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- Publisher
- Taylor & Francis
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- © 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
- ISSN
- 2474-9508
- DOI
- 10.1080/24749508.2018.1452465
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Abstract
GeoloGy, ecoloGy, and landscapes, 2018 Vol . 2, no . 3, 155– 168 https://doi.org/10.1080/24749508.2018.1452465 INWASCON OPEN ACCESS Seasonal variation of soil enzyme activities in relation to nutrient and carbon cycling in Senna alata (L.) Roxb invaded sites of Puducherry region, India a a b Savitha Veeraragavan , Ramamoorthy Duraisamy and Sudhakaran Mani a b d epartment of ecology & environmental s ciences, pondicherry University, puducherry, India; d epartment of environmental s ciences, Tamil nadu a gricultural University, c oimbatore, India ABSTRACT ARTICLE HISTORY Received 27 s eptember 2017 The present study is to understand the intrinsic ecological trait of invasive plant Senna alata a ccepted 3 March 2018 relating to soil characteristics and seasonality distributed in the Puducherry region located under tropical climate. Plant biomass, soil parameters linked with soil enzyme activities are examined in KEYWORDS six sites where S. alata is found growing. From the study, it is demonstrated that invasive species s oil enzymes; Senna alata l; S. alata exhibits heterogeneity in invaded soil characteristics but variation in aboveground heterogeneity-seasonality; biomass (AGB) of S. aalata is not significant. Soil moisture and soil enzymes activities and AGB are inconsistency; litterfall significantly positively correlated whereas pH and EC negatively correlated. Seasonal variation in total nitrogen, phosphorus, and elements viz. Mg, Ca, and Na is inconsistent in their seasonality. Based on observations made and results obtained in the study, it is stated soil characteristics and their seasonality in the S. alata invaded soil is, site-specific resulting in heterogeneity; but such heterogeneity is not exhibited in AGB in six sites and it is, therefore, reported that such an idiosyncratic trait of S. alata is one of the potential traits influencing successful invasion by S. alata as mono-species population. The statistical analyses also confirmed the observation made. The outcome of the study would help to prepare management programmes to check spreading of invasive species so as to restore native plant diversity in and around Puducherry region. 1. Introduction eco-biological aspects to understand the mode and suc- cess of invasion and spreading are very limited and such Biological invasions by alien species are widely recog- gap would weaken the speed management programmes nized as a significant component of global environmen - targeting invasive plants under such circumstances, it is tal change, oen ft resulting in irrevocable loss of native learned from our intensive field survey covering almost plant biodiversity and even livelihood of those who all parts of the Puducherry region, that Senna alata one sustain on locally available native plants. Consequently, of the invasive species under family Fabaceae, is coming in the recent years, it is becoming more important to up both in urban and rural areas of Puducherry region. understand the ecology of invasive plant species as they To chalk out management programme targeting spread are capable of not only altering biodiversity but also of invasive population in the Puducherry region, basic functioning of the ecosystem (Marcelino & Verbruggen, inputs relating to the ecology of the invasive plant and 2015; Powell, Chase, & Knight, 2011; Tylianakis, Didham, its habitat structure pertaining to the study region are Bascompte, & Wardle, 2008). It is widely reported that very much required. er Th efore, keeping these aspects as invasive plants can cause alternation in abundance or objectives, presently an attempt has been made to study species composition, diversity and also soil microbes and examine aboveground biomass (AGB) of the plant (Quist et al., 2014; Rusterholz, Salamon, Ruckli, & Baur, species linked with soil characteristics and soil enzyme 2014; Stefanowicz, Stanek, Nobis, & Zubek, 2016; Uddin activities by fixing six sites where S. alata are profusely & Robinson, 2017). Recently, Gibbons et al. (2017) have growing. Moreover, as of now a comprehensive study on found invasive plants with their unique species-specific ecological characteristics of S. alata and its habitat, are trait, changes invaded soil environment from surround- not available and hence present investigation has been ing native soil characters. Khadka (2017) has empha- undertaken. The outcome of the study would bring out sized the need for such study in Nepal as the invasion status and influence of invasive species on soil charac- of Mikania micrantha, directly and indirectly, modifies teristics linked with enzyme activity in an alien-invaded natural resources which are the rural household liveli- hoods. As pointed by Marcelino and Verbruggen (2015) region. It would be a substantial contribution to the CONTACT Ramamoorthy d uraisamy d.ramamoorthy01@gmail.com © 2018 The a uthor(s). published by Informa UK limited, trading as Taylor & Francis Group. This is an open a ccess article