Correlation among soil enzyme activities under different forest system management practices

Ecological Engineering 37 (2011) 1123–1131

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Correlation among soil enzyme activities under different forest system management practices S. Salazar a , L.E. Sánchez b , J. Alvarez a , A. Valverde a , P. Galindo c , J.M. Igual c , A. Peix a , I. Santa-Regina a,∗ a

IRNASA-CSIC, Cordel de Merinas 40-52, 37071 Salamanca, Spain Mathematics Department, Universidad de Antioquia, Colombia c Statistics Department, Universidad de Salamanca, Spain b

a r t i c l e

i n f o

Article history: Received 28 July 2010 Received in revised form 28 January 2011 Accepted 15 February 2011 Available online 31 March 2011 Keywords: Soil biochemical properties Enzymatic activities Phosphate solubilising bacteria Castanea sativa Quercus pyrenaica Forest management practices

a b s t r a c t Soil enzyme activities were performed in three permanent no-till and unfertilised plots located in the South of Salamanca province (Spain), the first in a Castanea sativa Mill. paraclimax coppice (CC), the second in a chestnut orchard (CO) and the third in a Quercus pyrenaica Wild. climax forest (Oak), adjacent to the CO plot. We hypothesized that the activities of dehydrogenases, ureases, acid phosphatases, arylsulphatases and ␤-glucosidases in different forest ecosystems are involved in the carbon, nitrogen and phosphorus cycling and we report their relationship with each other and with physical, chemical and general biochemical parameters of the soils. The main aim of the study was to detect biological criteria for sustainable development in natural degenerate forests of Mediterranean Europe. For this, we used sweet Chestnut (C. sativa Mill) and Oak Q. pyrenaica Wild as models to better define the ecological conditions of these natural resources in terms of nutrient balance, physiology and biological diversity of their communities, to relate them to the conditions of exploitation and land-use changes, for the characterization of sustainable ecological systems. Furthermore, soil respiration was high and significantly different in the chestnut coppice stand than the other two stands, chestnut orchard and oak. Correlations between soil biochemical and soil microbiological variables showed that the three different forest management practices had also a strong effect on soil function conditions. In a discriminate analysis, CC and Oak were discriminated clearly, while CO was in the middle of the biplot sharing some properties with each of the two different groups. Thus, we proposed a soil property transition from the best soil structure and function properties at one chestnut management properties with low tree densities (CC and CO) to other with the worst ones at highest tree density conditions (Oak). According to natural soil conditions in Oak, we assumed that most of the enzyme activities reached their highest levels at highest C and N soil contents but at lowest soil base saturation percentage while they were not at all associated with P soil availability. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The long-term sustainability of forest management systems is highly dependent upon maintaining soil properties (Schoenholtz et al., 2000). Because of their main role in organic matter decomposition and nutrient cycling, soil microorganisms are key components of terrestrial ecosystems. Growth, productivity and nutrient balance may mainly depend on the soil microbiota composition and microbial activity.

∗ Corresponding author. Tel.: +34 923 219 606; fax: +34 923 219 609. E-mail address: [email protected] (I. Santa-Regina). 0925-8574/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2011.02.007

Soil microbiological and soil biochemical properties are sensitive to both environmental stress and changes in management practices. Therefore, they are regarded as useful indicators of soil quality (Yakovchenko et al., 1996; Burylo et al., 2007), by citing some examples, soil respiration is a good indicator of both overall soil biological activity and soil quality (Fisk and Fahey, 2001), whereas phosphatase activities are considered especially useful indicators of both positive and negative effects of soil management practices on soil quality (Jordan et al., 1995). In regard to soil biochemical properties, phosphorus is an essential plant nutrient, the second in importance after nitrogen (Igual et al., 2005), that forms insoluble compounds, both in acid and in alkaline soils, thus limiting its availability to the plant (Bar-Yosef, 1991; Kiedrzynska et al., 2008; Ronkanen and Klove, 2009). However, a great fraction of soil

