Soil is one of the most diverse habitats on earth and contains the most diverse assemblages of living organisms. Biological activity in soils is largely concentrated in the topsoil. The biological components occupy a tiny fraction (<0.5%) of the total soil volume and make less than 10% of the total soil organic matter. This living component consists of plant roots and soil organisms (Breure 2004). It has been estimated that only between 1 and 5% of all microbes on earth have been named and classified. Fungi are a diverse component of soil microbial communities, in which they function as decomposers, mycorrhizal mutualists, and pathogens.

The fossil record of fungi dates back to the early phanerozoic and into the Proterozoic geological era (Pirozynski et al., 1988). The existence of fossil fungi indicated their evolutionary significance besides solving certain phylogenetic complexities. The taxonomic diversity includes an important part of an ecosystem’s diversity controlled by genetic diversity which can be greater than the number of recognized species. Studying the ecological interactions of these organisms is challenging because of the extremely high diversity of soil fungi, the complexity of the substrate, and the difficulty of direct observation of these communities (Bridge and Spooner 2001).

Fungi are not only beautiful but play a significant role in the daily life of human beings besides their utilization in industry, agriculture, medicine, food industry, textiles, bioremediation, natural cycling, as biofertilizers and many other ways. They are diverse group of organisms comprising both single-celled and multicellular filamentous forms. Fungal bio technology has become an integral part of the human welfare (Manoharachary et al., 2005).

Most of the mycologists tend to rely upon culture- based methods in ecological investigation of soil fungi. The data provide only a selective, and invariably biased, window on diversity. Diversity of macro fungi has been extensively investigated globally during the last decade or so (Schmit et al. 1999). The records show over 27000 fungi recorded in India so far. This may be the largest biotic community second in line with insects (Manoharachary et al. 2005). Around 205 new genera have been described from India of 4979 genera distributed in 484 families in 103 orders which are in four phyla of the true fungi of the Kingdom Eukaryota.

A wide range of fungi occur in different environments having various ecological functions. Their ability to grow in artificial media has long been exploited to isolate them from different environments and also specific media have been developed to select certain groups of microbes. However only around 5-10% of fungi can be cultured artificially. Fungi like Fusarium, Gliocladium, Penicillium and Trichoderma are stress tolereant. Majority of fungi are mesophiles with maximum growth between 25 and 30^° C (eg. Penicillium crysogenum, Mucor mucedo etc.), some like Candida scotti are psychrotolerant and others like Thixomicor, Thermomyces etc. are thermoleterant and grow above 40^°C. Aspergillus and Penicillium are xerotolerant having the capacity to grow on dry materials with low metric potential (a_w) while fungi like some Pichia species are osmotolerants and they grow at very low osmotic potential. True fungi are ubiquitous in the environment and fulfill a range of important ecological functions, particularly those associated with nutrients and carbon cycle processes in the soil (Christensen, 1989). Among them several species may have the same functions which may result in functional redundancy. Equally some species may interact to perform functions not possible by any individual species. Therefore biodiversity is the interaction of all these elements. Above everything microbial driven soil processes play key roles in mediating global climate change (De Zwart et al., 2003), today’s greatest challenge in front of everyone on this planet.

Unprecedented interest in microbial biodiversity in Western Ghats has arisen for three primary reasons; first little is known about it when comparing to other environments, second its unexplored vastness and its potential for commercial exploitation and finally new techniques for quantifying based on molecular biology.

Biological diversity in the Western Ghats faces threats due to the exploitation of its habitat. Inventorying and monitoring of the biological biodiversity of the Western Ghats is therefore an important challenge before the community of systematists, biogeographers and ecologists in India. In the 21^st century, fungi will not only be used for understanding their unique mode of life, but also for findings of general applicability to higher organisms, adaptation to harsh environmental conditions, aging and death (Ramesh Maheshwari 2005). Threat to them throughout the globe is of concern because they play a significant role in human welfare (Moore et al., 2001). However it has been estimated that only between 1 and 5% of all microbes on earth have been named and classified.