distributed under the terms of the creative c ommons a ttribution license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 156 S. VEERARAGAVAN ET AL. annals of the ecology of invasives, particularly S. alata 14 × 23 m (in the beginning of the study) and further (L.) Robx. season to season. The AGB per metre square is found out using digital hand weighing scale in the field itself and later dry weight is found out. Values are converted 2. Materials and methods to AGB per square metre area (dry wt.). The same pro- 2.1. Study region cedure is followed for AGB assessment in all sites for all seasons. Soil samples are collected every season from six Puducherry is located along the Coromandel Coast study sites to analyze soil parameters and soil enzymes of peninsular India with the geographical coordinates activities. About 500 g of soil sample is collected from 11°52′N, 79°45′E, and 11°59′N and 79°52′ E covering 10 to 15 cm depth in 6 points in each site where ever an area of 480 km (Figure 2). The total rainfall received plant samples are taken for AGB estimation. Six soil by the Pondicherry region is 1463.5 mm; during mon- subsamples are pooled together; about 500 g of from soon 710 mm and pre-monsoon 334 mm. The monthly pooled soil is taken for analysis. The same sampling mean temperature ranges between 26.0 and 34.2 °C. procedure is followed in other sites for soil sample col- Weather data relating to temperature, rainfall and rel- lection. Enzymes analyses are done within 48–72 h from ative humidity for the study period (2015) is presented the time of collection. Tofind out variation between sites in Tables 1 and 2. The study region experiences four and correlation among parameters, collected data are seasons viz. Post-monsoon (January–March) summer subjected to ANOVA and Pearson Correlation. To find (April–June), pre-monsoon (July–September), and the relationships between different variables, principal monsoon (October–December). Because of its geo- component analysis (PCA) was performed on all the graphical position (along Coromandel Coast of India) data (Figure 3). the region receives rainfall during two seasons i.e. during north-east monsoon (October–December) and a short spell of rain during south-east monsoon (from late sum- 2.3. Methods of soil analysis mer till early pre-monsoon). As the relative humidity Soil bulk density is determined by volumetric flask ranges between 64 and 88% the region is moderately method (Bashour & Sayegh, 2007). Soil water hold- wet throughout the year. ing capacity is determined by gravimetric method (Margesin & Schinner, 2005). Soil moisture content 2.2. Study species and sampling sites is determined by (Hesse, 1971). Soil texture is deter- mined by International pipette method (Piper, 1966) S. alata (L.) Roxb. Syn. Cassia alata (L), a medicinally and percentage values of sand silt and clay are calcu- important member of the genus Senna, is an invasive spe- lated.Soil reaction (pH) and electrical conductivity (1:2 cies from Mexico, South America (Figure 1). The study is soil–water suspensions) is determined by potentiome- conducted in six sites where S. alata is growing profusely, try, conductometry method (Waring & Bremner, 1964). located in and around the Puducherry region Figures 1 Total nitrogen (N), sulfur total organic carbon (TOC) and 2 and the geographical coordinates of study sites are was determined by Elemental CHNS Vario El cube (Liu, also recorded using GPS (Table 1). The study is under - Charrua, Weng, Yuan, & Ding, 2015). Extractable phos- taken during January–December 2015 that covers four phorus was determined by sodium bicarbonate method seasons’ viz. post-monsoon, summer, pre-monsoon, (Olsen, 1954). Extractable potassium and sodium are and monsoon. AGB of S. alata is estimated by quadrant determined by Flame photometer (Richards, 1954). method (harvest method) using 50 cm × 50cm quadrat. Nitrate – nitrogen is determined by chromotropic acid Since destructive method is adopted for estimation of spectrophotometric method (Sims & Jackson, 1971). AGB, harvesting of plant is done from six quadrants in Soluble calcium and magnesium are determined by each site during each season. The total area covered by soil extract titration with EDTA (Richards, 1954). Soil study plant varies from site to site i.e. from 10 × 15 m to Table 1. different sites in geographical coordination and soil physical parameters of six study sampling sites in p uducherry, India. Sites Parameters (S1) (S2) (S3) (S4) (S5) (S6) (P-Value) lattitude (n) 11°54′ 39.081″ 11°50′ 22.459″ 12°1′ 9.9235″ 11°55′ 55.413″ 11°56′ 42.458″ 11°54′ 30.265″ langitude (e) 79°48′ 41.270″ 79°47′ 10.476″ 79°41′ 57.546″ 79°46′ 3.116″ 79°48′ 18.574″ 79°44′ 39.097″ s oil bulk density g/ 0.91 ± 0.01 0.86 ± 0.06 0.94 ± 0.03 0.80 ± 0.03 0.726 ± 0.03 0.85 ± 0.03 0.026 c cm3 Water holding capac- 1.42 ± 0.06 1.63 ± 0.16 1.81 ± 0.35 1.32 ± 0.11 1.45 ± 0.34 1.75 ± 0.30 ns ity % s and % 37.57 ± 0.61 47.35 ± 0.60 73.42 ± 0.45 35.45 ± 0.74 58.23 ± 0.40 49.2 ± 0.29 0.000 a silt % 26.26 ± 1.11 35.16 ± 0.27 12.72 ± 0.38 11.33 ± 0.68 18.09 ± 0.31 16.45 ± 0.41 0.000a clay % 5 ± 0.58 16.16 ± 0.60 32.24 ± 0.38 17.32 ± 0.66 23.4 ± 0.46 15 ± 0.58 0.000 a notes: Mean ± standard error; (n = 3); ns = non significant, a: