Pedodiversity analysis at large scales: an example of three fluvial terraces of the Henares River (central Spain)

Geomorphology 62 (2004) 123 – 138 www.elsevier.com/locate/geomorph

Pedodiversity analysis at large scales: an example of three fluvial terraces of the Henares River (central Spain) A. Saldan˜a a,*, J.J. Iba´n˜ez b a

Departamento Interuniversitario de Ecologı´a, Universidad de Alcala´, Ctra. Madrid-Barcelona Km 33,6, 28871 Alcala´ de Henares, Madrid, Spain b Centro de Ciencias Medioambientales (CSIC), Serrano 115 bis, 28006 Madrid, Spain Received 18 July 2002; received in revised form 18 July 2003; accepted 16 February 2004 Available online 26 April 2004

Abstract This paper presents the results of the application of richness and diversity indices and the fitting of four probabilistic distribution models to a soil chronosequence developed on the fluvial terraces of the Henares River (NE Madrid) as a means to analyse the soil mantle evolution of the valley during the Quaternary. Richness – area relationships were also investigated. Calculations were performed considering individuals (i.e., pedons) and the area covered by a given soil type, at different hierarchy levels of the USDA taxonomic classification (Great Group, Subgroup and family) as well as for diagnostic horizons. Abundance of Great Groups and Subgroups (richness or pedorichness) increases from low (young) to high (old) terraces. It has also been found that the higher the detail in the classification, the higher the pedodiversity values. Diagnostic horizons accounted for the genetic pedodiversity; its value decreases with soil development in the first meter of the solum; this trend changes with increasing observation depth. Therefore, genetic pedorichness and pedodiversity increase in this example with the age of geomorphologic surfaces. However, an appropriate quantification of the genetic diversity should consider the entire solum depth. It is also shown that pedodiversity increases with the area considered. On the other hand, the fit of sampled areas to the probabilistic distribution models used by ecologists is less conclusive. Regarding richness – area relationships, the low and middle terraces fitted a logarithmic function, while the high terrace fitted a power model, in agreement with the ecological literature on biotaxa – area relationships. D 2004 Elsevier B.V. All rights reserved. Keywords: Taxonomic pedodiversity; Genetic diversity; Abundance distribution models; Diversity – area relationships; Chronosequence; Divergent pedogenesis

1. Introduction Landscape is a mixture of patches that vary in size, shape and arrangement and that are under the continuous influence of natural and anthropogenic * Corresponding author. Tel.: +34-91-8856410; fax: +34-918854929. E-mail address: [email protected] (A. Saldan˜a). 0169-555X/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2004.02.007

events (Forman and Godron, 1986; Turner and Gardner, 1991; Forman, 1995); the same applies to soilscapes (Fridland, 1976; Hole and Campbell, 1985; Iba´n˜ez et al., 1987). The necessity to understand the importance of landscape dynamics, heterogeneity and environmental changes is increasingly acknowledged (Forman, 1995). Techniques to quantitatively analyse and interpret factors influencing landscape functioning and diversity are needed, with

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particular emphasis on landscape structure and spatial patterns (Turner and Gardner, 1991). In the light of this approach, diversity has been one of the techniques normally used in ecological analyses (e.g., Simpson, 1949; Margalef, 1958; O’Neill et al., 1988; Patrono and Feoli, 1997). Biological diversity exists and can be analysed at many different levels, from genetic diversity to diversity in communities or ecosystems. Depending on the context, any of these nested hierarchy levels can be of predominant importance. Biological diversity can be quantified in many different ways at all these levels. The question is if the number of higher taxa provides a better measure of biodiversity than the number of species. The debate is still open, with researchers focusing mainly on the species level while other authors place their attention on higher taxa. For example, Williams and Gaston (1994) found a close relationship between the number of families present in different plots and the number of species present. They argue that complete counts of organisms are impractical and that indirect solutions, cheaper and faster are needed. In the last few years, biodiversity has appeared in the popular press, world conventions, international conferences, reports, scientific papers, meetings, etc. showing an exponential growth since early nineties (Harper and Hawksworth, 1995). However, the diversity of soils and landforms has hardly received any attention although their spatial and temporal variation may produce important quantitative and qualitative changes in the landscape. It is only in the last few years that the term diversity has also caught the attention of scientists working on soils and other fields within the earth sciences who are creating a forum and research projects on geodiversity. Measurements of diversity were introduced to pedology few years ago (Iba´n˜ez, 1986, 1995, 1996; Iba´n˜ez et al., 1990, 1994, 1995a; McBratney, 1992, 1995). Recently, several pedologists have started to investigate and discuss the value of the proposed methods and techniques (Yaalon, 1996a, 1998; Wilding and Nordt, 1998; Van Meirvenne, 1998; Florea, 1998; Dazzi and Monteleone, 1998; Finke et al., 1998; Phillips, 1999, 2001). Several approaches to pedodiversity analysis have been proposed: taxonomic pedodiversity (the diversity of soil classes), functional pedo-

