Microbial community structure and resource availability drive the catalytic efficiency of soil enzymes under land-use change conditions

Soil Biology & Biochemistry 89 (2015) 226e237

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Microbial community structure and resource availability drive the catalytic efficiency of soil enzymes under land-use change conditions Alexander Tischer a, Evgenia Blagodatskaya b, c, *, Ute Hamer a, d a

Institute of Soil Science and Site Ecology, Dresden University of Technology, Pienner Str. 19, 01737 Tharandt, Germany Institute of Physicochemical and Biological Problems in Soil Science, Russian Academy of Sciences, Institutskaya 2, 142290 Pushchino, Russia c €ttingen, Büsgenweg 2, 37077 Go €ttingen, Germany Department of Soil Science of Temperate Ecosystems, University of Go d Institute of Landscape Ecology, WWU e University of Münster, Heisenbergstraße 2, 48149 Münster, Germany b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 April 2015 Received in revised form 14 July 2015 Accepted 16 July 2015 Available online 30 July 2015

The turnover of nutrients bound to organic matter is largely mediated by extracellular hydrolytic enzymes (EHEs) produced by soil microorganisms. However, little is known about the environmental drivers (e.g., soil pH, C content, C:N ratio) of the catalytic properties of EHEs and their functional link to the structure of soil microbial communities. We linked catalytic properties, Km and Vmax, determined by MichaeliseMenten kinetics, to a set of environmental and microbial properties in the soils of a land-use sequence ranging from undisturbed natural forest to pastures of different ages and to secondary succession in the Andes of southern Ecuador. The sensitivity of the substrate affinity constant (Km) and the maximum rate (Vmax) of six EHEs (b-cellobiohydrolase (CBH), b-glucosidase (BG), N-acetylglucosaminidase (NAG), a-glucosidase (AG), xylanase (XYL), acid phosphomonoesterase (AP)) to changing environmental conditions was tested by fluorogenic substrates. We used the Vmax-to-Km ratio (Ka) as a proxy for the catalytic efficiency and the signature membrane phospholipid fatty acids as a proxy of microbial community structure. Microbial communities adapted to environmental changes, selected for enzymes with higher substrate affinity (Km) and catalytic efficiency (Ka) compared with pure cultures. Along the land-use sequence, catalytic efficiency increased from natural forest to young pasture, while it decreased during long-term pasture use and secondary succession. This is consistent with three to five times faster turnover of tested substrates (estimated based on MichaeliseMenten kinetic parameters) at the young pasture compared with the long-term pasture and secondary succession. Environmental drivers of the Km were enzymespecific (e.g., the pH for XYL, the C:N ratio for AP, and the C availability for NAG) and differed from those for Vmax. A decoupled response of Vmax and Km to land-use changes observed for AG, BG, CBH, XYL, and AP, implies divers consequences for ecosystem processes mediated by these enzymes. A high abundance of Gram(
) bacteria triggered the catalytic properties (Km and/or Ka) of enzymes decomposing cellulose, hemicellulose, starch, and monophosphoesters. The importance of climatic factors for catalytic properties of EHEs was emphasized by the Ka values extracted from the literature and demonstrated good correspondence of Ka between soils from geographically distinct experimental plots. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Extracellular enzyme activity MichaeliseMenten kinetics Isoenzymes PLFA Soil organic matter turnover The Andes of southern Ecuador

1. Introduction The cycling of major biogenic elements such as carbon (C), nitrogen (N), and phosphorus (P) in terrestrial ecosystems is susceptible to global change phenomena like increases in temperature

* Corresponding author. Department of Soil Science of Temperate Ecosystems, €ttingen, Büsgenweg 2, 37077 Go €ttingen, Germany. University of Go E-mail address: [email protected] (E. Blagodatskaya). http://dx.doi.org/10.1016/j.soilbio.2015.07.011 0038-0717/© 2015 Elsevier Ltd. All rights reserved.

(Allison et al., 2010) and changes in land use (Don et al., 2011). Especially in the tropics, the conversion of natural forests to arable land and pastures as well as the abandonment of degraded arable land is increasing all over the world (Bai et al., 2008). These shifts in land use are the main factors changing environmental properties such as soil pH (Ehrenfeld et al., 2005), and quantity and quality of organic compounds (e.g., C, N, P molar ratios) important for the metabolism of heterotrophic microbial communities (Cleveland and Liptzin, 2007). The co-occurrence of ecosystems differing in age after forest conversion represents a soil environmental gradient

A. Tischer et al. / Soil Biology & Biochemistry 89 (2015) 226e237

under similar climatic conditions. An example of a human-induced environmental gradient is the conversion of natural forests to pastures. In the study area, the south Ecuadorian Andes, burning forest biomass raises the pH (þ2 pH units) and increases the nutrient availability of the topsoils of the newly established pastures. During land use, additional burning and nutrient translocation into plant biomass cause a decrease in the pH and nutrient stocks in the soil (Hamer et al., 2013). Such changes in the soil environment and nutrient availability cause substantial variation in the biomass and composition of microbial communities in the study area (Tischer et al., 2014b) as well as in other ecosystems (Lauber et al., 2008; Fierer et al., 2009). These changes affect the relative domination of organisms exhibiting various microbial life strategies (Fierer et al., 2007) that are differentiated by growth rate and substrate affinity for the enzyme systems (e.g., copiotrophs with high growth rate and low substrate affinity vs. oligotrophs with low growth rate and high substrate affinity Killham and Prosser, 2015). In turn, such changes affect the turnover and sequestration of nutrients in soil (Cusack et al., 2011; Schimel and Schaeffer, 2012). The decomposition of organic material (OM) is largely mediated by extracellular hydrolytic enzymes (EHEs) produced by soil microorganisms (Swift et al., 1979). Due to functional redundancy, various microbial taxa produce a diverse set of enzymes (isoenzymes) that target the same substrate but differ in biochemical potential (Stres and Tiedje, 2006) and enzymatic adaptation to environmental constraints (Khalili et al., 2011). Thus, the catalytic properties of isoenzymes performing, e.g., the hydrolytic breakdown of polymers to smaller molecules, can differ significantly depending on the soil properties, nutrient availability, and quality and amount of substrate (Wallenstein et al., 2011). The substratedependent catalytic behavior of EHEs approximated with MichaeliseMenten kinetics (the velocity of the enzymeesubstrate reaction as a function of substrate concentration) is a useful tool for determining the sensitivity of EHEs to changing environmental conditions (Marx et al., 2005; Cusack et al., 2011; German et al., 2012; Stone et al., 2012). The parameter of the MichaeliseMenten equation, the Michaelis constant (Km), represents the substrate concentration at the half-maximal enzymatic rate. This Km value is used as an indicator of the apparent affinity of the enzyme to the particular substrate (German et al., 2012) and characterizes the rates of enzymatic reactions at low substrate concentrations, a common situation in soil (Hobbie and Hobbie, 2012). The second kinetic parameter of the MichaeliseMenten equation is the maximum rate of the enzyme-mediated reaction (Vmax) at saturating substrate concentrations. In soil, this parameter represents the potential enzyme activity and depends on the overall isoenzyme concentration (Wallenstein and Weintraub, 2008). The number of studies on Vmax focus either on the fine-scale distribution and catalytic potential of EHEs (Marx et al., 2005) or on the temperature sensitivity and effects of nutrient enrichment on the catalytic properties of EHEs (German et al., 2012; Stone et al., 2012). Less attention is paid, however, to the catalytic behavior of enzymes at substrate limitation. This is mainly due to a lack of methods for determining enzyme activities at substrate concentrations that occur in nature (Hobbie and Hobbie, 2012). Assays based on fluorogenically-labeled (4-methylumbelliferone, 4-MUF) substrates are sensitive to detect enzyme activity at substrate concentrations ranging from micromoles to nanomoles (Marx et al., 2001). Studies that link the apparent substrate affinities of EHEs to the soil microbial community structure are needed (Wang et al., 2012) in order to test how shifts in enzyme functioning are related to microbial community composition (Cusack et al., 2011). Since the parameters Vmax and Km are often interrelated (Kovarova-Kovar and Egli, 1998), the Vmax-to-Km ratio was suggested as better

