Applied Soil Ecology 33 (2006) 67–78
www.elsevier.com/locate/apsoil
Soil mesofaunal responses to post-mining restoration treatments
Pilar Andrés a,*, Eduardo Mateos b
a
Center for Ecological Research and Forestry Applications (CREAF), Autonomous University of Barcelona,
08193 Bellaterra, Barcelona, Spain
b
Department of Animal Biology of the Faculty of Biology, University of Barcelona,
Diagonal 645, 08028 Barcelona, Spain
Received 19 January 2005; received in revised form 8 August 2005; accepted 24 August 2005
Abstract
As soil destruction is one of the most conspicuous environmental impacts of opencast mining, mining companies are compelled
to restore the exploited areas and to develop monitoring programs to test the effectiveness of the restoration treatments. Soil physical
and chemical parameters are usually used as soil quality indicators, but bioindicators are more promising as they evaluate the global
soil capability to support its ecological functions. In this work, we used soil mesofaunal bioindicators to evaluate four post-mining
restoration treatments (soil spreading, soil spreading + grass and herb sowing, soil spreading + tree planting and soil spreading + sowing + planting). Twelve years after restoration none of the treatments had achieved the restoration of the preoperational forest
soil conditions. Soil spreading was the least effective treatment. Soil spreading + sowing and soil spreading + sowing + planting
generated grassland soil conditions. Soil spreading + planting induced an incipient forest soil structure. Number of taxa,
collembolan and oribatid species diversity and communites structure were the most sensitive mesofaunal parameters to evaluate
the soil restoration treatments.
# 2005 Elsevier B.V. All rights reserved.
Keywords: Soil mesofauna; Soil quality; Bioindicators; Restoration; Mining
1. Introduction
Landscape degradation is one of the most negative
consequences of opencast mining. As social concern for
landscape quality maintenance is growing in Europe,
laws compelling mining companies to carry out postmining land restoration or rehabilitation are being
implemented by environmental authorities. An evaluation of the success of minesite restoration is usually
required prior to declaring the mine’s closure.
After mining, the ecosystem may recover spontaneously if the resultant mineral substrate and the
* Corresponding author. Tel.: +34 93 581 46 80;
fax: +34 93 581 41 51.
E-mail address: pilar.andres@uab.es (P. Andrés).
0929-1393/$ – see front matter # 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.apsoil.2005.08.007
environmental conditions are adequate, but in most
cases, deficient physical, chemical and biological soil
conditions (e.g. unbalanced granulometry, low organic
matter content, soil biota shortage) or isolation from
colonization sources impede initiation of secondary
succession or slow it down to a level incompatible with
the social requirement for rapid solutions (Ash et al.,
1994; Bradshaw, 1997).
In these cases, a wide range of restoration
techniques, of different intensities and costs, may be
applied depending on resource availability and on
ecosystem resilience. Commonly, restoration works
include combinations of the following treatments: (a) in
situ improvement of the physical and chemical substrate
conditions; (b) top-soiling and later improvement and
spreading of the recovered soil; (c) soil stabilization and
protection with herbaceous species and (d) shrubs and
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P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
tree planting (Alcañiz et al., 1996; Green et al., 1999;
Shopper, 1993; Vallejo et al., 1999).
To evaluate the success of restoration methods, two
tools are needed: a clearly defined reference state and a
robust and easily measurable indicator or indicators
system. When land restoration is the goal, undisturbed
neighboring ecosystems may be used as reference sites.
Land units supporting the designated land use, over a
similar geological substratum and sharing climatic
conditions, may be useful when the goal is rehabilitation or reclamation.
Even though ecological restoration implies recreating the entire ecosystem structure, function and
diversity, restoration projects often are only evaluated
based on the reestablishment of the pre-disturbance
dominant vegetation (Longcore, 2003). In the Mediterranean areas, secondary plant succession is slow,
with an early dominance of annuals that are progressively substituted by forbs, grasses and woody species.
Percentage of woody plants and plant cover are
expected to increase with time after restoration.
Nevertheless, the frequent coexistence in time of plants
belonging to different life forms may question the value
of their frequency distribution as a maturity indicator,
and recent studies (Bonet and Pausas, 2004) conclude
that plant cover in semi-arid Mediterranean systems is
not dependent on plot age. Plant species diversity is
expected to exhibit a peak attributable to immigration
during the early period of 15 years since restoration, but
plant diversity correlates more with the vegetation
characteristics of the neighbouring undisturbed forest
than with the restoration age (UB/CEIB-ESAB/
CREAF, 2001).
Therefore, several environmental compartments
must be monitored, with soil being the most strategic,
given its well known key function in supporting the
whole ecosystem (Bezdicek, 1996). The ‘‘post-restoration’’ soil quality is commonly monitored by using soil
physical and chemical indicators, such as soil erodibility (Ojeda et al., 2003; Sort and Alcañiz, 1996), soil
porosity and structure (Sort and Alcañiz, 1999a,b) or
ability to support vegetation (Jorba and Andrés, 2000;
Moreno, 2000; Zas and Alonso, 2002). More recently,
landscape indicators, such as the ‘‘complexity habitat
index’’ (Ludwig et al., 2003) are being calculated.