diversity (i.e., the soil behaviour under different uses) and diversity of soil properties (McBratney, 1995). The present paper deals mainly with the analysis of taxonomic pedodiversity and proposes a coarse approximation to measuring genetic pedodiversity (i.e., the diversity of the diagnostic horizons) within the context of a soil chronosequence. In ecology, as well as in pedology, indices of negentropy (Shannon diversity index) and evenness summarise in a simple number a large amount of information. The addition of richness measures enables a better understanding of diversity patterns (Iba´n˜ez et al., 1995a). The purpose of this paper is to show the application of richness and diversity indices to the analysis of soil diversity (i.e., pedodiversity) considering the proportion of soil types taken as individuals (i.e., pits and augerings) and as the proportion of the soil maps which is characterised by a given soil type. Those soil maps have been obtained by conventional soil mapping (Saldan˜a, 1997). Two hypotheses are to be tested here: (1) soil evolution will converge toward some characteristic, zonal soils (decreasing pedodiversity); (2) soil cover diversity will increase with time. Different hierarchy levels of soil classification have been analysed. Besides, the number of diagnostic horizons identified in the area accounts for the genetic pedodiversity computation, as they are defined precisely considering morphologic, chemical and physical characteristics resulting from soil evolution. In pedology, diversity indices have generally been applied at broad and medium scales (Iba´n˜ez et al., 1990, 1995a; Florea, 1998; Phillips, 2001; Guo et al., 2003). In response to suggestions from some soil scientists (e.g., Yaalon, 1998; Odeh, 1998), the present analysis refers to small pieces of river terraces where detailed sampling was carried out.

2. Materials and methods 2.1. Study area characteristics The study area is in the provinces of Madrid and Guadalajara, Spain, between 40j30V and 40j50V N and 3j10V and 3j30V W (Fig. 1), 40 km NE of Madrid, on the southern slope of the Ayllo´n moun-

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Fig. 1. Study area.

tain range. It includes the Henares River valley, a tributary of the Jarama River, which in turn flows into the Tajo River. The altitude varies from 600 to 900 m above sea level. The climate is continental Mediterranean, with hot dry summers and cold winters, with two rainy peaks in autumn and spring. The annual mean temperature is 14 jC and the annual mean rainfall is 400 mm (IMN, 1992). The soil moisture regime is xeric and the soil temperature regime is mesic (USDA, 1994). The land is mainly used for rainfed agriculture; in particular wheat, barley and sunflowers occur. Irrigated sunflowers and maize are produced on the floodplain and low terraces. Natural vegetation occurs only in marginal areas with poor agricultural productivity; it is mainly

the degradation stage of the original climax forest formation. 2.2. Geomorphological and pedological setting During the Quaternary, the Henares River formed a large number of terraces, with up to 20 topographic levels along its right bank and a series of incised glacis-terrace levels on its left bank. This asymmetry is the result of climatic fluctuations, tectonic movements and lithologic-structural controls. Consecutive topographic steps are labelled T-36 to T-13 from lower to higher (ITGE, 1990). Most of them constitute wide flat surfaces, characterised by basal gravel layers enplaced during torrential periods over which finer

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material deposited. The lower terraces were formed mainly by river overflow, as indicated by the presence of levees. The structure of the depositional system on the higher terraces is difficult to recognise, because the latter have been affected by erosion and are currently restricted to small hilly remnants. The large terraces, such as T-22 (with a prominent scarp) and T25 for example, are probably of climatic origin according to Bull (1990). Few levels, e.g., the T-23 and T-24 terraces with inconspicuous morphology, constitute complex-response terraces caused by internal adjustments of the depositional system to minor changes in the base level, probably after a climatic fluctuation or a tectonic movement. Dissection processes worked perpendicular to the Henares River. Large alluvial fans are located at the bottom of the talus of the high and middle terraces, indicating torrential conditions that stopped sharply (Saldan˜a, 1997). Neotectonics has been suggested as the origin of some of the topographic levels in the Henares River valley, although its influence is still not clear. There was a period of instability after the deposition of T-25, suggested by relief subsidence and terrace inversion in the Manzanares and Jarama River valleys (Pe´rezGonza´lez, 1971, 1994). The granulometric and petrographic composition of the terrace sediments is very similar throughout, with quartzite, quartz and limestone pebbles within a sandy matrix. Calcareous pebbles are absent from the higher terraces (ITGE, 1990). The ages of the terraces range from Early Pliocene to Late Pleistocene and Holocene (Pe´rez-Gonza´lez and Asensio, 1973; Gallardo et al., 1987; Pe´rez-Gonza´lez and Gallardo, 1987; ITGE, 1990; Jime´nez-Ballesta et al., 1993) as indicated in Table 1. Soil development increases from the lower (and younger) terraces to higher levels. Entisols (Typic Xerorthents and Typic Xerofluvents) occur in the floodplain. Inceptisols are developed on the three lower terraces, with Typic Xerochrepts on the T-30 level and Calcixerollic and Typic Xerochrepts on the next two levels, depending on the presence or absence of calcic horizons. Calcixerollic and Typic Xerochrepts were also found on the T-27 level, but with redder colours. The petrocalcic and argillic horizons, reported to exist at this level, could reflect inclusions of older and more developed soils. Typic and Calcic

Table 1 Altitude and age of the terraces of the Henares River in the study area and soil types developed on them Topographic level

Altitude (m)

T-36 T-35 T-30

605 605 610

– – 5

T-29b

615

10

T-27

620

15

T-26

625

20

T-25b

635

30

T-24

650

45

T-23

655

50

T-22

670

65

T-20

700

95

T-19

710

105

T-17

730

125

T-16

750

145

T-15b

770

165

T-13

793

188

a b

Elevation (m)

Agea

Main soil types

Holocene Holocene Late Pleistocene Late Pleistocene Middle Pleistocene Middle Pleistocene Middle Pleistocene Middle Pleistocene Middle Pleistocene Middle Pleistocene Early Pleistocene Early Pleistocene Early Pleistocene Early Pleistocene Early Pleistocene Early Pleistocene

Entisols Entisols Entisols, Inceptisols Inceptisols Inceptisols, Alfisols Inceptisols, Alfisols Alfisols Alfisols Alfisols Alfisols Alfisols Alfisols Alfisols Alfisols Alfisols Alfisols

According to ITGE, 1990. Sample areas.