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microbiota is known to be able to mobilize phosphorus in soil (Igual et al., 2001). Consequently, the role of microorganisms in the soil phosphorus cycling is a natural process widely known (Oehl et al., 2001) and the processes that take place may be managed, in order to optimize the ecosystem functioning. Nutrient cycling in soils involves biochemical, chemical and physicochemical reactions, with the biochemical reactions being catalyzed by enzymes. Both microbial species and soil animal communities release enzymes into the environment in order to degrade macromolecular and insoluble organic matter and, thus, soil enzymatic activity is of primary interest in understanding plant litter decomposition. They are involved in energy transfer, environmental quality and crop productivity (Dick, 1994; Tabatabai, 1994) and often are used as indices of microbial activity and soil fertility (Deng and Tabatai, 1996, 1997; Bandick and Dick, 1999). At ecosystem scale, enzyme activity in soils is a function of microbial and root activity, which are, in turn, regulated by moisture, temperature and substrate quality (nutrient availability) (Tabatabai, 1994). The dehydrogenase activity is considered to be an indicator of the oxidative metabolism in soils and thus of the microbial activity (microbial redox system and oxidative activities) (Trevors, 1984). However, the relationship between an individual biochemical property and the total microbial activity is not always obvious, especially in the case of complex systems like soils, where the microorganisms and processes involved in the degradation of the organic compounds are highly diverse (Nannipieri et al., 1990). Soil ureases are one of the most important enzymes involved in soil organic matter mineralization, they release N–NH4 + through urea hydrolysis and are essential in the chain of hydrolysis of amino compounds, which are supplied to the soil from plants and to a lesser extent from animals and microorganisms. In addition, this enzyme is part of microbial products that can be accumulated in cell free forms because they are highly resistant to environmental degradation (Zantua and Bremner, 1977). Acid phosphatase enzymes are assumed to have an essential role for the cycling of phosphorus in forest ecosystems, particularly where its availability may limit plant productivity (Speir and Ross, 1978). Increased production of phosphatase enzymes by plant roots and microorganisms can be induced when phosphorus is limited. Consequently, an increase in phosphatase activity may reflect a high demand for this macronutrient. The arylsulphatase enzymes, which are produced by both plants and microorganisms, are responsible for catalyzing the hydrolysis of organic sulphate ester with an aromatic radical liberating (R-OH) and (SO4 2− ) (Tabatabai and Bremner, 1970; Dick, 1994). Among glucosidases, we may emphasize the ␤-glucosidases that are capable of breaking down labile cellulose and other carbohydrate polymers. Its action is fundamental in order to liberate the nutrients of organic compounds by reducing their molecular size and producing smaller organic structure and thus facilitate future microbe enzyme activities (Szegi, 1988). Management practices (e.g. crop rotation, mulching, tillage and application of fertilizers and pesticides) may have diverse effects on various enzymes and microbial activities such as respiration and nitrogen mineralization (Tabatabai, 1994). Therefore, changes in both enzymes and microbial activities could alter the nutrients availability for plant uptake and these changes are potentially sensitive indicators of soil quality (Dick, 1994). We monitored the activities of five enzyme groups: dehydrogenases, ureases, acid phosphatases, arylsulphatases and ␤-glucosidases in different forest ecosystems. We report the activities of a variety of these enzymes involved in the carbon, nitrogen and phosphorus cycling and we discuss their relationship which each other and with physical, chemical and general biochemical parameters of

the soils. The main aim of the study was to detect biological criteria for sustainable development in natural degenerate forests of Mediterranean Europe. For this, we used sweet Chestnut (Castanea sativa Mill) and Oak Quercus pyrenaica Wild as models to better define the ecological conditions of these natural resources in terms of nutrient balance, physiology and biological diversity of their communities, to relate them to the conditions of exploitation and land-use changes, for the characterization of sustainable ecological systems. 2. Materials and methods 2.1. Site description The study area is situated on the Sierra de Francia area, Salamanca Province (Spain). Three permanent no-till and unfertilised plots were established in fields within a diverse forest management practice (FMP): the first in a C. sativa Mill. paraclimax coppice (CC), the second in a chestnut orchard (CO) and the third in a Q. pyrenaica Wild. climax forest (Oak), that is adjacent to the CO plot. The densities of trees are (tree ha−1 ): 1892, 382 and 2960 for CC, CO and Oak, respectively (Table 1). The study area is dominated by granite substrates and the soil pH is generally acid, although with intercalations of limestone. The soil type varies in depth and among plots, being classified as umbric Regosol in coppice stand and umbric Leptosol in oak and orchard plots (F.A.O., 1989). There are enclaves of vegetation typical of the Euro-Siberian region, with taxa such as: Ilex aquifolium L., Aconitum napellus ssp. castellanum L., Actaea spicata L., Monotropa hypopitys L., Atropa belladona L., Hypericum montanum L., Neottia nidus-avis (L.) Rich., Paris quadrifólia L., and Corylus avellana L. 2.2. Sample collection and soil analyses In each stand five soil samples were collected separately under the canopy of the trees from the top 10 cm of the soil surface. After sampling, the mineral soil samples were sieved (2 mm) to remove stone and large roots and analyzed for microbiological and biochemical variables and then were stored for no more than 15 days after collection at 4 ◦ C. For counts of total and phosphate solubilising microbiota, soil subsamples of 10 g were placed in dilution bottles with 90 ml sterile water and shaken for 5 min. Then, ten-fold soil dilutions were spread-plated in YED-P (Peix et al., 2001), with the addition of 50 ␮g ml−1 cycloheximide by triplicate to determine total microbiota and phosphate solubilising bacteria counts. Plates were incubated for 7 days at 28 ◦ C and the colony-forming units (CFU g−1 soil) were counted. Potential respiration was measured in laboratory as CO2 evolution from the soil samples at 55% of WHC, after incubation in tight containers for 3 d at 28 ◦ C. The CO2 output from the soil was determined by NaOH absorption followed by titration with HCl (Alef, 1995). Organic carbon was determined on oven-dried samples using a WÖSTHOFF CARMOGRAPH 12 device. Determination of the total nitrogen content was performed using a 3 BRAN LUEBBE Autoanalyzer (Salazar, 2008). Soil P available was determined by the Bray method (Tan, 1996). 2.3. Enzyme assays Dehydrogenase (E.C. 1.1.1) activity was estimated by the addition of 3% aqueous solution of 2,3,5-triphenyltetrazolium chloride (TTC) to 6 g soil followed by incubation at 37 ◦ C for 24 h. The triphenyl formazan (TPF) was then extracted with