National and international monitoring programs have
proposed soil health bioindicators, including microbial
biomass and soil respiration (Ortiz, 1998), N mineralization, microbial diversity and others related to the
soil faunal communities (Andrés, 1999; Schloter et al.,
2003) but there is no agreement about which soil biota
bioindicators are most indicative of the final success of
restoration or of the suitability of the successional
process in a restored mine (Ekschmitt et al., 2003).
Soil invertebrates may be appropriate tools in
indicating the degree to which soil is affected by
human activities (Kimberling et al., 2001; Ruf, 1998;)
or land use intensification (Ponge et al., 2003) as they
are sensitive to the anthropogenic disturbance, well
correlated with soil functions and are helpful for
elucidating ecosystem processes (Doran and Zeiss,
2000). Physical, chemical or landscape derived indicators may be less expensive than bioindicators in timeeffort or expertise (Ekschmitt et al., 2003), but they do
not necessarily reflect the soil’s capability to maintain
its essential ecological functions, due to their sensitivity
to the time of sampling in relation to certain management and environmental events (van Bruggen and
Semenov, 2000).
In this work, we used soil mesofaunal bioindicators
to evaluate the success of four post-mining restoration
treatments in recreating the biological forest soil
conditions.
2. Materials and methods
This experiment is part of a multidisciplinary study
conducted in 2000–2001 to use a set of vegetal and soil
physical, chemical and biological indicators to evaluate
the quality of 12 limestone quarries restored in
Catalonia from 1987 to 1997 by different methods.
During this period, four main restoration methods were
applied by the mining companies: soil spreading over
the mineral layer (soil spreading treatment), soil
spreading followed by sowing of herbaceous species
(sowing treatment), soil spreading followed by planting
of shrubs and trees (planting treatment) and soil
spreading followed by sowing and planting (mixed
treatment). Lolium perenne (ryegrass), Festuca arundinacea (tall fescue), Dactylis glomerata (orchardgrass),
Medicago sativa (lucerne) and Onobrichys sp. were the
grasses and herbs usually sown. Pinus halepensis
(Aleppo pine), Pinus pinea (stone pine) and Quercus
ilex (holm oak) were the most commonly used trees, and
Spartium junceum (Spanish broom), Pistacia lentiscus
(mastic tree), and Quercus coccifera (kermes oak) were
the most commonly planted shrubs.
The experimental design for soil faunal bioindicators
included 10 plots (two plots per treatment and two more
for reference). The eight treatment plots, each 300 m2 in
size, were located in four limestone quarries of the
Garraf region of Barcelona (Catalonia, Spain) restored
between 1988 and 1992. The two reference plots were
selected in the unexploited forest areas adjacent to two
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P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
Table 1
General plot characteristicsa
Plots
Plots code
Quarry
HC
PD
Sowing 1
Sowing 2
Mixed 1
Mixed 2
Planting 1
Planting 2
Spreading 1
Spreading 2
Reference 1
Reference 2
A1
A2
B1
B2
C1
C2
D1
D2
E1
E2
Santa Margarida
Vallcara
Santa Margarida
Vallcarca
Corral del Carro
Vallcarca
Santa Margarida
El Tirano
Santa Margarida
Garraf
92.6 6.5
99.7 0.6
68 8
55.7 17
91.3 6.5
1.7 2.1
59 10.4
85.3 20.4
Data unavailable
Data unavailable
1.23 0.15
0.63 0.17
0.6 0.07
1.48 0.16
1.65 0.61
0.36 0.08
1.72 0.47
1.39 0.52
Data unavailable
Data unavailable
HC, herbaceous cover in % (mean S.E.); PD, plant diversity by the Shannon index in bits (mean S.E.).
a
Sources: UB/CEIB-ESAB/CREAF, 2001.
underlying upper 5 cm of the mineral layer was sampled
with a cylindrical borer of 5 cm diameter and 5 cm
depth. Samples were immediately transported to the
laboratory for mesofaunal extraction in BerleseTullgren funnels (2 mm diameter mesh) for 15 days
under constant light and temperature conditions.
Soil animals were first classified into wide taxonomic groups. Mites were later determined at the
suborder level. Mesostigmatid and prostigmatid mites
were determined at family level and Cryptostigmata
(oribatids) and collembolans at species level. This
classification was considered the most efficient to
determine the trophic structure of the arthropod
community and the species diversity of oribatids and
collembolans.
Given the contagious distribution and the high
micro-spatial heterogeneity of the soil fauna, we
considered soil samples as independent replicates for
the purpose of evaluating within-treatment variability.
For each plot, we calculated the mesofaunal density,
the number of taxa, the relative abundance of
of the quarries. Distances between the four quarries
were 3–18 km, and the geological substratum and
climate were the same for all of them, with an average
annual temperature of 14–16 8C and a mean annual
precipitation of 525–575 mm.