Haploxeralfs have been described on the six higher terraces. Petrocalcic Palexeralfs have been recorded at various levels, for example the T-25 near Meco. Typic Palexeralfs are the most common soils of the higher terraces. Calcic horizons have been identified on soils from levels T-27 to T-20, whereas deep petrocalcic horizons (more than 150 cm from the surface) occur on the T-15 level. Some of these soils exhibit vertic properties, such as cracks and slickensides. Therefore, the main pedogenic processes leading to soil formation include weathering, clay illuviation, decalcification, rubification and hardening (Medina, 1977; Vaudour, 1979; Dı´az, 1986; Gallardo et al., 1985, 1987; Iba´n˜ez et al., 1992; Gonza´lez-Huecas et al., 1993; Saldan˜a, 1997).

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2.3. Soil sampling A geopedologic map, obtained from the interpretation of aerial photographs at 1:18,000 scale and field observations, was used to select the proper location of three sample areas for description and sampling. Forty nine observation points on a square grid of 7 by 7 points (Saldan˜a, 1997) were established in the sample areas located on a Late Pleistocene terrace (A1 on T29), a Middle Pleistocene terrace (A2 on T-25) and an Early Pleistocene terrace (A3 on T-15), with terrace labelling according to ITGE (1990). Observations were separated by a 90-m distance and each sample area covered 291,600 m2. In the case of the low terrace, a multi-scale sampling grid was used with observations also at 30- and 10-m distances to detect spatial variation (Saldan˜a, 1997; Saldan˜a et al., 1998). Since the purpose of the investigation was to study the soil variability in the Henares valley, the sample design attempted to provide information about the soils in two ways: (1) on the one hand, several river terraces were sampled to have a complete image of the entire valley and to be able to establish an evolution model of the area; (2) on the other hand, to get information not only about the upper part of the soils but also downwards, different depths were sampled in each of the selected terraces. With these considerations in mind, and taking into account economic and temporal limitations for sampling, the design is shown in Fig. 1 that in some instances covered all the terrace tread available (this was the case of the high terrace to avoid the effects of dissection). Soils were described and analysed at all grid nodes either by augering or by excavation of full pits; in addition, samples were taken at standardised depths of 10– 20, 40 – 50 and 90– 100 cm. To allow soil characterisation, sand, silt, clay, calcium carbonate, organic carbon and soil reaction were analysed for all the 147 samples collected in A2 and A3 sample areas and the 387 samples of the A1 sample area. Particle-size distribution was determined by the Bouyoucos method, organic carbon by the Walkley– Black method, calcium carbonate with the Bernard calcimeter, and pH with a pHmeter in 1:2.5 soil – water mixtures (Saldan˜a, 1997). Soils were classified according to Soil Taxonomy (USDA, 1994) at Great Group, Subgroup and family hierarchy levels. Three soil maps were obtained by conventional mapping and the grid observations were also used to establish soil map unit

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borders. Table 2 shows the input data used for pedodiversity computations. It includes the number of profiles and the percentage of a given map unit characterised by a certain soil type at the selected hierarchy levels; the frequency of the diagnostic horizons identified in the profiles described in the study area accounted for the genetic pedodiversity. 2.4. Richness indices Once the study area is delimited and the constituent taxa (in the present research, the number of soil types) are identified and enumerated, richness indices are useful measures of diversity. From ecology, species richness provides an instantly comprehensible expression of diversity, provided that care is taken with sample size (i.e., all sample sizes are equal). However, focusing on information about the relative abundance of species has led to the development of a great range of diversity indices and models which go further than species richness (Magurran, 1988). Several indices have been derived in which use is made of the number of taxa recorded and the total number of individuals of all species (numerical species richness). Two of them are introduced here, the Margalef’s index and the Menhinick’s index. The Margalef’s index (Magurran, 1988) is calculated by applying the formula: DMg ¼

ðS  1Þ lnN

The Menhinick’s index (Whittaker, 1977) is calculated as follows: S DMn ¼ pffiffiffiffi N where S is the number of species (i.e., soil types) recorded and N is the total number of individuals (i.e., number of samples summed in every sample area) over all S species. Both indices take values from 0 to l. 2.5. Diversity indices Several techniques have been proposed to measure diversity (Magurran, 1988). However, indices based on the proportional abundance of objects are the most

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Table 2 Input data for pedodiversity calculations Pedotaxa

Table 2 (continued) (c) Family level

Sample area A1

Pedotaxa

A2

A3

A1

No. of Area No. of Area No. of Area profiles % profiles % profiles % (a) Great group level Xerochrepts Haploxeralfs Palexeralfs

