Does soil acidity explain altitudinal sequences in collembolan communities?

Abstract

Altitudinal changes in collembolan communities were studied by sampling soil microarthropods along a gradient from 950 to 2150 m a.s.l., under a wide range of forest vegetation types. A multivariate method showed that most changes in species composition followed changes in soil chemistry, humus forms and vegetation. A transition from mull to mor humus, with concomitant soil acidification, was observed with increasing elevation. It was observed that at a given elevation, changes in soil acidity occurring in the course of forest dynamics exerted the same effects than altitude, thus soil acidity explained better the composition of collembolan communities. Densities and local diversity of Collembola were observed to increase with soil acidity, which can be explained by (i) physiological adaptations to acid soils inherited from palaeozoic times and (ii) more habitat and food resources when organic matter accumulates at the top of the soil profile.

Full text

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Date of preparation: 29/05/2000 1
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Number of text pages: 28 3
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Number of tables: 2 5
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Number of figures: 5 7
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Title: DOES SOIL ACIDITY EXPLAIN ALTITUDINAL SEQUENCES IN COLLEMBOLAN 9
COMMUNITIES? 10
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Names of authors: Gladys Loranger*, Ipsa Bandyopadhyaya**, Barbara Razaka*** and Jean-François 12
Ponge 13
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Address: Museum National d'Histoire Naturelle, Laboratoire d'Écologie Générale, 4 avenue du Petit-15
Château, 91800 Brunoy, France 16
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* Present address: Université de Paris Sud, Laboratoire de Biologie Végétale, Bat. 362, 91405 Orsay 18
Cedex, France 19
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** Present address: Simantapalli, Santiniketan, Birbhum, West Bengal PIN-731235, India 21
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*** Present address: 11 rue Guichard, 94230 Cachan, France 23
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Corresponding author: Jean-François Ponge, Tel. +33 1 60479213, Fax +33 1 60465009, E-mail: 27
jean-francois.ponge@wanadoo.fr 28
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Abstract 1
2
Altitudinal changes in collembolan communities were studied by sampling soil microarthropods along 3
a gradient from 950 to 2150m a.s.l., under a wide range of forest vegetation types. A multivariate 4
method showed that most changes in species composition followed changes in soil chemistry, humus 5
forms and vegetation. A transition from mull to mor humus, with concomitant soil acidification, was 6
observed with increasing elevation. It was observed that at a given elevation changes in soil acidity 7
occurring in the course of forest dynamics exerted the same effects than altitude, thus soil acidity 8
explained better the composition of collembolan communities. Densities and local diversity of 9
Collembola were observed to increase with soil acidity, which can be explained by i) physiological 10
adaptations to acid soils inherited from palaeozoic times and ii) more habitat and food resources when 11
organic matter accumulates at the top of the soil profile. 12
13
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Keywords: Collembola, Altitude, Acidity, Humus form, Vegetation 15
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1. Introduction 18
19
Biocenoses of Collembola (Hexapoda) have been studied at scales varying from that of a regional 20
landscape (Gisin, 1943; Haybach, 1959; Cassagnau, 1961; Nosek, 1967; Ponge, 1980; Hågvar, 1982; 21
Pozo, 1986; Deharveng and Bedos, 1993; Ponge, 1993; Lauga-Reyrel and Lauga, 1995) to that of the 22
plant cushion or of the boulder (Bonnet et al., 1970; Booth and Usher, 1984, 1985). Scientists 23
endeavouring to find out factors which could explain the observed variations in species composition 24
are faced with a puzzling problem. If the scale is too large, discrepancies in the occupation of space 25
by species may arise quite independent of ecological factors, due to time-related processes such as 26
fragmentation of habitats or extinction-colonization processes (Christiansen and Bullion, 1978). If the 27
scale is too small, then interactions between species may overwhelm the selective action of ecological 28
factors (Usher, 1985; Usher and Booth, 1986). Thus the choice of an appropriate scale is a 29
prerequisite to any community study. The second more important aspect is the heterogeneity of the 30