MATERIALS AND METHODS:

Study site:

Soil samples were collected from thick vegetation sites of Shencottai (8^o 57' North; 77^o12' East), a small town in the south western Tamil Nadu bordering Kerala State for the present study. The average annual temperature of the sampling site is 20^oC to 24^oC with and average annual rainfall 1000mm to 2500mm. The climate in this area is humid and tropical. The sampling area is the type of moist deciduous forest. The mountain stretch is divided into four sites viz. bottom (659 ft. altitude), middle (1313 ft. altitude), sub-top (1637 ft. altitude) and top (1713 ft. altitude). The altitudes were measured with the help of a GPS device named Garmin GPS 12XL, made in Taiwan.

Table. 1: The physicochemical components of the 16 soil samples.

Sl. No.	Parameters	Samples taken
		Winter				Summer				Pre-Monsoon				Post-Monsoon
		1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	Ph	6.76	6.89	7.02	7.08	6.85	7.06	7.57	7.89	7.89	7.84	7.45	8.07	7.83	8.02	8.16	7.73
2	Elecron Conductivity (dsm^-1)	1.82	1.34	1.62	2.06	1.32	1.01	0.84	0.72	0.67	0.42	0.34	0.85	0.60	0.44	0.41	0.64
3	Organic carbon %	8.25	8.86	8.41	8.22	8.89	9.07	9.12	8.90	8.21	8.16	8.24	9.26	4.62	3.59	3.54	3.46
4	Organic matter %	15. 02	17. 09	16. 35	17. 46	16. 71	16. 22	17. 86	16. 96	16. 42	16. 12	16. 32	16. 22	8. 74	6. 78	7. 08	7. 12
5	Nitrogen %	1.29	1.77	2.04	1.67	1.49	1.64	1.77	1.61	1.72	1.54	1.62	2.08	1.02	1.06	1.21	1.20
6	Phosphorus %	0.48	0.35	0.44	0.41	0.42	0.58	0.59	0.32	0.44	0.48	0.42	0.46	0.27	0.37	0.38	0.36
7	Potassium %	2.27	2.44	2.41	2.28	2.45	2.55	2.44	2.29	2.65	2.58	2.67	2.69	2.06	2.14	1.68	1.87
8	Sodium %	0.52	0.64	0.31	0.62	0.48	0.56	0.57	0.59	0.42	0.42	0.45	0.36	0.69	0.64	0.68	0.64
9	Calcium %	2.25	2.38	3.08	2.64	3.29	3.26	3.05	2.97	4.22	4.26	4.08	3.68	4.78	4.77	4.68	4.68
10	Magnesium %	1.96	2.03	2.02	2.04	2.13	2.18	2.25	2.19	2.26	2.38	2.49	2.33	2.16	2.14	2.15	2.16
11	Sulphur %	0.22	0.23	0.25	0.30	0.36	0.44	0.21	0.43	0.33	0.34	0.36	0.39	0.68	0.69	0.64	0.67
12	Zinc (ppm)	2.64	2.48	2.34	3.08	3.04	2.84	2.79	2.87	2.68	3.08	2.67	2.89	1.53	1.57	1.84	1.58
13	Copper (ppm)	1.40	1.55	1.19	1.34	1.29	1.26	1.18	1.27	1.21	1.14	1.26	1.20	0.53	0.65	0.55	0.57
14	Iron (ppm)	15. 35	14. 66	15. 64	15. 10	17. 84	18. 08	18. 05	19. 04	16. 38	16. 17	18. 35	18. 24	10. 16	10. 24	9. 69	8. 47
15	Manganese (ppm)	4.58	4.72	4.52	4.26	4.69	4.54	4.64	4.42	5.08	4.77	4.48	4.57	2.38	2.59	2.54	2.52
16	Chromium (ppm)	0.49	0.43	0.61	0.47	0.49	0.64	0.62	0.89	0.64	0.62	0.53	0.69	0.77	1.05	0.84	0.94
17	Nickel(ppm)	0.04	0.05	0.06	0.04	0.06	0.06	0.08	0.04	0.06	0.08	0.06	0.05	0.21	0.22	0.24	0.22
18	Cobalt (ppm)	0.22	0.23	0.23	0.22	0.32	0.28	0.27	0.24	0.46	0.28	0.28	0.23	0.36	0.27	0.26	0.24
19	Cadmium (ppm)	0.11	0.12	0.13	0.11	0.13	0.12	0.14	0.12	0.23	0.17	0.18	0.16	0.26	0.24	0.27	0.28