49

100 41 8

86 14

2 20 27

2 35 63

1 1 1 19

1 1 1 34

14 13

31 32

(b) Subgroup level Calcic Xerochrepts 26 Typic Xerochrepts 23 Calcic Haploxeralfs Typic Haploxeralfs Petrocalcic Palexeralfs Calcic Palexeralfs Typic Palexeralfs Vertic Palexeralfs

58 42 11 30 2

26 60 4

6

10

(c) Family level Pedotaxa

Sample area A1

Fine loamy, mixed, mesic, Calcixerollic Xerochrepts Fine silty, mixed, mesic, Calcixerollic Xerochrepts Fine clayey, mixed, mesic, Calcixerollic Xerochrepts Fine loamy, mixed, mesic, Typic Xerochrepts Coarse loamy, mixed, mesic, Typic Xerochrepts Fine loamy, mixed, mesic, Calcic Haploxeralfs Fine clayey, mixed, mesic, Calcic Haploxeralfs

A2

22

A3 1

Sample area

Fine loamy, mixed, mesic, Typic Haploxeralfs Fine clayey, mixed, mesic, Typic Haploxeralfs Fine clayey, mixed, mesic, Petrocalcic Palexeralfs Fine clayey, mixed, mesic, Calcic Palexeralfs Fine clayey, mixed, mesic, Typic Palexeralfs Fine clayey, mixed, mesic, Vertic Palexeralfs

A2

A3

6

1

23

18

2

6

14

13

(d) Genetic horizons Sample area

1 Ochric Cambic Calcic Argillic Petrocalcic

3

a

20

1

3

3

9

1

b

A1

A2

A3

49 23 26

49

49 1 2 47 1b

22 49 3a

They appear within 150 cm from the surface. It appears below 150 cm from the surface.

frequently used. They consist of two components: (1) the variety (richness); and (2) the relative abundance (evenness) of species. The most common indices, used in ecology as a measure of the structural heterogeneity of a community, derive from the information theory. These are the Shannon diversity index (Shannon and Weaver, 1948) and the Brillouin index (Brillouin, 1956). The application of the Shannon index to pedodiversity computation has been a source of discussion (Iba´n˜ez et al., 1998; Camargo, 1999; Iba´n˜ez and de Alba, 1999). From a mathematical

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point of view, according to Martı´n and Rey (2000), there is no reason to reject its application as it was done by Iba´n˜ez et al. (1990, 1995a, 1998). Recently, Phillips (2001) has used the state probability function and the diversity index to analyse the soil evolution and the spatial structure of soil landscape variability. The Shannon index is calculated by applying the following formula: HV ¼ 

n X

pi *lnðpi Þ

i¼1

where pi is estimated as ni/N (ni is the area covered by the i-th pedotaxa and N is the total area under study). HV is usually found to fall between 1.5 and 3.5 and only rarely surpasses 4.5 (Marugan, 1988). The Brillouin index is calculated as follows: HB ¼

lnN !  Rlnðni Þ N

where N is the total number of pedotaxa and ni is the number of individuals belonging to the i-th pedotaxa. This index rarely exceeds 4.5 (Magurran, 1988). One major difference between the two indices is that the Shannon index will always give the same value provided the number of species and their proportional abundances remain constant. This is not the case of the Brillouin index, which depends on sample size (Magurran, 1988). The ratio of observed diversity to maximum diversity (this occurs when all elements are equally abundant) is taken as a measure of evenness (Pielou, 1969). In this paper, the above indices were applied to estimate the pedodiversity of the Henares River terraces. Three types of input were used in the formulas: (1) the number of individuals of a given pedotaxa; (2) the proportion of the area characterised by a given soil type; and (3) the number of diagnostic horizons identified in each sample area. The first two result in the taxonomic pedodiversity while the last one constitutes a first approximation to the genetic diagnostic horizon diversity, i.e., the genetic pedodiversity. The first two analyses were carried out for the three sample areas at three levels of the soil taxonomy classification, i.e., Great group, Subgroup and family (USDA, 1994).

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2.6. Abundance distribution models Species abundance distribution uses all the information gathered in a community and is the most complete mathematical description of the data; however it is a controversial matter (e.g., Iba´n˜ez and de Alba, 2000). Although taxa abundance data are described by families of distributions, diversity is usually examined in relation to four main models: lognormal distribution, geometric series, logarithmic series and MacArthur’s broken-stick model (Magurran, 1988). The plot on a rank/abundance curve of the four models represents a progression ranging from the geometric series where a few species are dominant with the remainder fairly uncommon, through the log series and lognormal distributions where species of intermediate abundance become more common and ending in the conditions represented by the brokenstick model in which species are more equally distributed (Magurran, 1988). Although they are commonly used in ecological studies, they have also been proposed as a technique to analyse pedodiversity (Iba´n˜ez et al., 1995a). Abundance model fitting was computed with the program Species Diversity and Richness (Henderson and Seaby, 1997), which provides the best fitting of the data to one of the models mentioned above. 2.7. Richness– area relationships This is another way to explore, quantify and compare the complexity of abiotic landscape structures in different areas and environments (Iba´n˜ez et al., 1995b, 1998). According to the theory of island biogeography (MacArthur and Wilson, 1963, 1967) so used in ecology (e.g., May, 1975; Tokeshi, 1993; Brown and Lomolino, 1998), the dependency of the number of species S (number of different taxonomic groups) on the area A (referring in particular to islands and other analogous habitats of patchy spatial occurrence) can be modelled in two ways: (1) by means of a linear relationship between log S and log A (log S =plog A + q for certain constants p and q), i.e., a power function with exponent p and prefactor eq (S = eqAp); and (2) by a linear relationship between S and log A (S = alog A + b for certain constants a and b), which is simply a logarithmic function of S in terms of A (or, alternatively, an exponential