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proxy of the catalytic efficiency (Ka) than Vmax and Km alone (Moscatelli et al., 2012). The Ka characterizes the inherent catalytic properties of enzymes (Moscatelli et al., 2012), associated with the competitive ability of soil microorganisms (Kovarova-Kovar and Egli, 1998). However, the sensitivity of enzyme catalytic efficiency to environmental gradients caused by land-use changes must be tested experimentally. The incorporation of catalytic properties of EHEs in soil C models (Allison et al., 2010) requires knowledge on the environmental drivers of the parameters that affect the Ka, i.e., the Vmax and the Km. Different Vmax drivers were reported for enzymes with various functions in a study of land-use change (Lauber et al., 2008; Tischer et al., 2014a). Specifically, the Vmax of EHEs that degrade cellulose and chitin was regulated by the amount of soil microbial biomass. In contrast, the Vmax of EHEs involved in degrading hemicelluloses and starch was mainly driven by the quantity and quality of the substrate input, and was not restricted by the abundance of soil microbes. The environmental drivers of the Km and the Ka remain unknown. Therefore, the present study set out to answer the question whether the apparent substrate affinities and catalytic efficiencies of six EHEs involved in C, N, and P cycling are associated with microbial community structure and with changes in soil chemical properties caused by land-use. We analyzed the kinetics of b-cellobiohydrolase (CBH), b-glucosidase (BG), b-xylanase (XYL), and aglucosidase (AG), which are involved in depolymerizing cellulose, hemicelluloses (Wong et al., 1988), and starch (Suzuki et al., 1976). Cellulose is the most abundant carbohydrate biopolymer on Earth that is hydrolyzed by CBH and BG to low-molecular-weight substances (cellobiose, glucose). Hemicelluloses are highly abundant polymeric sugar structures in the primary cell wall of plants (up to 30%) (Barton and Northrup, 2011). Major components of hemicelluloses are xylans, which are hydrolyzed by the action of xylanases to the monomer xylose. Starch, the major carbohydrate storage component of plants, is hydrolyzed by the action of a-1,4glucosidases to glucose (Barton and Northrup, 2011). We also analyzed the kinetics of N-acetylglucosaminidase (NAG) that hydrolyzes N-acetylglucosamine of fungal chitin and bacterial €gel-Knabner, 2006), and thus is linked to mipeptidoglycan (Ko crobial turnover and interacts with both C and N cycles in soil (Beier and Bertilsson, 2013). Particularly in tropical soils, large proportions of P are immobilized in organic, ester-linked P forms (Doolette and Smernik, 2011). Therefore, we investigated the catalytic properties of acid phosphomonoesterase (AP) that catalyze the hydrolysis of monophosphoesters, and then release phosphate for plant and microbial uptake (Nannipieri et al., 2011). The present study on soils in Southern Ecuador is a first attempt to establish linkages between environmental changes caused by land-use, microbial community structure and the catalytic properties of EHEs. The research questions of the study were: How land-use change from forest to pasture will affect the patterns of enzymes' catalytic properties, i.e., Km and Ka? Does microbial community structure matter for enzymes' catalytic properties? Is the rate of enzyme reaction related to substrate affinity or do they vary independently of each other? In addition, we linked our data to the results of available studies (MUF assays only) in order to test whether the observed relationships to environmental drivers hold true at the large geographic scale. 2. Materials and methods 2.1. Site description The study was conducted along a typical land-use sequence in the valley of Rio San Francisco, in the Cordillera Real, an eastern

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range of the South Ecuadorian Andes (3 580 S, 79 50 W) (Beck et al., 2008). The area has integrated the effects of forest disturbance, approximately 55 years of pasture use, secondary succession up to a stage dominated by shrubs, and pasture re-establishment after abandonment. The mean annual air temperature is 15.3  C, the mean annual precipitation is 2176 mm (ECSF 3 580 S and 79 040 W, alt. 1860 m a.s.l.), and no pronounced seasonality exists (Bendix et al., 2006). The natural forest site is characterized as an evergreen, lower montane forest. Pastures were established in the same year after the natural forest was clear cut and burned and then the pasture grass Setaria sphacelata (Schumach.) Stapf & C.E. Hubb. ex Chipp. was planted (Hartig and Beck, 2003). The young Setaria pasture (1930 m a.s.l.; 03 570 2600 S, 79 0201900 W) is 20 years old (established in 1990). The old Setaria pasture (2080 m a.s.l.; 03 570 5300 S, 79 040 3700 W) is 55 years old (1955) and is characterized by repeated, low-intensive burning with which the farmers maintain pasture use and avoid secondary succession, especially invasion by the tropical bracken fern (Pteridium arachnoideum (L.) Kuhn.). The abandoned pasture site (10 years after land abandonment (2001); 2100 m a.s.l.; 03 570 5100 S, 79 040 3700 W), a former Setaria pasture, is overgrown with bracken. The shrubland site (2150 m a.s.l.; 03 580 3000 S, 79 0501900 W) was abandoned approximately 20 years ago (1988) due to severe bracken infestation, and the subsequent succession resulted in a diverse set of herbaceous and shrubby plant species. The four studied soils were classified as Cambisol (WRB), while the soil of the old pasture was classified as Umbrisol (WRB) (FAO, 2006). A detailed description of the characteristics and land-use history of the study sites (except the reestablished pasture) is given in Tischer et al. (2014b). The reestablished pasture (Umbrisol; 2000 m a.s.l.; 03 580 0900 S, 79 0401100 W) is a former Setaria-pasture that was abandoned 11 years (~1997) before the re-establishment experiment described by Roos et al. (2010) was started in 2008 when Setaria sphacelata was planted. 2.2. Soil sampling At each study site, six randomly arranged, replicate plots (natural forest: five; re-established pasture: three) were selected, and at each plot (10  10 m), a composite soil sample consisting of 10 subsamples (mineral topsoil 0e5 cm) was taken with a soil auger (diameter: 6 cm). At the pasture sites, the dense root layer (about ~2e4 cm thick) above the mineral soil was not sampled. Immediately after the sampling was completed, stones and roots were carefully removed. The soil samples were stored at þ4  C for 1 month before the analyses of microbial community structure and enzyme functioning were applied. Storing of soil samples at þ4  C does not affect enzyme activities (DeForest, 2009) and prevents microbial phospholipid fatty acids from rapid degradation (Zelles, 1999). The samples from the replicate plots were treated individually for enzyme and PLFA measurement. 2.3. Soil chemical properties To measure the soil organic carbon (SOC) and total nitrogen (Nt) content, subsamples of soil were dried at 40  C and, ground and analyzed on a CNS analyzer (Vario EL, Heraeus). The total amount of P (Pt) was determined with acid digestion (HNO3, HF, HClO4) in a microwave oven (Tischer et al., 2014a) and subsequent inductively coupled plasmaeoptical emission spectrometry measurements (ICP-OES, CIROS, Spectro). Nutrient ratios (SOC:Nt, SOC:Pt, Nt:Pt) were calculated on an atomic basis. To extract the dissolved organic carbon (DOC) and total dissolved nitrogen (TDN), 60 g of field-moist soil was shaken with 0.5 M K2SO4 (1:3 soil:solution ratio) for 2 h on a reciprocating shaker (180 rpm). The