The selection of the experimental plots had to be
adapted to the quarries available. As the quarries were
restored by different mining companies, applying
different restoration plans, none of the quarries
available for monitoring included areas representative
of all four treatments. However, we chose plots of
similar age, climate, soil class and geological substrata
from four quarries in the same homogeneous area,
which included the four treatment types. The final
allocation of the plots, together with their general
characteristics, is summarized in Tables 1 and 2.
Sampling was conducted in May 2000, when the
biological activity in Mediterranean soils is high. We
sampled at random at six points per plot. At each
sampling point, the whole depth of the organic layer was
taken with a square 10 10 section sampling tool. The
Table 2
General plots soil characteristicsa
Plots
TC
GR
OM
CC
pH
WC
R
Sowing 1
Sowing 2
Mixed 1
Mixed 2
Planting 1
Planting 2
Spreading 1
Spreading 2
Reference 1
Reference 2
Silt clay loam
Loam
Loam
Loam
Loam
Silt loam
Loam
Loam
Clay loam
Loam
28.1
36.3
22.9
55.3
22.6
47.1
15.9
71.2
24.1
37.5
1.74
2.65
1.05
0.58
1.41
1.25
0.52
0.64
8.27
8.94
31.8
26.9
23.1
19.7
21
21.6
20.8
21.8
27.4
25.3
8.5
8.4
8.5
8.5
8.1
8.2
8.6
8.7
8.1
8.3
50.1
0.64
49.1
0.25
40.9
0.51
43.7
0.88
41.8
1.07
TC, soil textural class; GR, percentage of gravel; OM, percentage of soil organic matter; CC, percentage of clay; pH, pH measured in water; WC,
mean water content at saturation as %; R, mean soil respiration in g CO2 m 2 h 1.
a
Sources: UB/CEIB-ESAB/CREAF, 2001.
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collembolans and Acari, the Cryptostigmata/Prostigmata ratio, the vertical distribution and the trophic
structure of the mesofauna. Also, we calculated the
percentage of the four acarine suborders with respect to
the total number of Acari.
For collembolans and oribatids, we measured the
species diversity by means of the Shannon index. The
trophic structure of the mesofaunal community was
characterized by calculating the percentage of predators. The vertical distribution of the mesofauna was
calculated in terms of percentage of animals inhabiting
the upper soil organic layer.
Results were tested by means of nested-ANOVA with
two levels (level 1, treatments; level 2, plots within
treatments).
To characterize the structure of the oribatid and
collembolan communities, we compared treatments and
undisturbed forest for collembolan and oribatid species
abundance by means of the Wilcoxon’s signed rank test.
Detrended correspondence analysis (DCA) and
canonical correspondence analysis (CCA) were performed to look for similarity in species composition and
in the oribatid and collembolan community structure.
The community data matrix included 10 columns (one
per plot) and 74 rows (the number of collembolan and
oribatid species). The cells of the data matrix represent
the mean population density of each species. In the
variables data matrix, the nominal variable ‘‘treatment’’
(with five classes) was included by defining five dummy
environmental variables: ‘‘sowing’’, ‘‘planting’’,
‘‘mixed’’, ‘‘reference’’ and ‘‘spreading’’. Thus, the
variables data matrix included 10 columns and five
rows. Additionally, simple regression analyses were
conducted to test the utility of the soil physical and
chemical parameters in predicting the soil mesofaunal
community characteristics.
3. Results
the most abundant taxa in all plots. No significant
differences in their density or relative abundance were
found between treatments. The density and relative
abundance with respect to the total number of Acari of
the mesostigmatid and astigmatid mites were also
independent of the treatments. The density of the
cryptostigmatid (oribatid) mites was independent of the
treatments, but their relative abundance was affected by
the treatments (Table 6) with the lowest value measured
in the spreading treatment plots (Table 3). The relative
abundance of the oribatids was also dependent on the
plots (Table 6). The dominance of the prostigmatid
mites was dependent on the treatments and on the plots
(Table 6), with the lowest value in the reference plots
and the highest in the spreading treatment plots.
The Cryptostigmata/Prostigmata ratio was independent of the treatments.
3.3. Mesofaunal vertical distribution
Significant differences in vertical distribution were
found between treatments and between plots (Table 6).
More than 50% of the soil mesofauna occupied the
upper organic layers of the reference forest soils,
whereas most of them (approximately 89%) occupied
the deeper mineral layers in the sowing and in the soil
spreading treatments. Planted plots (with or without
sowing) showed an intermediate state, with about 33%
of the mesofauna inhabiting the upper organic layer.
3.4. Relative abundance of predators
Differences in percentage predators were not
significant but this trophic class was especially
abundant in the soil spreading treatment (Table 3)
due to the high abundance of predatory mites belonging
to the families Rhodacaridae (Mesostigmata), Bdellidae, Caligonellidae, Pseudochelidae and Rhagidiidae
(Prostigmata).
3.1. Total mesofaunal density and composition
3.5. Oribatid and collembolan community structure
Differences in total mesofaunal density (Table 3)
between treatments and reference plots were not
significant. The number of taxa per sample was significantly dependent on the treatments and significantly
higher in the reference plots than in the restoration
treatments (Table 6).