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function of A in terms of S). For most species groups in most habitats, after a steep increase, the curve tends to level off. The area at which the abrupt change in slope takes place is called the minimum area point. The latter has been considered as an index of how large a community must be to express its structural character, or to be representative of a community type (Forman, 1995). It is important to mention that this type of curve is widely used in phytosociology to distinguish among different communities (the so-called species accumulation curves) and to determine the minimum area for an adequate sampling protocol. Each of the previously mentioned abundance distribution models implies one of these relationships by the addition of the assumption that the total number of individuals is proportional to the area A (May, 1975). Then, each model of abundance distribution is related with one type of richness – area model, being the parameters of the corresponding models related by specific expressions. The lognormal and the brokenstick models yield power curves; the geometric and logarithmic series models yield logarithmic expressions for the richness – area curves. Pedorichness –area relationships were investigated for the three sample areas as a further step in the analysis of diversity and complexity of the soils of the Henares River valley.

3. Results and discussion 3.1. Richness indices At the Great Group level, A1 sample area is dominated by just one group, Xerochrepts; A2 is dominated by Haploxeralfs with the presence of Palexeralfs, while Haploxeralfs and Palexeralfs have been identified on A3 almost in equal proportion together with few Xerochrepts. So, richness in Great Groups increases from low to high terraces. The number of Subgroups also increases from low to high terraces: from the two Subgroups of the Xerochrepts found on A1 to the six Subgroups of Xerochrepts, Haploxeralfs and Palexeralfs found on A3 (Tables 2 and 3). The same applies at family level, with five families on A1 area to seven families observed on A3 sample area. This trend opposes the progressive evolution leading

to soil convergence and homogenisation, as shown by the low variability of the properties of soils on the high terraces (Saldan˜a et al., 1998). In the first case, the high number of taxa occurs because of the imprint of the depositional system related to short-distance variations in the soil parent material, which lead to more families (e.g., coarse loamy). In the high terraces, historical contingencies (such as erosion processes, tree fall, etc.) would explain this result inverting the soil homogenisation process. It could be argued that the area under study is rather homogeneous. For example, soils belonging to eight orders were identified in a region of similar dimensions in Venezuela (Zinck, personal communication). Obviously, more significant results should be expected for a higher number of taxa. The Margalef’s index and the Menhinick’s index are quantitative indicators of pedotaxa richness. At the three categories, both indices show the same behaviour with an increase from low to high terraces. This result agrees with the simple abundance measures shown in Table 3. Two possible interpretations, not excluding each other could be proposed to explain these results: (i) from a pedogeomorhic approach, and (ii) from the physics point of view. According to the first one, the pedorichness increase from young to old terraces could suggest the activity of other processes such as erosion and deposition of eroded material on the high terraces which obliterated the scarps; this could be the main reason for the appearance of Xerochrepts on A3, a terrace dominated by Palexeralfs and Haploxeralfs. The second approximation, in the light of the chaos theory, considers soils as NDS systems (Nonlinear Dynamical systems) that may be unstable, chaotic and self-organised. In such a case, the effects of minor variations in initial conditions (e.g., small granulometrtic or mineralogical differences) and/or historical contingencies (e.g., external forcing caused, for example by climate changes, perturbations or disturbances such as local erosion and deposition, bioturbation, tree fall, neotectonics, etc.) would persist and grow over time, causing divergent pedogenesis (see Iba´n˜ez et al., 1997; Phillips, 1998, 1999, 2001 and references therein for further details). This would lead to an increasing spatial complexity that would result in a pedorichness increase in contrast with a monotonic progress

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Table 3 Richness and diversity indices Sample area

Hierarchical level

S

DMg

DMn

H1V

H2 V

H Vmax

Evenness

HB

A1 A2 A3 A1 A2 A3 A1 A2 A3

Great Group

1 2 3 2 4 6 5 6 7

0 0.26 0.51 0.26 0.77 1.28 1.03 1.28 1.54

0.14 0.29 0.43 0.29 0.57 0.86 0.71 0.86 1.00

0 0.45 0.82 0.43 1.02 1.32 1.15 1.46 1.40

0 0.40 0.74 0.68 1.02 1.23 – – –

3.89 3.89 3.89 3.89 3.89 3.89 3.89 3.89 3.89

0 0.11 0.21 0.11 0.26 0.34 0.29 0.38 0.36

0 0.41 0.75 0.65 0.92 1.17 0.87 1.30 1.23

Subgroup

Family

S: Abundance; DMg: Margalef’s richness index (Magurran, 1988); DMn: Menhinick’s richness index (Whittaker, 1977); H1V: Shannon diversity index (soil type data) (Shannon and Weaver, 1948); H2V: Shannon diversity index (unit coverage data) (Shannon and Weaver, 1948); HB: Brillouin index (Brillouin, 1956).