soil suspensions were stored for 24 h at 4  C and subsequently centrifuged at 4000  g for 15 min (4  C). Afterward, the solutions were filtered through a 0.45-mm cellulose-nitrate filter, and the filtrates were analyzed for DOC and TDN with a multi-NC Analyzer (Analytik Jena, Germany). To determine the available phosphorus (P) fraction, the Bray-P method was applied. Field-moist soil equivalent to 5 g dry matter was extracted with 50 mL of a solution containing 0.03 M NH4F and 0.025 M HCl (Bray and Kurtz, 1945). Samples were shaken at 180 rpm for 1 min and subsequently filtered (low-phosphate filter, grade 131, Munktell, Germany). The inorganic P (Bray-Pi) content in the extracts was measured photometrically with a continuous flow auto analyzer at 880 nm (Skalar Analytik GmbH, Germany). The total Bray-P content of the extracts was determined with ICP-OES (CIROS, Spectro) measurements. The organic P (Bray-Po) fraction was calculated as the difference between the total Bray-P and Bray-Pi. On dried subsamples, soil pH (H2O) was measured potentiometrically in deionized water (1:2.5 soil:solution ratio), and exchangeable aluminum (Alex) was determined by extraction with 0.5 M NH4Cl solution (Tischer et al., 2014a) and subsequently analyzed with an ICP-OES (CIROS, Spectro). 2.4. Enzyme kinetics assay The chemical and biological characteristics of the soil samples used for the enzyme kinetics assay are presented in Table 1. The soil samples used for the kinetic assay represent average soil conditions for the respective study sites (Fig. 1). The activity of EHEs of the mineral soil was measured using fluorescently labeled (4-MUF) substrates according to Marx et al. (2005) and German et al. (2011). Six fluorescent enzyme substrates based on 4-MUF were used (Supplementary Table 1). Before the MUF substrates were diluted in sterile distilled water, they were dissolved in 2 mL of 2-methoxyethanol. To approximate the general acidic conditions of the soils, 0.5 g of field-moist soil samples were suspended and homogenized in 50 mL of 50 mmol sodium acetate buffer (pH 5.0) and subsequently sonicated for 2 min (50 J s
1, in an ice bath to maintain a constant temperature). Fifty microliters of each soil suspension were pipetted in microplates (96-well, flat bottom, black, polystyrene, Grainer bio-one GmbH, Germany) together with 50 ml of sodium acetate buffer (pH 5.0) and 100 ml of each substrate solution (saturation concentration) and were preincubated at 30  C (slightly shaken without air circulation). The time from the addition of the substrate solution to the fluorescence measurement was kept constant during the enzyme assay for all samples (DeForest, 2009). The selected pH and temperature were used to achieve biochemically-defined enzymeesubstrate reactions that corresponded to the environmental conditions of the sites (German et al., 2011). The substrate concentrations used to determine the MichaeliseMenten kinetics of each enzyme ranged between 1 and 360 mmol g
1 soil. Fluorescence was read after 30, 60, and 120 min with a microplate fluorescence reader (Bio-Tek Instruments, Inc., U.S.) at a 360 nm excitation wavelength and a 460 nm emission wavelength. For the calibration curves, solutions were prepared using subsamples of the soil suspensions of each soil and MUF. Following Marx et al.'s (2001) method, background fluorescence was investigated by measuring the rate at which fluorescence increased. Taking the decrease in fluorescence in the presence of soil into account, calibration curves and controls were included in every series of enzyme measurements. The activity of the tested EHEs were expressed in the same units as the MUF release in mmol g
1 h
1 or in nmol g
1 h
1, and the substrate affinity (Km) was expressed as the amount of substrate in mmol g
1. Three analytical replicates were measured for each soil sample for each site.

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Table 1 Soil chemical and biological variables of the soil samples used for the study of enzyme kinetics (mineral topsoil 0e5 cm). Site


1

SOC [mmol g ] Nt [mmol g
1] Pt [mmol g
1] DOC [mmol g
1] TDN [mmol g
1] Bray-Pi [nmol g
1] Bray-Po [nmol g
1] pHH2 O Alex [mmolc g
1] MBC [mmol g
1] MBN [mmol g
1] MBP [mmol g
1]

Natural forest

Young pasturea

Old pastureb

Re-established pasturec

Abandoned pastured

Shrublande

8984 428 14.1 12.3 4.3 21.6 101.5 3.76 52.8 110.1 14.1 2.5

7097 475 33.3 17.0 2.5 64.7 303.6 5.21 2.1 208.3 28.3 8.4

4405 285 15.9 3.9 0.4 50.1 108.1 4.62 44.4 116.5 15.5 4.5

7962 366 16.6 13.9 1.0 29.1 118.4 4.66 93.9 89.4 11.0 2.8

6167 227 11.9 5.0 0.8 22.5 77.9 4.52 62.0 63.7 5.9 2.6

7588 278 8.2 6.8 0.5 46.9 77.5 3.93 85.7 83.0 9.3 2.2

SOC Soil organic carbon (dry combustion, CNS-analyzer). Nt Total soil nitrogen (dry combustion, CNS-analyzer). Pt Total soil phosphorus (acid digestion, ICP-OES). DOC Dissolved organic carbon (K2SO4-extract). TDN Total dissolved nitrogen (K2SO4-extract). Bray-Pi Inorganic phosphorus (Bray-method). Bray-Po Organic phosphorus (Bray-method). Alex Exchangeable aluminum in mmol charge (NH4Cl-extract). MBC Microbial biomass carbon (chloroform-fumigation method, KEC ¼ 0.45). MBN Microbial biomass nitrogen (chloroform-fumigation method KEN ¼ 0.54). MBP Microbial biomass nitrogen (chloroform-fumigation method, Bray-extraction, KEP ¼ 0.40). a Young pasture was established in 1990. b Old pasture was established in 1955. c The re-established pasture was established in 1957 and was abandoned in 1997; re-establishment started in 2008. d Abandoned since 2001, forest conversion in the late 1950s. e Abandoned since 1988, forest conversion in the late 1950s.