3.2. Relative abundance of the taxa
Mites (67–79% of the total mesofauna) and
collembolans (4–21% of the total mesofauna) were
Twenty-six collembolan and at least 47 oribatid
species (the Oppia species are grouped, except for O.
concolor) were identified overall (Tables 4 and 5).
For collembolans and oribatids, the species diversity
measured by Shannon index was significantly affected
by the treatments (Table 6). For oribatids, the diversity
in the reference and in the sowing treatment was
significantly greater than in the other treatments. For
collembolans, it was greater in the reference than in any
restoration treatment. For both taxa, the lowest species
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P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
Table 3
Effect of restoration treatments on the density of various taxa (individuals/m2) and on some soil community indicators (mean S.E.; n = 12)
Treatments
Reference
Spreading
Pseudoscorpionida
Phalangida
Araneida
Acari
Isopoda
Pauropoda
Penicillata
Julida
Glomerida
Geophilomorpha
Symphyla
Protura
Diplura
Collembola
Microcoryphia
Zygentoma
Orthoptera
Isoptera
Embioptera
Psocoptera
Homoptera
Thysanoptera
Coleoptera
Hymenoptera
Diptera
Immature insects
135 55
88
17 11
17909 2549
17 11
390 192
177 95
127 91
17 17
175 175
11170 2011
135 63
102 56
562 172
42 42
1644 1480
17 11
261 85
126 74
320 185
100 33
Total density
Taxa/sample
% Of predators
% Fauna in organic layer
% Mesostigmata a
% Cryptostigmataa
% Astigmataa
% Prostigmata a
Cryptos/Prostig
26363 3321
7.9 0.6
18.0 1.5
56.6 6.1
13.4 2.1
71.2 2.3
2.4 1
13 1.3
6.6 1
a
88
88
424 175
772 422
3719 585
88
88
85 85
42 42
Sowing
Planting
Mixed
12104 2414
88
21339 9039
42 42
42 42
25315 6698
42 42
42 42
42 42
136 91
88
42 42
42 42
42 42
42 42
1197 616
85 57
42 42
3444 1069
42 42
2403 574
42 42
85 85
2507 1006
88
88
85 85
88
127 88
127 91
344 163
88
135 98
1464 900
102 57
25 18
288 180
144 64
346 159
209 143
1281 686
254 109
42 42
227 86
92 75
609 253
709 289
228 98
814 368
25 18
516 225
51 42
220 91
14126 2420
4.7 0.5
28.1 6.0
11.9 4.13
8.4 2.5
46.4 7.4
8.8 4.3
36.5 9.3
5.2 2.2
31567 6753
5.3 0.5
20.4 2.5
10.9 3.3
10.7 2.9
52.2 4.6
8.8 2.7
28.3 3.6
2.4 0.4
17364 3295
5.1 0.3
16.9 3.8
33.0 7.4
11.6 3.5
67.5 7.6
3.5 1
17.4 4.7
10.6 3.5
26739 9784
5.8 0.4
20.5 3.7
30.9 8.7
14.5 4.9
53.1 6.6
6.2 2.9
26.2 6.2
4.5 1.9
% of Mesostigmata, Cryptostigmata, Astigmata and Prostigmata with respect to the total number of Acari.
diversity was found in the soil spreading treatment. As
shown in Table 7, differences in the diversity were
coupled with differences in the dominant species.
When considering the oribatid and collembolan
species composition and abundance, the highest
similarity between the treatments and the reference
plots was obtained with the planting and with the mixed
treatments for oribatids and with the sowing and mixed
treatments for collembolans (Tables 4 and 5).
3.6. Multivariate analysis
The first two axes of the DCA explained 36% of the
variance. The ordination diagram displayed different
species composition by plots (Fig. 1). Except for the
spreading treatment, the plots are not associated by
treatments and do not display any clarifying spatial
distribution pattern.
The first two axes of the CCA (Fig. 2) explained
32.1% of the variance within species. The speciesenvironment correlation coefficient was 0.965 for the
first axis and 0.952 for the second. The correlation
coefficients (inter-set correlations) of the five dummy
environmental variables with the first canonical axis
were: 0.96 for reference, 0.21 for sowing, 0.19 for
planting, 0.40 for mixed and 0.23 for spreading.
With the second axis, the correlation coefficients were:
0.10 for reference, 0.89 for sowing, 0.07 for
planting, 0.62 for mixed and 0.13 for spreading.
By Monte Carlo permutation tests on the effect of the
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P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
Table 4
Collembolan density in individuals/m2 (mean S.E.; n = 12), species composition, species diversity (Shannon index) and similarity between
treatments and reference plots with regard to species abundance by means of the Wilcoxon’s test
Reference
Xenylla brevisimilis (Gama, 1964)
Ceratophysella tergilobata (Cassagnau, 1954)
Ceratophysella denticulata (Bagnall, 1941)
Friesea mirabilis (Tullberg, 1871)
Friesea truncata (Cassagnau, 1958)
Pseudachorudina meridionalis (Bonet, 1929)
Choreutinula inermis (Tullberg, 1871)
Deutonura cf sinistra (Denis, 1935)
Protaphorura cf nemorata (Gisin, 1952)
Mesaphorura sp.