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Table 1 General characteristics of stands selected in Honfría forest area. Confidence intervals p = 0.05. For each site, mean values in the same column followed by different letters are significantly different. Stand

C. sativa coppice (CC)

C. sativa orchard (CO)

Q. pyrenaica (Oak)

Altitude (m.a.s.l.) Soil type L.A.I. (m2 m−2 ) leaf area index Long term mean P (mm) (annual rainfall) Mean annual temperature (◦ C) Size of trees (DBH cm) Tree age (years) Tree height (m) Diameter at breast height (cm) Shoot density (shoot ha−1 ) Basal area (m2 ha−1 )

1015 Umbric Regosol 2.9 1590 10.8 5.35–19.3 70 15.3 ± 1.3c 12.90 ± 1.7b 1 892 ± 100b 28.40 ± 8c

950 Umbric Leptosol 3.1 1530 11.1 18.9–22.7 85 8.90 ± 0.8a 20.40 ± 3.0c 382 ± 30a 18.50 ± 4a

950 Umbric Leptosol 2.5 1530 11.1 5.2–23.6 75 12.2 ± 1.0b 11.60 ± 1.5b 2 960 ± 125c 26.50 ± 7c

methanol and determined colorimetrically at 485 nm. Acid phosphomonoesterase (E.C. 3.1.3.2.), ␤-glucosidase (E.C. 3.2.1.21) and arylsulphatase (E.C. 3.1.6.1) activities were determined by incubating soils with a substrate containing p-nitrophenyl sulphate solution and quantified spectrophotometrically, measuring the optical density of its coloration and comparing it with the optical density of a standard solution of p-nitrophenol liberated by an enzymatic hydrolysis. In these cases, the enzymatic activity is expressed in ␮mol p-nitrophenol g−1 h−1 . Acid phosphomonoesterase activity was determined by using p-nitrophenyl phosphate as substrate and incubating it at pH 5.0 (Modified Universal Buffer) and 37 ◦ C. After 30 min, 2 M CaCl2 was added (to stop the reaction and to avoid the brown coloration caused by organic matter) and the liberated p-nitrophenol was extracted with 0.2 M NaOH (Tabatabai and Bremner, 1969). The activity of ␤-glucosidase was determined as described previously for phosphomonoesterase activity, with the modification that the substrate was p-nitrophenyl-␤-glucopyranoside and the liberated p-nitrophenol was determined by THAM–NaOH (0.1 M, pH 12; Eivazi and Tabatabai, 1988). Arylsulphatase activity was determined with p-nitrophenyl sulphate as substrate, incubating soils at pH 5.8 (acetate buffer 0.5 M) and 37 ◦ C for 1 h (Tabatabai and Bremner, 1970). For urease activity determination, we used the Kandeler and Gerber (1988) method. After the addition of aqueous (controls) or a buffered urea solution (samples) to 5 g of soils samples, they were incubated for 2 h at 37 ◦ C. Liberated ammonium was extracted with potassium chloride solution after being shaken for 30 min and filtered in order to prevent the interference of possible precipitates. The method used to determine urease was based on the reaction of sodium salicylate with NH3 in the presence of sodium dichloroisocyanurate which forms a green-colored complex under alkaline pH conditions, and the extinction was measured at 690 nm. 2.4. Statistical analyses Canonical correlation analysis (CCA) was used to interpret the relationships between soil microbiological and soil biochemical variables (Nash and Chaloud, 2002). The CCA was intended to derive a linear combination (canonical variate) of the two sets of variables and to indicate which specific variable was responsible for discriminating among the forest management practices. Therefore, the variance of the original sets of variables was redistributed into a few pairs of canonical variates to maximize the correlation between them. These canonical correlation coefficients were interpreted as ordinary Pearson correlation coefficients and their significance evaluated via a chi-square statistic. The proportion of variance of a canonical variate explained by an original variable was measured by a squared canonical correlation (ri 2 ) or eigenvalue. The value of ri 2 can be considered as the

