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Biodiversity indicators in European ground waters:
towards a predictive model of stygobiotic species
richness
FABIO STOCH*, MALVINA ARTHEAU
†
, ANTON BRANCELJ
‡
, DIANA M. P. GALASSI* AND
FLORIAN MALARD
§
*Dipartimento di Scienze Ambientali, University of L’Aquila, L’Aquila, Italy
†
Laboratoire d’Ecologie des Hydrosyste
`mes, Universite
´Paul Sabatier, Toulouse, France
‡
National Institute of Biology, Ljubljana, Slovenia
§
Laboratoire d’Ecologie des Hydrosyste
`mes Fluviaux, Universite
´Claude Bernard Lyon, Villeurbanne, France
SUMMARY
1. Estimates of species richness obtained from exhaustive field inventories over large
spatial scales are expensive and time-consuming. For this reason, efficiency demands the
use of indicators as ‘surrogates’ of species richness. Biodiversity indicators are defined
herein as a limited suite of taxonomic groups the species richness of which is correlated
with the species richness of all other taxonomic groups present in the survey area.
2. Species richness in ground water was assessed at different spatial scales using data
collected from six regions in Europe. In total, 375 stygobiotic species were recorded across
1157 sites and 96 aquifers. The taxonomic groups collected from more than one site and
with more than two species (Oligochaeta, Gastropoda, Cyclopoida, Harpacticoida,
Ostracoda, Isopoda, Amphipoda, Bathynellacea and Acari) were used to develop
nonparametric models to predict stygobiotic biodiversity at the aquifer scale.
3. Pair-wise correlations between taxonomic groups were low, i.e. variation in species
richness of a single taxonomic group did not usually reflect variation of the other groups.
In contrast, multiple regressions calculated between species richness of any combination of
taxa and extra-group species richness along the six regions resulted in a number of
significant relationships.
4. These results suggest that some taxonomic groups (mainly Copepoda and Amphipoda
and, to a lesser extent, Oligochaeta and Gastropoda) combined in different ways across the
regions, were good biodiversity indicators in European groundwater ecosystems.
However, the uneven distribution of taxonomic groups prevented selection of a common
set of indicators for all six regions. Faunal differences among regions are presumably
related to both historical and ecological factors, including palaeogeography, palaeo-
ecology, geology, aquifer fragmentation and isolation, and, less clearly, anthropogenic
disturbance.
Keywords: biodiversity, ground water, indicators, predictive model, stygobionts
Introduction
Species richness is a simple measure of biodiversity
and a widely used criterion for conservation planning.
Unfortunately, exhaustive field inventories over large
spatial scales are expensive and time-consuming.
Considering that it is impractical to monitor compre-
hensively every taxonomic group living in a habitat or
Correspondence: Fabio Stoch, Dipartimento di Scienze Ambientali, University of L’Aquila, Via Vetoio, Coppito, I-67100 L’Aquila,
Italy. E-mail: fstoch@faunaitalia.it
Freshwater Biology (2009) 54, 745–755 doi:10.1111/j.1365-2427.2008.02143.x
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd 745
even in a small site, efficiency demands the use of
indicators as ‘surrogates’ of species richness (Pearson,
1994; Prance, 1994; van Jaarsveld et al., 1998; Moritz
et al., 2001; Favreau et al., 2006). Moreover, the surro-
gacy approach allows taxonomic identification of a
limited set of species or taxonomic groups and to
design monitoring plans based on a restricted data set,
bypassing practical impediments in recognising and
identifying all members of a community (Mac Nally &
Fleishman, 2004). Finally, biodiversity indicators may
be of paramount importance in selecting priority areas
for conservation, being good candidates as umbrella
species, i.e. species whose conservation is expected to
confer protection to a large number of naturally co-
occurring species (Fleishman, Murphy & Brussard,
2000; Fleishman, Blair & Murphy, 2001; Roberge &
Angelstam, 2004).
McGeoch (1998) used the name ‘biodiversity indi-
cators’ to define a limited suite of taxa, the diversity of
which reflects diversities of other taxa. Lindenmayer,
Margules & Botkin (2000) extended the definition to
(i) species whose presence or absence indicates pres-
ence or absence of some other species; (ii) keystone
species and (iii) dominant species in a community.
More recently biodiversity indicators were simply
defined as ‘species with occurrence patterns that are
correlated with the species richness of a larger group
of organisms’ (Mac Nally & Fleishman, 2004). Recent
work demonstrated that also higher taxa may be good
surrogates of species richness (Ba
´ldi, 2003), and some
taxonomic groups above species level appear to
adequately serve the role of biodiversity indicators
(Ricketts, Daily & Ehrlich, 2002; Vessby et al., 2002;
Vanclaj, 2004).
