ISSN 2320 -5083 Journal of International · Journal of International ... Highway Engineer, Infrastructure, Ramboll, ... Simpson’s index describes the probability that a second individual

Journal of International Academic Research for Multidisciplinary

ISSN 2320 -5083

A Scholarly, Peer Reviewed, Monthly, Open Access, Online Research Journal

Impact Factor – 1.393

VOLUME 1 ISSUE 11 DECEMBER 2013

A GLOBAL SOCIETY FOR MULTIDISCIPLINARY RESEARCH

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Editorial Board

Dr. Kari Jabbour, Ph.D Curriculum Developer, American College of Technology, Missouri, USA.

Er.Chandramohan, M.S System Specialist - OGP ABB Australia Pvt. Ltd., Australia.

Dr. S.K. Singh Chief Scientist Advanced Materials Technology Department Institute of Minerals & Materials Technology Bhubaneswar, India

Dr. Jake M. Laguador Director, Research and Statistics Center, Lyceum of the Philippines University, Philippines.

Prof. Dr. Sharath Babu, LLM Ph.D Dean. Faculty of Law, Karnatak University Dharwad, Karnataka, India

Dr.S.M Kadri, MBBS, MPH/ICHD, FFP Fellow, Public Health Foundation of India Epidemiologist Division of Epidemiology and Public Health, Kashmir, India

Dr.Bhumika Talwar, BDS Research Officer State Institute of Health & Family Welfare Jaipur, India

Dr. Tej Pratap Mall Ph.D Head, Postgraduate Department of Botany, Kisan P.G. College, Bahraich, India.

Dr. Arup Kanti Konar, Ph.D Associate Professor of Economics Achhruram, Memorial College, SKB University, Jhalda,Purulia, West Bengal. India

Dr. S.Raja Ph.D Research Associate, Madras Research Center of CMFR , Indian Council of Agricultural Research, Chennai, India

Dr. Vijay Pithadia, Ph.D, Director - Sri Aurobindo Institute of Management Rajkot, India.

Er. R. Bhuvanewari Devi M. Tech, MCIHT Highway Engineer, Infrastructure, Ramboll, Abu Dhabi, UAE Sanda Maican, Ph.D. Senior Researcher, Department of Ecology, Taxonomy and Nature Conservation Institute of Biology of the Romanian Academy, Bucharest, Romania Dr. Reynalda B. Garcia Professor, Graduate School & College of Education, Arts and Sciences Lyceum of the Philippines University Philippines Dr.Damarla Bala Venkata Ramana Senior Scientist Central Research Institute for Dryland Agriculture (CRIDA) Hyderabad, A.P, India PROF. Dr.S.V.Kshirsagar, M.B.B.S,M.S Head - Department of Anatomy, Bidar Institute of Medical Sciences, Karnataka, India. Dr Asifa Nazir, M.B.B.S, MD, Assistant Professor, Dept of Microbiology Government Medical College, Srinagar, India. Dr.AmitaPuri, Ph.D Officiating Principal Army Inst. Of Education New Delhi, India Dr. Shobana Nelasco Ph.D Associate Professor, Fellow of Indian Council of Social Science Research (On Deputation}, Department of Economics, Bharathidasan University, Trichirappalli. India M. Suresh Kumar, PHD Assistant Manager, Godrej Security Solution, India. Dr.T.Chandrasekarayya,Ph.D Assistant Professor, Dept Of Population Studies & Social Work, S.V.University, Tirupati, India.

JOURNAL OF INTERNATIONAL ACADEMIC RESEARCH FOR MULTIDISCIPLINARY Impact Factor 1.393, ISSN: 2320-5083, Volume 1, Issue 11, December 2013

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ASSESSMENT OF MICROBIAL DENSITY AND DIVERSITY IN THE RHIZOSPHERE OF BAMBUSA BAMBOS (L.)