Neotullbergia sp.
Parisotoma notabilis (Schäffer, 1896)
Cryptopygus albaredai (Selga, 1962)
Isotomiella minor (Schäffer, 1896)
Folsomia manolachei (Bagnall, 1939)
Folsomides parvulus (Stach, 1922)
Archisotoma sp.
Isotomurus sp.
Entomobrya sp. 1
Entomobrya sp. 2
Lepidocyrtus lanuginosus (Gmelin, 1788)
Lepidocyrtus lignorum (Fabricius, 1793)
Pseudosinella sp.
Heteromurus major (Monniez, 1889)
Sminthurinus aureus (Lubbock, 1862)
Bourletiella sp.
Total collembola
Species diversity
Wilcoxon’s test p-values
450 140
382 218
Spreading
Sowing
Planting
Mixed
42 42
202 109
1124 740
42 42
42 42
475 475
170 96
88
17 11
85 85
202 160
42 42
25 18
321 153
85 85
340 130
17 17
401 365
170 72
17 11
176 60
740 249
236 107
976 665
42 42
42 42
85 57
42 42
382 236
127 66
212 171
263 254
144 91
687 326
712 430
159 55
88
17 11
42 42
3719 585
2.03 0.14
–
1197 616
0.36 0.19
0.048
170 170
42 42
202 162
177 76
42 42
59 42
42 42
127 91
85 85
679 471
85 57
340 220
340 253
42 42
42 42
496 211
178 76
42 42
42 42
59 59
152 90
51 51
88
88
3444 1069
1.06 0.21
0.338
2403 574
1.08 0.20
0.180
2507 1006
0.56 0.20
0.21
five dummy environmental variables, only ‘‘reference’’
was significant (99 permutations, F-ratio = 1.77,
p = 0.003). Thus, the collembolan and oribatid communities were different in the reference undisturbed
forests and in the restored areas. Additionally, even
though the dummy environmental variable ‘‘sowing’’
was not significant, the ‘‘sowing’’ treatment plots were
distant from the others in the second axis, and we
concluded that faunal communities obtained by sowing
were different from those obtained by applying any
other treatment.
3.7. Soil environmental parameters versus soil
bioindicators
Fig. 1. Detrended correspondence analysis (DCA) ordination diagram
for collembolan and oribatid species showing treatment plots (A1 and
A2, sowing; B1 and B2, mixed; C1 and C2, planting; D1 and D2,
spreading; E1 and E2, reference). Total inertia = 2.299, axis 1 eigenvalue = 0.493, axis 2 eigenvalue = 0.334.
Significant correlations with the mesofaunal diversity and species richness were found for soil organic
matter content, soil pH and clay content (Table 8). The
highest coefficients of determination (R2) were found
between organic matter content and collembolan
diversity and richness and between organic matter
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P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
Table 5
Oribatid density in individuals/m2 (mean S.E.; n = 12), species composition and species diversity (Shannon’s index) and similarity between
treatments and reference with regard to species abundance, by means of the Wilcoxon’s test
Reference
Allogalumna subaequale (Mihelčič)
Aphelacarus acarinus (Berlese)
Arthrodamaeus reticulatus (Berlese)
Berlesezetes auxiliaris (Grandjean)
Camisia spinifer (Koch)
Carabodes aerolatus (Berlese)
Carabodes willmanni (Bernini)
Ceratozetes conjunctus (Mihelčič)
Phyllozetes emmae (Berlese)
Cosmochthonius lanatus (Michael)
Chamobates cuspidatus (Michael)
Damaeus flagellifer (Michael)
Epilohmannia cylindrica (Berlese)
Eueremaeus granulatus (Mihelčič)
Eupelops acromios (Hermann)
Eupelops sp.
Galumna sp.
Haplozetes sinuatus (Pérez-Íñigo Jr.)
Hemileius initialis (Berlese)
Hermanniella dolosa (Grandjean)
Hypovertex sp.
Incabates sp.
Liochthonius sp.
Lucoppia burrowsi (Michael)
Metabelbella sp.
Microzetes petrocoriensis (Grandjean)
Neoliodes ionicus (Sellnick)
Oppia
Oppia concolor (Koch)
Oribatella berlesei (Michael)
Oribatula tibialis (Nicolet)
Papillacarus aciculatus (Berlese)
Passalozetes africanus (Grandjean)
Peloribates europaeus (Willmann)
Pilobates carpetanus (Pérez-Íñigo)
Pilogalumna sp.
Platyliodes sp.
Rhysotritia sp.
Scheloribates laevigatus (Koch)
Scheloribates sp.
Scutovertex sculptus (Michael)
Sphaerochthonius splendidus (Berlese)
Steganacarus magnus (Nicolet)
Tectocepheus velatus (Michael)
Trimalaconothrus sp.