Unfortunately, with few exceptions (Mac Nally
et al., 2002), there is still little empirical evidence to
support the expectation that species richness within a
particular group is correlated with species richness of
co-occurring taxa (Lawton et al., 1998; Ricketts et al.,
2002; Vessby et al., 2002). Pair-wise correlations
between groups are usually very low as well (Bilton
et al., 2006). Although most broad-scale assessments
of freshwater biodiversity, which mainly focussed on
evaluating environmental quality, have relied on
selected indicator taxa (Sauberer et al., 2004; Bilton
et al., 2006), it has rarely been explicitly tested how
well such putative surrogate taxa reflect the overall
community composition (Paavola et al., 2003). Briers
& Biggs (2003) examined the mean and range of
cross-taxon correlations between species richness of
several insect families of pond macroinvertebrates,
and were able to identify Coenagrionidae (Odonata)
and Limnephilidae (Trichoptera) as biodiversity indi-
cators, although good correlations were not obtained
at taxonomic ranks higher than the family level. In
contrast, Heino et al. (2003, 2005) criticised the use of
single taxonomic groups as indicators of insect biodi-
versity in headwater streams. Bilton et al. (2006),
exploring cross-taxon species richness relationships
among macroinvertebrates of freshwater ponds,
observed that patterns of cross-taxon congruence in
species richness were highly variable among taxa and
study sites, making the use of a single taxon as a
predictor of overall macroinvertebrate species rich-
ness problematic. For this reason, Bilton et al. (2006),
following Su et al. (2004), advocated the use of
indicators of community similarity between ponds
instead of indicators of species richness.
According to Vanclaj (2004), these conclusions may
be unnecessarily pessimistic, because such indicators
may not be necessarily used to infer species richness
within other groups, but rather to extrapolate infor-
mation on overall species richness. Moreover, Mac
Nally & Fleishman (2002, 2004) and Fleishman et al.
(2005) pointed out that it is unlikely that indicator
species from a single taxonomic group will provide
information on species richness of the entire biota at
spatial scales meaningful for most land-use decisions,
suggesting the use of combinations of indicator
species. Mac Nally & Fleishman (2004) argued that
prediction of species richness should be regarded as a
testable hypothesis in the form of a statistical model,
i.e. a function of the occurrence of indicator species.
The present study explores the possibility of infer-
ring stygobiotic species richness in ground water
using a surrogacy approach, since predictive models
of biodiversity indicators in ground water do not
currently exist. The large data sets assembled during
the PASCALIS project (Gibert et al., 2005), including
almost all stygobiotic taxa recorded from European
subterranean waters, offers a unique opportunity to
investigate whether a limited suite of biodiversity
indicators may be identified in groundwater commu-
nities. The main goal of this analysis was to develop a
statistical model to select potential indicators of
stygobiotic species richness based on the assumptions
of Mac Nally & Fleishman (2002, 2004). Moreover, the
consistency of models across different spatial scales,
746 F. Stoch et al.
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd, Freshwater Biology,54, 745–755
using data sets from karstic and porous aquifers from
different European regions, are examined.
Methods
Sampling design
The data set was derived from the PASCALIS project
as described by Gibert et al. (2005). Data were
collected following a stratified random sampling in
six regions distributed in Europe: the Walloon karst
(Belgium), the southern Jura (eastern France), the
Roussillon region (southern France), the Picos de
Europa, Cantabria (northern Spain), the Lessinian
massif (northern Italy) and the Krim massif (Slovenia).
In each region, the sampling strategy involved collec-
tion of stygobiotic species at 192 sites, evenly distrib-
uted among four habitat types: (i) unsaturated karst;
(ii) saturated karst; (iii) hyporheic zone and (iv)
phreatic zone in unconsolidated sediments, along
four hydrogeographic basins. The sampling proce-
dure adopted is reported by Malard et al. (2002).
The 192 sites were mostly distributed in caves for the
unsaturated karst; caves, springs and wells for the
saturated karst; hyporheic habitats for the upper
porous aquifers; and wells or piezometers for the
saturated zone of porous aquifers.
Biological data set
Once collected, groundwater invertebrates were
sorted and counted. For each taxonomic group, all
specimens were identified at the species level, when-
ever possible. Only Nematoda and Turbellaria were
identified at the genus level and, for this reason, were
excluded from further analyses. According to the
degree of adaptation and specialisation to life in
ground water, each species was assigned to one of the
main ecological categories (Gibert et al., 1994): sty-
gobionts (i.e. species strictly confined to the ground-
water environment, as they complete their life cycle in
ground water, and show morphological and physio-
logical adaptations to subterranean habitats), stygo-
philes (i.e. species with incipient adaptation to life in
ground water, but able to live in both surface and
subsurface environments and related ecotones such as
springs and the hyporheic zone of streams and rivers),
and stygoxenes (i.e. species which enter ground water
accidentally through fast or slow infiltration pathways
connecting surface waters to ground water). Only
stygobionts were retained for the statistical analyses.