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sampled site or region, which could be due both to abiotic factors, such as lithology, climate and 1
aspect (Ponge, 1980; Ponge and Delhaye, 1995; Theurillat et al., 1998), and to biotic factors such as 2
vegetation dynamics (Bernier, 1996; Miles 1979). 3
4
In a previous study on the Macot forest (Savoy, France) the heterogeneity of humus forms has been 5
shown to reflect that of the forest patchwork, varying according to altitude, phases of the forest cycle 6
and competition between the spruce forest and the bilberry heath (Bernier and Ponge, 1994; Bernier, 7
1996). Since ecological factors affecting humus forms, and humus forms themselves, were known to 8
affect collembolan communities (Ponge, 1980, 1983; Hågvar and Abrahamsen, 1984; Ponge 1993), it 9
has been decided to sample these animals at the scale of the eco-unit (Oldeman, 1990). As defined 10
by Oldeman (1990), eco-units are unit components of the forest patchwork. They are made of trees 11
and other organisms which have undergone a common history following a disturbance event, the so-12
called zero-event, that created locally the eco-unit. In mountain coniferous forests of the French 13
northern Alps, most frequent disturbances are storms and cutting operations. At the montane level in 14
the Macot forest the forest renewal resulted from an improvement in humus form which occurred 15
before trees actually died and the canopy was opened. Improved humus allowed the rapid 16
establishment of a new cohort of Norway spruce (Picea abies) regardless of any long-lasting 17
successional processes (Bernier and Ponge, 1994; Bernier, 1996; Ponge et al., 1998). At the 18
subalpine level, the regeneration niche of spruce and other conifers was mostly decaying wood but the 19
subalpine heath competed strongly with the forest, thus decreasing the size of forest eco-units 20
(Bernier 1996, 1997). 21
22
The present study tested whether the scale of the eco-unit accounted for major variations in species 23
composition observed over an altitudinal gradient ranging from 950 to 2150m a.s.l., and explained 24
these variations. For that purpose collembolan communities and humus profiles were sampled near 25
each other at the approximate centre of the different kinds of eco-units which formed the forest 26
patchwork, care being taken to exclude micro-scale factors such as dead wood, stones, moss 27
cushions and proximity of tree trunk bases, all of which are known to influence collembolan 28
communities (Cassagnau, 1961; Bonnet et al., 1970; Ponge, 1980; Wolters, 1983; Arpin et al., 1984; 29
Kopeszki, 1992a, 1992b, 1993; Setälä and Marshall, 1994; Kopeszki, 1997), 30

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1
2
2. Methods 3
4
2.1 Study sites and sampling design 5
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Five sites were selected along an altitudinal range from 950 to 2150 m, i.e. the altitudinal range of the 7
communal forest of Macot La Plagne (Savoy, France), which nearly covers a north facing slope along 8
the river Tarentaise. Each site was characterized by a variety of vegetational types, according to 9
phases of sylvigenesis (Oldeman, 1990) and competition between heath and forest (Bernier, 1997). 10
Different kinds of forest and heath eco-units with similar aspect and soil type were identified by 11
vegetational features on as small scale as possible (at most 0.5 ha). Sites have been described in 12
detail in previously published papers, together with results concerning humus profiles and earthworm 13
communities (Bernier and Ponge, 1994; Bernier, 1996, 1997). A total of 37 eco-units were used for 14
sampling humus profiles and soil fauna. Soil microarthropods were sampled by forcing a 15cm 15
diameter x 10cm height aluminum cylinder into the topsoil, flush to the ground surface, at the 16
approximate centre of each eco-unit. The place chosen for sampling humus and fauna was devoid of 17
fallen wood and moss cushions and further than 0.5m from a tree base or stump. Sites were sampled 18
in a week during June 1991, after snowmelt and before summer drought. Soil samples were 19
immediately placed into sealed plastic bags then transported to the laboratory for extracting 20
microarthropods. 21
22
2.2 Collection and identification of fauna 23
24
Animals were extracted within a week by the dry funnel method (Macfadyen, 1957) into 96% ethyl 25
alcohol. Collembola were sorted, mounted in chloral-lactophenol (lactic acid, chloral hydrate, phenol 26
25:50:25 v/w/v), and they were identified at the species level under a light microscope with phase 27
contrast at x400 magnification. A list of the 65 identified species is given in Table 1. Morphological 28
characters fitted well with published descriptions of these species except that i) the fourth mucronated 29
hair did not exist on the third pair of legs of Hypogastrura cf. affinis, ii) the sensilla s was not flame-like 30

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on the third thoracic segment of Mesaphorura cf. italica, iii) the a2 hair was lacking on the fifth 1
abdominal tergite of Xenylla cf. brevicauda, iv) there was only a single hair on the mucrodens of 2
Xenylla cf. xavieri. 3
4
2.3 Multivariate statistics 5
6
Data (densities of animals per unit surface) were treated by correspondence analysis, a multivariate 7
method using the chi-square distance (Benzécri, 1969, 1973; Hill, 1974; Greenacre, 1984). This 8
method has been already used successfully to analyse changes in the species composition of 9
collembolan communities, with or without a priori hypotheses concerning the possible influence of 10
external factors (Bonnet et al., 1970, 1976, 1979; Ponge, 1980; Ponge and Prat, 1982; Poursin and 11
Ponge, 1982; Ponge, 1983; Gers and Izarra, 1983; Arpin et al., 1984; Poursin and Ponge, 1984; Pozo, 12
1986; Ponge, 1993; Lauga-Reyrel and Lauga, 1995; Loranger et al., 1998; Salmon and Ponge, 1999). 13
Variations in species composition are analysed without resorting to the suspected influence of external 14
factors, but rather factors are extracted from multiple measurements in order to explain the trends 15
depicted by main inertia axes (eigen vectors) of a between-species chi-square distance matrix. 16
Variables (species) and samples are simultaneously projected on a space formed by the first factorial 17
axes, i.e. those explaining better the global variation. Variables and samples are indicated by points, 18
the bulk sample being thus represented by a cloud of points. Each species is projected in the vicinity 19
of samples to the species composition of which it contributes the best. The proximity of species and 20
samples and the contribution of the different species to the factorial axes allow detection of gradients 21
or discontinuities in the species composition, following one or several of the first factorial axes, which 22
are linearly independent. Each factorial axis represents a dimension of the sub-space into which the 23
cloud of data has been projected. The introduction of additional (passive) variables helps interpretation 24
of factorial axes in ecological terms when these variables prove to be well-correlated with factorial 25
axes. Additional variables are projected as if they had been used in the analysis but they do not 26
influence to any extent the formation of the factorial axes. Their projection is a point in the vicinity of 27
the samples (and species) which it characterizes best. For example if pH has been measured, this 28
parameter will be represented by a unique point falling near the samples exhibiting the highest pH. 29
30