Xenillus tegeocranus (Hermann)
Zetorchestes flabrarius (Grandjean)
Immature Cryptostigmata
Total oribatids
Species diversity
Wilcoxon’s test p-values
42 34
192 152
944 227
351 132
17 17
50 36
297 221
83 41
1122 419
33 26
271 145
17 17
117 50
88
67 40
Spreading
144 91
42 42
Sowing
Planting
Mixed
42 42
1756 888
85 57
42 42
88
101 69
42 42
127 91
552 193
483 261
42 42
88
42 42
85 57
85 57
85 57
1001 422
135 70
33 33
328 177
500 424
308 290
42 42
42 42
17 17
88
25 25
433 271
33 26
17 17
168 90
88
42 42
85 57
42 42
25 25
88
167 167
3473 1257
88
42 34
233 108
127 127
492 261
601 472
466 317
532 376
42 42
340 181
398 144
170 114
449 241
951 283
202 133
88
17 17
42 42
42 42
229 229
1061 466
485 143
150 101
42 42
652 232
88
133 50
2341 1716
93 85
88
1969 1696
218 102
42 42
177 103
1103 425
244 152
127 88
305 254
125 125
85 57
85 57
1001 463
297 98
25 25
3471 2086
1220 630
541 238
88
51 43
2503 574
3853 1562
3142 1123
2301 416
9782 6536
12719 1881
2.54 0.18
–
6039 1662
1.42 0.22
0.007
13693 4531
2.06 0.13
0.002
8862 2374
1.50 0.13
0.127
16055 8865
1.44 0.19
0.132
74
P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
Table 6
Results of the nested ANOVA for the effects of treatments and plots
within treatments on various faunal parameters
d.f.
Number of taxa per sample
Treatments
4
Plots (in treatments)
5
Error
50
MS
F
18.8
4.3
3.0
6.13
1.41
P
Treatments
Oribatidsa
Collembolans
Sowing
T. velatus
A. acarinus
P. notabilis
L. lignorum
L. lanuginosus
Planting
S. laevigatus
T. velatus
O. tibialis
Folsomides sp.
Entomobrya sp. 2
Isotomurus sp
Mixed
Galumna sp.
S. splendidus
X. brevisimilis
C. denticulada
Spreading
Galumna sp.
O. tibialis
T. velatus
Pilogalumna sp
Isotomurus sp.
L. lanuginosus
Reference
Oppia ssp.
Ch. cuspidatus
Platyliodes sp.
L. lanuginosus
X. brevisimilis
I. minor
C. tergilobata
0.0004
0.2368
% Cryptostigmata with respect to the total acari number
Treatments
4
1371.7
4.66
Plots (in treatments)
5
1899.7
6.46
Error
50
294.0
0.0028
0.0001
% Prostigmata with respect to the total acari number
Treatments
4
1028.7
3.72
Plots (in treatments)
5
1504.3
5.44
Error
50
276.5
0.0099
0.0004
% Fauna inhabiting the organic layer
Treatments
4
4192.3
Plots (in treatments)
5
1842.3
Error
50
333.5
12.57
5.52
<0.0001
0.0003
<0.0001
0.3524
Oribatid species diversity
Treatments
Plots (in treatments)
Error
Table 7
Species contributing 50% of the total collembolans and oribatids for
each treatment
a
4
5
48
2.9
0.3
0.3
9.64
1.13
Collembolan species diversity
Treatments
4
Plots (in treatments)
5
Error
39
3.4
0.2
0.3
8.65
0.50
<0.0001
0.7704
Immature oribatids were not included in counting.
large population sizes and ability to occupy a wide
range of ecosystems, microhabitats and niches make the
soil arthropods useful to monitor year-to-year ecosystem environmental changes (Kremen et al., 1993;
content and oribatid specific diversity. Soil pH
explained more than 50% of the variability in the
collembolan species richness and specific diversity and
the percentage of clay explained approximately 60% of
the oribatid specific diversity.
4. Discussion
The aim of the post-mining restoration treatments
was to accelerate secondary succession towards the
recovery of the former forest in the newly exposed soils.
Given that soil matrix, soil micro-, meso- and macrobiota and plant interactions evolve throughout the
successional process, different restoration methods
should be compared for effectiveness, at a given
moment, by evaluating the maturity of the restored areas
by means of physical, chemical and biological
indicators sensitive to the restoration age.
Together with plants, many soil arthropods sensitive
to ecological changes occurring over time after
restoration have been recognised as efficient indicators
of ecosystem maturity for conservation planning as well
as land reclamation monitoring (Greenslade and Majer,
1993; Kremen et al., 1993). Short generation times,
Fig. 2. Canonical correspondence analysis (CCA) ordination diagram
for collembolan and oribatid species showing treatment plots (A1 and
A2, sowing; B1 and B2, mixed; C1 and C2, planting; D1 and D2,
spreading; E1 and E2, reference). Arrows pointed towards dummy
environmental variables (1, sowing; 2, mixed; 3, planting; 4, spreading; 5, reference). Total inertia = 2.299, axis 1 eigenvalue = 0.418,
axis 2 eigenvalue = 0.320. Theo environmental variables scales are
multiplied by two in relation to plots and species scale.