2.5. Enzyme kinetics and kinetic parameters Experimental data were analyzed with several models for enzymeesubstrate reactions (Panikov et al., 1992). For our data, the MichaeliseMenten model: [V ¼ (Vmax*[S])/(Km þ [S])],

(1)

where V is the reaction rate as a function of the substrate concentration (S), described the curves best. The MichaeliseMenten constant or apparent substrate affinity of EHEs (Km) and the maximum velocity of the enzymeesubstrate reaction (Vmax) were calculated by fitting the model presented in equation (1) to our

experimental dataset with ModelMaker (Vers. 3.03, Cherwell Scientific Publishing Ltd, Oxford, 1997). Following Moscatelli et al.'s (2012) method, we calculated the catalytic efficiency (Ka) as the
1 ratio of Vmax to Km, expressed in nmolMUF mmol
1 Substrate h . The turnover time (Tt) of the substrates entering the soil was calculated for low- and high-substrate conditions according to the following equation: Tt (days) ¼ (Km þ S)/Vmax (Panikov et al., 1992; Larionova et al., 2007). For the low-substrate concentration, the amount of substrate was set equal to the modeled Km (S ¼ Km) while for the high-substrate concentration 400 mmol substrate per g
1 dry soil was used (S ¼ 400) in order to achieve substrate saturating conditions for the calculation of turnover time of added substrates.

Fig. 1. Triplot of a redundancy analysis (RDA) showing the variability in the soil properties (left panel) and microbial community structure (right panel) along the studied land-use sequence (mineral topsoil 0e5 cm). The first two axes accounted for 57.5% of the variability in the PLFA data, and for 82.2% of the relationships between the microbial community structure and the selected soil data. Pt ¼ total phosphorus, Bray-Pi ¼ inorganic Bray-P, Bray-Po ¼ organic Bray-P, SOC ¼ soil organic carbon, H3Oþ ¼ concentration of hydronium ions ¼ 10
pH(soil), Al ¼ exchangeable aluminum, SOC:Nt and SOC:Pt were calculated with the total element concentrations on a molar basis.

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2.6. Soil microbial biomass and microbial community structure

2.7. Statistical analysis

Microbial biomass carbon (MBC) and nitrogen (MBN) were estimated according to the chloroform fumigationeextraction procedure (Vance et al., 1987). For each sample, one subsample was extracted in 0.5 M K2SO4 (1:5 soil:solution ratio). A second subsample was fumigated with chloroform for 24 h in a vacuum atmosphere, followed by extraction in 0.5 M K2SO4. C and N were measured with a multi-NC Analyzer (Analytik Jena, Germany). For determining the MBC and MBN concentrations, the difference between the fumigated and non-fumigated extracts was calculated, and extraction efficiency of 0.45 and 0.54 was used for MBC and MBN (Joergensen, 1996; Joergensen and Mueller, 1996), respectively. Microbial biomass phosphorus (MBP) was estimated using the chloroform-extraction procedure adapted for acid soils (Chen and He, 2004). For each sample, one subsample was extracted in 0.03 M NH4F and 0.025 M HCl (1:5 soil:solution ratio), and the second subsample was extracted after 24 h of fumigation. The inorganic P content in the extracts was measured photometrically with a continuous flow auto analyzer at a wavelength of 880 nm (Skalar Analytik GmbH, Germany). Phosphate adsorption after cell lysis of microbial biomass was taken into account for the MBP calculations of each sample (for the used P adsorption constants (KP), see Tischer et al. (2014b)). For calculating the MBP concentrations, the difference between the fumigated and non-fumigated extracts was calculated, and the conversion factor of kEP ¼ 0.40 (Brookes et al., 1982) was applied. The microbial nutrient ratios (microbial C:N, microbial C:P, microbial N:P) were calculated on an atomic basis. In order to approximate microbial community composition total phospholipid fatty acids (PLFA) were extracted and analyzed according to Zelles' et al. (1995) method. Briefly, field-moist soil (equivalent to 6.25 g dry matter (DM)) samples were extracted with a one-phase extraction mixture containing chloroform: methanol: phosphate buffer (1:2:0.8). Phospholipids were separated on a silicic acid column (2 g/12 mL, Varian) and subjected to a mild alkaline hydrolysis. The unsubstituted fatty acid methyl esters (FAMEs) were derived from the phospholipid fraction (aminopropyl column: 0.5 g/3 mL, Machery & Nagel) and separated according to their degree of saturation on a benzenesulfonylpropyl column (0.5 g/3 mL, Varian) as saturated, monounsaturated, and polyunsaturated FAMEs. Directly prior gas chromatographic analysis, monounsaturated fatty acids were derivatized with dimethyl disulfide (DMDS, Fluka). To identify and quantify the FAMEs, a gas chromatograph equipped with flame ionization detector (GC 2010, Shimadzu) and a polar capillary column (30 m/0.25 mm BPX70, 0.25 mm film, 30 m  0.25 mm, SGE) was used. Methyl nonadecanoate (19:0, SigmaeAldrich) was used as an internal standard and was added to the sample directly before sample injection. The temperature program was 40  C for 2 min, increase to 100  C at 20  C min
1, hold for 1 min, increase to 140  C at 5  C min
1, and finally to 250  C at 2  C min
1. The detector was maintained at 260  C and the injector at 240  C. Peaks were identified by comparison with standards (BAME and FAME mix, 10Me16:0, SigmaeAldrich). Identified PLFAs (30 in total) were assigned to specific microbial groups. Gramnegative bacteria (Gram(
)) were represented by the fatty acids of cy17:0, cy19:0, 16:1n7c, 18:1n7c, 18:1n9c; Gram-positive bacteria (Gram(þ)) by i15:0, a15:0, i16:0, i17:0; actinomycetes by 10Me16:0, 10Me18:0; fungi by 18:2n6,9c, 18:2n6,9t; and protozoa/animal by 20:4 (Ratledge and Wilkinson, 1988; Zelles, 1999). The relative abundances of single PLFA markers were expressed in mol% of the total amount of PLFA in the samples in order to normalize for differences in PLFA biomass.