A biological data matrix, based on pres-
ence ⁄absence of species, was created for all the
stygobiotic species of eleven higher-level taxa: Poly-
chaeta, Oligochaeta, Gastropoda, Cladocera, Calano-
ida, Cyclopoida, Harpacticoida, Ostracoda, Isopoda,
Amphipoda, Bathynellacea, Thermosbaenacea, Acari,
Coleoptera collected at each site. For each of the six
regions and each taxonomic group, total species
richness, mean number of species per site and
standard deviation of the number of species per site,
and frequency of occurrence were reported (Table 1).
Polychaeta, Calanoida, Thermosbaenacea and Cole-
optera, each represented in the data set by a single
species recorded from one site only, as well as
Cladocera, collected with only two rare species, were
excluded from data analysis.
Data analysis
Biodiversity indicators are defined as a limited suite
of taxonomic groups the species richness of which is
correlated with the extra-group species richness, i.e.
species richness of all the other taxonomic groups
present in the study area. This definition was applied
to the taxa listed in Table 1, and the survey unit is
given by the sum of all the sampling sites belonging to
each habitat type. Within-group species richness was
never directly correlated with total species richness,
because the two data sets were not independent.
Using total richness, S, as the response variable
instead of extra-group species richness would artifi-
cially enhance the correlation coefficient, especially
when dealing with speciose taxa (Briers & Biggs,
2003).
Cross-taxon correlations were calculated using
Spearman’s rank correlation coefficient. Moreover,
the extra-group species richness in each of the six
regions was modelled as a function of the within-
group species richness. Following Mac Nally &
Fleishman’s (2004) recommendations, any possible
model involving the independent variables (in this
case, the within-group species richness of each of
the nine higher-level taxa listed in Table 1) and all
their possible combinations (i.e. all possible pairs,
trios and so forth) were considered, and 2
9
models
were tested. Following Culver et al. (2003), rank-
order multiple-regression was performed, both
Biodiversity indicators in European ground waters 747
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd, Freshwater Biology,54, 745–755
because of the small number of aquifers (16 within
each region) analysed, and uncertainties in the
distribution of data. Rank regression is well suited
to analyse data that have monotonic, but usually
nonlinear, relationships. The ‘best’ models, i.e. those
models that optimised fitting errors, were selected
for each combination of taxa based on adjusted R
2
values.
Bonferroni corrections (Shaffer, 1995) were applied
to correct the alpha level (0.05) when assessing the
statistical significance of Spearman’s rank correlation
coefficients. Unfortunately, the Bonferroni approach is
very conservative especially when the number of
comparisons becomes large and when the tests are not
independent. Therefore, the less restrictive approxi-
mate false discovery rate approach (Benjamini &
Hochberg, 1995) was followed with regression models
as well; corrections were calculated separately for
each combination of taxa within which models were
compared.
The entire data set was stored in Microsoft Excel
and all the routines to perform correlations and
regressions were written by F. Stoch in Microsoft
Visual Basic for Applications. Accuracy of results of
correlation analysis was tested using
SPSSSPSS
13.0 for
Windows.