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In order to give the same weight to all parameters, all variables (discrete as well as continuous) were 1
transformed into X = (x-m)/s + 20, where x is the original value, m is the mean of a given variable, and 2
s is its standard deviation. The addition to each standardized variable of a constant factor of 20 allows 3
all values to be positive, correspondence analysis dealing only with positive numbers (normally 4
counts). Following this transformation, factorial coordinates of variables can be interpreted directly in 5
term of their contribution to the factorial axes: the farther a variable is projected from the origin of the 6
axes (barycentre) along a given direction (along a factorial axis) the more it contributes to this axis. 7
Variables were doubled in order to allow for the dual nature of most parameters (the absence of a 8
given species is as important as its presence, low pH values are as important as high pH values). To 9
each variable X was thus associated a twin X' varying in an opposite sense (X' = 40 – X). Such a 10
doubling proved useful when dealing with ecological gradients (Ponge et al., 1997) or when it was 11
judged interesting to classify samples according to their bulk abundance, besides changes in species 12
composition (Loranger et al., 1998). Originally, correspondence analysis was performed to deal only 13
with count numbers. Later it has been extended to other types of variables (Greenacre, 1984). The 14
transformations used here give to correspondence analysis most properties of the well-known 15
principal components analysis (Hotelling, 1933), while keeping the advantage of the simultaneous 16
projection of rows (variables) and columns (samples) onto the same factorial axes and the robustness 17
due to the principle of distributional equivalence. 18
19
2.4 Soil chemical analyses 20
21
Once the extraction of microarthropods was completed, dried samples from the top first 10cm were 22
used for chemical analyses. Samples were sieved to 2mm, homogenized, then chemical analyses 23
were performed on several sub-samples. Water pH and potassium chloride pH were measured on a 24
5g sub-sample diluted with deionized water (soil:water 1:1 w/w). A 50g sub-sample was crushed with 25
pestle and mortar, then sieved at 200 µm for further analyses. Cation exchange capacity was 26
measured on a 10g sub-sample by percolating the soil with 1N calcium chloride until saturation of 27
exchange sites then displacing calcium with 1N potassium nitrate. Determination of calcium and 28
chloride content was performed in the filtrate by flame nitrous oxide-acetylene atomic absorption 29
photometry, and complexometry with a Technicon® autoanalyser, respectively. Exchangeable cations 30

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(Ca, Mg, K, Na) were determined on a 10g sub-sample after displacement of sorbed cations with 1
ammonium nitrate. Potassium and sodium were determined on the filtrate by flame emission 2
photometry, calcium and magnesium by flame atomic absorption photometry. Total carbon and 3
nitrogen were determined with a CHN Carlo Erba® analyser on a 5mg sub-sample. Total bases (Ca, 4
Mg, K, Na), iron and manganese were determined on 1g sub-sample after boiling with concentrated 5
hydrochloric acid. Potassium and sodium were determined by flame air-acetylene emission 6
photometry, magnesium, iron and manganese by flame air-acetylene atomic absorption photometry, 7
and calcium by flame nitrous oxide-acetylene atomic absorption photometry. Total phosphorus was 8
determined on a 1g sub-sample with a Techicon® autoanalyser after treatment with concentrated 9
hydrogen peroxide followed by boiling with perchloric acid. 10
11
2.5 Other data 12
13
Other data were used as additional variables in the multivariate analysis of Collembola communities. 14
Particle size distribution was calculated on the same samples as for chemical analyses, i.e. on the top 15
first 10cm of soil (litter comprised) after extraction of fauna and sieving at 2mm the dried material. 16
Humus form was determined during sampling of humus profiles, part of which have been thoroughly 17
described in Bernier and Ponge (1994) and Bernier (1996). The study of humus profiles helped to 18
notice the presence of main moss, herb, shrub and tree species in the litter. Species richness and total 19
abundance of Collembola were added, too. 20
21
Some other analyses were done using the age of trees forming the eco-units into which Collembola 22
were sampled. The age was calculated either by recording successive whorls on the stem of young fir 23
or spruce trees or by counting annual increments on a probe taken as near as possible from the 24
ground (correction was made by adding the age of saplings of similar height growing in the same site). 25
26
27
3. Results 28
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3.1 Influence of altitude and soil chemistry 30