All variables were described with the mean and standard error (SE) or standard deviation (SD), respectively. Data were checked for normality and homogeneity of variance, and, if necessary, were transformed using the BoxeCox transformation (Osborne, 2010). Differences in the Km between sites were tested with one-way analysis of variance (ANOVA), and pairwise differences were tested with Tukey's honestly significant differences (HSD) test (P < 0.05) (StatSoft, 9th Edn, Tulsa/OK, 2009). To elucidate differences in kinetic properties between tested enzymes in the same sample (dependent sample), repeated-measures ANOVA (RMANOVA) and then the post-hoc LSD test (P < 0.05) were performed (StatSoft, 9th Edn, Tulsa/OK, 2009). Because the calculation of the enzyme catalytic parameters was based on three analytical replicates of one sample for each site, non-parametric Kendall rank correlation statistics were performed to assess associations between the soil chemical/biological properties and the parameters of the enzyme kinetics. Significant correlations at the level of P < 0.05 were considered. To elucidate the associations between the microbial community structure (PLFA data) and the respective soil chemical properties, a redundancy analysis (RDA) was conducted for an extended dataset (see Tischer et al., 2014a, n ¼ 5e6; reestablished pasture n ¼ 3) using Canoco 4.5 for Windows (ter Braak and Smilauer, 2002). Based on the Monte Carlo permutation test (499 permutations, P < 0.05), significant chemical properties were selected and plotted with the PLFA data in an ordination diagram. The RDA ordination diagram focused on inter-sample distances and was based on a covariance matrix (centered PLFA data only). For clarity, PLFA markers that did not contribute to the variability of the dataset or were not well represented by the ordination diagram (short arrows) were removed from the figure (but were included in the calculation). Linear- and non-linear regressions were performed using the respective functions in OriginPro 8.5.0 (OriginLab Corp., 8th Edn., Northampton, 2008). For the analysis of relationships between Vmax and Km (Fig. 3), we calculated linear regressions for i) the dataset including all sites (full model) and ii) a reduced dataset (reduced model) excluding the young pasture site (two to three-fold larger amount of microbial biomass, pH-value more than one unit higher) in order to take non-linear responses of the catalytic properties of the EHEs into account. 3. Results 3.1. Microbial community structure (PLFA) in relationship to soil environment The redundancy analysis of the PLFA marker content clearly distinguished the different land-use types (contribution of the two axes accounted for 57.5 of variability of the PLFA data). The relationships between the PLFA markers and the environmental properties were even stronger and reached 82.2% of the variability as a cumulative effect of the first two axes (e.g., axis 1 contributed 68% of PLFA to environment relationships) (Fig. 1). Both axes mainly represented gradients in the relative abundance of PLFA markers indicative of Gram(
) bacteria. Thus, high relative proportions of 18:1n9c, 18:1n7c, and 16:1n7c were observed at the active and abandoned pasture compared with the other sites tested. The gradient of the environmental properties indicated by the first axis of RDA was associated with soil pH, SOC and Pt concentrations, and stoichiometric soil C:N:P ratios. Specifically, the highest proportions of 18:1n9c, 18:1n7c, and 16:1n7c were related to the high soil pH, high Pt, and narrow soil C:N:P ratios. Soil microbial biomass

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was highest at the young pasture while further pasture use and secondary succession reduced MBC to or below forest level (Table 1). 3.2. Apparent substrate affinity (Km) of enzymes involved in C cycling The relationships between the substrate concentration and enzyme activity were hyperbolic and thus were well described with MichaeliseMenten kinetics (Fig. 2). Substrate saturation was obtained by each enzyme in each sample (Supplementary Fig. 1). Fig. 2 shows the enzyme substrate behavior for the natural forest as well as for AP as an example. Substrate affinity varied substantially among the enzyme types (Supplementary Table 2). The highest substrate affinities (i.e., lowest Km values) were obtained for CBH and for BG. The substrate affinities of AG and XYL were significantly lower than for CBH and BG. The affinities of individual enzyme types varied between sites.

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The substrate affinities of AG and CBH decreased after the forest was converted to pasture, but increased again during further pasture use and land abandonment. In contrast, the substrate affinity of BG increased from forest to pasture, and was significantly higher in the shrubland compared to the natural forest. The forest conversion to pasture initially had no effect on the substrate affinity of XYL. However, during the course of pasture use, the substrate affinity of XYL increased, and then decreased to the forest level during land abandonment and secondary succession. 3.3. Substrate affinities (Km) of enzymes involved in N and P cycling The substrate affinities of NAG and AP were significantly lower than for CBH and BG. No differences between sites were observed for the Km of NAG (Supplementary Table 2). Lowest substrate affinities for AP were found in the natural forest and the shrubland, while for old and abandoned pastures, the substrate affinities of AP were the highest.

Fig. 2. MichaeliseMenten plots (enzyme activity as a function of the substrate concentration) of a) b-glucosidase, xylanase, N-acetylglucosaminidase, and acid phosphatase measured at the natural forest site (mineral topsoil 0e5 cm) and b) acid phosphatase along the land-use sequence in southern Ecuador (mineral topsoil 0e5 cm) are shown. Values are means ± SD (n ¼ 3).

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3.4. Substrate affinity in relation to Vmax and catalytic efficiency of extracellular enzymes Different relationships were revealed between substrate affinity and potential enzyme activity. NAG was the only enzyme, for which the Vmax and the Km were significantly correlated, disregarding whether the young pasture was included (full model) or not (reduced model). The catalytic properties of BG and CBH exhibited pronounced variability in the Km, while the Vmax varied slightly along the land-use sequence (Fig. 3). For the catalytic properties of XYL and AG, neither the full nor the reduced model showed significant relationships between Vmax and Km. However, the exclusion of the shrubland from the latter would result in a strong positive relationship between the catalytic properties (adjusted r2 ¼ 0.9978, P < 0.001, regression not shown). Similarly, AP demonstrated no relationship between Vmax and Km. However, two different groups of sites with the first consisting of the old-, the reestablished-, and the abandoned pasture showed a double increase in Vmax at similar Km values while the second group demonstrated a decrease in Vmax as Km increased from young pasture to the forest and the shrubland. Larger dataset is required within each of revealed groups to draw the conclusions about the relevance of these patterns. Despite the varied relationships between Vmax and Km, their ratio, i.e., the catalytic efficiency of enzymes (Ka), demonstrated a surprisingly clear trend in the land-use gradient. Catalytic efficiency (Ka) commonly increased from natural forest to young pasture but gradually decreased during long-term pasture use and land abandonment back to or below the forest level, with the exception of cellulolytic enzymes at the shrubland site (Fig. 4). 3.5. Relationship of substrate affinities of EHEs to microbial community structure and soil chemical properties