significantly different at p ≤ 0.001, b: significantly different at p ≤ 0.01, and c: significantly different at p ≤ 0.05, nainarmandapam (s1), Reddichavadi (s2), lingareddypalayam (s3), Moolakulm (s4), Thattanchavady (s5), Villiyanur (s6). GEOLOGY, ECOLOGY, AND LANDSCAPES 157 Table 2. Weather report of puducherry, India. and Eivazi and Tabatabai (1988). Cellulase activity is determined by the method given by Schinner and von Post-mon- Premon- soon Summer soon Monsoon Mersi (1990) and Saccharase activity is determined by Temperature 81.33 74.33 70 86.67 the method described by Schinner and von Mersi (1990). (°c ) In all analyses, triplicate samples are run for each param- Rainfall (mm) 9.67 86.32 111.35 236.87 Humidity (%) 81.33 74.34 70 86.67 eter and mean values are used for data analysis. −1 −1 3. Result enzymes viz. urease activity (μg NH -N g dwt 2 h ) is by method described by Tabatabai and Bremner (1972). Among all studied soil physical parameters, soil water Acid phosphatase and alkaline phosphatase by Tabatabai holding capacity show no variation between sites. The and Bremner (1969) method; β-glucosidase activity is soil texture significantly differs in all the six sites except determined by methods described by Tabatabai (1982) clay (p < 0.683). Soil moisture content is higher in Figure 1. s tudy area. s ource: a uthor. Figure 2. Cassia alata l. 158 S. VEERARAGAVAN ET AL. Postmonsoon 1400 5 4.5 glucosidase 1000 xylanase 3.5 Cellulase 2.5 Saccharase carbon 1.5 0.5 0 0 Site 1 Site 2 Site 3 Site 4Site 5Site 6 Summer 600 4.00 3.50 glucosidase 3.00 xylanase 2.50 Cellulase 300 2.00 Saccharase 1.50 carbon 1.00 0.50 0 0.00 Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Pre-Monsoon 160 30 glucosidase xylanase 80 15 Cellulase Saccharase carbon 0 0 Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Monsoon 1200 9 glucosidase 800 6 xylanase Cellulase Saccharase 400 3 carbon 0 0 Site 1 Site 2 Site 3 Site 4Site 5Site 6 Figure 3. Relationship between soil enzyme activity and Toc during in different season post-monsoon, summer, pre-monsoon, −1 −1 −1 −1 monsoon in the unit of enzyme activity in of β-glucosidase (μg p-np g dwt h ) cellulase (glucose equivalent μg g dwt 24 h ), −1 −1 −1 −1 saccharase (glucose equivalent μg g dwt 3 h ), Xylanase (glucose equivalent μg g dwt 24 h ), Toc (g/kg) in six sites in p uducherry, India. monsoon (13.78%) and least in the summer (1.23%). (Tables 3 and 4). The AGB is higher in the post-monsoon Table 3 indicates variation in all measured parameters (2333.3 gm/m ) with a second peak in pre-monsoon and between sites and seasons (except in summer (p < 0.19). least in summer (1200 gm/m ). Except during summer pH and EC show higher values during summer; (p < 0.046) variation in AGB is not significant between between seasons the variation is significant (p < 0.016) sites. TOC is higher in the pre-monsoon (12.37 g/kg) lesser Enzyme activities Enzyme activities Enzyme activities Enzyme activities Carbon Carbon Carbon Carbon GEOLOGY, ECOLOGY, AND LANDSCAPES 159 Table 3. s oil physico-chemical properties in post-monsoon and summer season six study sites in puducherry, India. Post-monsoon Summer Season S1 S2 S3 S4 S5 S6 P-value S1 S2 S3 S4 S5 S6 P-value pH 7.40 ± 0.29 6.80 ± 0.27 7.52 ± 0.35 6.10 ± 0.49 8.15 ± 0.19 6.98 ± 0.34 0.016 c 8.73 ± 0.10 7.84 ± 0.06 8.54 ± 0.27 7.30 ± 0.42 7.44 ± 0.23 8.27 ± 0.33 0.000a ec (ms/cm) 101.28 ± 0.36 98.00 ± 0.58 58.67 ± 0.33 20.80 ± 0.15 32.17 ± 0.60 85.03 ± 0.52 0.000 a 124.00 ± 0.58 103.27 ± 0.41 167.67 ± 0.33 189.97 ± 0.55 61.50 ± 0.20 169.72 ± 0.37 0.000 a s oil moisture 9.47 ± 0.25 9.86 ± 0.71 8.65 ± 0.60 10.31 ± 0.66 8.09 ± 0.27 7.57 ± 0.38 0.031c 1.23 ± 0.15 1.46 ± 0.17 1.78 ± 0.28 2.58 ± 0.25 3.67 ± 0.11 2.31 ± 0.40 ns (%) Biomass(gm/ 2333.33 ± 2013.33 ± 2160 ± 0.02 2186.66 ± 2086.667 ± 2026.66 ± ns 1486.67 ± 2233.33 ± 2293.33 ± 1926.67 ± 2226.67 ± 1946.67 ± 0.046c m ) 0.029 0.02 0.02 0.02 0.02 0.022 0.01 0.03 0.017 0.01 0.02 Toc (g/kg) 3.9 ± 0.37 3.23 ± 0.28 3.32 ± 0.40 1.382 ± 0.23 1.364 ± 0.27 1.767 ± 0.19 0.000 a 2.79 ± 0.17 1.90 ± 0.35 3.17 ± 0.54 1.86 ± 0.44 1.52 ± 0.56 2.3 ± 0.53 ns Total nitrogen 0.25 ± 0.03 0.29 ± 0.04 0.19 ± 0.03 0.10 ± 0.00 0.15 ± 0.00 0.17 ± 0.00 0.002b 2.76 ± 0.41 2.01 ± 0.26 2.64 ± 0.29 2.05 ± 0.41 1.01 ± 0.27 1.57 ± 0.18 0.016 c (g/kg) extractable 3.81 ± 0.06 5.42 ± 0.07 9.75 ± 0.05 9.83 ± 0.06 5.24 ± 0.05 7.46 ± 0.04 0.000 a 27.76 ± 0.03 35.78 ± 0.03 36.86 ± 0.03 75.40 ± 0.29 24.76 ± 0.03 19.86 ± 0.04 0.000a phosphorus (g/kg) extractable 4.62 ± 0.77 7.42 ± 0.62 7.47 ± 0.75 7.16 ± 0.65 6.28 ± 0.74 11.64 ± 0.48 0.000 a 33.94 ± 2.04 58.30 ± 0.33 70.87 ± 0.38 75.66 ± 0.41 51.75 ± 10.16 88.5 ± 10.50 0.000 a potassium (g/kg) extractable 3.52 ± 0.70 3.01 ± 0.72 2.72 ± 0.63 2.76 ± 0.48 3.71 ± 0.01 2.81 ± 0.45 ns 23.50 ± 0.37 20.31 ± 0.59 12.55 ± 0.73 14.58 ± 0.62 21.49 ± 0.56 12.49 ± 0.33 0.000 a sodium (g/ kg) extractable c al- 11.09 ± 1.02 11.86 ± 0.55 13.65 ± 0.68 17.23 ± 0.90 12.49 ± 0.91 15.82 ± 0.67 0.000a 14.37 ± 0.41 14.95 ± 0.52 14.40 ± 0.99 16.28 ± 0.40 17.40 ± 0.42 16.87 ± 0.22 0.000a cium (g/kg extractable 4.65 ± 0.60 7.17 ± 0.37 4.7 ± 0.61 4.73 ± 0.62 3.17 ± 0.18 5.73 ± 0.46 0.036c 6.72 ± 0.57 7.21 ± 0.45 7.77 ± 0.43 7.63 ± 0.27 7.63 ± 0.47 6.73 ± 0.34 ns magnesium (g/kg) n itrate nitro- 0.37 ± 0.07 2.38 ± 0.40 1.89 ± 0.42 1.15 ± 0.22 1.53 ± 0.30 1.27 ± 0.39 0.016c 2.14 ± 0.43 2.10 ± 0.53 1.87 ± 0.57 1.741 ± 0.44 0.97 ± 0.54 1.60 ± 0.36 ns gen (g/kg) sulphur (g/kg) 0.19 ± 0.01 0.46 ± 0.20 0.58 ± 0.03 0.05 ± 0.00 0.03 ± 0.00 0.05 ± 0.00 0.001 ns 0.86 ± 0.13 0.61 ± 0.20 0.75 ± 0.17 0.59 ± 0.15 0.79 ± 0.13 0.78 ± 0.10 ns notes: Mean ± standard error; (n = 3) ns = non significant, a: significantly different at p ≤ 0.001, b: significantly different at p ≤ 0.01 and c: significantly different at p ≤ 0.05. nainarmandapam (s1), Reddichavadi (s2), lingareddypalayam (s3), Moolakulm (s4), Thattanchavady (s5), Villiyanur (s6). 