multiple regression correlation between the two sets of variates, which measures the adequacy of the overall fitted model (Gittins, 1985). The CCA for each forest management practice was interpreted through a biplot representation that displayed a matrix of canonical correlations between two sets of standardized variables X1 (microbiological variables) and X2 (biochemical variables). To show in which sense the structure correlations were optimal, it was derived a “rank r weighted least-square approximation” between the two canonical variate matrices. In this latter approximation, we took as weight matrices the inverses of the sample correlation matrices; this choice made the loss function independent of linear transformation of X1 and X2 . Additionally, an alternative and univariate ANOVA approach was used to examine each variable separately to see if a significant difference existed between any pairs of the FMP examined but independently of the variable correlations occurring in them. 3. Results 3.1. Matrix correlations Among the FMPs, there were few correlations between soil microbiological and soil biochemical variables, thus only five to nine of seventy two possible correlations were significant and higher than r = 0.5 (Table 2). Firstly, soil moisture was highly correlated with soil respiration in CC and Oak and also correlated well with ␤-glucosidase activity, but only in CC, although this enzyme activity mainly responded to C soil content. In the same direction, there was a similar correlation between soil moisture and acid phosphatase activity, but again C soil content better explained this latter enzyme activity in CC. However, these former two enzyme activities responded differently in the other two FMPs, acid phosphatase activity was mediated by N soil content in CO, while soil base saturation (K) percentage was the best variable for explaining both ␤-glucosidase and dehydrogenase activities in Oak. Once more in CO examination, arylsulphatase activity was more related to C soil content than N soil content that, in turn, became a better variable to explain both acid phosphatase and dehydrogenase activities. The correlations described above will be better understood from the following detailed comparison of each variable in each FMP separately (Table 3). More natural forest conditions in Oak contained the highest C and N soil contents, but at the same time, the lowest soil base saturation (K) percentage; in both cases Oak differed significantly from the other two FMPs, but no differences were found in relation to P soil contents. Consequently, the above mentioned transition was primarily conditioned by differences in either C and N soil contents or base saturation (K) percentage while was not constrained by changes of P soil contents.

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Table 2 Pearson correlation matrix between soil microbiological and soil biochemical parameters. Values in bold indicate significance level at p < 0.001. CC: chestnut coppice; CO: chestnut orchard and Oak: Quercus pyrenaica forest. ns = not significant. Plots

Variables

pH (water)

Soil moisture

C

N

C/N

P

Ca

K

CC

Soil respiration Total microbiota P solubilising bacteria Dehydrogenase Urease Acid phosphatase Arylsulphatase ␤-Glucosidase

ns ns ns ns ns ns ns ns

0.577 ns ns ns ns 0.471 ns 0.516

ns ns ns ns ns 0.519 ns 0.604

ns ns ns 0.514 ns ns ns 0.486

ns ns ns ns ns ns ns ns

ns ns ns ns ns ns ns ns

ns ns ns 0.584 ns ns ns ns

ns ns ns 0.558 ns ns ns ns

CO

Soil respiration Total microbiota P solubilising bacteria Dehydrogenase Urease Acid phosphatase Arylsulphatase ␤-Glucosidase

ns ns ns 0.495 ns ns ns ns

ns ns ns ns ns ns ns ns

ns ns ns ns ns ns 0.581 ns

ns ns ns 0.562 ns 0.518 0.493 ns

ns ns ns ns ns ns ns ns

0.514 ns ns ns ns ns ns ns

ns ns ns ns ns ns ns ns

ns ns ns 0.640 ns ns ns ns

Oak

Soil respiration Total microbiota P solubilising bacteria Dehydrogenase Urease Acid phosphatase Arylsulphatase ␤-Glucosidase

ns ns ns ns ns ns ns ns

0.507 ns ns ns ns ns ns ns

ns ns ns ns ns ns ns ns

ns ns ns ns ns ns ns ns

ns ns ns ns ns ns ns ns

0.501 ns ns 0.497 ns ns ns ns

ns ns ns ns ns ns ns ns

ns ns ns 0.578 ns ns ns 0.503

Independently of which variable is to be analyzed, it was noteworthy that in CC and CO, either C and N soil contents or base saturation (K) percentage were closely related to any enzyme activity, such as dehydrogenase, acid phosphatase or ␤-glucosidase. However, in Oak, C and N soil contents were not good explanatory variables for these same enzyme activities, instead they were replaced by soil base saturation percentage as the only variable able to explain the activity of both dehydrogenase and ␤-glucosidase activities. As a result, according to natural soil conditions in Oak, we assumed that most of the enzyme activities reached their highest levels at highest C and N soil contents but at lowest soil base saturation percentage while were not associated at all with P soil availability. In a similar way, soil respiration, as a measure of soil function, was significantly higher in CC than the other two FMPs. This situation made possible the correlation between P soil contents and soil respiration in CO and Oak but not in CC in spite of all CMPs having similar P soil contents. Finally, summarizing the correlations between both sets of microbiological and biochemical variables, the transition estab-