In addition, a strong positive relationship (P ¼ 0.003) between the dissolved organic C and Ka of NAG was revealed (Fig. 5c). The EHEs involved in C cycling showed positive associations with a particular marker for the soil microbial community structure. Briefly, the higher the abundance of Gram(
) bacteria, the higher the catalytic efficiency of AG and XYL (Supplementary Fig. 4aeb). Likewise, the Ka of CBH was positively related to the abundance of actinomycetes (Supplementary Fig. 4c). The not mentioned enzymes showed no significant relationships to soil chemical and biological properties. 3.7. Substrate turnover under low and high substrate availability The fastest turnover of added substrate (1e7 days) was observed for 4-MUF-phosphate (AP) for high- and low-substrate availability (Supplementary Table 3). The turnover time of 4-MUF-2-deoxy-2acetamido-b-D-glucoside (NAG), 4-MUF-b-D-glucoside (BG), and 4-MUF-b-D-xylopyranoside (XYL) was intermediate (weeks to a month), while the dynamics of the degradation of 4-MUF-a-Dglucoside (AG) and 4-MUF-b-D-cellobioside (CBH) were slow (up to several years). Remarkably, the smallest differences (four to seven times) in turnover time between low and saturated substrate amounts were detected for 4-MUF-phosphate (AP), while the highest differences (up to 1000 times) were observed for cellobioside (CBH). Under low and high substrate availability, the substrate turnover was three to four times faster at the young pasture site. Natural forest and shrubland exhibited comparable turnover rates under high substrate concentrations. In contrast, the turnover of 4-MUF-phosphate (AP) and 4-MUF-2-deoxy-2-acetamido-b-Dglucoside (NAG) at low substrate concentration was faster at the natural forest compared to the shrubland, while the opposite was observed for the degradation of cellulose- and hemicellulose-like compounds. 4. Discussion

Kendall rank correlation revealed significant (P < 0.05) relationships between the substrate affinities of AG, BG, XYL, and AP and the soil microbial community structure (Supplementary Table 3 and Supplementary Fig. 2). The Km of AG was inversely related to Gram(
) bacteria marker 18:1n7c but was positively related to 10Me18:0, a PLFA marker for actinomycetes. The Km of XYL and AP was inversely correlated with the abundance of Gram(
) bacteria markers (16:1n7c, 18:1n7c): The higher the abundance, the higher the substrate affinity. No associations between PLFA data and substrate affinity were observed for CBH and NAG. Furthermore, the Kendall rank correlation applied to environmental properties revealed a significant positive relationship between the substrate affinity of NAG and DOC. In contrast, the substrate affinity of CBH was inversely related to the soil C:Pt and Nt:Pt ratios. To find plausible determinants for the Km and the Ka of the six EHEs, we plotted our data with data from available studies (Marx et al., 2005; German et al., 2012) against environmental properties, namely, SOC, soil C:N, pH, and mean annual temperature (MAT) (Supplementary Fig. 3aed). We found environmental factors regulated the Km of XYL, NAG, and AP. The Km values of XYL were inversely related to soil pH (P ¼ 0.01) with higher substrate affinities at higher soil pH. The Km values of AP were positively related to the soil C:N ratio (P ¼ 0.023). The MAT explained 66% of the variability in the catalytic efficiency of CBH (P ¼ 0.003): The lower the MAT, the higher the catalytic efficiency of CBH (Supplementary Fig. 3b). 3.6. Relationship of catalytic efficiencies of EHEs to microbial community structure and soil chemical properties The catalytic efficiency of NAG and AP showed significant positive relationships with microbial biomass C and soil N (Fig. 5aeb).

4.1. Microbial biomass and community structure as affected by land-use change We observed a three-fold increase in microbial biomass after forest conversion to pasture, while biomass decreased significantly during the course of land abandonment and secondary succession (Table 1). Total amount of microbial biomass was regulated by shifts in resource availability (N, P) and stoichiometry as well as by soil pH (Tischer et al., 2014a). These relationships were confirmed along large-scale environmental gradients (Wardle, 1992). In the present account, such gradients were associated with increases in the abundance of Gram(
) bacteria at active pasture sites (Fig. 1). Changes in the microbial community structure indicated that, at the level of the PLFA profiles, the abundance of the microbial groups was susceptible to human-induced changes in soil properties (Allison and Martiny, 2008; Fierer et al., 2009). Such feedback between land use and microbial community has been found for various systems (Rousk and Bååth, 2011; Deyn et al., 2011) and, as discussed below, significantly affected the catalytic properties of EHEs (Waldrop et al., 2000; Tischer et al., 2014a). 4.2. Substrate affinities (Km) of extracellular enzymes in soil The Km values obtained in our study corresponded well to the published range of the substrate affinities of the EHEs involved in degrading cellulose (CBH, BG) and starch (AG) (Marx et al., 2005; German et al., 2012). Substantial variation in the catalytic properties of the EHEs involved in hydrolysis of hemicelluloses (XYL), chitin and peptidoglycan (NAG), and monophosphoesters (AP) occurred between ecosystems (Supplementary Fig. 3 and

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233

Fig. 3. Kinetic properties (Km and Vmax) of b-cellobiohydrolase (CBH), b-glucosidase (BG), N-acetylglucosaminidase (NAG), a-glucosidase (AG), xylanase (XYL), and acid phosphatase (AP) along the land-use sequence in southern Ecuador (mineral topsoil 0e5 cm) are shown. Values are means ± SD (n ¼ 3), and the parameters are the statistics of the linear regression analyses.

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Fig. 4. The catalytic efficiency (Ka ¼ Vmax-to-Km ratio) of a) b-cellobiohydrolase (CBH), b-glucosidase (BG), acid phosphatase (AP), and b) N-acetylglucosaminidase (NAG), aglucosidase (AG), and xylanase (XYL) along the land-use sequence in southern Ecuador (mineral topsoil 0e5 cm) are shown.

Fig. 5. The catalytic efficiency (Ka ¼ Vmax-to-Km ratio) of N-acetylglucosaminidase (NAG) and acid phosphatase (AP) as a function of a) microbial biomass C (MBC, CFE method) and b) the soil total nitrogen (Nt) as well as c) the catalytic efficiency of N-acetylglucosaminidase as a function of the dissolved organic C (DOC) of the mineral topsoil samples (0e5 cm) along the land-use sequence in southern Ecuador are shown. Parameters are statistics of the linear regression analyses.