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1
The first factorial axis extracted 14.4% of the total variance. Despite this low value, this axis was the 2
only one clearly interpretable on the basis of the data collected in the present study. Thus further order 3
axes were considered as background noise and they were ignored in the following. Projection of 4
samples and main and additional variables was thus only done on axis 1. At first sight the significance 5
of this axis can be found in the altitudinal gradient. Figure 1 and Table 1 show that axis 1 was strongly 6
correlated with altitude, the correlation being even better when based on logarithms of factorial 7
coordinates. If we consider axis 1 of correspondence analysis as a compound index of species 8
composition, this means that the species composition of collembolan communities varied according 9
with altitude, but that the observed variation decreased when higher elevation was reached. For 10
instance more variation in species composition occurred from 1000 to 1500m than from 1500 to 11
2000m. 12
13
Figure 2 shows the projection of collembolan species and some additional variables such as humus 14
form, altitude, and vegetation. The species composition at 2150m (the upper limit of the forest) did not 15
differ greatly from that at 1850m (the subalpine forest), even though the difference in elevation was 16
300m (Fig. 1). Most changes occurred at the montane level from 950m (the lower montane level) to 17
1550m (the upper montane level). Species typical of upper slope forest were Archaphorura absoloni 18
(AAB), Isotoma nivalis (INI), Mesaphorura tenuisensillata (MTE), Ceratophysella denticulata (CDE), 19
Folsomia sensibilis (FSE), Pogonognathellus flavescens (PFL), Hypogastrura cf. affinis (HAF), Friesea 20
claviseta (FCL), Lepidocyrtus lignorum (LLI). Species typical of lower slope forest were Protaphorura 21
armata (PAR), Mesaphorura hylophila (MHY), Pseudosinella edax (PED), Willemia intermedia (WIN), 22
Lepidocyrtus lanuginosus (LLA), Lipothrix lubbocki (LLU), Pseudosinella alba (PAL), Folsomia 23
penicula (FPE), Parisotoma notabilis (PNO), Arrhopalites gisini (AGI), Allacma sp. (ASP), Bourletiella 24
sp. (BSP), Xenylla cf. brevicauda (XBR), Sminthurinus aureus (SAU), Tomocerus minor (TMI). 25
26
Humus forms varied from mull (macrofaunal activity dominant) to moder (mesofaunal activity 27
dominant) then to mor (poor faunal activity) according to axis 1, but it should be highlighted that the 28
position of moder and mor was quite similar. These two humus forms were thus inhabited by the same 29
community although mor was characterized by scarcity of animal faeces. The projection of plant 30

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species along axis 1 reflected their preferential position along the altitudinal gradient, with silver fir 1
(Abies alba) and hazel (Corylus avellana) as typical lower slope species, and Alpen rose 2
(Rhododendron ferrugineum) as typical upper slope species. Most other common plant species were 3
centered around the origin, thus indicating their wide distribution over the studied altitudinal gradient. 4
5
Particle size distribution did not seem to vary along the studied altitudinal gradient, lower and higher 6
values of all categories being centered around the origin (Fig. 3). On the contrary, variables describing 7
chemical properties of soils were stretched along axis 1, indicating strong chemical variations with 8
altitude (Fig. 4). The topsoil of the upper slope forest was characterized by higher acidity, expressed 9
by i) lower pH (water as well as potassium chloride pH), ii) lower content in total bases (chiefly calcium 10
and magnesium), iii) higher exchangeable acidity (D pH), iv) accumulation of organic matter (more C 11
and N), and by a lower iron content. The C/N ratio did not vary at all according to axis 1, and some 12
other chemical features such as cation exchangeable capacity (and exchangeable bases), total 13
phosphorus, potassium, sodium and manganese were roughly centered around the origin, thus 14
indicating that they did not contribute greatly to axis 1. 15
16
Local species richness and abundance of Collembola increased along Axis 1 (Fig. 4). Both were 17
significantly correlated between themselves and with Axis 1 (Table 2). Local species richness was 18
negatively correlated with water pH, but neither abundance nor local species richness were correlated 19
significantly with elevation. 20
21
3.2 Influence of vegetation 22
23
We may wonder whether vegetation influenced directly or indirectly collembolan communities 24
independently of altitude. The fact that vegetation factors were not represented by lower-order axes of 25
correspondence analysis might indicate either that vegetation did not influence collembolan 26
communities or that this influence was superimposed on that of altitude and soil chemistry. Two 27
arguments favour the second hypothesis, i) the existence of cycling processes embracing both soil 28
properties and development of the forest ecosystem at the montane level (Bernier and Ponge, 1994; 29
Bernier 1996), ii) demonstration that the influence of altitude was superimposed on that of soil acidity, 30