lished among FMPs can be shown in a discriminate analysis (Fig. 1). In the ordination plot, CC soil samples tended to separate from the remaining samples along the first horizontal axis (F1) while CO and Oak soil samples tended to disperse more along the second vertical axis (F2) rather than the first horizontal axis (F1). It was evident a total separation between the two extreme-tree density FMPs (CC and Oak) while CO remained between them sharing low tree density properties in the first case and identical soil conditions in the second one. 3.2. Canonical correlation analysis (CCA) Since the aim was to interpret CCA using biplot representations (Gabriel, 1971) of either weight or correlation structure matrices, we determined how many dimensions were necessary to explain the relationships between both sets of latent variables. According to this, the first and second two canonical variates were the only significant ones (p < 0.001) (Table 4) besides having the highest singular values among all possible correlations (ri > 0.886 for all FMP) (Table 5). Furthermore, the second canonical correlation was also

Table 3 Individual ANOVAs for each variable in both soil microbiological and soil biochemical parameters. CC: chestnut coppice; CO: chestnut orchard and Oak: Quercus pyrenaica forest. Numbers presented are mean values. Values in bold indicate significance level at p < 0.001. Variables

Groups

Soil respiration Total microbiota P solubilising bacteria Dehydrogenase Urease Acid phosphatase Arylsulphatase ␤-Glucosidase

Soil microbiological parameters

Pr > F 0.004 0.235 0.046 <0.001 0.027 0.003 <0.001 0.001

CC 17.612a 79.850a 16.150a 31.750a 31.390a 41.174a 20.482a 8.855a

pH (water) Soil moisture C N C/N P Ca K

Soil biochemical parameters

0.000 0.094 <0.001 <0.001 0.032 0.786 0.005 <0.001

5.238a 20.730ab 72.646a 25.290a 16.727b 73.055a 371.882a 197.468a

CO 13.972b 79.233a 9.467b 17.933b 26.997ab 22.636b 10.610b 6.768b 5.128ab 18.830b 63.421b 20.523b 17.506ab 82.027a 328.500a 152.367b

Oak 14.231b 79.033a 28.167ab 11.033b 25.623b 31.392ab 11.742b 5.291b 4.965b 22.593a 89.178c 27.540c 18.054a 72.017a 156.443b 87.412c

S. Salazar et al. / Ecological Engineering 37 (2011) 1123–1131

Fig. 1. Canonical correlation analysis between soil samples taken in each of the three chestnut management plots considered (CO, CC and Oak).

high for each FMP, then as have stated before, the first two pairs of canonical variables were highly correlated. Therefore, it was seen that variable weights were well represented in the first two canonical coordinates for all FMPs. Hereafter, as have stated before only those linear combinations of an original variable with a variate in both significant canonical pairs identified. But as clarification, a coefficient or weight value indicates the relative contribution of each standardized variable, in the presence of others, to a specific canonical variate by both magnitude and direction (positive or negative) when the biplot representation is being examined (Fig. 2). The inter-set structure correlations (Table 6) describe the relationship between the original variable and its canonical variate or its opposite. These types of correlations did not reflect multivariate relationships, instead each correlation value functioned as one of many univariate relationships between one variable and its canonical variate, but without considering the existence of other variables. Additionally, these inter-set structure correlations were obtained by a DVS of a matrix pondered by their inverse matrices and represented in biplot representations (Fig. 2). The CCA revealed one useful relationship pattern between soil microbiological and soil biochemical variables. The highest correlation found between the variables’ information, the rank of the model, the standardized coefficients and the canonical inter-set structure helped to show an interesting pattern of relationships.