160 S. VEERARAGAVAN ET AL. Table 4. s oil physico-chemical properties inpre-monsoon and monsoon seasons of six study sites in puducherry, India. Pre-monsoon Monsoon Season S1 S2 S3 S4 S5 S6 P-value S1 S2 S3 S4 S5 S6 P-value pH 6.83 ± 0.09 6.03 ± 0.27 8.68 ± 0.27 6.75 ± 0.21 6.61 ± 0.26 7.48 ± 0.25 0.011 c 7.50 ± 0.23 6.77 ± 0.19 8.02 ± 0.16 6.33 ± 0.38 7.11 ± 0.28 7.03 ± 0.49 0.016 c ec (ms/cm) 98.50 ± 0.58 87.63 ± 1.11 120.35 ± 0.78 169.00 ± 0.53 35.00 ± 0.41 36.50 ± 0.32 0.000 a 33.42 ± 0.72 47.50 ± 0.55 82.57 ± 0.61 38.37 ± 0.59 53.57 ± 9.54 56.63 ± 0.59 0.000 a s oil moisture 2.95 ± 0.34 4.26 ± 0.42 3.17 ± 0.18 3.02 ± 0.26 3.38 ± 0.31 2.46 ± 0.33 0.000 a 12.22 ± 0.26 13.23 ± 0.57 13.14 ± 1.49 12.43 ± 0.26 13.78 ± 0.59 10.81 ± 0.61 0.018 c (%) Biomass(gm/ 1200.00 ± 1460 ± 0.01 1260 ± 0.023 1640 ± 0.01 1353.333 ± 1346.66 ± ns 1598.00 ± 2073.33 ± 2240 0.03 2100 ± 0.02 2153.333 ± 2280 ± 0.02 ns m ) 0.015 0.02 0.01 0.024 0.02 0.02 Toc (g/kg) 14.51 ± 0.32 22.33 ± 0.30 19.57 ± 0.34 26.43 ± 0.35 14.67 ± 0.39 20.66 ± 0.38 0.000 a 7.22 ± 0.20 8.122 ± 0.17 5.83 ± 0.38 6.25 ± 0.48 5.2 ± 0.16 7.42 ± 0.21 0.000 a Total nitrogen 0.53 ± 0.04 0.80 ± 0.01 1.00 ± 0.06 1.50 ± 0.06 0.81 ± 0.02 1.06 ± 0.07 0.000 a 1.87 ± 0.26 2.74 ± 0.33 1.80 ± 0.21 1.92 ± 0.25 2.75 ± 0.02 1.90 ± 0.23 0.010 b (g/kg) extractable 22.65 ± 0.74 28.52 ± 0.45 17.09 ± 0.96 18.44 ± 0.28 16.51 ± 0.54 12.87 ± 0.44 0.000a 23.62 ± 0.73 34.58 ± 0.76 32.23 ± 0.44 30.36 ± 0.65 33.57 ± 0.64 19.61 ± 0.78 0.000 a phosphorus (g/kg) extractable 16.32 ± 0.06 18.73 ± 0.03 17.33 ± 0.03 15.67 ± 0.03 7.18 ± 6.97 21.35 ± 0.03 ns 23.3 ± 0.73 23.23 ± 0.98 24.76 ± 0.34 15.43 ± 0.48 18.41 ± 0.64 13.45 ± 0.56 0.000 a potassium (g/kg) extractable 6.72 ± 0.28 3.14 ± 0.55 7.54 ± 0.23 7.66 ± 0.13 4.50 ± 0.35 7.84 ± 0.10 0.000 a 2.34 ± 0.01 2.03 ± 0.01 1.39 ± 0.01 1.46 ± 0.01 2.26 ± 0.01 1.25 ± 0.01 0.000 a sodium (g/ kg) extractable calcium (g/kg 6.87 ± 0.22 6.82 ± 0.61 8.08 ± 0.37 7.67 ± 0.30 7.03 ± 0.29 7.82 ± 0.45 0.000a 1.197 ± 0.287 0.903 ± 0.327 1.943 ± 0.455 1.560 ± 0.306 2.323 ± 1.090 4.583 ± 2.504 0.000 a extractable 2.83 ± 0.27 3.58 ± 0.25 2.98 ± 0.30 3.17 ± 0.31 3.34 ± 0.43 4.04 ± 0.09 ns 2.01 ± 0.16 3.23 ± 0.56 2.06 ± 0.26 2.91 ± 0.50 1.85 ± 0.33 2.36 ± 0.43 ns magnesium (g/kg) nitrate nitro- 0.07 ± 0.00 0.04 ± 0.00 0.64 ± 0.27 0.64 ± 0.27 0.08 ± 0.00 0.42 ± 0.02 0.039c 0.67 ± 0.03 1.874 ± 0.18 1.39 ± 0.31 1.15 ± 0.22 2.14 ± 0.27 1.81 ± 0.29 0.008b gen (g/kg) sulphur (g/kg) 0.45 ± 0.02 0.44 ± 0.02 0.26 ± 0.04 0.25 ± 0.04 0.37 ± 0.03 0.36 ± 0.03 0.000a 0.80 ± 0.03 0.88 ± 0.02 0.83 ± 0.02 0.62 ± 0.04 0.50 ± 0.04 0.72 ± 0.04 0.000a notes: Mean ± standard error; (n = 3), ns = non significant, a: significantly different at p ≤ 0.001, b: significantly different at p ≤ 0.01 and c: significantly different at p ≤ 0.05. nainarmandapam (s1), Reddichavadi (s2), lingareddypalayam (s3), Moolakulm (s4), Thattanchavady (s5), Villiyanur (s6). GEOLOGY, ECOLOGY, AND LANDSCAPES 161 in the summer (1.90 g/kg). There is no significant sea- changes (Caldwell, 2006), Moreover, soil enzyme activi- sonal variation in TOC except during summer (Table 6). ties are mostly vary in quantity and level of activity due Macronutrients viz. potassium (88.5 g/kg), sodium to each soil type as well as above ground vegetation and (23.50 g/kg), calcium (17.40 g/kg), and Magnesium such changes are more pronounced in case of invasive (7.77 g/kg), sulphur (0.86 g/kg) are significantly higher plants invaded soil. One such region is selected in the in summer and significantly vary between sites. Total present study were invasive plant S. alata is profusely nitrogen (2.76 g/kg), nitrate nitrogen (2.14 g/kg), and growing as monospecies population in the Puducherry phosphorus (75.40 g/kg) show inconsistent values both region. The outcome and observations made during site-wise and season-wise. Total nitrogen showed higher different seasons in six sites are discussed in two lev- values during summer in three sites (p-0.016) but dur- els. Firstly, it is tried to find out whether there exists ing monsoon in other three sites (p-0.010). Similarly, variation in the soil characteristics and enzyme activity nitrate nitrogen showed higher values during summer among the six sites with densely growing as monospe- in four sites (p-0.5791) and higher values during pre- cific population of S. alata; secondly, the seasonality of monsoon in two sites exhibiting inconsistency. During enzyme activities at the face of various soil characteris- other two seasons viz. pre-monsoon and post-mon- tics and AGB of the invasive plant, have been examined soon, it showed lesser variation (p-0.016 and 0.036 and