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the latter being known to vary according to the forest cycle (Ponge and Bernier, 1995; Bernier, 1996). 1
In order to verify this hypothesis at the montane level (lower and upper), coordinates of the eco-units 2
along axis 1 and pH (water) values were simultaneously crossed with the mean age of the trees (Fig. 3
5). At 950m, where pH values vary from 5 to 7 it appears that these variations closely follow that of 4
axis 1 coordinates in a chronosequence. At 1550m, where pH values vary from 3.5 to 5, coordinates 5
along axis 1 did not vary to the same extent, especially during the time of most active growth of trees 6
(55 to 60 years), but they follow the same trend as pH values. This means that at the montane level 7
the species composition of collembolan communities (depicted by axis 1) varies during the forest cycle 8
as does soil acidity. 9
10
11
4. Discussion 12
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4.1. Altitude, vegetation and soils 14
15
These results suggest that the effects of altitude, vegetation and soils on collembolan communities are 16
superimposed, and are probably reinforced by a number of positive feed-back loops involving climate, 17
nutrient availability, plant secondary metabolism and soil foodwebs (Perry et al., 1989; Ponge et al., 18
1997, 1998; Northup et al., 1998; Ponge, 1999; Ponge et al., 1999). Since it has been demonstrated 19
that soil acidity varies cyclically under the development of vegetation (Ponge and Bernier, 1995; 20
Bernier, 1996), it follows that the effect of vegetation on soil collembolan communities is probably 21
through acidification and deacidification of the soil beneath. Such reversible effects in the course of 22
vegetation dynamics have been already observed on earthworm communities, and on the humus 23
forms they build (Miles, 1985; Bernier and Ponge, 1994; Ponge and Delhaye, 1995). Collembolan 24
communities are sensitive to soil acidity (Ponge, 1980, 1983; Hågvar and Abrahamsen, 1984; Pozo, 25
1986; Ponge, 1993; Van Straalen and Verhoef, 1997), although pH itself is not responsible for the 26
observed changes in species composition (Hågvar, 1990; Salmon and Ponge, 1999). The joint effect 27
of vegetation and altitude upon collembolan communities is best measured by a combination of pH, 28
exchange acidity, redox potential, nutrient availability, free forms of aluminum and other toxic metals, 29
accumulation of poorly humified organic matter, remanence of plant secondary metabolites, toxicity of 30

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the soil atmosphere (Lafond, 1950; Wilde, 1954; Ovington, 1954; Verdier, 1975; Ritchie and Posner, 1
1982; James and Riha, 1984; Ulrich, 1986; Muller et al., 1987; Sexstone and Mains, 1990; Kuiters, 2
1990; White, 1994; Northup et al., 1995). When altitude increases, i) erosion impoverishes upper 3
slope soil to the benefit of lower slope soils, ii) mineralization is slowed by low temperature, and thus 4
organic matter tends to accumulate, iii) plants produce more secondary metabolites, in particular 5
phenolic compounds, which inhibit proteins and make nitrogen, sulphur and phosphorus unavailable, 6
iii) humification is slowed, and thus small organic molecules may act as ligands which leach metals 7
and bases down the soil profile (podzolization). When trees grow actively, i.e. when forest eco-units 8
are in the aggradation phase (Oldeman, 1990), the uptake of nutrients by roots exceeds their release 9
through decomposition of litter and weathering of mineral particles, thus temporarily impoverishing the 10
soil locally. Thus from the point of view of soil acidification altitude and vegetation dynamics may have 11
similar side effects on soil collembolan communities. 12
13
4.2 Acidification effects 14
15
Now, let us examine whether the present data explain the acidification hypothesis. If we compare the 16
distribution of species along axis 1 (Figs. 2, 3, 4) with the classification of acidophilic and acido-17
intolerant temperate lowland species by Ponge (1980, 1983, 1993), we can notice that three acido-18
intolerant species, namely Mesaphorura hylophila (MHY), Pseudosinella alba (PAL) and Folsomia 19
penicula (FPE), appear on the negative side of axis 1, and none on the positive (acid) side. On the 20
contrary acidophilic species such as Mesaphorura macrochaeta (MMA), Micranurida pygmaea (MPY), 21
Protaphorura lata (PLA, = P. subuliginata), Willemia anophthalma (WAN), Friesea claviseta (FCL), 22
Friesea mirabilis (FMI), appear on the positive (acid) side of axis 1. An exception is the position of 23
Willemia intermedia (WIN), an acidophilic species according to Ponge (1993), which is here on the 24
negative side of axis 1. 25
26
Some species found to live here at higher elevation have been frequently recorded in northern 27
coniferous forests (Bödvarsson, 1973; Bååth et al., 1980; Hågvar, 1982; Hågvar and Abrahamsen, 28
1984; Huhta et al., 1986; Fjellberg, 1998), such as Archaphorura absoloni (AAB), Mesaphorura 29
tenuisensillata (MTE), Hymenaphorura sibirica (HSI, = H. polonica), Willemia denisi (WDE = W. 30