DR. VENNILA.S* DR. T. KALAISELVI**

DR. S.UMESH KANNA*** DR. K.T.PARTHIBAN****

*Senior Research Fellow, Forest College & Research Institute, Mettupalayam, Tamil Nadu, India

**Professor (Agricultural Microbiology), Center of Excellence in Biofuels Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India ***Forest College & Research Institute Tamil Nadu Agricultural University, Mettupalyam, Tamil Nadu, India ****Forest College & Research Institute Tamil Nadu Agricultural University, Mettupalyam, Tamil Nadu, India

ABSTRACT

The rhizosphere soil microbial density, diversity and fertility status of the bamboo at

different locations viz., Anaikatty, Barliyar, Mammaram, Gudalur and Mettupalayam were

studied. The impact of bamboo on soil microbes and the microbial diversity of non-

rhizosphere soil were also assessed. In general, the density of microflora varied widely

among the various locations as well as between bamboo rhizosphere and non-rhizosphere

soils. The density of bacteria and actinomycetes were found to be higher in Anaikatty

rhizosphere soil and fungal population was higher in Mettupalayam non-rhizosphere soil.

Among the bamboo rhizosphere, Anaikatty harboured greater number of microbial

population while Barliyar rhizosphere soils exhibited greater microbial diversity. Comparing

rhizosphere and non-rhizosphere soils, non-rhizosphere were microbialy more diverse.

Among natural and managed ecosystem, natural ecosystems had more number of bacteria and

actinomycetes, while managed ecosystem recorded greater number of fungi.

KEYWORDS: Rhizosphere, Microbial Density, Microbial Diversity,

INTRODUCTION

Bamboo is a versatile multipurpose tree species, which plays a vital role in the

domestic economy of India. Bamboos are woody tree-like grasses, grouped into the sub

family Bambusoideae or tribe Bambuseae under the family Graminae (Poaceae). It is

indigenously found in tropical countries. A total number of 75 genera and 1250 species are

reported to occur in the world (Sodestrom and Ellis, 1987). It is a fast growing plant, the

largest possible biomass is obtained within a very short time of 4 to 6 years. After that the

sustained yield can be obtained on annual basis and there is no need for replanting for the

next 25 to 30 years until flowering or death occurs. Hence, it is known as ‘poor man’s

timber’ and ‘green gold’.


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Bamboo has not only gained importance as raw material in cottage industries but is

also used in large scale industries as pulp and paper (Maheswari and Satpathy, 1990). About

325 paper mills spread over the country meet about 60 per cent of their fibrous raw material

requirement from bamboo. As indicated above, industrial demand for paper is rising but

bamboo reserve is dwindling fast. Currently, Bambusa bambos is being grown in several

regions of India to meet out the increasing demand of long fibre pulping material

(Shanmughavel and Francis, 2001).

Microorganisms constitute less than 0.5 per cent (w/w) of the soil mass, yet they have

a major impact on soil properties and processes. The seemingly rigid mass of clay, sand and

silt is home for a complex microbial community including bacteria, fungi, actinomycetes,

algae, protozoa and viruses. The soil bacteria and fungi play pivotal roles in various

biogeochemical cycles (BGC) (Wall and Virginia, 1999) and are responsible for the cycling

of organic compounds. Soil microorganisms also influence above ground ecosystems by

contributing to plant growth and soil fertility (Yao et al., 2000). Thus, the intimate

association of microbes and soil particulates is critical for total ecosystem survival. Hence,

the integrity of the total ecosystem, above and below ground, rests on the stability of the soil

microbial community. The destruction of the soil micro biota through mismanagement or

environmental interference results in decline or even death of the above ground plant

population. Thus, an understanding of soil microbes, their properties and the nature of the

interaction with and within their environment is essential. With this background, the study on

assessment of rhizosphere microbial diversity of bamboo was undertaken.