P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
Table 8
Best regressions found between mesofaunal parameters and soil
abiotic factors (n = 10)
pH (H2O)
Organic matter
% Clay
Collembolan diversity (H)
R2 = 0.919
R2 = 0.56
p = 0.012
p < 0.0001
Simple ( )
Logarithmic
Oribatid diversity (H)
R2 = 0.946
p < 0.0001
Logarithmic
R2 = 0.62
p = 0.034
Logarithmic
Number of collembolan species
R2 = 0.684
R2 = 0.53
p = 0.009
p < 0,0001
Simple ( )
Simple (+)
Longcore, 2003). However, soil invertebrate communities are subject to natural stochastic variation, and
their diversity, distribution and abundance may be
determined by a combination of several factors, such as
vegetative cover (Al-Assiuty et al., 1999; Kallimanis
et al., 2002), soil nutrient status, organic mater content
(Ponge et al., 2003) and soil texture or soil acidity
(David et al., 1999) which may differ in importance
from one invertebrate group to another (Berch et al.,
2001; David et al., 1999). In this sense, the use of soil
invertebrates as bioindicators requires first to establish
how the different species respond to a variety of
environmental factors (Hodkinson and Jackson, 2005).
In our plots, an additional source of variability comes
from the fact than they belonged to different mining
companies and were restored with similar but not
identical soil materials. This last factor may explain
why, in some cases, plot effects are as strong as
treatment effects.
As for vegetation, post-restoration recovery of the
invertebrate community is slow and not shorter than
15 years (Neumann, 1991; Webb, 1994), with 80–102
years estimated for the forest collembolan community
to recover (Addison et al., 2003). The colonization
of newly restored soils by the soil fauna coming from
the adjacent undisturbed forests depends mainly on
active locomotion for the macrofauna and on aerial
immigration or on phoresy for mesoarthropods (St.
John et al., 2002). The immigration strategies are
taxon-specific, with collembola and trombidiidae
establishing first within 15 months (Wanner and
Dunger, 2002). The maximal mesofaunal density is
usually achieved during the 2–3 year ‘‘pioneer’’
phase, which is followed by a drastic reduction in
density to levels of less than 20% within the following
75
10 years (Koehler, 1998). Subsequent changes in
taxonomic composition and relative abundance may
be related to successional changes in factors, such as
plant cover, soil pH and organic matter content, etc.
(Black et al., 2003).
In our work, we found no differences in total
mesofaunal density between the undisturbed forests and
the 12-year-old restored plots, but these simple density
data, obtained from a unique sampling campaign, are
not enough to evaluate the success of the soil
community recovery and may reflect a transitory state
in the still young restoration successional process.
On the other hand, similar densities may correspond
to different soil community structures indicative of
different soil maturity stages. In this sense, the richness
of taxa was higher in the undisturbed forests than in any
treatment, due to the presence of some large, mobile,
typical forest taxa, such as the jumping Bristletails
(Microcoryphia), woodlice (Isopoda), centipedes
(Geophilomorpha) or millipedes (Glomerida) in the
reference plots. As a progressive increase of these
macroarthropods may be expected from grassland to
scrubland and forests (Ferguson, 2001), we may
conclude that the richness of taxa is indicative of the
maturity of the vegetal community of the restored areas.
From this perspective, the soil faunal diversity in our
plots was still not that expected for a mature forest, but
rather showed the characteristics of a successional
intermediate stage.
The dominance of the mite suborder Prostigmata was
an interesting indicator. The prostigmatid mites are
abundant in temperate meadows (Kethley, 1990) and
lawns (Koehler, 1998). Prostigmata are also favored by
anaerobiosis and are indicative of human soil disturbance (Clapperton et al., 2002; Philips, 1990). In our
work, Prostigmata (belonging in most cases to the
families Bdellidae, Caligonellidae and Rhagidiidae)
were most abundant in the spreading treatment,
indicating the low degree of effectiveness of this
treatment in facilitating the recovery of the undisturbed
forest soil conditions.
The Cryptostigmata/Prostigmata ratio has been
discussed by many authors as an indicator of the
organic matter content in soils (Cepeda-Pizarro and
Withford, 1989; Kethley, 1990). Given that Cryptostigmata are representative of undisturbed sites whereas
Prostigmata abundance increases with disturbance
(Clapperton et al., 2002), high values of the ratio
correspond with undisturbed forest soils, since low
values are characteristic of the forest transformation to
more immature stages (Sgardelis and Usher, 1994). In
our work, this ratio was maximal in the undisturbed
76
P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
forests and in the planting treatment and minimal in the
spreading treatment.
In mature Mediterranean soils, mesofauna inhabit
mainly the upper organic layers, from which they
migrate to the deeper layers or to the litter depending on
how favorable or adverse climatic and trophic conditions are (Andrés, 1998; Andrés et al., 1999). In forests,
the soil mesofauna are particularly concentrated in the
aboveground/underground interface area, where litter is
abundant and where niche diversity is the highest.