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aspinata), Xenylla boerneri (XBO), Anurida granulata (AGR), Pogonognathellus flavescens (PFL), 1
Lepidocyrtus lignorum (LLI), Pseudanurophurus binoculatus (PBI), Anurophorus laricis (ALA). All 2
these species, except Anurophorus laricis (ALA), have been commonly found by Ponge (2000a) under 3
beech in a range of acidic soils of the Belgian Ardennes. Thus most of them do not seem to be 4
restricted to northern coniferous forests, but rather to strongly acidic conditions. 5
6
4.2 Adaptations 7
8
An alternative hypothesis to the influence of soil acidification is the adaptation of collembolan 9
communities to climate conditions prevailing at higher elevation, i.e. more sun and snow and colder 10
mean temperatures. Special adaptations to these conditions have been registered in alpine as well as 11
circumpolar collembolan populations, such as dark pigmentation (Rapoport 1969), cold hardness 12
strategies and cryoprotectants (Zettel et al., 1989; Block, 1983; Vannier, 1994), efficiency of low 13
temperature metabolism (Block and Tilbrook, 1975; Burn, 1984), behavioural response to changes in 14
barometric pressure (Zettel, 1984), long distance migration strategies (Hågvar, 1995). Some typical 15
alpine species are present at high elevation only in our samples, such as Deutonura conjuncta (DCO), 16
Hypogastrura meridionalis (HME), Xenylla obscura (XOB), Folsomia sensibilis (FSE), Folsomia 17
inoculata (FIN), Isotoma nivalis (INI), Vertagopus montanus (VMO). The presence of Hypogastrura 18
meridionalis (HME) and Xenylla obscura (XOB) is remarkable, since these two species were only 19
known from the Pyrenees and the Himalayas, respectively. For all these species we cannot find any 20
proof of their strong acidophily in the literature, thus they rather seem to be adapted to cold climate 21
conditions which are to be found at the upper subalpine level. 22
23
Species which are well-known from lowland sites for their acidophily are Willemia anophthalma 24
(WAN), Friesea mirabilis (FMI), Micranurida pygmaea (MPY) and Protaphorura lata (PLA). They could 25
be thought not to live at the same sites than the mountain species Deutonura conjuncta (DCO), 26
Hypogastrura meridionalis (HME), Xenylla obscura (XOB), Folsomia sensibilis (FSE), Folsomia 27
inoculata (FIN), Isotoma nivalis (INI) and Vertagopus montanus (VMO). In fact densities of these two 28
groups were highly correlated, as verified by their Spearman rank correlation coefficient (Sokal and 29
Rohlf, 1995). Thus it can be predicted that species tolerant of acidity will be tolerant of altitude. Soil 30

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acidification, which is known to have been favoured by atmospheric pollution during the last three 1
decades in Scandinavia and Central Europe (Wittig, 1986; Falkengren-Grerup, 1987), may cause less 2
dramatic changes in mountain biocenoses of Collembola than in lowland biocenoses, and perhaps 3
even mountain biocenoses may be favoured by atmospheric pollution. This prediction is in partial 4
agreement with results of the survey done from 1968 to 1990 by Rusek (1993) in the Tatra mountains. 5
The increase in numbers of the formerly rare boreo-alpine Pseudanurophorus binoculatus recorded by 6
this author can be attributed to acid deposition, but the disappearance of Folsomia alpina, a typical 7
inhabitant of alpine pioneer ecosystems on dolomite and limestone (Nosek, 1967), does not follow the 8
above prediction. It should be noted that our results hold only for siliceous bedrocks and that at the 9
alpine level a decrease in species richness and total abundance of Collembola has been observed 10
(Lauga-Reyrel and Lauga, 1995). The subalpine level seems to be most favourable to Collembola, but 11
not the alpine level and even less the nival level. 12
13
We observed an increase in local species richness and abundance according to axis 1 of 14
correspondence analysis (Fig. 4), which could be better explained by an increase in soil acidity than 15
by an increase in elevation (Table 2). Since soil acidification implies a decrease in litter decomposition 16
rates (Ulrich, 1986) and is reinforced by slow humification of organic matter (Stevenson, 1994) we 17
may expect a correlation between acidification and accumulation of soil organic matter, at least on 18
siliceous bedrocks. This has been demonstrated in previous studies on the same sites (Bernier and 19
Ponge, 1994; Bernier, 1996) and can be explained at first sight by an increase in available habitat and 20
food. Since most collembolan species are known to ingest humus (Gilmore and Raffensperger, 1970; 21
Wolters, 1987; Saur and Ponge, 1988; Ponge, 1991) it can be postulated that the more humus will 22
accumulate the more food and habitat will be available to Collembola. A similar increase in local 23
species richness and abundance of Collembola has been observed from the montane to the subalpine 24
level by Deharveng and Bedos, (1993), which could be seemingly explained by the observed increase 25
in soil acidity. 26
27
These results indicate a physiological adaptation of the whole group to acid soils, that corroborates 28
with the primitive nature of Collembola, which appeared as soon as the Silurian age, and then radiated 29
during the Devonian age (Rolfe, 1985; Dunger, 1987). At this time topsoils were probably strongly 30