Table 1 Distribution of stygobiotic taxonomic groups in the six European regions studied
Taxon
Cantabria Roussillon Jura
SMean SD Frequency SMean SD Frequency SMean SD Frequency
Polychaeta – – – – – – – – – – – –
Oligochaeta 12 0.11 0.38 9.9 17 0.65 1.00 39.6 4 0.11 0.36 9.4
Gastropoda 2 0.05 0.22 5.2 4 0.23 0.47 21.4 6 0.83 0.96 54.2
Cladocera – – – – 2 0.01 0.10 1.1 1 0.04 0.20 4.2
Calanoida – – – – – – – – – – – –
Cyclopoida 10 0.43 0.66 34.4 12 0.48 0.77 33.7 11 0.84 0.84 60.9
Harpacticoida 10 0.18 0.45 16.1 8 0.11 0.40 8.0 9 0.73 0.89 50.5
Ostracoda 5 0.15 0.48 10.9 9 0.21 0.56 16.6 10 0.84 0.93 55.2
Isopoda 3 0.17 0.37 16.7 8 0.35 0.63 27.3 7 0.33 0.54 29.7
Amphipoda 6 0.11 0.34 10.9 7 0.58 0.72 45.5 10 1.11 0.85 79.2
Bathynellacea 13 0.22 0.59 15.1 4 0.07 0.28 7.0 2 0.13 0.35 12.5
Thermosbaenacea – – – – – – – – – – – –
Acari 6 0.13 0.53 7.8 1 0.01 0.07 0.5 – – – –
Coleoptera – – – – – – – – 1 0.01 0.07 0.5
Total 67 1.56 1.83 – 72 2.70 2.72 – 61 4.98 2.93 –
Wallonia Lessinia Krim
SMean SD Frequency SMean SD Frequency SMean SD Frequency
Polychaeta – – – – 1 0.01 0.07 0.5 – – – –
Oligochaeta 3 0.11 0.31 10.9 15 0.26 0.64 16.8 22 0.45 0.68 36.4
Gastropoda 1 0.00 0.07 0.5 2 0.12 0.37 10.7 14 0.61 0.94 36.9
Cladocera 1 0.02 0.16 2.5 – – – – – – – –
Calanoida – – – – – – – – 1 0.01 0.07 0.5
Cyclopoida 7 0.31 0.51 28.2 12 0.85 0.88 59.4 13 1.03 1.00 63.1
Harpacticoida – – – – 24 0.86 1.00 56.3 18 0.81 1.21 41.7
Ostracoda 5 0.19 0.49 15.8 7 0.12 0.34 11.7 11 0.25 0.64 16.6
Isopoda 3 0.08 0.30 7.9 2 0.04 0.19 3.6 3 0.04 0.20 4.3
Amphipoda 9 0.54 0.69 45.5 12 0.31 0.60 24.9 9 0.51 0.74 38.5
Bathynellacea – – – – 6 0.07 0.25 6.6 6 0.13 0.41 10.7
Thermosbaenacea – – – – 1 0.01 0.07 0.5 – – – –
Acari 5 0.05 0.23 5.4 7 0.17 0.41 15.2 8 0.35 0.76 21.9
Coleoptera – – – – – – – – – – – –
Total 34 1.32 1.50 – 89 2.80 2.31 – 105 4.19 3.29 –
S, total species richness; Mean, mean number of species per site; SD, standard deviation of the number of species per site; Frequency,
frequency of occurrence (i.e. percentage of sites where a taxonomic group was recorded).
748 F. Stoch et al.
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd, Freshwater Biology,54, 745–755
Results
In total 375 stygobiotic species were recorded across
the 1157 sites and 96 saturated and unsaturated
aquifers sampled in the six regions (Table 1). In term
of species richness, groundwater assemblages were
dominated by the Crustacea Copepoda, with 114
species collected (1 Calanoida, 52 Cyclopoida and 61
Harpacticoida). Oligochaeta were represented by 70
species, but serious limitations to classify the species
reliably may have lowered the real number of stygo-
biotic species. Amphipoda and Ostracoda were rep-
resented by 43 and 41 species, respectively, while
Isopoda (26 species), Bathynellacea (28), Gastropoda
(29) and Acari (19) were relatively species poor.
Cladocera were recorded with two species, and
Polychaeta, Thermosbaenacea, Coleoptera with one
species only, found in a single site.
Total species richness in the study regions ranged
from 34 in Wallonia to 105 in the Krim massif. The
mean number of species per sample was highly
variable, too, ranging from 1.32 in Wallonia to 4.98
in the Jura. The distribution of species richness within
the stygobiotic taxa differed among regions as well
(Table 1).
Pair-wise cross-taxon Spearman’s rank correlations
between groups were usually weak (Table 2), i.e. the
variation in species richness of a single taxonomic
group was usually not representative of the variation
of other groups. Moreover, the cross-taxon correlations
were highly variable among both taxa and regions. The
percentage of significant cross-taxon congruencies per
region ranged between 5.7%(Cantabria) and 19.4%
(Lessinia), and the correlations between within-group
and extra-group species richness calculated for each
taxon were weak as well (Table 3).
The results of the rank-order multiple-regressions
(best models, selected on the basis of the highest
adjusted R
2
) are reported in Table 4 and illustrated in
Fig. 1. For practical reasons, only combinations of
three or less indicators out of a total of nine potential
taxa are reported.
This method extracted useful combinations of bio-
diversity indicators (Table 4), more efficiently for the
western regions (Cantabria, Roussillon, Jura) and, to a
less extent, for the other three regions. For example,
using trios of potential indicators, Copepoda and
Amphipoda, together with Gastropoda and Ostra-
coda, were selected in the western regions. These
groups explained over 70%of the rank-order varia-
tion of species richness of the remaining groups found
in the same area. In the eastern areas (Lessinia and
Krim), Copepoda and Amphipoda were selected as
well, together with Oligochaeta and Bathynellacea.