Supplementary Table 4). One possible explanation for this variation might be related to differences in soil pH and MAT (Supplementary Fig. 3). The Km of the soil EHEs demonstrated much higher substrate affinity (i.e., low Km) compared to the Km of CBH and XYL originated from pure cultures of bacteria and fungi (Km of CBH: 20e24,300 mmol L
1 (Voutilainen et al., 2009; Wang et al., 2012), mez et al., 2001; Wagschal et al., Km of XYL: 366e870 mmol L
1 (Go 2009)). Since the same substrates were used in the soil and pure cultures enzyme assays, the low Km of CBH and XYL suggests that the soil EHEs, produced by a different set of microorganisms, are more efficient in substrate catalysis than the purified enzymes from selected single organisms. This finding is especially indicative, given that the interactions of EHEs with the soil environment, e.g., immobilization by clay and humus (Nannipieri and Gianfreda, 1998), entrapment in soil micro-aggregates (Marx et al., 2005), and competitive inhibition (Stone et al., 2012) potentially increase the Km values of the soil EHEs (Marx et al., 2005). 4.3. Does microbial community structure matter for enzymes' catalytic properties? In a previous study, we found that enzyme activities measured at substrate saturation (Vmax) were not related to the composition of the soil microbial community (Tischer et al., 2014a). In contrast, the Km (AG, XYL, AP) as well as the Ka (AG, XYL) demonstrated inverse relationships to the PLFA markers indicative of Gram(
) bacteria. Specifically, we observed a strong link between Gram(
)

bacteria and hydrolysis of monophosphoesters with the higher the bacterial abundance, the higher the substrate affinity of AP (Supplementary Table 3). One possible explanation is that Gram(
) bacteria are adapted to degrade the organic P of microbial residues. A source of monoester-bound P in soil are lipopolysaccharide €gel-Knabner, 2006) with lipid A unique to the outer polymers (Ko membrane of Gram(
) bacteria (Wilkinson, 1988). The re-cycling of these resources proceeds within a few days (van Veen et al., 1987), which confirms our turnover time of 4-MUF-phosphate (Table 2) corresponds well to recently reported values of bacterial biomass turnover (median 112 h) (Sinsabaugh et al., 2015). The strong positive links between the catalytic efficiency of AP and MBC as well as Nt (Fig. 5aeb) indicate that a fast re-utilization of monoester-bound P occurs when the microbial biomass and subsequently the absolute P demand are high. In contrast, decreases in microbial biomass and soil N alleviated the turnover of P bound as phosphomonoesters (e.g., mononucleotides, sugar phosphates, and polyphosphates) in soils. These constraints on substrate turnover were also supported by the calculated turnover times for old and abandoned pasture sites (Table 2) (Turner and Haygarth, 2005). Under the non-saturating substrate concentrations (when the decomposition is mainly governed by the Km), the fast turnover time of starch, hemicellulose, and monophosphoesters was associated with Gram(
) bacteria, the most abundant microbial group in our samples. These associations indicate that at least a proportion of Gram(
) bacteria were oligotrophs that produce EHEs with a

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235

Table 2 Turnover time Tt (in days) of added MUF-substrates at two different substrate concentrations in mineral topsoil (0e5 cm) samples of the sites across the land-use sequence. Values represent mean with standard deviation in parenthesis (analytical replicates n ¼ 3). Turnover time was calculated by (Km þ S)/Vmax. For the low substrate concentration, the amount of substrate was set equal to the modeled Km while for the high substrate concentration, 400 mM substrate g
1 soil was used. Site

Turnover time (days) AG

Natural forest Young pasture Old pasture Re-established pasture Abandoned pasture Shrubland

BG

XYL

CBH

NAG

AP

S at Vmax S at Vmax/2 S at Vmax S at Vmax/2 S at Vmax S at Vmax/2 S at Vmax

S at Vmax/2 S at Vmax S at Vmax/2 S at Vmax

S at Vmax/2

646 283 897 486 850 626

4.5 2.2 7.0 4.6 9.1 1.5

0.61 0.41 0.68 0.78 1.10 1.10

(89) (15) (74) (30) (72) (30)

58 78 44 69 56 208

(31) (13) (14) (14) (19) (30)

51 24 61 65 79 55

(2) (1) (3) (3) (4) (4)

1.8 0.5 1.4 2.0 3.0 0.6

(0.3) (0.1) (0.3) (0.3) (0.6) (0.2)

80 27 100 107 192 47

(9) (1) (3) (6) (23) (2)

13 4 5 12 24 11

(5) (1) (1) (2) (7) (2)

477 99 269 4481 572 311

(34) (5) (18) (33) (53) (26)

(1.7) (0.4) (1.8) (1.7) (2.8) (0.5)

52 21 117 36 80 70

(4) (1) (5) (2) (3) (4)

5.5 3.0 10.7 4.6 8.7 7.0

(1.1) (0.5) (1.5) (0.5) (0.7) (1.3)

2.6 2.1 4.6 5.2 7.3 3.8

(0.07) (0.09) (0.21) (0.26) (0.22) (0.02)

(0.05) (0.05) (0.07) (0.13) (0.10) (0.02)

Enzymes and substrates: AG a-glucosidase: 4-MUF-a-D-glucoside (338.1 g mol
1). BG b-glucosidase: 4-MUF-b-D-glucoside (338.1 g mol
1). XYL Xylanase: 4-MUF-b-D-xylopyranoside (308.28 g mol
1). CBH Cellobiohydrolase: 4-MUF-b-D-cellobioside (500.45 g mol
1). NAG 1,4-b-N-acetylglucosaminidase: 4-MUF-2-deoxy-2-acetamido-b-D-glucoside (379.36 g mol
1). AP Phosphomonoesterase: 4-MUF-phosphate (256.15 g mol
1).

high substrate affinity (Km) (Fierer et al., 2007) and/or high catalytic efficiency (Ka). We did not observe a relationship between the microbial community structure (i.e., fungal markers) and the Km of NAG. In addition to the methodological drawbacks of the PLFA method in accounting soil fungal biomass (Frostegård et al., 2011), the lack of an association can be explained as follows: i) The production of NAG is not restricted to fungi only (Geisseler et al., 2010), and ii) NAG degrades fungi- and bacteria-derived residues consisting of N€ gel-Knabner, 2006). Fungal chitin and bacacetylglucosamine (Ko terial peptidoglycan are abundant components of microbial cell walls; thus, the increased activity of NAG reflects the fast turnover and re-utilization of microbial biomass. This was highlighted by the fact, that there are no reports of quantitatively significant longterm accumulation of chitin in nature (Beier and Bertilsson, 2013). We speculated that increases in biomass turnover by NAG led to an increase in the DOC concentrations, as a consequence of the direct dissolution of the N-acetylglucosamine structures in soil (Beier and Bertilsson, 2013). This was confirmed by the close positive relationship between the Km of NAG and the Vmax, as well as between the Ka of NAG and the DOC (Supplementary Table 3). Direct effects of NAG activity on DOC pool, however, can be weakened by alternative pathways of chitin degradation, such as deacetylation of chitin to chitosan, that do not directly led to the formation of soluble products (Beier and Bertilsson, 2013). Our data suggest that domination of certain phylogenic groups in the structure of microbial community (e.g., G(
) bacteria) is responsible for substrate affinity of enzymes performing specific functions (i.e., the final steps of the degradation of the starch, hemicelluloses, and monophosphoesters). Such a domination can strongly alter the turnover time of specific substrates especially at their low concentrations. 4.4. Is the maximum rate of the enzyme reaction related to substrate affinity? The changes in substrate affinity did not correlated with the Vmax values for most of the enzymes tested (Fig. 3). These data demonstrated a decoupling of the kinetic parameters of the MichaeliseMenten equation and pointed to the high flexibility of cellulolytic enzyme systems to environmental constraints (Bradford, 2013). Furthermore, the conversion from forest to pasture was accompanied by the increased catalytic efficiency of most enzymes. In most cases, however, this increase was due to the