Table 2a: One way (Season-wise) Descriptive

Seasons	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean		Minimum	Maximum
Seasons	N	Mean	Std. Deviation	Std. Error	Lower Bound	Upper Bound	Minimum	Maximum
Winter	648	1.14	1.331	.052	1.03	1.24	0	9
Summer	516	.75	.907	.040	.67	.83	0	5
Pre-Monsoon	612	.62	.741	.030	.56	.68	0	4
Post-Monsoon	612	.75	1.035	.042	.67	.83	0	8
Total	2388	.82	1.055	.022	.78	.86	0	9

Sampling:

Each sample consisted of 5 regularly spaced subsamples from each spot.These subsamples in one location were pooled, mixed and sieved (2 mm mesh) in sterile conditions and then considered as one sample. In the same way all the samples were collected from each location from the whole stretch of the mountain as climbed from foot to the top. In such a way four soil samples were collected in each season (winter, summer, pre-monsoon and post-monsoon) of 2010 at the sites mentioned above. Altogether sixteen (four for a season) soil samples were collected and analyzed. Extra quantity of soil was collected for the analysis of physichochemical properties. The samples were collected in the first week of January, April, June and September. The sampling points were identified very carefully through all the seasons and they were collected at the depth of 0-15 cm from the forest floor (Hu et al., 2006) in labeled sterile polythene bags and taken in ice-packed coolers to the laboratory for microbiological and physicochemical analysis (Anderson and Ingram 1993) from the same locations. Correlations among these soil-related variables and measures of soil fungal diversity were analyzed to determine differences among the soil-related variables.

Analysis of Physicochemical Parameters and correlation studies:

The physicochemical parameters of the soil samples were analyzed at the Soil Testing Laboratory, Dept. of Agriculture, Govt. of Tamil Nadu, Tiruchirapalli-20. (Table 1).

Isolation and Identification of fungal isolates:

The samples were plated onto Potato Dextrose Agar (PDA) medium with chloramphenical (40 mg/l). The plates were incubated at 22 – 25⁰C. After a week the fungal colonies were observed and subjected to view the morphology by lactophenol cotton blue staining technique (Gilman, 1998). The identification of fungal taxa was based on illustrated Genera of imperfect fungi (Barnett, 1965), Hyphomycetes (Subramanian 1971) and Manual of soil fungi (Gilman, 1957, 1998).

Statistical Analysis:

The frequency of occurrence of fungal isolates in different seasons were statistically analyzed using ANOVA.

RESULTS AND DISCUSSION:

Fungal community composition was most closely associated with changes in soil nutrient status. The results suggest that specific changes in edaphic properties, not necessarily land-use type itself, may best predict shifts in microbial community composition across a given landscape. Carbon, N, Mg and Fe were high in soils collected from forests thus influencing fungal diversity. Greater demand for nutrients by plants during rainy season (the peak vegetative growth period) limited the availability of nutrients to soil microbes and, therefore, low microbial C, N and P. Weak correlations were also obtained for the relationships between microbial C, N and P and soil physicochemical properties.