Materials and Method

Bamboo rhizosphere soil samples were collected from different locations viz.,

Anaikatty, Barliyar, Mammaram, Gudalur and Mettupalayam. Among these locations,

Mettupalayam plantation was artificially raised where as the other locations were natural

strands. For comparison, non-rhizosphere soil samples adjacent to bamboo plantations were

also collected and analysed for microbial population. The population density of bacteria,

fungi, actinomycetes, diazotrophic bacteria and phosphate solubilizers were enumerated

using serial dilution and plating technique (Parkinson et al., 1971).


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Location details

Sl.No Locations Latitude Longitude Altitude Forest type

1. Anaikatty 110 00’N 77012’E 400 MSL 6A.Southern tropical thorn forest

2. Barliyar 11o21’N 77o55’E 500 MSL 5A.Southern tropical dry deciduous forest

3. Mammaram 11o22’N 79o58’E 750 MSL 5A.Southern tropical dry deciduous forest

4. Gudalur 11o43’N 79o49’E 1000 MSL 5B/E9Bamboo brakes

5. Mettupalayam 11o19’N 77o56’E 320 MSL 6A. Southern tropical thorn forest

(Champion and Seth, 1968)

Assessment of microbial diversity indices

Measures of diversity are frequently seen as indicators of the well being of any

ecosystem. They also serve as a measure of the species diversity in the ecosystem. The

following indices were worked out to assess and compare the diversity and distribution of

different microorganisms at different locations.

Alpha diversity indices

After assessing the microbial density, the diversity was calculated using various

indices. The alpha diversity indices viz., richness, evenness and dominance were calculated

by using the following standard formulae

Richness indices

a) Species number (Magurran, 1987)

This represents the total species number in each sample.

b) Margalef’s D (Clifford and Stephenson, 1975)

Margalef’s D has been favourite index for many years. It is calculated as the species

number minus one divided by the logarithm of the total number of individuals. This program

uses the natural logarithm.

Dmg = (S – 1)/ ln N

Where,

S = the number of species recorded

N = the total number of individuals summed overall S species

c) Shannon diversity index (Batten, 1976)

This presents the Shannon-Wiener (also known as Weaver) diversity index for each

sample and is defined as:

H’ = pi ln pi

Where, pi = the proportion of individuals in the ith species

This program calculates the index using the natural logarithm.


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Dominance indices

a) Simpson’s index (Simpson, 1949)

Simpson’s index describes the probability that a second individual drawn from a

population should be of the same species as the first.

D = [Ni (Ni – 1)] / [NT (NT – 1)]

Where, Ni = the number of individuals in the ith species and

NT = the total individuals in the sample

So the larger its value, the greater the diversity. The statistic 1 – C gives a measure of the

probability of the next encounter (by the collector or any animal moving at random) being

from another species (Hurlbert, 1971).

b) Berger parker diversity index (Berger Parker, 1979 and May, 1975)

A simple dominance measure is the Berger Parker index. The index expresses the

proportional importance of the most abundant species.

D = Nmax / N

Where,

Nmax = the number of individuals in the most abundant species

N = the total number of individuals in the sample

This simple index was considered by May (1975) to be one of the best. It is a simple

measure of the numerical importance of the most abundant species.

c) McIntosh index (McIntosh, 1967)

This index was calculated using the following formula Proposed by McIntosh (1967)

as

D = N – U / N – (N)

U = (ni2)

Where,

N = the total number of individuals in the sample

U is given by the expression

U = [n(i)]

Where,

n(i) is the number of individuals in the ith species and the summation is undertaken

over all the species.