towards a single soil type, or few soil types (convergent pedogenesis). Thus, our detailed field studies corroborate the results previously obtained by Phillips (2001) at smaller scales in fluvial chronosequences of the United States: the fitting to power law distributions and increase of diversity with the time. In any case, future researches are needed to give more empirically support to this hypothesis.

pedodiversity values decrease with increasing detail of classification (i.e., 0.82 and 0.31 for Great Group and family levels, respectively. See Table 3). Brillouin index based on the number of soil type data shows slightly lower values than the Shannon

3.2. Diversity indices The taxonomic pedodiversity (Shannon diversity) was calculated considering, on the one hand, the number of soil profiles belonging to a given pedotaxa and, on the other hand, considering the unit coverage area of pedotaxa. It has been found out that pedodiversity values calculated on profile data are slightly higher than those calculated on unit coverage data (Table 3; Fig. 2a and b), although differences are not significant as indicated by the Mann – Whitney U-test (U = 17). The taxonomic pedodiversity of soil-type data increases from low to high terraces at high hierarchy levels, and has a maximum for the A2 sample area at family level (Fig. 2). This verifies one of the hypotheses of this research. The latter can be explained because the Shannon index takes into account not only the richness but also the evenness of the elements present. In this particular case, although richness is higher for the old terrace, evenness (the other component of the diversity index) is higher for the middle terrace. It is also interesting to notice that ranges of

Fig. 2. Taxonomic pedodiversity calculated on: (a) soil type data; (b) unit coverage data.

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index, but the same trend as the latter which is indicated by the correlation table (Table 4). Pielou’s evenness index exhibits a different behaviour for the different hierarchy levels. At Great Group and Subgroup levels, evenness increases from low to high terraces, with a larger increase for the latter level. In contrast, evenness shows its maximum for the middle terrace at family level (Fig. 3). The same reasoning already explained for the taxonomic pedodiversity applies here. A last but very important consideration, regards the effect of the classification system on pedodiversity. In the case of the USDA Soil Taxonomy system (used in this research), both field and lab data may change pedodiversity calculations. An example is that vertic properties come out earlier than the presence of calcic horizons. In the present study, it happens that some of the Vertic Palexeralfs described on A3 have calcic horizons below 100 cm but this feature is not reflected in the final classification. Thus, the number of Vertic Palexeralfs is very high resulting in a decrease in pedodiversity values. In the case of the lab data, it may happen that identical soils with slight percent base saturations differences (34% and 36%) would be classified in different orders (Ultisols and Alfisols), which would be translated in an ‘‘artificial’’ pedodiversity increase. 3.3. Taxonomic pedorichness and pedodiversity at different scales The taxonomic pedorichness and pedodiversity (Shannon formula) based on soil-type data were calculated at different distance intervals for the A1 Table 4 Correlation matrix of diversity indices (values significant at p < 0.05 in bold) Index

S

DMg

S DMg DMn H1V Evenness HB

1.00 1.00 1.00 0.94 0.94 0.93

1.00 1.00 0.94 0.94 0.93

DMn

1.00 0.94 0.94 0.93

H1V

Evenness

HB

Fig. 3. Pielou’s evenness index.

sample area. Table 5 shows that pedodiversity increases with distance (i.e., with the area). This could be expected as the closer the observations, the more similar they tend to be. This reasoning is straightforward when the position of the sampling scheme falls inside a soil map unit, but it cannot be extended when it is located at the edge of two or more map units. Therefore, the richness – area relationship, which is rule in the ecological literature, has also been proposed in pedodiversity analysis (Iba´n˜ez et al., 1998; Iba´n˜ez and de Alba, 2000; McBratney et al., 2000; Phillips, 2001). 3.4. Genetic pedorichness and pedodiversity Ochric, argillic, cambic, calcic and petrocalcic are the five diagnostic horizons identified in the study area. However, this number changes according to the soil depth considered. This is especially true for old soils, which in the study area would be those of the A2 and A3 sample areas. In the latter case, the presence of thick argillic horizons results in low pedodiversity figures. However, in some instances, these values may increase with the depth considered as some horizons appear below 100 cm from the Table 5 Taxonomic pedorichness and pedodiversity at different lags for A1 (soil type data)

1.00 1.00 1.00

1.00 1.00

1.00

S: Abundance; DMg: Margalef’s richness index (Magurran, 1988); DMn: Menhinick’s richness index (Whittaker, 1977); H1V: Shannon diversity index (soil type data) (Shannon and Weaver, 1948); HB: Brillouin index (Brillouin, 1956).

Lag (m)

S

H1V

10 30 90

3 6 5

0.76 1.14 1.15

S: Abundance; H V: Shannon diversity index (Shannon and Weaver, 1948).