discussed. respectively). Enzymes activity viz. Acid and Alkaline phosphatase, urease, β-glucosidase, cellulose, xylanase, 4.1. Heterogeneity in soil parameters enzyme saccharase are higher in the monsoon and less in sum- activities among selected sites mer whereas saccharase and acid phosphates showed Studies on various physico-chemical parameters and higher activity during post-monsoon. Site wise and sea- enzymes activity of soil in six sites revealed that varia- son wise values are presented in tables. tions in their soil characteristics except for soil profile Correlation analysis shows a positive correlation viz. sand, silt, and clay, is not significant. Similarly, between soil moisture and electrical conductivity except soil nitrogen and activities of enzyme cellulose (r = 0.576*, 0.535*). Positive correlation between total and xylanase, other parameters and activities of other nitrogen, TOC, phosphorus, magnesium, acid phos- enzyme differ from each factor significantly though phatase, alkaline phosphatase, urease, β-glucosidase, the study plant is growing densely as a monospecies cellulose, xylanase (r = 0.663**, 0.647**, 0.607**, 0.948**, population in all sites. Besides, AGB of S. alata also 0.729**) (Table 6). There is a strong negative correla- showed varying level at any point of time during the tion between total nitrogen, TOC with soil moisture study period in all six sites. The ANOVA results and (r = −0.704**, −0.599**). Table 6 shows strong neg- Pearson correlation also reflected similar trend. It is ative correlation soil moisture between total nitro- presumed that such a spatial heterogeneity found in gen and TOC, phosphorus, nitrate, nitrate nitrogen soil parameters where the invasive plant S. alata grow- (r = −0.704**, −0.599**, −0.536**, −0.693*, −0.544*) in ing as nonspecific population, might be the important summer. Table 5 indicates soil enzymes and physico- characteristics of an invasive plant. It has been already chemical parameters in pre-monsoon there a strong pos- reported for other invasive plants that alien species itive correlation in the electrical conductivity between establish as monospecific population and significantly TOC, sodium (r = 0.698**, 0.743**); TOC and phos- change the properties of native ecosystems (Vilà et al., phorus (r = 0.782**) and others are showing no signifi- 2011). These invasive can alter the biogeochemistry cant correlation. There is a negative correlation between of ecosystems mediated by secondary metabolites soil moisture and sodium (−0.658*). Total nitrogen and released by invasive species as well as plant–plant and nitrate nitrogen are positively correlated (r = 0.740**). plant–microbe interactions (Weidenhamer & Callaway, Table 6 shows a negative correlation of pH with cellulose 2010). This is also again support the observation made (r = 0.600**). PCA explains 85% of the total variance in the present study those variations between the sites. in the correlation matrix. Dim 1 and 2 show 68% for Besides, one of the basic factors that influence hetero- post-monsoon; 62% for summer; 73% for pre-monsoon, geneity in soil characteristics is the type and quantity and 66% for monsoon. Season-wise correlated explained of AGB presence of dense vegetation provide the soil variables are depicted in Figure 4(a)–(d) which demon- adequate cover, thereby reducing the loss in nutri- strate grouping of positively correlated variables and ents that are necessary for plants growth and energy negatively correlated variables during different seasons fluxes catalyzed by soil enzyme activities (Moraghebi, with their level of significance (Table 7). Matinizadeh, Khanjani, Teimouri, & Afdideh, 2012). It is also more clear from present field survey that dense 4. Discussion foliage, continuous litter fall, shady under storey cli- e enzy Th me levels in soil systems vary in amounts pri- mate of the soil, and the prevailing moisture facili- marily due to the fact that each soil type has different tate a conducive environment supported diversified amounts of organic matter content, composition, and micro-organisms – the producers of various enzymes activity of micro-organisms and intensity of biochemical and trigger various soil biochemical changes leading 162 S. VEERARAGAVAN ET AL. Table 5. s oil enzyme activities in post-monsoon and summer six study sites in puducherry, India. Post-monsoon Summer Season S1 S2 S3 S4 S5 S6 P-value S1 S2 S3 S4 S5 S6 P-value a cid phos- 178.76 ± 4.71 287.10 ± 3.62 194.71 ± 2.65 288.86 ± 3.06 189.80 ± 3.49 254.02 ± 3.45 0.000a 171.36 ± 2.75 188.70 ± 3.12 168.69 ± 3.08 195.49 ± 2.93 118.22 ± 2.39 202.54 ± 3.24 0.000a phatase (p-n itrophe- −1 nol μg g dwt −1 h ) alkaline 178.92 ± 3.66 243.78 ± 3.11 185.22 ± 2.39 237.41 ± 3.78 189.84 ± 3.03 212.93 ± 5.63 0.000a 162.82 ± 2.93 180.00 ± 3.95 154.39 ± 2.31 216.19 ± 3.36 142.10 ± 3.04 183.22 ± 2.98 0.000a phosphatase (p-n itrophe- −1 nol μg g dwt −1 h ) Urease (μg 130.08 ± 2.98 719.60 ± 2.71 108.78 ± 3.40 556.94 ± 3.34 195.83 ± 3.35 346.14 ± 5.31 0.000a 252.88 ± 3.78 356.46 ± 4.88 138.80 ± 3.10 261.25 ± 4.75 173.18 ± 2.74 255.11 ± 3.61 0.000a −1 nH -n g dwt −1 2 h ) β-glucosidase 190.60 ± 5.08 282.35 ± 3.39 177.20 ± 5.27 229.47 ± 3.31 134.82 ± 3.08 319.39 ± 2.95 0.000a 105.96 ± 3.38 106.34 ± 4.60 104.50 ± 4.06 