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Evenness indices

Evenness (E), is a measure of how similar the abundances of different species or

categories are in a community, when all species in a community are equally abundant. The

evenness index should be maximum and decreases towards zero as the relative abundance of

the species diverge away from evenness. Evenness ranges from zero to one. When evenness

is close to zero, it indicates that most of the individuals belong to one or a few species or

categories. When the evenness is close to one; it indicates that each species or category

consists of the same number of individuals.

a) Shannon’s equitability (Pielou, 1969)

Equitability or evenness refers to the pattern of distribution of the individuals among

the species. This measure of equitability compares the observed Shannon-Wiener index

against that distribution of individuals between the observed species which would maximize

diversity. If H is the observed Shannon-Wiener index, the maximum value is log (S), where

S is the total number of species in the habitat. Equitability assumes a value between 0 and 1

with one being complete evenness. Therefore, the index is

H H

EH = ------- = -------

Hmax InS

Where, H = Shannon Wiener index

b) McIntosh equitability (Pielou, 1969)

McIntosh equitability (E) was calculated using the following formula

N – U

E = -------------

N – N/S

Where,

N = The total number of individuals in the sample

U is given by the expression

U = [n(i)]

S = The total number of species


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Beta diversity indices - similarity measures

Beta diversity measures the increase in species diversity along transects and is

particularly applicable to the study of environmental gradients. It measures two attributes,

the number of distinct habitats within a region and the replacement of species by another

between disjoint parts of the same habitat. All of the selected samples in the active data set

will be used to calculate the indices.

Estimating beta diversity employs similarity measures. This technique looks at the

similarity of pairs of sites, either in terms of species presence and absence (qualitative data)

or by taking species abundances into account (quantitative data). Although, there are a vast

range of these coefficients, two of these use presence and absence data while the other two

require abundance data.

1. Jaccards measure (Southwood, 1978)

This was calculated using the equation

Cj = j / (a + b – j)

Where,

j = the number of species found in both sites

a = the number species in site A

b = the number species in site B

2. Sorenson measure (qualitative) (Janson and Vegelius, 1981)

This measure was similar to the Jaccard index and uses identical variables.

Cs = 2j / (a+b)

Where,

J = the number of species found in both sites

a = the number species in site A

b = the number species in site B

3. Sorenson measure (quantitative) (Bray and Curtis, 1957)

A version of the Sorenson measure uses quantitative data

CN = 2jN / (aN + bN)

Where, j = the number of species found in both sites

aN = the number of individuals in site A

bN = the number of individuals in site B

4. Morista Horn index (Wolda, 1983)

This index is calculated from the equation


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CMH = 2 (ani x bni) / (da + db) aN x bN

Where, aN = the number of individuals in site A

bN = the number of individuals in site B

ani = number of individuals in the ith species in A

bni = number of individuals in the ith species in B

da = ani2 / aN2

db = bni2 / bN2

Result and Discussion

The rhizosphere is most certainly an area of intense biological activity within the soil

ecosystem. It is represented by a diversity of microbial population, the complete range of

plant microbe and microbe – microbe interactions and the relative inclusiveness of all

essential biogeochemical processes for total ecosystem development. Hence, the productivity

of the microbial community and the above ground community is interlocked with the

viability and stability of the microbial community.

The microbial density in bamboo rhizosphere soil collected from various locations

compared with non-rhizosphere soil. In general, the density of bacteria and actinomycetes

was higher in rhizosphere soils. Among the locations, Anaikatty rhizosphere soils recorded

maximum population of bacteria (585.75 x 107 CFU. g-1 soil) and actinomycetes (1999.5 x

102 CFU. g-1 soil) the least in bamboo rhizosphere soils of Mettupalayam. On the contrary,

maximum fungal population (43.75 x 106 CFU. g-1 soil) was noticed in non rhizosphere soil

samples of Mettupalayam. The soils of Gudalur registered for the lowest population of

actinomycetes owing to their acidic pH (Meiklejohn, 1957 and Alexander, 1978). In general,

the actinomycetes population was found to be the least among the microbes analysed. This

may be due to cool temperature and acidic environment prevalent in various locations except

Anaikatty and Mettupalayam. Similarly, the wide variation in bacterial and actinomycetes

population among locations and between rhizosphere and non rhizosphere soils may be due

to varied physico-chemical properties of the soils viz., pH, organic carbon and available

nutrients. This is in accordance with the report of Sushma Shail et al. (1997) that changes in

average number of fungi and bacteria is Kumaun Himalaya soils are positively correlated

with soil moisture and negatively with soil pH. The highest available nitrogen and organic

matter content of the Anaikatty soils may be responsible for greater bacterial and

actinomycetes observed in the study area.