Trios of indicators explained over 60%of extra-group
species richness rank-order variation. Finally, Cyclo-
poida and Oligochaeta significantly contributed to
explaining species richness of the other groups in the
Walloon karst, explaining approximately 53%of
extra-group species richness rank-order variation.
Discussion
The results obtained by the present analysis support
the contention that some taxonomic groups may be
used as biodiversity indicators (Vanclaj, 2004). Reli-
ability of the surrogacy approach is still debatable and
questionable in some respects (van Jaarsveld et al.,
1998; Favreau et al., 2006). However, although the
regressions with most explanatory power include
different taxa combinations in different regions, the
statistical methodology adopted here strengthens the
potential of some taxonomic groups to serve as
indicators of overall species richness across European
ground waters. This conclusion is supported by the
consistency of the biological data set used. In general,
Copepoda and Amphipoda appear to be reliable
predictors of the residual species richness in almost
all the regions analysed. The explanation of such
behaviour is probably traceable in the high taxonomic
diversification of these crustacean groups in ground
water, which probably reflects a wide range of trophic
and spatial niche diversification, although niche par-
tition is still largely unknown (Galassi, 2001).
Unfortunately, these results also suggest that the
selection of biodiversity indicators requires re-calibra-
tion, depending on the groundwater region under
study. The uneven distribution of various taxonomic
groups of stygobionts in European ground waters
(Malard et al., 2009; Galassi et al., 2009; Dole-Olivier
et al., 2009), absence of some speciose groups from
some regions (e.g. stygobiotic Harpacticoida from
Wallonia), and geographical variation in the degree
of cross-taxon congruence, prevent selection of a single
set of indicators able to cover all of the six analysed
regions equally well. These observations are in line
with the conclusions drawn by Bilton et al. (2006), who
noticed high variability of indicator groups among the
Biodiversity indicators in European ground waters 749
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd, Freshwater Biology,54, 745–755
regions they sampled. In the same way, Faith &
Walker (1996) and Su et al. (2004) observed that the
relationships between indicator groups and target
groups can be weak or absent in some areas because
different factors may drive their distributions. Accord-
ing to Williams (1998), the prevailing factors that
might promote particularly tight indicator relation-
ships are: (i) similarity of taxa in terms of eco
logical requirements; (ii) similar palaeogeographical
and palaeoecological events in all regions, which may
have led to a common scenario of vicariance and
would result in uniform distribution patterns of taxa
across the regions; (iii) similar means for passive
dispersal, if any and (iv) similar patterns of biotic
interactions, although this last aspect is virtually
unknown for groundwater communities (Culver,
1994; Strayer, 1994). Given the above observations,
the differences observed in species richness, taxo-
nomic composition and strength of biodiversity indi-
cator relationships across European ground waters are
probably related to both historical and ecological
factors. Rundle et al. (2002) proposed a schematic
Table 2 Cross-taxon correlations amongst stygobiotic species richness of the nine taxonomic groups retained for analyses of data from
six European regions
Taxa
Cantabria Roussillon Jura Wallonia Lessinia Krim
R
s
P-value R
s
P-value R
s
P-value R
s
P-value R
s
P-value R
s
P-value
Oligochaeta – Acari 0.08 0.779 0.31 0.241 – – )0.02 0.945 0.08 0.780 0.03 0.899
Oligochaeta – Amphipoda 0.37 0.162 0.48 0.059 0.55 0.027 )0.27 0.317 0.49 0.054 0.10 0.709
Oligochaeta – Bathynellacea 0.34 0.194 0.21 0.430 0.23 0.387 – – )0.17 0.538 0.41 0.116
Oligochaeta – Cyclopoida 0.34 0.191 0.44 0.089 0.15 0.571 )0.16 0.544 )0.39 0.133 0.37 0.162
Oligochaeta – Gastropoda – – 0.49 0.055 0.13 0.621 )0.30 0.257 0.52 0.041 )0.12 0.661