A. Saldan˜a, J.J. Iba´n˜ez / Geomorphology 62 (2004) 123–138

surface (e.g., petrocalcic). This is the case for example of the intermediate or the high terraces, where the petrocalcic horizon has been described at approximately 100 and 180 cm, respectively. Thus, considering only 100 cm below the surface, the highest richness is for A3, but pedodiversity is lower for the latter case. As already explained, the Shannon index considers not only richness but also evenness. In the case of the A1, richness is low but evenness is high, as the three diagnostic horizons are more or less equally distributed (Table 2d). On A3, two diagnostic horizons (ochric and argillic) represent more than 90% of the total, yielding a low genetic pedodiversity value. Considering 150 cm below the surface, the trend is maintained but diversity is slightly higher on A2 and A3 sample areas because the petrocalcic horizon appears between 100 and 180 cm resulting in a richness increase (Table 6). Then, if the whole solum had been considered, richness would have increased from low to high terraces, i.e., according to age although diversity would be higher for the middle terrace (Tables 2d and 6). Actually, several soil scientists (e.g., Glinka, 1931) claimed that the soil survey must include all the solum and the C horizon independently of its depth. Such criticism is supported both on theoretical grounds and by current demands of pedologic information (e.g., soil hydrology; see Iba´n˜ez and Boixadera, 2002 and references therein). According to several authors, classification criteria used by FAO (1990), WRB (ISSS-ISRIC-FAO, 1998) and USDA (USDA, 1994) of soil taxonomies reflect a strong ‘‘agronomic bias’’ (Yaalon, 1995, 1996b; Iba´n˜ez et al., 1997). As it has been shown, this bias may change the quantification of the genetic pedodiversity, either by underestimation (high hierarchy levels) or overestimation (family level). This is particularly important for older and stable geomorphologic surfaces where deep soils are found (Paton et al., 1995; Ollier and Pain, 1996; Richter and MarkeTable 6 Genetic pedorichness and pedodiversity at the following depths: (a) within 100 cm; (b) more than 100 cm below the surface Sample area

A1 A2 A3

Pedorichness

Pedodiversity

(a)

(b)

(a)

(b)

3 3 4

3 4 5

1.04 1.04 0.83

1.04 1.14 0.87

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witz, 1995; Iba´n˜ez et al., 2000; Iba´n˜ez and Boixadera, 2002). As it was already stated in early works (Iba´n˜ez et al., 1995a, 1998), the utility of pedodiversity to analyse soil landscapes has been shown, not only at broad scales but also at detailed scales, as indicated also by Phillips (2001). Nevertheless, more examples should be considered to make generalizations. 3.5. Abundance distribution models Rank/abundance were plotted considering the number of profiles belonging to a given soil type at family level (Fig. 4) and the output of the program Species Diversity and Richness is shown in Table 7a. It can be seen that observations of sample areas fit quite well, both to a lognormal distribution, and to geometric and logarithmic series, as the ecological theory predicts. In biological assemblages, the former is the most common for larger assemblages with many individuals and taxa, in which soil types of intermediate abundance are common (Fig. 4a) (Ganis, 1991). However, when biological assemblages consist of a small number of individuals and taxa, logarithmic series and geometric series are most frequently used (Fig. 4b and c); this is also the case for small samples of lognormal distribution assemblages, disturbed communities and the first stages of ecological successions (Magurran, 1988). In the latter distributions, few taxa are dominant while the rest are very scarce. Since the sampled areas analysed here must be considered as small samples of the terraces (the larger assemblage), these fits to several of the above-mentioned distributions were expected; this comprises a certain ambiguity as it is also found in biological communities (Magurran, 1988). In the ecological literature, the fit of datasets to a power law function has been explored to analyse taxa –area relationships (see below) but not for biological assemblages. However, with the emergence of fractal geometry in the scientific literature, the fit of very disparate datasets in nature to the power law (a signature of possible underlying fractal structures) is beginning to be analysed (Schroeder, 1991; Korvin, 1992; Iba´n˜ez and de Alba, 2000). Sample areas A1 and A2 exhibit a good fit to this statistical distribution, with R-square values above 0.8; the power fit might be the most appropriate also for sample area A3, but

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A. Saldan˜a, J.J. Iba´n˜ez / Geomorphology 62 (2004) 123–138 Table 7 Fitting parameters of soil families to abundance distribution models most used by ecologists (a) For sample areas Model

Geometric Broken stick Logarithmic Lognormal

A1

A2

A3

v2 (obs.) P

v2 (obs.)

P

v2 (obs.) P

No 4.21 7.37 2.42

3.33 3.51 6.98 2.46

65 48 13 65

8.89 7.62 4.28 5.03

– 38 12 66

18 11 37 28

(b) For different lags Model

10 m

30 m

v2 (obs.) P

v2 (obs.)

P

10.2 16.4 14.9 0.1E + 38

7 2.30 0.6 13.1 0.5 26.4 0 5.79

Geometric 7.51 Broken stick 10.5 Logarithmic 16.5 Lognormal 5.14

11 6 0.3 27

90 m v2 (obs.) P 32 4 0.01 33

P: probability.

Fig. 4. Abundance models at family level considering the number of soil types. (a) Sample area A1; (b) sample area A2; (c) sample area A3. A: Fine loamy Calcixerollic Xerochrept; B: Fine loamy Typic Xerochrept; C: Coarse loamy Typic Xerochrept; D: Fine clayey Calcixerollic Xerochrept; E: Fine silty Calcixerollic Xerochrept; F: Fine clayey Typic Haploxeralf; G: Fine clayey Calcic Haploxeralf; H: Fine loamy Typic Haploxeralf; I: Fine clayey Calcic Palexeralf; J: Fine loamy Calcic Haploxeralf; K: Fine clayey Petrocalcic Palexeralf; L: Fine clayey Typic Palexeralf; M: Fine clayey Vertic Palexeralf.