111.77 ± 4.90 103.02 ± 3.53 111.35 ± 3.20 0.000a (p-n itrophe- −1 nol μg g dwt −1 h ) Xylanase (Glu- 60.02 ± 1.97 81.34 ± 2.32 66.68 ± 2.15 82.75 ± 2.00 69.297 ± 2.30 82.075 ± 2.10 0.000a 51.50 ± 1.49 57.79 ± 1.41 33.72 ± 1.36 65.50 ± 0.32 44.682 ± 0.37 64.691 ± 0.36 0.000a cose equiva- −1 lent μg g dwt −1 24 h ) s accharase 380.68 ± 947.86 ± 457.48 ± 1038.46 ± 328.75 ± 9.62 765.19 ± 0.000a 127.17 ± 2.70 216.16 ± 5.41 132.71 ± 3.64 119.27 ± 6.67 102.71 ± 3.28 101.25 ± 2.76 0.000a (Glucose 10.38 16.36 10.35 27.00 18.15 equivalent −1 μg g dwt −1 3 h ) c ellulose (Glu- 257.00 ± 6.20 603.00 ± 7.00 228.52 ± 5.44 235.65 ± 6.97 328.64 ± 8.55 571.18 ± 6.40 0.000a 222.87 ± 8.61 456.89 ± 217.30 ± 6.35 190.39 ± 5.35 331.95 ± 472.72 ± 0.000a cose equiva- 12.40 12.05 18.85 −1 lent μg g dwt −1 24 h ) notes: Mean ± standard error; (n = 3), ns = non significant, a: significantly different at p ≤ 0.001, b: significantly different at p ≤ 0.01 and c: significantly different at p ≤ 0.05. nainarmandapam (s1), Reddichavadi (s2), lingareddypalayam (s3), Moolakulm (s4), Thattanchavady (s5), Villiyanur (s6). GEOLOGY, ECOLOGY, AND LANDSCAPES 163 Table 6 s oil enzyme activities in pre-monsoon and monsoon six study sites in puducherry India. Pre-monsoon Monsoon Season S1 S2 S3 S4 S5 S6 P-value S1 S2 S3 S4 S5 S6 P-value a cid phos- 156.15 ± 3.43 151.42 ± 2.31 134.09 ± 2.46 134.53 ± 3.37 79.00 ± 2.97 114.10 ± 2.59 193.04 ± 2.66 187.19 ± 3.97 140.91 ± 3.73 188.24 ± 1.79 125.16 ± 2.40 153.68 ± 1.81 0.000a phatase (p-n itrophe- −1 nol μg g dwt −1 h ) alkaline 119.79 ± 3.70 105.65 ± 3.36 111.44 ± 3.76 174.78 ± 3.69 127.44 ± 3.76 123.41 ± 2.93 0.000a 308.59 ± 5.58 412.12 ± 7.00 309.12 ± 3.60 443.96 ± 327.17 ± 6.99 442.36 ± 5.45 0.000a phosphatase 13.60 (p-n itrophe- −1 nol μg g dwt −1 h ) Urease (μg 35.74 ± 1.71 57.90 ± 2.04 111.14 ± 3.45 110.06 ± 3.69 121.71 ± 3.10 100.42 ± 1.70 0.000a 432.79 ± 415.45 ± 9.70 516.85 ± 1109.34 ± 264.65 ± 6.70 456.79 ± 0.000a −1 nH -n g dwt 10.06 13.71 34.82 10.18 −1 2 h ) β-glucosidase 76.60 ± 2.64 69.49 ± 2.53 85.40 ± 1.78 88.78 ± 2.69 82.24 ± 2.92 66.01 ± 2.05 0.000a 273.64 ± 7.67 336.10 ± 241.80 ± 7.63 327.13 ± 9.95 256.27 ± 8.99 357.56 ± 9.70 0.000a (p-n itrophe- 10.43 −1 nol μg g dwt −1 h ) Xylanase (Glu- 56.35 ± 1.77 70.68 ± 1.17 50.40 ± 1.76 76.06 ± 2.46 56.570 ± 2.29 64.537 ± 1.71 0.000a 91.68 ± 2.71 83.44 ± 2.35 100.83 ± 2.04 88.74 ± 2.07 99.325 ± 2.80 97.507 ± 2.73 0.000a cose equiva- −1 lent μg g dwt −1 24 h ) s accharase 105.57 ± 3.15 145.55 ± 2.39 111.53 ± 1.27 118.38 ± 2.31 121.98 ± 2.99 117.06 ± 2.43 0.000a 262.07 ± 6.45 360.55 ± 331.55 ± 268.07 ± 5.46 232.37 ± 238.94 ± 8.61 0.000a (Glucose 10.35 11.30 11.95 equivalent −1 μg g dwt −1 3 h ) c ellulose (Glu- 43.43 ± 2.88 49.42 ± 1.36 23.39 ± 0.72 59.28 ± 3.35 39.56 ± 3.16 58.21 ± 3.35 ns 321.84 ± 8.76 865.22 ± 6.77 327.18 ± 6.53 781.29 ± 5.47 443.85 ± 6.54 891.83 ± 4.80 0.000a cose equiva- −1 lent μg g dwt −1 24 h ) notes: Mean ± standard error; (n = 3), ns = non significant, a: significantly different at p ≤ 0.001, b: significantly different at p ≤ 0.01 and c: significantly different at p ≤ 0.05. nainarmandapam (s1), Reddichavadi (s2), lingareddypalayam (s3), Moolakulm (s4), Thattanchavady (s5), Villiyanur (s6). 164 S. VEERARAGAVAN ET AL. to spatial heterogeneity among six sites. Shao, Yang, and Wu (2015) have affirmed that vegetation type is an important factor influencing the spatial-temporal variation of soil enzyme activities and labile organic carbon. These results are also found to be similar with the findings of (Krämer & Green, 2000; Sinsabaugh, Benfield, & Linkins, 1981; Sinsabaugh & Linkins, 1988; Sinsabaugh & Moorhead, 1994). To reinforce the present observation further, it is relevant to cite specific reports on heterogeneity in soil character - istics invaded by a particular alien species wherever they grow viz. the exotic plant Mikania micrantha invasion in south-east China (Li, Zhang, Jiang, Xin, & Yang,2006) and B. thunbergii in New Jersey (Kourtev, Ehrenfeld, & Häggblom, 2003) and three sites of Eupatorium adenophorum (Sun, Gao, & Guo, 2013). Variation in plant inputs to soil due to the invasion, has the potential to cause variation in enzyme composition (Hernández & Hobbie, 2010) and might be a spatial heterogeneity effect (Sun et al., 2013). To substantiate the present observation on variabil- ity in soil quality and enzyme activity, the success of invasion of S. alata that has been reported to possess potential allelopathic property by virtue of its phytoch- meical constituents (Rodrigues, Souza Filho, Ferreira, & Demuner, 2010) and these are capable of interfering with chemical ecology of given invaded region. Though there is no report on the exact chemistry of root exu- dates of S. alata, it could not be ignored because plants modify the soil environment through root exudates that aeff ct soil structure, and mobilize and/or che- late nutrients and long-term impact of litter and root exudates can modify soil nutrient pools or nutrient cycles differently from native species (Weidenhamer & Callaway, 2010). Moreover, plant released-compounds such as polysaccharides, aromatic compounds, and esters, together with detritus and root exudates serve as sources of substrates for enzymatic degradation and provide the energy and