Oligochaeta – Harpacticoida 0.44 0.091 0.45 0.080 0.18 0.508 – – 0.77 <0.001 0.06 0.833
Oligochaeta – Isopoda 0.42 0.107 0.16 0.557 0.15 0.580 )0.43 0.097 0.35 0.189 )0.04 0.888
Oligochaeta – Ostracoda 0.18 0.502 0.64 0.007 0.31 0.238 )0.04 0.888 0.07 0.804 )0.16 0.543
Gastropoda – Acari 0.20 0.450 0.30 0.264 – – 0.22 0.413 )0.32 0.234 0.26 0.340
Gastropoda – Amphipoda 0.25 0.344 0.15 0.568 0.08 0.768 0.43 0.095 0.83 <0.001 0.71 0.002
Gastropoda – Bathynellacea 0.35 0.182 0.21 0.437 )0.38 0.143 – – 0.25 0.359 0.28 0.290
Gastropoda – Cyclopoida )0.01 0.971 0.68 0.004 0.04 0.890 0.44 0.086 )0.53 0.033 )0.06 0.816
Gastropoda – Harpacticoida )0.10 0.712 0.62 0.010 0.36 0.168 – – 0.36 0.168 )0.50 0.049
Gastropoda – Isopoda )0.06 0.832 )0.36 0.175 0.78 <0.001 0.44 0.088 0.69 0.003 0.39 0.137
Gastropoda – Ostracoda 0.49 0.055 0.27 0.314 0.48 0.061 0.32 0.229 )0.08 0.781 0.34 0.193
Cyclopoida – Acari 0.42 0.101 0.09 0.745 – – 0.21 0.428 0.33 0.212 0.41 0.118
Cyclopoida – Amphipoda 0.13 0.620 0.17 0.526 0.08 0.775 0.61 0.012 )0.31 0.246 )0.04 0.890
Cyclopoida – Bathynellacea )0.17 0.522 0.29 0.271 )0.23 0.389 – – )0.41 0.113 0.40 0.123
Cyclopoida – Harpacticoida )0.05 0.863 0.73 0.001 0.65 0.007 –– )0.29 0.276 0.50 0.051
Cyclopoida – Isopoda )0.01 0.961 )0.29 0.277 0.43 0.100 0.47 0.067 )0.32 0.230 )0.50 0.047
Cyclopoida – Ostracoda 0.03 0.910 0.51 0.044 0.26 0.321 0.54 0.030 0.16 0.554 )0.36 0.176
Harpacticoida – Acari )0.35 0.178 0.39 0.133 – – – – 0.27 0.313 0.25 0.342
Harpacticoida – Amphipoda )0.26 0.323 0.30 0.258 0.12 0.658 – – 0.38 0.151 )0.41 0.113
Harpacticoida – Bathynellacea 0.73 0.001 0.42 0.104 )0.22 0.422 – – 0.06 0.827 )0.04 0.892
Harpacticoida – Isopoda 0.18 0.500 )0.15 0.574 0.38 0.152 – – 0.14 0.597 )0.46 0.072
Harpacticoida – Ostracoda )0.15 0.583 0.44 0.090 )0.04 0.891 – – 0.06 0.832 )0.56 0.023
Ostracoda – Amphipoda 0.59 0.017 0.19 0.471 0.31 0.245 0.34 0.197 )0.12 0.652 0.51 0.044
Ostracoda – Bathynellacea 0.03 0.918 0.53 0.034 )0.47 0.069 – – )0.28 0.302 )0.03 0.908
Ostracoda – Isopoda 0.27 0.304 )0.09 0.732 0.49 0.054 0.45 0.077 )0.28 0.289 0.60 0.014
Ostracoda – Acari 0.06 0.817 0.23 0.395 – – 0.05 0.843 0.63 0.008 )0.23 0.384
Isopoda – Acari 0.10 0.708 )0.30 0.256 – – )0.09 0.738 )0.49 0.055 )0.22 0.409
Isopoda – Amphipoda 0.47 0.066 0.02 0.945 0.23 0.395 0.35 0.184 0.69 0.003 0.49 0.053
Isopoda – Bathynellacea )0.07 0.805 )0.37 0.153 )0.41 0.116 – – 0.39 0.133 )0.15 0.570
Amphipoda – Acari 0.36 0.176 0.18 0.503 – – 0.12 0.646 )0.29 0.278 0.24 0.374
Amphipoda – Bathynellacea )0.24 0.364 0.19 0.482 )0.08 0.762 – – 0.18 0.516 0.25 0.347
Bathynellacea – Acari )0.25 0.349 0.15 0.572 – – – – )0.50 0.050 0.40 0.126
R
s
, Spearman’s rank correlation coefficient; P, probability values.
Significant relationships after Bonferroni correction of alpha for individual tests are shown in bold.
750 F. Stoch et al.
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd, Freshwater Biology,54, 745–755
representation of the main factors affecting freshwater
species pools at different spatial scales. Accordingly,
palaeogeographical and palaeoecological events are of
primary importance as they shape particular palaeo-
biogeographical scenarios. These lead to quite differ-
ent species assemblages, which reflect the different
geological and climatic events in distinct geographical
areas. For instance, glaciated areas underwent drastic
impoverishment of regional species pools during the
Quaternary, as in some parts of the Jura massif, where
several stygobiotic species show wide ecological tol-
erance, accompanied by a relatively wide geographical
distribution. A more extreme situation occurred in the
Walloon region. Here entire groups of stygobionts are
missing, probably reflecting low habitat heterogeneity
compared to the other regions examined, together
with the strong residual effects of the Quaternary
glaciations as the presence of permafrost (Martin et al.,
2005). On the contrary, the highest stygobiotic species
richness is located in the southernmost regions of
Europe, which were much less affected by glaciations.