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acid, due to chemical properties of primitive plants (lichens, bryophytes, pteridophytes, later on 1
gymnosperms), scarcity of base-rich susbtrates, and to acid rains (Elmi and Babin, 1996; Lethiers, 2
1998). Thus it may be thought that acidophilic species appeared sooner in the evolution than acid-3
intolerant species, which lost some important physiological adaptations, this idea being reinforced by 4
the examination of phylogenetic trees (Ponge 2000b). Contrary to more recently evolved invertebrate 5
groups such as terrestrial oligochaetes which increase in species richness in richer soils 6
(Abrahamsen, 1972a, 1972b), soil acidity does not decrease the species richness of Collembola, at 7
least in mountain and boreal sites where acid environments played the role of refuges for primitive 8
adaptations. 9
10
11
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Figure captions 1
2
Fig. 1. Correlation between the first factorial axis of correspondence analysis and altitude. Bars are 3
standard errors of the mean factoral coordinates at each altitude. ** = significant at the 0.01 4
threshold (d.f. = 35). 5
6
Fig. 2. Correspondence analysis. Projection of collembolan species and some additional variables on 7
the first factorial axis. Collembolan species (higher values only) are indicated by a three-letter 8
code in bold type. Origin of the axis (barycentre) is indicated by an arrow. For the sake of 9
clearity codes or names of variables have been displaced horizontally, their projection on axis 10
1 being indicated only by their vertical position. 11
12
Fig. 3. Correspondence analysis. Projection of collembolan species and particle size classes on the 13
first factorial axis. Collembolan species (higher values only) are indicated by a three-letter 14
code in bold type. Higher values of particle size percentages are indicated in bold type, while 15
lower values are in italic. Otherwise as for Fig. 2. 16
17
Fig. 4. Correspondence analysis. Projection of collembolan species, soil chemical variables and 18
population estimates on the first factorial axis. Collembolan species (higher values only) are 19
indicated by a three-letter code in bold type. Higher values of soil chemical variables and 20
population estimates are indicated in bold type, while lower values are in italic. S = sum of 21
exchangeable bases. D pH is the difference between water pH and KCl pH. Soil chemical 22
variables and population estimates significantly correlated with axis 1 (Spearman rank 23
correlation) were placed into boxes. Otherwise as for Fig. 2. 24
25
Fig. 5. Changes in the course of time of pH and axis 1 coordinates at the montane level. 26
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codes 950m 1550m 1750m 1850m 2150m
AAB Archaphorura absoloni 01330 1698 1787 2252
AGI Arrhopalites gisini 7 0 0 0 0
AGR Anurida granulata 096 81 24 136
ALA Anurophorus laricis 0334 9281 2417 1120
ASE Arrhopalites sericus 35 34 32 057
ASP Allacma sp. 7 0 0 0 0
BSP Bourletiella sp. 14 0 0 259 0
CDE Ceratophysella denticulata 0124 03929 215
CTH Cryptopygus thermophilus 0 0 0 0 11
DCO Deutonura conjuncta 028 8 0 23
DSY Deutonura sylvatica 0 0 0 40 23
EMA Entomobrya marginata 0 0 267 251 464
ENI Entomobrya nivalis 0136 477 170 0
FCL Friesea claviseta 0447 226 1205 0
FIN Folsomia inoculata 0 0 1140 0 0
FLA Folsomia lawrencei 2695 2965 89 1180 0
FMA Folsomia manolachei 0 0 0 1948 68
FMI Friesea mirabilis 0323 412 0 0
FPE Folsomia penicula 4449 4544 4737 0 0
FQU Folsomia quadrioculata 0 6 0 0 0
FSE Folsomia sensibilis 0175 105 73 1562
HAF Hypogastrura affinis 0 6 40 129 68
HME Hypogastrura meridionalis 0 0 243 13921 0
HSI Hymenaphorura sibirica 0 0 186 0 0
IMI Isotomiella minor 1118 2960 1997 1536 12133
INI Isotoma nivalis 01086 1277 1835 6146
ITI Isotoma tigrina 0 0 8 0 0
LLA Lepidocyrtus lanuginosus 2165 741 6629 1334 0
LLI Lepidocyrtus lignorum 0 0 404 356 249
LLU Lipothrix lubbock i 120 624 8 0
LVI Lepidocyrtus violaceus 0175 849 978 0
MCR Mesaphorura critica 768 049 0
MHY Mesaphorura hylophila 835 475 000
MIT Mesaphorura italica 149 232 89 0 0
MMA Mesaphorura macrochaeta 311 3712 574 178 170
MMI Megalothorax minimus 99 51 0 0 238
MPY Micranurida pygmaea 57 628 816 49 192
MSY Mesaphorura sylvatica 011 089 407
MTE Mesaphorura tenuisensillata 0351 558 113 1879
NMU Neanura muscorum 085 113 137 11
OBI Orchesella bifasciata 0141 113 0 0
OCR Oncopodura crassicornis 023 000
OEM Odontella empodialis 0 0 194 493 68
PAL Pseudosinella alba 672 368 000
PAR Protaphorura armata 3027 0 0 0 0
PAS Pseudachorutella asigillata 028 49 719 192
PBI Pseudanurophorus binoculatus 0198 40 8113
PCA Paratullbergia callipygos 64 0 8 0 215
PED Pseudosinella edax 424 0 0 0 0
PFL Pogonognathellus flavescens 0113 388 8 0
PLA Protaphorura lata 0 0 1568 1043 1098
PNO Parisotoma notabilis 11282 2563 574 275 2456
PPA Pseudachorutes parvulus 594 102 32 65 11
PSE Pseudisotoma sensibilis 0340 49 0 0
SAU Sminthurinus aureus 21 0 0 0 0
SIN Schoettella inermis 0 0 0 8 0
TMI Tomocerus minor 7 0 8 0 0
VCI Vertagopus cinereus 0 0 16 65 0
VMO Vertagopus montanus 028 16 16 0
WAN Willemia anophthalma 177 3027 234 73 939
WDE Willemia denisi 42 1358 024 668
WIN Willemia intermedia 28 0 0 0 0
XBO Xenylla boerneri 0 0 453 011
XBR Xenylla brevicauda 3516 74 14373 0 0
XOB Xenylla obscura 01777 8711 26359
XXA Xenylla xavieri 0 6 32 73 0
Total 31923 31276 50517 37575 59554
Mean species richness 13.3 17.1 17.9 14.4 19.6
Bulk species richness 27 43 46 40 32
Number of samples 8 10 577
Table 1. Mean abundance of Collembolan species (ind m-2) in the five investigated sites
1