elements’ necessary for enzyme synthesis (Hernández & Hobbie, 2010). Sun et al. (2013) showed that the soil microbial composition was signifi - cantly different in the three Eupatorium adenophorum invaded sites which are the principle causative bio factor in enzyme activity. Therefore, it is concluded primarily that invasive plant S. alata could invade any site successfully and modify it by virtue of its invasive trait and exhibit heterogeneity in the soil characteristics and create an allelopathic regime in the invaded region/soil and flourish as monospecific population; besides invasive plants are capable of modifying ecosystem function and such changes can be species specific and site-specific Figure 4. (a,b,c,d). principal component analysis with three sets of variables: enzymes, nutrients measured the symbols indicate (Dassonville et al., 2008). The second level of study the corresponding score group means ± se and the arrows relates to seasonality in soil characteristic, enzyme represent variable eigenvectors in the space plotted by the activities, and AGB of S. alata. first two pca axes. The projection of the lines along each axis indicates their relative importance. GEOLOGY, ECOLOGY, AND LANDSCAPES 165 Table 7. c orrelation between soil enzymes and physico-chemical properties six study sites in puducherry, India. pH EC SM ABG TN TOC Sulphur EP N-N E-K E-Na E-Ca E-Mg ACP ALP URA β- GLU XYL CEL SAC pH 1 ec 0.287* 1 sM −0.235* 0.066 1 aBG −0.212 0.019 0.362** 1 Tn −0.103 −0.294* 0.030 −0.022 1 Toc 0.397** 0.412** −0.271* −0.505** −0.058 1 sul- 0.106 −0.233* 0.024 0.153 0.700** −0.180 1 phur e p 0.156 0.169 −0.114 −0.335** 0.391** 0.712** 0.110 1 n- n −0.210 −0.208 0.171 0.261* 0.455** −0.544** 0.423** −0.213 1 e K −0.122 −0.329** −0.493** 0.072 0.466** −0.293 0.451** −0.035 0.240* 1 e-na 0.056 −0.289* −0.731** −0.097 0.304** −0.200 0.309** 0.010 0.182 0.676** 1 e-ca −0.227 −0.157 −0.343** 0.296* −0.223 −0.675** −0.079 −0.587** 0.349** 0.442** 0.531** 1 e-Mg −0.177 −0.198 −0.518** 0.132 0.035 −0.459** 0.124 −0.397** 0.342** 0.606** 0.690** 0.793** 1 acp −0.441** 0.128 0.273* 0.326** −0.286* −0.543** −0.186 −0.490** 0.363** −0.040 −0.160 0.518** 0.340** 1 alp −0.403** −0.147 0.788** 0.363** 0.407** −0.297* 0.319** 0.049 0.361** −0.160 −0.456** −0.318** −0.366** 0.214 1 URa −0.504** −0.204 0.592** 0.315** 0.158 −0.375** 0.168 −0.107 0.318** −0.120 −0.286* −0.074 −0.065 0.492** 0.755** 1 β- Gl U −0.350** −0.067 0.852** 0.392** 0.122 −0.392** 0.120 −0.188 0.341** −0.344** −0.576** −0.169 −0.287* 0.453** 0.885** 0.730** 1 Xyl 0.142 0.255* −0.005 −0.430** −0.145 0.744** −0.281* 0.460** −0.446** −0.416** −0.398** −0.662** −0.536** −0.290* 0.026 0.013 −0.042 1 cel −0.472** −0.318** 0.588** 0.375** 0.283* −0.488** 0.304** −0.200 0.494** −0.003 −0.234* 0.009 −0.019 0.380** 0.841** 0.700** 0.837** −0.150 1 sac −0.303** 0.274* 0.489** 0.306** −0.462** −0.403** −0.371** −0.457** 0.237* −0.427** −0.427** 0.311** 0.102 0.823** 0.235* 0.474** 0.560** −0.097 0.321** 1 notes: N = 72 *correlation is significant at the ≥0.05 level of interval, **correlation is significant at the ≥0.01 level of interval, ns: not significant, ec: electrical conductivity, Tn: total nitrogen, T oc: total organic carbon, sm; s oil moisture, aBG: above ground biomass, ep: extractable phosphorus, e-K: extractable potassium, e-na: extractable sodium, e- c a: extractable calcium, e-Mg: extractable magnesium, acp : acid phosphatase, alp : alkaline phosphatase, URa: urease, β-Gl U: β-gulucosidase, Xyl: xylanase, cel: cellulase and sac saccarase. 166 S. VEERARAGAVAN ET AL. 4.2. TOC on soil enzyme activities (P), and sulphur (S) cycles. Fioretto, Papa, Curcio, Sorrentino, and Fuggi (2000) reported that cellulase in e r Th elationship between TOC and enzyme activities soils are derived mainly from plant debris incorporated is not significant as the principle source of carbon i.e. into the soil (Table 7). e Th relatively dense structure of litter is almost continuously added by the densely grow- plants and a greater accumulation of litter and fine roots ing invasive plant almost throughout the year. During in the understory of forest and plantation may favour the summer, the plant species showed a reduction in the growth of microbial populations which in turn pave way biomass of aboveground parts; but large amount of litter for higher level of enzyme activity particular enzymes fall was contributed to the soil during summer. Which related to degradation of plant debris. β-glucosidase contribute to higher amount of TOC; secondly because present in plant debris decomposing in the ecosystem of high temperature and lesser soil moisture the micro- (Ajwa & Tabatabai, 1994; Martinez & Tabatabai, 1997). bial activity is low resulting in lesser utilization of carbon Glucosidase enzyme plays an important role in soils by the microbes. Under such conditions more of TOC because it is involved in catalyzing the hydrolysis and is left unused in the soil (Figure 3). TOC is also highly biodegradation of cellulose in plant debris and the accu- influenced by the type of vegetation and its level of litter mulation of C in microbial biomass. The more density of production under shady microclimate. Vegetation type plants cover provides microclimate condition resulting is an