However, it is important to note that the influence of
palaeogeography and palaeoecology may date back
much further, as for the Lessinian and the Krim
massifs, which are characterised by the development
of very ancient karst (Boccaletti et al., 1990; Galassi
Table 3 Correlations between within-group and extra-group species richness in six European regions
Taxon
Cantabria Roussillon Jura Wallonia Lessinia Krim
R
s
P-value R
s
P-value R
s
P-value R
s
P-value R
s
P-value R
s
P-value
Oligochaeta 0.64 0.008 0.71 0.002 0.46 0.075 )0.21 0.428 0.64 0.008 0.18 0.511
Gastropoda 0.42 0.107 0.58 0.018 0.39 0.139 0.42 0.102 0.44 0.085 0.16 0.552
Cyclopoida 0.18 0.516 0.66 0.006 0.41 0.118 0.71 0.002 )0.37 0.160 0.40 0.120
Harpacticoida 0.22 0.403 0.69 0.003 0.43 0.095 – – 0.53 0.034 )0.30 0.266
Ostracoda 0.28 0.295 0.61 0.013 0.18 0.507 0.53 0.034 0.07 0.800 )0.16 0.565
Isopoda 0.27 0.320 )0.08 0.765 0.48 0.062 0.34 0.193 0.38 0.149 )0.04 0.872
Amphipoda 0.31 0.242 0.46 0.071 0.29 0.279 0.35 0.181 0.49 0.054 0.46 0.074
Bathynellacea 0.15 0.589 0.35 0.185 )0.30 0.265 – – )0.09 0.742 0.58 0.019
Acari )0.08 0.765 0.31 0.245 – – 0.07 0.796 0.05 0.866 0.43 0.099
R
s
, Spearman’s rank correlation coefficient; P, probability value.
Significant relationships after Bonferroni correction of alpha for individual tests are shown in bold.
Table 4 Statistical models with two or three potential indicators of extra-group species richness selected using rank-order multiple-
regression
Region and taxa R
2adj
FP(F)b
1
P(b
1
)b
2
P(b
2
)b
3
P(b
3
)
Cantabria
Amphipoda + Bathynellacea 0.44 6.88 0.009 0.70 0.005 0.48 0.036
Gastropoda + Harpacticoida + Amphipoda 0.70 12.75 <0.001 0.67 0.003 0.66 0.001 0.41 0.022
Roussillon
Oligochaeta + Harpacticoida 0.71 19.61 <0.001 0.43 0.016 0.62 0.002
Gastropoda + Ostracoda + Amphipoda 0.77 17.50 <0.001 0.53 0.002 0.48 0.004 0.35 0.024
Jura
Gastropoda + Cyclopoida 0.44 6.81 0.010 0.54 0.032 0.54 0.018
Gastropoda + Cyclopoida + Amphipoda 0.74 15.17 <0.001 0.61 0.0014 0.47 0.004 0.44 0.007
Wallonia
Oligochaeta + Cyclopoida 0.53 9.60 0.003 )0.17 0.374 0.76 0.001
Oligochaeta + Cyclopoida + Acari 0.52 6.32 0.008 )0.18 0.379 0.79 0.002 )0.12 0.578
Lessinia
Oligochaeta + Cyclopoida 0.47 7.67 0.006 0.75 0.004 )0.05 0.818
Oligochaeta + Cyclopoida + Bathynellacea 0.64 9.93 0.001 0.98 <0.001 0.20 0.335 0.27 0.205
Krim
Harpacticoida + Amphipoda 0.39 5.72 0.017 0.08 0.743 0.73 0.007
Harpacticoida + Amphipoda + Bathynellacea 0.63 9.34 0.002 0.00 0.998 0.64 0.004 0.47 0.027
R
2adj
, model-adjusted squared correlation coefficient; b
1
,b
2,
b
3
, estimated regression coefficients.
All relationships are statistically significant under the approximate false discovery rate.