increase in the Vmax values while the substrate affinity was not significantly affected. Therefore, strong correlations between the maximum rate of the enzyme reaction and substrate affinity are rather the exception than the rule since both parameters were regulated by different factors. 4.5. The drivers of catalytic properties at larger spatial scales Higher substrate affinities of XYL and AP were associated with the higher abundance of Gram(
) bacteria (Supplementary Table 3) and were driven by soil pH and the C:N ratio (Fig. 1). This is in line with broad-scale analyses of the distribution of bacteria along gradients in soil pH and C:N ratios (Fierer et al., 2009). The Km values of NAG were positively related to the SOC and the DOC. It supports the idea that when nutrient availability decreases, the competitive species produce enzymes with high affinity to substrate (low Km) and reutilize microbial residues for maintenance function (Stone et al., 2012; Bradford, 2013). Remarkably, the range of the catalytic efficiency in our study (e.g., Fig. 4) corresponded very well to the Ka values of subtropical grassland in California: Ka: BG 85.8, XYL 15.5, CBH 58.0, NAG 27.0 nmolMUF h
1 mmol
1 Substrate; German et al. 2012, supplementary tables. Such a good correspondence in Ka values between soils from geographically distinct experimental plots with similar climatic conditions (e.g., MAT ¼ 17  C) may indicate the importance of climatic factors for the catalytic properties of soil enzymes. The inverse relationships between the MAT and the Ka of CBH (Supplementary Fig. 3) indicated that climate warming can retard the terminal steps of cellulose turnover (German et al., 2012). 4.6. Conclusions Land-use change caused significant gradients in soil chemical and biological properties strongly affecting the catalytic properties of EHEs (Supplementary Table 5). Young pasture was characterized by highest catalytic efficiency and by fastest substrate turnover, which decreased during long-term pasture use. Environmental drivers of the Km were enzyme-specific (e.g., the pH for XYL, the C:N ratio for AP, and the C availability for NAG). Furthermore, we revealed that the Km and Vmax were driven by different soil properties. This caused a decoupled response of Vmax and Km to land-use changes for majority of tested enzymes. Such a decoupling of both parameters led to alterations of catalytic efficiency of EHEs and

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thus, affected the turnover rates of respective substrates in soil. The recycling of N and P from dead microbial biomass might be one important function of soil EHEs (i.e., NAG, AP) that is associated to oligotrophic microorganisms with highly efficient enzyme systems. The in-depth analysis of this research area deserves further attention. Acknowledgments We thank the two anonymous reviewers for their highly valuable comments which helped us to substantially improve the manuscript. The authors gratefully acknowledge the financial support by the DFG (German Research Foundation) for the subproject B3.1 within the DFG research Unit 816 “Biodiversity and Sustainable Management of a Megadiverse Mountain Ecosystem in South Ecuador” (HA 4597/1-2). Contribution of EB was supported by Russian Science Foundation (Project No. 14-14-00625). We thank Guido Ehrlich and Maximilian Kirsten for their assistance in collecting and preparing soil samples for laboratory measurements. We are grateful to Manuela Unger (TU Dresden) for her skillful and tedious laboratory work. The cooperation with Dr. Marion Schrumpf (Max Planck Institute for Biogeochemistry, Jena) and the access to the laboratory facilities of the MPI for Biogeochemistry, Jena is gratefully acknowledged. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.soilbio.2015.07.011. References Allison, S.D., Martiny, J.B.H., 2008. Resistance, resilience, and redundancy in microbial communities. Proceedings of the National Academy of Sciences of the United States of America 105, 11512e11519. Allison, S.D., Wallenstein, M.D., Bradford, M.A., 2010. Soil-carbon response to warming dependent on microbial physiology. Nature Geoscience 3, 336e340. Bai, Z.G., Dent, D.L., Olsson, L., Schaepman, M.E., 2008. Proxy global assessment of land degradation. Soil Use and Management 24, 223e234. Barton, L., Northrup, D.E., 2011. Microbial Ecology. Wiley-Blackwell, Hoboken, N.J. Beck, E., Bendix, J., Kottke, I., Makeschin, F., Mosandl, R., 2008. Gradients in a Tropical Mountain Ecosystem of Ecuador. Springer-Verlag, Berlin, Heidelberg. Beier, S., Bertilsson, S., 2013. Bacterial chitin degradation-mechanisms and ecophysiological strategies. Frontiers in Microbiology 4, 149. Bendix, J., Homeier, J., Cueva Ortiz, E., Emck, P., Breckle, S.W., Richter, M., Beck, E., 2006. Seasonality of weather and tree phenology in a tropical evergreen mountain rain forest. International Journal of Biometeorology 50, 370e384. Bradford, M.A., 2013. Thermal adaptation of decomposer communities in warming soils. Frontiers in Microbiology 4. Bray, R.H., Kurtz, L.T., 1945. Determination of total, organic and available forms of phosphorus in soils. Soil Science 59, 39e45. Brookes, P.C., Powlson, D.S., Jenkinson, D.S., 1982. Measurement of microbial biomass phosphorus in soil. Soil Biology & Biochemistry 14, 319e329. Chen, G.-C., He, Z.-L., 2004. Determination of soil microbial biomass phosphorus in acid red soils from southern China. Biology and Fertility of Soils 39, 446e451. Cleveland, C., Liptzin, D., 2007. C:N:P stoichiometry in soil: is there a “Redfield ratio” for the microbial biomass? Biogeochemistry 85, 235e252. Cusack, D.F., Silver, W.L., Torn, M.S., McDowell, W.H., 2011. Effects of nitrogen additions on above- and belowground carbon dynamics in two tropical forests. Biogeochemistry 104, 203e225. DeForest, J.L., 2009. The influence of time, storage temperature, and substrate age on potential soil enzyme activity in acidic forest soils using MUB-linked substrates and l-DOPA. Soil Biology & Biochemistry 41, 1180e1186. Deyn, G.B., de Quirk, H., Bardgett, R.D., 2011. Plant species richness, identity and productivity differentially influence key groups of microbes in grassland soils of contrasting fertility. Biology Letters 7, 75e78. Don, A., Schumacher, J., Freibauer, A., 2011. Impact of tropical land-use change on soil organic carbon stocks e a meta-analysis. Global Change Biology 17, 1658e1670. Doolette, A.L., Smernik, R.J., 2011. Soil organic phosphorus speciation using spectroscopic techniques. In: Bünemann, E., Oberson, A., Frossard, E. (Eds.), Phosphorus in Action: Biological Processes in Soil Phosphorus Cycling. Springer Berlin Heidelberg, pp. 3e36. Ehrenfeld, J.G., Ravit, B., Elgersma, K., 2005. Feedback in plant-soil system. Annual Review of Environment and Resources 30, 75e115.

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