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From the analysis of either the inter-set structure correlations (Table 6) or the biplot representations (Fig. 2), we found the best soil structure and function properties in the lowest tree density FMP (CC) and the worst one in the highest tree density conditions (Oak). For example, in the first canonical variate axis, CC had the major number of relationships between soil microbiological and soil biochemical variables, where dehydrogenase and ␤-glucosidase activities and, in less extent acid phosphatase activity, respond to both C and N soil contents and soil base saturation percentage changes while soil respiration was more related to soil moisture. Also, in the second canonical variate axis, arylsulphatase and acid phosphatase activities along with P solubilising bacteria counts were strongly associated with C/N ratio. However, when considering the other two FMPs, C and N soil contents were less powerful in explaining any enzyme activities but soil base saturation percentage was still an important explanatory variable. Since CO was considered to be intermediate between the other two FMPs because of its dual arrangement of low tree density with Oak forest soils alike, then it was expected to match similar patterns to the other FMPs assessed. In this case, we found that there was not a significant relationship presented in the first canonical variate except that between soil respiration and soil moisture or soil base saturation percentage. Although dehydrogenase and arylsulphatase activities had the greatest response to C and N soil contents in the second canonical variate. As expected, in natural forest conditions of Oak, C and N soil contents were not related to any canonical variate axes while soil base saturation percentage was still explaining both dehydrogenase and ␤-glucosidase activities. At the same time, it was noticeable that the relationship between soil respiration and soil moisture did not change due to the FMP applied and also that dehydrogenase and ␤-glucosidase activities were the only ones to have high responses for all FMPs. Finally, other differences found when comparing FMPs were related to P soil content, which was the best variable for predicting the behaviour of both dehydrogenase and ␤-glucosidase activities but only in natural forest conditions of Oak. 4. Discussion To fully understand the ecological properties in soil, we will need to encompass both the biotic and abiotic interactions and responses of various factors to reflect soil diversity complexities. Furthermore, since the correlations declined towards lower values from one FMP to another it therefore implied a transition from CC, with the highest number of correlations, to Oak with the lowest number of them. This ongoing transition can be explained by examining the strength of each correlation change through the FMPs.

Table 4 Test of significance of the canonical correlation analysis (CCA) between biochemical parameters and nutrients. DF: degree of freedom. Values in bold indicate significance level at p < 0.001. Axis

Lambda

F

DF1

DF2

Test of Lambda (Wilks) F1 F2 F3

0.025 0.114 0.315

3.137 2.146 1.427

72 56 42

269.138 242.258 214.521

p <0.0001 <0.0001 0.055

Table 5 Canonical correlation (ri ) for each chestnut management practice. ri

F1

F2

Canonical correlation (ri ): singular values or eigenvalues CC 0.886 0.798 CO 0.895 0.869 Oak 0.967 0.872

F3

F4

F5

F6

F7

0.591 0.773 0.804

0.541 0.629 0.654

0.491 0.491 0.519

0.245 0.322 0.446

0.191 0.292 0.301

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Table 6 Inter-set structure correlation of soil microbiological parameters and soil biochemical parameters. ns = not significant. Variable

Groups

CC F1

F2

F1

F2

F1

F2

Respiration Total microbiota P solubilising bacteria Dehydrogenase Urease Acid phosphatase Arylsulphatase ␤-Glucosidase

Soil microbiological parameters

−0.549 ns ns −0.765 ns −0.525 ns −0.668

ns ns 0.578 ns ns 0.693 0.725

−0.604 ns 0.406 0.485 ns ns ns ns

0.724 ns ns 0.700 ns ns 0.526

−0.896 ns ns −0.707 ns ns ns −0.636

ns ns ns ns ns ns ns ns

pH (water) Soil moisture C N C/N P Ca K

Soil biochemical parameters

ns −0.562 −0.757 −0.681 ns ns −0.536 −0.785

ns ns ns ns 0.525 ns ns ns

0.488 −0.577 ns ns ns ns ns 0.778

ns ns 0.671 0.743 ns ns ns ns

ns −0.522 ns ns ns −0.649 −0.467 −0.479

ns 0.614 ns ns −0.607 ns ns ns

Quality of available organic compounds is the most important factor to consider in assessing soil microbial community relationships (Wardle and Giller, 1996), because it provides information about the soil substrate biochemical composition that, in turn, it reveals nutrient availability to microorganisms. Soil organic matter has been considered an important indicator of soil quality because of its character of nutrient sink and source that can enhance soil physical and chemical properties and also promote biological activity (Jimenez et al., 2002). Despite the significance of OM content in the soil quality, this parameter did not offer much response to environmental changes in the soil. In contrast, the biological processes involved in its transformation are more sensitive to environmental perturbations of the different origin. For example, the results of Sardans et al. (2008) confirm that moderate increase of soil temperature in spring, autumn or winter when soil water content is greater, can imply a great increase in soil urease activity in comparison with changes among different seasons, where great changes in soil temperature have only moderate effects on soil enzyme activity. Factors influencing soil microbial activity exert control over soil enzyme production and nutrient availability (Sinsabaugh et al., 1993). Soil enzyme activities are “sensors” of soil microbial status and soil physico-chemical conditions (Baum et al., 2003). A significant problem in the valuation of biochemical properties among the evaluated sites is that we cannot use these properties as valid indicators and independent of the soil type, season or ecological perturbation (Nannipieri et al., 2002). Some studies have focused their efforts in determining indexes that could be adapted to particular situation as those elaborated by Trasar-Cepeda et al. (1998), in soils of Galicia. These authors established indexes that discriminated also among acid forest soils under climax vegetation and soils under some management types, restoration activities or different degrees of contamination (TrasarCepeda et al., 2000). However, there is an alternative to formulation of different indexes, consisting in the use of a multivaried statistics of the biochemical data to find what type of enzymatic activity explain better the variability of data and show differences among soils submitted to different conditions of management (Kandeler et al., 1999). Under this perspective, the variance contained in the principal axes of the canonical ordination shows that the enzyme dehydrogenase was, in general, the microbiological variable that explained more variance in the first canonical variable in each one of the three sites studied, while no one biochemical variable presented a gradient of response to the imposed conditions among sites.

