness patterns (Guéorguiev, 1977).
Habitat availability, like number of all available caves, is related
to centres of troglobiotic diversity (Christman & Culver, 2001).
It was also proposed that surface primary production should
have influence on subterranean biodiversity (Culver et al.,
2006). Subterranean habitats mostly lack primary producers
(with exceptions of chemoautotrophs in some systems) and are
dependent from organic matter income from surface. What is the
most important in the case of troglobiotic diversity in Dinaric
karst remains to be tested.
Despite the Dinaric karst having been recognized a global
hotspot according to richness and special elements of its subterranean fauna (Sket, 1999a,b; Sket et al., 2004; Sket, 2005a), there
is no unified system of conservation of the unique fauna in this
area. Most protection activities are aimed at individual speciesrich caves, and differ among the seven countries where Dinarides
are spreading. For example, in Slovenia the troglobiotic beetles
along with amphibian Proteus anguinus Laurenti are protected by
law at the individual species level, while the rest of the subterranean fauna is protected through general habitat protection of
caves (Sket, 2005b). A similar situation is found in Croatia, with
individual species protection of all subterranean fauna (Gottstein
Matocec et al., 2002). For long-term conservation of cave fauna
in whole Dinaric karst, establishment of a network of important
subterranean areas in the region is necessary. Choosing the areas
should be based on species richness, as well as endemism, rarity,
and complementarity (Reyers et al., 2000; Michel et al., in press).
Troglobiotic beetles are appropriate group to be regarded as indicator
of terrestrial troglobiotic diversity centres in Dinaric karst as well
as its karstic surroundings. They present nearly half of the terrestrial
troglobiotic fauna (Sket et al., 2004), of which they are considered
best-studied group. Hotspots and species richness patterns of
troglobiotic beetles in Dinaric karst that we identified present
first candidate areas to be included in such network for terrestrial
troglobiotic fauna.
ACKNOWLEDGEMENTS
We are particularly indebted to late Slovenian speleobiologist
Egon Pretner for his extensive work in collecting data on
© 2007 The Authors
Diversity and Distributions, 14, 95–105, Journal compilation © 2007 Blackwell Publishing Ltd
103
M. Zagmajster et al.
subterranean beetles in Dinaric karst. We are grateful to Slavko Polak
(Notranjska Museum Postojna) who offered advice and help in
search for relevant literature. Tomaz Skrbinsek (University of
Ljubljana) gave valuable support with GIS work, and Andrej Blejec
(University of Ljubljana) with statistical analyses. Tomaz Podobnikar
(Slovenian Academy of Sciences and Arts) prepared initial grid
network and projection files for the research area. We are thankful
to Peter Trontelj (University of Ljubljana) who provided comments
on a previous version of the manuscript. We are also grateful to
two anonymous referees for their helpful comments. The work
was financially supported by the Slovenian Research Agency.
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SUPPLEMENTARY MATERIAL
The following supplementary material is available for this
article:
Appendix S1 Graphical presentation of values of Moran’s I and
Geary’s c autocorrelation indices of number of troglobiotic beetle
species per cells at different scales.
Appendix S2 Graphical presentation of values of jackknife 1,
jackknife 2, bootstrap and ICE estimators of troglobiotic beetles
richness in Dinaric karst, calculated at all different grid cell
sizes.
Appendix S3 Graphical presentation of values of Chao2 estimator of troglobiotic beetles richness in Dinaric karst, calculated at
all different grid cell sizes, with upper and lower values of 95%
confidence intervals.
Appendix S4 Spearman’s rank correlation coefficient between
number of sampled localities and number of troglobiotic species
per cells of different sizes.
This material is available as part of the online article from:
http://www.blackwell-synergy.com/doi/abs/10.1111/j.14724642.2007.00423.x
(This link will take you to the article abstract).
Please note: Blackwell Publishing is not responsible for the content or functionality of any supplementary materials supplied by
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directed to the corresponding author for the article.
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105