important factor influencing the spatial-tempo- in more activity of micro-organisms which are the main ral variation of soil enzyme activities and labile organic source of these enzymes in the soil (Grierson & Adams, carbon (Shao et al., 2015). Ehrenfeld (2003) stated that 2000; Moraghebi et al., 2012; Sedia & Ehrenfeld, 2006). soil carbon (C) and nutrients pools are oen m ft odified e diff Th erences in the sources of substrate availability by invasions and that the direction and magnitude of and composition may lead to the changed behaviours the impacts were probably determined by the composi- of the activity of hydrolytic enzymes, such as, urease, tion of the invaded community and soil properties. Soil and β-glucosidase in soils. Such enzymatic disparity of enzymes’ involvement in biogeochemical cycles of the enzyme activities is revealed also in other studies (Tian, soil will result in the transforming and cycling of soil Dell, & Shi, 2010). Enzymatic activity depends on tem- labile organic carbon pool (Shao et al., 2015). Based on perature, soil moisture, and flora (Błońska, 2010).The the carbon as of energy for the microbes involved in moisture response function of β-glucosidase, xylanase, enzymatic activity, due to unfavourable soil microcli- alkaline phosphatase, urease, cellulase activity almost mate, the enzyme activity is higher during post-mon- stable during the year β-glucosidase were closely related soon and monsoon but lesser during summer. However, to plant growth and the biomass amount (Shao et al., β-glucosidase, urease, and acid phosphatase showed 2015). The results indicated that vegetation type is an positive correlation with AGB and nutrients viz. phos- important factor influencing the spatial-temporal var - phorus and nitrogen with TOC. iation in enzyme activities and labile carbon. Caldwell, Griffiths, and Sollins (1999) found that the relationship 4.3. Seasonality in soil characteristics and enzyme between major C- and P-processing enzymes changed activity under different soil and vegetation regime. Total nitrogen also showed no significant seasonal Seasonality of soil characteristics showed significant variation due to variation inplant biomass and litter fall. relation with certain factors. Due to previous rainy sea- Liao et al. (2008) conducted a meta-analysis and found son, the soil retains its wetness during post-monsoon that on average, with much higher litter decomposition and influences microbial activities triggering more rates and increases in soil nitrogen mineralization and enzyme activities resulting in more availability of nutri- nitrification. Seasonal variation of plant biomass was ents. Therefore, during post-monsoon above ground the potential driving factor for them that are associated biomass showed higher values. The enzyme activity is with C, N, and P cycling (Shao et al., 2015). Stepwise also higher as suitable environmental factors viz. mois- regression analysis showed that vegetation was the best ture, ambient temperature, and pH. EC also showed predictor of N cycling metrics the higher productivity, higher values due to presence of more elements/ions the higher the rates of net mineralization, nitrification, in the soil due to higher microbial oxidation process. During summer, there is less enzyme activity influenced and microbial biomass-N (Corbin & D’antonio, 2011). by higher temp and low soil moisture and these factors However, there exists significant relation between soil reduce the availability of the nutrients to the root system nitrogen and enzyme urease. These two factors showed resulting in lesser growth and above ground of plants; higher values during post-monsoon when there is high on the other hand due to higher temp, leaves and other AGB production and lesser during summer. Similar plant parts stated drying and triggers higher amount of observation is also made by Dormaar, Johnston, and litter fall. The effects of enzyme activities on the decay of Smoliak (1984) and Błońska (2010) that the activity major litter components have been found to couple with of urease clearly increases in winter and decrease in major nutrient cycles, such as nitrogen (N), phosphorus summer. GEOLOGY, ECOLOGY, AND LANDSCAPES 167 Usually extractable P is negatively correlated with Disclosure statement the percentage of clay, organic carbon and positively No potential conflict of interest was reported by the authors. correlated with pH but here no such correlation because of a higher rate of litterfall which contains more P. Such References varying levels of both soil parameter and enzyme activ- ities in alien invaded soil have been already reported Ajwa, H.A., & Tabatabai, M.A. (1994). Decomposition of different organic materials in soils. Biology and Fertility of in different plants. For instance, Amaranthus viridis Soils, 18(3), 175–182. invasion significantly increased the concentration of − Bashour, I., & Sayegh, A.H. (2007). Methods of analysis for soil total P in Senegal (Sanon et al., 2009). Soil NO soils of arid and semi-arid regions. 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Journal
Geology Ecology and Landscapes – Taylor & Francis
Published: Jul 3, 2018
Keywords: Soil enzymes; Senna alata L; heterogeneity-seasonality; inconsistency; litterfall