CO

Oak

˜ Quilchano and Maranón (2002) reported that differences in plant species composition and soil properties could be responsible for the variation in dehydrogenase, and also that this enzyme activity was positively and significantly correlated with soil pH but no correlation with total soil C and N. However, Leiros et al. (2000) reported a clear positive relationship with soil C, probably in the forest ecosystems they studied soil microorganisms are nutrient rather than C limited. Since dehydrogenase did not respond to the variation of C contents or the C:N ratio, rather site factors (forest canopy, species composition, soil texture, soil pH and available nutrients) and seasonal sampling data were the greater determinants of the variation in dehydrogenase than management factors ˜ (Quilchano and Maranón, 2002). Nevertheless, it is also very difficult to compare the values obtained with different experimental protocols (especially in the case of enzymatic activities), either because there are no standardized methods or due to the soil samples been subjected to different pre-treatments prior to their analysis, such as pH, sample collection, and sample storage, prior to their analysis (Gil-Sotres et al., 2005). Soil basal respiration is considered to reflect the availability of C for microbial maintenance and is a measure of basic turnover rates in soil (Insam et al., 1991). The growth or microbial activity measured as soil respiration was higher in chestnut coppice stand than in the chestnut orchard and oak stands, that were significantly similar. At the same time, the only correlated nutrient with this microbiological variable was the P, but only in the chestnut orchard and oak stands. This result shows that the high microbial activity was independent of the P availability and the other biotic and abiotic regulating the transformation of this nutrient in the soil are responsible of the established differences. Therefore, there was a clear indication that the proposed transition was largely due to soil condition changes affected by the type of FMP implemented, which went from one FMP with low tree density (CC and CO, about 1892 and 382 trees ha−1 , respectively) to other with high and natural tree density (Oak, about 2960 trees ha−1 ). It needs to be clarified that CO and Oak plots were adjacent to each other, hence CO soil conditions were highly conditioned by the natural Oak forest prior to conversion. This null correlation also involves that the enzymatic reaction of the acid phosphatase, associated with the transformation of organic P, neither was activated by the lower P availability. As it was stated before, the FMP altered microbial community function but not the counts on total and PSB microbiota, since there were no significant differences in total culturable microbiota or phosphate solubilising bacteria among them. We speculate that

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Fig. 2. Biplot representation between biochemical parameters and nutrients based on CCA.