Biodiversity indicators in European ground waters 751
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd, Freshwater Biology,54, 745–755
Cantabria
(Gastropoda, Harpacticoida, Amphipoda)
0
4
8
12
16
Roussillon
(Gastropoda, Ostracoda, Amphipoda)
0
4
8
12
16
Jura
(Gastropoda, Cyclopoida, Amphipoda)
0
4
8
12
16
3
0
4
8
12
16
Observed richness
Fitted richness
0
4
8
12
16
Observed richness
Wallonia
(Oligochaeta, Cyclopoida)
0
4
8
12
16
9
Lessinia
(Oligochaeta, Cyclopoida, Bathynellacea)
Krim
(Harpacticoida, Amphipoda, Bathynellacea)
Fitted richnessFitted richness
1612840
1612840
161284
0
24181260
1260 391260
Fig. 1 Fitted versus observed extra-group species richness in six European regions for rank-order multiple-regression models selected
on the basis of the highest adjusted R
2
for three indicator groups (two for Wallonia).
752 F. Stoch et al.
2009 The Authors, Journal compilation 2009 Blackwell Publishing Ltd, Freshwater Biology,54, 745–755
et al., 2009) which emerged from the sea in the
Tertiary. The old age of these karstic aquifers, together
with a less drastic influence of the Quaternary glaci-
ations, led to high diversification of species (especially
among Copepoda, Amphipoda and Oligochaeta),
which was maintained over time (Galassi et al., 2009).
Influences of other environmental features are
superimposed on this basic scenario. These include,
for example, habitat heterogeneity and fragmentation,
a frequent situation in unsaturated karst, where a high
degree of endemism occurs within some taxonomic
groups such as Copepoda, Amphipoda, Oligochaeta
and Bathynellacea (Pipan & Culver, 2005; Galassi et al.,
2009). Equally important is the role of anthropogenic
disturbance in affecting composition and structure of
the groundwater assemblages (Hancock, 2002; Lafont
et al., 2006). An increase in organic matter and nutrient
supply alters assemblage composition, leading to
population declines or disappearance of some stygo-
biotic and other sensitive species (Rundle & Ormerod,
1991; Malard et al., 1994; Rundle & Ramsay, 1997;
Malard, 2001; Moesslacher, Griebler & Notenboom,
2001; Paran et al., 2005). Finally, although care was
taken in the PASCALIS sampling design to distribute
sites evenly among aquifers and habitat types, sam-
pling effort and efficiency across the six regions were
not exactly the same (Dole-Olivier et al., 2009).
These factors vary in relative importance in differ-
ent regions. This may allow for greater co-variation of
species richness of different taxa in some areas such as
the western regions (i.e. Cantabria, Roussillon and
Jura), which influences the strength of indicator
relationships. Indicator selection models appear to
be less efficient in the eastern regions (i.e. Lessinia and
Krim), which show higher biodiversity and greater
habitat fragmentation (Galassi et al., 2009). Finally,
models should be applied with caution to Wallonia,
where the effects of Quaternary glaciations and the
intensity of land use may have heavily influenced the
groundwater assemblages (Martin et al., 2005).
It is still unknown to what extent the different
ecological and historical factors shape groundwater
assemblages (Stoch, 1995; Ward et al., 1998; Galassi,
2001; Gibert & Deharveng, 2002; Rundle et al., 2002;
Galassi et al., 2009) and which factors are key in
determining geographical variation of cross-taxon
correlations in European ground waters. Towards the
goal of drawing general and thus transferable infer-
ences about the nature of ecological assemblages, Mac
Nally & Fleishman (2004) argued for developing and
testing hypotheses that explain why a particular set of
indicators encompasses fundamental information
about a whole community. If this can be achieved, both
researchers and natural resource managers striving to
improve the monitoring and conservation of stygobi-
otic biodiversity, may be better informed by reliable
studies on selected groups than by impractical attempts
to survey the entire groundwater fauna.
Acknowledgments
B. Arconada, R. Araujo, M. Bodon, C. Boutin,
A. Camacho, M. Creuze
´des Cha
ˆtelliers, C. Debroyer,
W. Decraemer, P. De Laurentiis, A. Di Sabatino, G. &
M. Falkner, F. Fiers, M. Ghamizi, N. Giani, R. Ginet,
N. Guil, D. Jaume, J. Juget, G. Magniez, F. Margari-
tora, P. Marmonier, E. Martinez Ansemil, C. Meisch,
M. Messouli, A. Navas, S. Prevorc
ˇnik, M.A. Ramos,
B. Sambugar, B. Sket, J. Van Goethem, F. Velkovrh,
K. Wouters are greatly acknowledged for their valu-
able contributions to data acquisition. Two anony-
mous reviewers constructively criticised the first draft
of the manuscript which helped improve particularly
the presentation of statistical results. This study was
supported by the PASCALIS project (Protocols for the
ASsessment and Conservation of Aquatic Life In the
Subsurface) funded by the European Community
under its 5th Framework Programme (contract no.
EVK2-CT-2001-00121).
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