PONGE
28
Abundance Species richness Water pH Elevation
Abundance
Species richness 0.65
Water pH -0.22 -0.48
Elevation 0.19 0.31 -0.60
Axis 1 0.51 0.72 -0.70 0.73
Table 2. Spearman rank correlation coefficients between some ecological
factors and axis 1 of correspondence analysis (n = 37). Significant values
at P = 0.05 are in bold type, others in italic
1
2

PONGE
29
y = 0,0493Ln(x) - 0,362
R2= 0,5184 **
-0.03
-0.02
-0.01
0
0.01
0.02
0.03
800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200
Axis 1 of CA
Elevation (m)
1
Fig. 1 2
3

PONGE
30
1
Fig. 2 2
3
Axis 1
950m
1550m
1750m
1850m
2150m
AAB
AGI
AGR
ALA
ASE
ASP BSP
CDE
CTH
DCO
DSY
EMA
ENI
FCL
FIN
FLA
FMA
FMI
FPE
FQU
FSE
HAF
HME
HSI
IMI
INI
ITI
LLA
LLI
LLU
LVI
MCR
MHY
MIT
MMA
MMI
MPY MSY
MTE
NMU
OBI
OCR OEM
PAL
PAR
PAS
PBI
PCA
PED
PFL
PFL
PNO
PPA
PSE
SAU
SIN
TMI
VCI
VMO
WAN
WDE
WIN
XBO
XBR
XOB
XXA
MULL
MODER
MOR
Hazel
Honeysuckle
Cow-wheat
Hylocomium
Rhytidiadelphus
Silver fir
Norway spruce
European larch
Whortleberry
Cowberry
Wood-sorrel
Hair-grass Wood-rush
Alpen rose
Cembro pine
Homogyne
+

PONGE
31
1
Fig. 3 2
3
Axis 1
AAB
AGI
AGR
ALA
ASE
ASP BSP
CDE
CTH
DCO
DSY
EMA
ENI
FCL
FIN
FLA
FMA
FMI
FPE
FQU
FSE
HAF
HME
HSI
IMI
INI
ITI
LLA
LLI
LLU
LVI
MCR
MHY
MIT
MMA
MMI
MPY MSY
MTE
NMU
OBI
OCR OEM
PAL
PAR
PAS
PBI
PCA
PED
PFL
PFL
PNO
PPA
PSE
SAU
SIN
TMI
VCI
VMO
WAN
WDE
WIN
XBO
XBR
XOB
XXA
Clay
Fine silt
Coarse silt
Fine sand
Coarse sand
Clay
Fine silt
Coarse silt
Fine sand
Coarse sand
+

PONGE
32
1
Fig. 4 2
3
Axis 1
AAB
AGI
AGR
ALA
ASE
ASP BSP
CDE
CTH
DCO
DSY
EMA
ENI
FCL
FIN
FLA
FMA
FMI
FPE
FQU
FSE
HAF
HME
HSI
IMI
INI
ITI
LLA
LLI
LLU
LVI
MCR
MHY
MIT
MMA
MMI
MPY MSY
MTE
NMU
OBI
OCROEM
PAL
PAR
PAS
PBI
PCA
PED
PFL
PFL
PNO
PPA
PSE
SAU
SIN
TMI
VCI
VMO
WAN
WDE
WIN
XBO
XBR
XOB
XXA
Total K
Total Na
Total Mg
Total Ca
Total Mn
Total Fe
Total P
pH H2O
pH KCl
D pH
C
N
C/N
Exch Ca
Exch Mg
Exch K
Exch Na
S
CEC
S/CEC
Total K
Total Na
Total Mg
Total Ca
Total Mn
Total Fe
Total P
pH H20
pH KCl
D pH
C
N
C/N
Exch Ca
Exch Mg
Exch K
Exch Na
S
CEC
S/CEC
+
Species richness
Species richness
Abundance
Abundance

PONGE
33
4.5
5
5.5
6
6.5
7
7.5-0.031
-0.029
-0.027
-0.025
-0.023
-0.021
-0.019
-0.017
-0.015
0
20
30
40
70
105
160
pH
Axis 1
Time (yrs)
950 m
Axis 1
pH
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
5.1-0.015
-0.01
-0.005
0
0.005
0.01
0.015
0.02
0.025
10
30 before
canopy
closure
30 after
canopy
closure
55
60
160
190
215
pH
Axis 1
Time (yrs)
1550 m
Axis 1
pH
1
Fig. 5 2