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Alpha diversity

The richness indices for bacteria, fungi and actinomycetes of various locations were

calculated using Shannon Weiner indices (Table 2; Fig1). The indices varied with location,

as well as between rhizosphere and non-rhizosphere microbial population. The Shannon

Weiner index varied between 0.02132 and 0.6832, (Table 2) where as Margalef’s D index

ranged from 0.0911 to 0.1147.The relative abundance of microbial population was measured

using Simpson, Berger Parker and McIntosh indices. The Simpson index value ranged from

0.0062 (Anaikatty – rhizosphere) to 0.4900 (Mettupalayam – non-rhizosphere) and the

Berger Parker index varied from 1.0031 (Anaikatty – rhizosphere) to 1.7519 (Mettupalayam

– non-rhizosphere). The McIontosh index ranged between 0.0031 (Anaikatty – rhizosphere)

and 0.2859 (Mettupalayam – non-rhizospehre) (Table 2). This may be due to wider variation

in population level among the microbes viz., bacteria, fungi and actinomycetes encountered in

this particular location. There is no variation in diversity in terms of species number since all

kinds of microbes viz., bacteria, fungi and actinomycetes were observed in all locations.

Secondly, the dominance measures which describe the distribution of species abundances

were used to study the dominance. Similar to richness indices, in the present study, among

rhizosphere soils, Barliyar rhizosphere soil recorded maximum value for all the three

dominance indices used, while Anaikatty soils recorded the least. Since the dominance

indices are decided by the number of species, the total number of individuals as well as by the

level of most abundant species (Magurran, 1985), Anaikatty soils recorded the least due to

uneven distribution of microbes even though they harboured greater number of microbes.

The evenness in microbial population between different locations was estimated using

McEven and ShannEven indices (Fig 2). The lowest McEven value of 0.0074 was observed

for the rhizosphere soil samples of Anaikatty. While non-rhizosphere soil samples of

Mettupalayam recorded the highest value of 0.6763 (Table 2). Since, high evenness occurs

when species are equal or virtually equal in abundance is conventionally equated with high

diversity (Magurran, 1985). Eventhough rhizosphere soils had more number of microbes

compared to non- rhizosphere soils, in terms of microbial diversity, the non- rhizosphere soils

were found to be more diverse. This may be due to uneven distribution of various kinds of

microbes in rhizosphere soil.

Similarity measures

The Beta diversity and similarity measures were also calculated for different

locations. The similarity measures such as qualitative (Jaccard and Sorenson’s) and


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quantitative (Sorenson’s and Morista Horn) measures were estimated. Since, all the three

microbes viz., bacteria, fungi and actinomycetes were recorded in all the locations, qualitative

difference was not noticed and hence the value is one for all the locations for both

rhizosphere and non-rhizosphere soil samples. The quantitative value for Sorenson’s index

varied from 0.1935 to 0.8297. The Morista Horn varied from 0.8494 to 0.9999 (Table 3).

The Jaccard and Sorenson’s qualitative indices are designed to one in cases of

complete similarity and zero if the sites are dissimilar and have no species in common. In the

present study, the calculated qualitative indices value is one for all the locations since all

three microbes were obtained from different study area. But quantitative difference in

microbial population was observed among locations. Among the comparisons,

Mettupalayam vs Mamaram recorded the maximum value. Hence, the microbial diversity

and density of these two locations may be similar. The indices are minimum for

Mettupalayam vs Anaikatty and thus they may be dissimilar. Even though plethora of reports

(Andren et al., 1995; Kennedy and Smith, 1995; Bengtsson, 1998; Bardgett and Shine, 1999;

Griffiths et al., 2001; Jones et al., 2001; Brodie et al., 2002) are available for microbial

diversity of agricultural lands and grasslands, not much work has been done for forests tree

species particularly for bamboo. The microbial diversity of few shola tree species of

Longwood, Tiger hill and Thai shola of Nilgiris was reported by Venkatachalam (2003).