insufficient data are available at the moment to demonstrate this. The same procedure was applied at different scales for the observations of the A1 sample area. As shown in Table 7b, observations at 10-m distance fit a

lognormal series, at 30-m distance fit a geometric series although the probability values are really low, while at 90-m distance they fit a lognormal and a geometric distribution model. In this case, no clear trend can be established in relation with the area increase. It has been shown that abundance distribution models change with age although the mathematical relationship that higher evenness means a more equitable distribution model (broken-stick) has not been clearly obtained. Besides, more observations would be also necessary to detect that they also change with the area as it was already shown by Iba´n˜ez et al. (1998) at coarse scales, i.e., more equitable models fit the observations when the area increases (see Section 3.3). Table 8 Pedorichness for the three sample areas with increasing area Area (m2)

S1

S2

S3

400 1,600 3,600 14,400 32,400 129,600 291,600

2 2 3 5 6 6 6

1 1 2 3 3 6 6

1 1 1 2 2 3 7

S1, S2, S3: pedorichness for sample areas A1, A2 and A3, respectively.

A. Saldan˜a, J.J. Iba´n˜ez / Geomorphology 62 (2004) 123–138

3.6. Pedorichness –area relationships Pedorichness (at family level)– area relationships were investigated to detect which model, power or logarithmic, better fits the observations of each sample area. Table 8 shows that pedorichness increases with area. Both logarithmic and power functions gave a good fit for all sample areas (Fig. 5). However, Fig. 5a shows that the curves for the first two terraces (sample areas A1 and A2) seem to approach an asymptote. This implies that no additional soil types can be expected to appear with further increase of area. Such a pattern was not detected at the older terrace (sample area A3). This is in agreement with the results obtained using geostatistical tools: a larger area should be sampled to capture the spatial variability of the A3 sample area (Saldan˜a et al., 1998). Such results, although very interesting, must be used with caution, in view of the low number of samples and taxonomic categories found in the study area. In any case, many studies in ecology and biogeography have demonstrated that

Fig. 5. Pedorichness – area relationships. (a) Sample area A1 (showing a logarithmic fit); (b) sample area A3.

135

taxa –area relationships could be simulated well with both logarithmic and power distributions in small assemblages (Lomolino and Weiser, 2001).

4. Conclusions Diversity statistics used in conservation biology have been shown to be applicable to the analysis at a large scale of soil distributions on fluvial terraces, even when the plots under analysis are rather homogeneous from the pedologic and geomorphologic points of view. The investigation focussed on the diversity of soils classified in different categories of the USDA Soil Taxonomy; the family level, a practical category defined mainly for agronomic purposes, was the lowest category considered. There is not much difference in calculating pedodiversity with coverage data or soil profile number. The high correlation between indices used here suggested that a single index, i.e., the Shannon index, commonly used in many disciplines, might be enough to summarise all the information regarding diversity. Taxonomic pedorichness and pedodiversity increased with terrace age and area. This suggested a divergent soil evolution similar to the one recently defined by Phillips (2001) on the terraces of the coastal plain in North Carolina (Talbot transect). The latter is explained because the closer the observations, the more similar they tend to be, showing the complementarity between diversity tools and geostatistical analyses. Genetic pedodiversity decreased with age. Quantification of this type of pedodiversity was influenced by soil depth and soil classification schemes. Regarding soil depth, it is suggested that the analysis of the entire solum, and not the standard depths applied by soil surveyors following Soil Taxonomy (USDA, 1994) criteria, should be considered as an appropriate way for pedodiversity quantification. This is a critical point when considering old and stable geomorphologic surfaces where deep soils are found. Pedorichness –area relationships for the three sampled areas could be fitted well with both logarithmic and power law models. Such ambiguity was also observed in ecology with species –area relationships. The underlying causation of this pattern is rather pervasive in ecology. It is intriguing that the same results appear when non-biological assemblages are

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studied. A fine scale sampling is another innovation of this research, since only analysis at coarser scales were carried out in the past. However, the small number of taxa categories and the small area studied here are also major shortcomings in order to reach more straightforward conclusions. This research seems to support the applicability of the nonlinear dynamics to the soil system. Soil evolution depends on minor variations in initial conditions and/or eventual changes in the boundary conditions and may approach different chaotic attractors or different stability points (see Iba´n˜ez et al., 1997 and Phillips, 1999 for further details). This is the divergent pedogenesis postulated by Phillips (2001), which may lead to increasing complexity and pedodiversity in space and time (Iba´n˜ez et al., 1997; Phillips, 1999, 2001). The applicability of lognormal or power law distributions to older and richer soil assemblages, the increase of pedodiversity in time, the suitability of the power law model to taxa – area relationships, and the decrease of the values of connectance indices for soil assemblages (Saldan˜a and Iba´n˜ez, 2002) are signatures that support the Nonlinear Dynamical systems hypotheses.

Acknowledgements Dr. D.H. Yaalon, Dr. J.D. Phillips and Dr. Y. Pachepsky are greatly acknowledged for their comments on this manuscript. One anonymous reviewer also provided constructive comments on an earlier version of the text. This paper has been funded by the projects NAT89-0996 and CLI95-1815-CO2-01, supported by the CICyT (Spain). Ms. Saldan˜a is grateful to the Centro de Ciencias Medioambientales (CSIC, Spain), the Regional Government of Madrid (Spain) and the ITC (The Netherlands) for their economic support.

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