During the first post-restoration stages, the newly spread
soil layer is vertically homogeneous, and the later
progression through stratification is promoted by the
development of the vegetation cover and by its surface
and sub-surface supply of organic matter. Therefore, the
mesofaunal concentration in the upper soil layers may
be taken as an indicator characteristic of mature forest
soil stratification. In our restored plots, mesofaunal
stratification was mainly favored by the treatments
including planting (with or without sowing), whereas
sowing or soil spreading resulted in most animals
inhabiting the deeper soil layers.
After soil restoration, the secondary succession
process implies an increase in the structural diversity
and energy availability of the ecosystem, which
facilitate the development of higher trophic levels
(Ferguson, 2001). The predators/decomposers ratio is
then expected to be higher in forests than in grasslands
or scrublands. On the other hand, since polyfagous taxa,
with opportunistic feeding habits, are more tolerant to
disturbance than predators, a decline in the predators/
decomposers ratio may be related to soil degradation
(Kimberling et al., 2001). Contrary to what we
expected, the index did not discriminate between
restored and undisturbed plots, probably because this
index may be useful in detecting soil degradation
processes but not in evaluating the soil maturity state in
a particular ecosystem.
Diversity indices are considered insensitive when
calculated for the soil mesofauna as a whole (Ekschmitt
et al., 2003; Siepel and van de Bund, 1988; van Straalen,
1998) but their resolution may be improved when
calculated for more homogeneous taxa. In our treatments, the maximal specific diversity for collembolans
and oribatids was found in the reference forests, and
sowing appeared as a promising treatment in restoring
the oribatid diversity. The spreading treatment plots
exhibited the lowest species diversity for both taxa. As
biodiversity is dependent on the combination of
multiple environmental and ecological factors, the
interpretation of this index is difficult in terms of the
success of restoration treatments. However, as in other
studies, we found a significant correlation between
biodiversity and some specific soil characteristics, such
as soil organic matter content for oribatids and
collembolans (Noble et al., 1996; Ponge et al., 2003),
and soil pH for collembolans (Loranger et al., 2001; van
Straalen and Verhoef, 1997).
Comparison of the oribatid and collembolan community structure between treatments by means of rankspecies plots was not successful in our plots because of
overlapping curves (Krüger and Scholtz, 1998). An
alternative attempt by applying a Wilcoxon’s pairedranks test to the relative abundance of the oribatid and
collembolan species suggested than the mixed treatment was the most adequate in order to promote the
recovery of the community structure of both groups.
Finally, as van Straalen (1998) and Addison et al. (2003)
have suggested, the greatest data coherence was
achieved by using multivariate analyses. As the
solutions coming from the CCA and the DCA were
different, and the species-environment correlation
values in CCA were high, we inferred that the
contribution of the treatments to the species variability
was small (Ter Braak, 1986). But, as Gauch (1982)
pointed out, an ordination diagram that explains only a
low percentage of variance may be quite informative. In
our case we may conclude that none of the treatments
was successful in restoring a soil forest faunal
community.
Overall, two main conclusions may be extracted
from this work, one concerning the most efficient
mesofaunal indicators for post-mining restoration,
and the other concerning the evaluation of the postmining restoration treatments. Even if they are
relatively expensive in terms of classification effort,
the multivariate statistical methods applied to the
collembolan and oribatid community composition
provide the most useful information in order to detect
similarities or differences between the restored and
the reference soils. Even so, additional indices, such
as the collembolan or oribatid species diversity,
directly related to the progressive organic matter
accumulation in the upper soil layers, may be used as
bioindicators of the forest soil recovery (Ponge et al.,
2003).
In our work, the soil mesofaunal trends suggested
that 12 years after restoration, the areas treated by soil
spreading and herbaceous species sowing are as
herbaceous ecosystems, characterized by high densities
of prostigmatid and astigmatid mites living mainly in
the mineral soil layers. Where the new soil has been
planted with shrubs and trees, the bioindicators suggest
the beginnings of a soil forest structure, with animals
P. Andrés, E. Mateos / Applied Soil Ecology 33 (2006) 67–78
living mainly in the upper soil layers, and a high
Cryptostigmata/Prostigmata ratio. When the treatment
was sowing and planting trees over the new soil, the
restored areas are in an intermediate stage between the
forest and the herbaceous systems structure. The
greatest gap between treated and undisturbed areas
was found where the restored soil was not later
vegetated. However, differences between the restored
and the undisturbed areas are not easy to interpret in
terms of ‘‘restoration success’’. As the direction and
speed of the secondary succession depends on a
multiplicity of factors (such as the pre-disturbance
characteristics of the ecosystem, the colonization rate
from the adjacent ecosystems, the physical disturbances
of the biotope or the community endogenous processes)
which are continuously evolving in time (McCook,
1994; Terradas, 2001), the description of the ecosystem
variables at a given moment do not allow one to predict
its future evolution.
Some authors (Parmenter and MacMahon, 1990)
have suggested that the pre-disturbance ecosystem
recovery is not possible after mining. Our restored plots
may become stabilized in the situation described, may
progress through forest characteristics or may even
regress to more simple stages. Monitoring in time is
necessary to clarify such matters.
Acknowledgment
This work was supported for the CICyT-FEDER
2FD97-1644-C03-03 project.
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