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the microbial community structure could be maintained constant among FMPs due to the lack of fluctuations in P soil contents but, at the same time, without responding to changing levels of either C and N soil contents or base saturation (K) percentage. There are contradictory reports concerning the relationship between this enzyme and the inorganic nutrient content in the soil (Speir and Ross, 1978). Kang and Freeman (1999) could not find a direct inverse relationship between this enzyme activity and nutrient concentration in each forest ecosystem. Microorganisms compete with plants for P and the annual P demand of microorganisms can exceed that of plants. This suggests that soil microorganisms which acquire P more efficiently are more rapid in their response to an increased supply of P, or have greater demand for P than the oak trees (Schneider et al., 2001). This is likely the main reason for the low P, since Al and Fe are the key factors controlling P-fixation and availability of P for microbes in soils (Giesler et al., 2004). On the contrary, organic matter, carbon and nitrogen are the components that are really originating the differences in the quality of soil among evaluated sites. At the ecosystem level, can be considered the use of properties that are related to the cycles of C, N and P, especially when related to the transformation of the organic matter in the soil; or properties related to the size, diversity and activity of microbial biomass as well as to the activity of the soil hydrolytic enzymes (Gil-Sotres et al., 2005). Likewise, it was found that the associated enzymes with the transformation of these nutrients, as arylsulphatase, ␤-glucosidase and dehydrogenase, were also indicators of the differences between the different managements. In Tables 2 and 6 we can see how these three enzymes separate clearly the oak, chestnut coppice and chestnut orchard sites, whereas these nutrients are the only ones in allowing to follow differences among the three sites. Otherwise, this result allows us to ratify that the phosphatases are not good indicators to differ the soil quality among sites due to the P content which do not vary significantly. Normally the 40% of the consulted published papers used a general biochemical parameter such as microbial biomass C, dehydrogenase activity, soil respiration, nitrogen mineralization capacity, FDA hydrolysing capacity, while the remaining 60% considered a specific biochemical parameter such as urease or phosphatase activities (Gil-Sotres et al., 2005). The carbon amounts explain the differences found among sites, positive correlations were establish among carbon and ␤glucosidase and the acid phosphatases in the chestnut coppice, whereas in chestnut orchard and oak stands this correlation was not found. A lot of studies have confirmed the positive correlations among these enzymes and organic carbon (Frankenberger and Tabatabai, 1991; Dick et al., 1988; Eivazi and Tabatabai, 1990; Deng and Tabatai, 1997; Santruckova et al., 2004). Likewise, the enzyme arylsulphatase was also positively correlated to the carbon, but in the oak stand differences were observed according to the cover type. Nevertheless, other studies have reported that ␤glucosidase was also correlated with the nitrogen (Dick et al., 1988). In this study we established a small correlation in orchard and oak stands; however, the hydrogenases in the coppice and oak stands were strongly correlated with the nitrogen, whereas the acid phosphatases and the arylsulphatases were only correlated with the oak stand. Our results indicate that the enzymatic activity of ␤-glucosidase, arylsulphatase and dehydrogenase is lower in the oak stand, where the highest organic matter and total nitrogen available amounts are present. Previous studies of Eivazi and Tabatabai (1990) support ␤-glucosidase activity and was found to be positively correlated with organic C. The functional similarity between the communities of microorganisms among sites showed that neither the residual materials which are involved in the litter of the three sites nor

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the soil type nor the quality of management of residues has high impacts on the nature of community. The correlations show that the activity of ␤-glucosidase and arylsulphatase in chestnut coppice stand that were the enzymes more associated with the organic matter components. These enzymes were also related with nitrogen and this indication can correspond as the quality of soil organic matter as the nitrogen availability. Many studies have reported strong positive correlations among acid phosphatase activity, ␤glucosidase activity and microbial biomass (Kandeler and Eder, 1993; Nannipieri et al., 1983). Phosphatase activity is largely the parameter most related with the organic matter components. However, we detected a lack of the same high dependence of urease and ␤-glucosidase activities on SOC. As also reported by Wick et al. (2002) for ␤-glucosidase, we found that ␤-glucosidase and urease activities correlated more with WSC than with SOC, probably due to the relation of these enzymes with the conversion of specific compounds as one fraction of the whole soil organic matter stock rather than total content. Moreover, these enzyme activities seem not to be influenced by climatic conditions, as also observed by Trasar-Cepeda et al. (1998) and this enforces the idea that they may respond to soil organic matter quality and nutrient availability, since correlation with available phosphorus was observed. The three enzyme activities implied in the cycles of C (␤-glucosidase), N (urease) and P (acid phosphatase) inform on the biochemical potential of a soil and its possible resilience (Taylor et al., 2002). The arylsulphatase activity decreases in the oak stand, which contains a higher amount of organic matter. Deng and Tabatai (1997) reported a strong correlation between the arylsulphatase activity and organic carbon amount, bearing the hypothesis that the enzymes in the soils are limited by the clay and humic colloids and the association with the humic substances is an effective form to protect these enzymes in the environment of the soil. For Speir et al. (1984), the arylsulphatase is not very highly correlated with any other enzymatic activity; this apparent lack of coupling between the sulphur cycle and the other major nutrient cycles. For arylsulphatase and urease showing that the total and extracellular activity of these enzymes are mainly associated with microbial biomass in soils (Dodor and Tabatabai, 2005). It is also possible that variation in microbial community structure and activity induced by decomposition of crop residue may modulate microbial biomass and extracellular enzymes that are released into soil by microbial biomass (Ritz et al., 1997).

5. Conclusions The obtained results when we apply a CCA analysis show the narrow relation between the two groups of variables in the chestnut coppice stand, being significant the two first canonical variables, showing that these two whole set of data were not independents. The structure of correlation among the variables is different in three plots. For soil microbiological and soil biochemical parameters, the variables dehydrogenase and K contribute more to the first pair of canonical variates. Correlations between soil biochemical and soil microbiological variables showed that the three different forest management practices had also a strong effect on soil function conditions. In a discriminate analysis, CC and Oak were discriminated clearly, while CO was in the middle of the biplot sharing some properties with each of the two different groups. Thus, we proposed a soil property transition from the best soil structure and function properties at one chestnut management properties with low tree densities (CC and CO) to other with the worst ones at highest tree density conditions (Oak). According to natural soil conditions in Oak, we

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