Dave and Desai (2006) reported microbial diversity of marine salterns of Gujarat using

morphological, physiological and biochemical parameters. The leaf colonizing lichen

diversity (folicolous) of Andaman Nicobar islands, Palani, Nilgiris hills and the north east

was reported earlier (Serusiaux, 1989; Pinokiyo and Singh, 2004). Our results are in

confirmity with these reports.

Conclusion

The long term productivity, resilience of stresses and conservation of biodiversity are

among the issues that need to be emphasized if forests are to be managed in a sustainable

manner. Soil microorganisms fulfill critical ecological roles, such as decomposition and

nutrient cycling. The present study revealed that Anaikatty rhizosphere soils ranked first in

terms of density, while the diversity was greater in rhizosphere soils of Barliyar. Comparison

between rhizosphere and non-rhizosphere soils indicated that non-rhizosphere soils were

more diverse.


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Table 1. Microflora of bamboo rhizosphere and non-rhizosphere soils of different locations

Location

Bacteria (X x 107 CFU. g-1 soil)

Fungi (X x 106 CFU. g-1 soil)

Actinomycetes (X x 102 CFU. g-1 soil)

R S R S R S

Anaikatty 585.75a 2.00a 15.50a 19.50b 1999.50a 11.25a

Barliyar 66.50b 3.43a 13.00a 5.25b 827.13ab 16.75a

Mammaram 73.75b 15.50a 11.75a 10.00b 377.63b 6.25a

Gudalur 122.25b 4.50a 15.25a 6.75b 139.88b 3.25a

Mettupalayam 33.25b 4.75a 20.75a 43.75a 170.50b 8.00a

Mean 176.30 6.04 15.25 17.05 702.93 9.10

In column, values followed by a common letter are not significantly different at the 5% level by DMRT


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Table 2 Microbial diversity indices - Alpha diversity indices

Indices Anaikatty Barliyar Mammaram Gudalur Mettupalayam

R S R S R S R S R S

Richness

Shannon weiner 0.02132 0.5955 0.1667 0.3627 0.1477 0.1698 0.0608 0.4528 0.0927 0.6832

Margalef’s D 0.0911 0.1125 0.1049 0.1147 0.1035 0.1045 0.0969 0.1048 0.1014 0.1088

Species Number 3 3 3 3 3 3 3 3 3 3

Dominance

Simpson’s 0.0062 0.4054 0.0753 0.2077 0.0648 0.0779 0.02180 0.0825 0.0366 0.4900

Berger Parker 1.0031 1.3936 1.0408 1.1334 1.0347 1.0423 1.0112 1.4236 1.0190 1.7519

McIntosh index 0.0031 0.2289 0.0384 0.1099 0.0329 0.0397 0.0110 0.1694 0.0184 0.2859

Evenness

Shaneven 0.0194 0.5420 0.1518 0.3301 0.1345 0.1546 0.0553 0.4270 0.0844 0.6219

McEven 0.0074 0.5415 0.0909 0.2600 0.0779 0.0940 0.0259 0.0352 0.0436 0.6763

Table 3 Microbial diversity - Similarity measures

Index MTP vs Anaikatty MTP vs Barliyar MTP vs Mammaram MTP vs Gudalur

R S R S R S R S

Jaccard – qualitative 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Sorenson – qualitative 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Sorenson – quantitative 0.1935 0.7068 0.6899 0.8297 0.8101 0.4063 0.5697 0.6728

Morista Horn quantitative 0.9998 0.8494 0.9996 0.9606 0.9998 0.9229 0.9999 0.8592


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0.3

0.4

0.5

0.6

0.7

dex

valu

e

Fig. 1. Microbial diversity base


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0.3

0.4

0.5

0.6

0.7

dex

valu

e

Fig. 2. Microbial diversity ba


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