The total organic carbon and organic matter varied with season to season and so the other inorganic contents. For example, the organic matter was 17.09 % in the winter season while it was 8.74 % in the post-monsoon season in the same site. The water holding capacity of the soils in the forest floor varied as per its organic and other materials’ contents. The pH also influenced greatly the fungal population in the soil. It is a universal truth that fungi mostly like acidic pH for growth. The pH of the soil samples ranged from 6.76 to 8.16 but most of the soil samples showed alkaline pH (Table1) and this may be suspected as one of the reasons why so many fungal species were not present which was contrary to what would be usual. The fungal population is high in the soil during the winter season and also the soil pH is acidic during this time. This shows why the population is higher in winter than the other seasons. The variations may probably be attributed to bioclimatic factors acting on and/ or interfering with, fungal survival and dispersal (Sarquis and Oliveira 1996 and Bergen and Wagner-Merner 1977). It is noticed that seasonal changes cause population shifts and changes to fungal communities (Lodge and Cantrel, 1995).

89 fungal species were isolated in this study. The occurrence of genera in terms of the number of species were Aspergillus (25 species) in first place followed by Penicillium (14 species), Alternaria and Fusarium (6 species each) and Curvularia, Mucor and Trichoderma (5 species each). Other fungal species did not show high population. Though second in place the data regarding Penicillium were coincident with those reported by several authors who mention the constant presence of this particular fungus in the mycoflora from different areas in the world (Calvo et al., 1980 a and b and Almeida and De-Almeida, 1997).

The occurrence of Aspergillus genera in highest number has also been recorded in the studies conducted by Fatma F. Migahed et al., (2003) of the soil samples of the sand beaches of Alexandria. The number of colonies of almost all individuals occurred more in the winter season and it may be attributed to the colder weather which is favorable for the fungal growth. Reasons for not finding most of the commonly found individuals include weather, soil pH, microclimatic phenomena (Cibula 1974), as well as poor collecting.

To test the difference between frequencies of organisms in different seasons, between sites and between individuals One-Way ANOVA was used (Table 2, 3 a, b and c and 4 respectively). From one-way ANOVA it is evident that there was significant difference between seasons, no significant difference between sites and there is significant difference between individuals.

Table 2b: ANOVA

	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	94.778	3	31.593	29.414	0.000
Within Groups	2560.583	2384	1.074
Total	2655.361	2387

Table 2c: Post Hoc tests Homogenous subsets Duncan^a,,b

Season	N	Subset for alpha = 0.05
Season	N	1	2	3
Pre-Monsoon	612	0.62
Summer	516		0.75
Post-Monsoon	612		0.75
Winter	648			1.14
Sig.		1.000	0.974	1.000

Means for groups in homogeneous subsets are displayed.

a. Uses Harmonic Mean Sample Size = 592.666.

b. The group sizes are unequal. The harmonic mean of the group sizes is used. Type I error levels are not guaranteed.

Table 3b: ANOVA Values

	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	0.949	3	0.316	0.284	0.837
Within Groups	2654.412	2384	1.113
Total	2655.361	2387

Table 4: ANOVA (for all the individual species) Values

	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	608.306	88	6.913	7.763	0.000
Within Groups	2047.056	2299	0.890
Total	2655.361	2387

Figure 1: Shows the number of fungal species being abundant in the winter season in all the four sites

CONCLUSION:

In our studies the soil samples represented the main reservoir of fungi. Some soil fungi are potential pathogen to human, animals and plants. A potential plant pathogen named Sarocladium oryzae which occurs rarely was isolated in the present study. The forest, farmyard, park soils as well as sediments of the rivers and oceans naturally contain humus and organic materials which are the best factors for the growth of keratinolytic and saprophytic fungi. (S.Ali-shtayeh and Rana 2000). Fungi are important decomposers of all sorts of complex organic molecules, including those materials high in cellulose, keratin, chitin, and lignin. Mycorrhizal fungi form symbiotic associations with the roots of 75-80% of vascular plants (Hawksworth 1991; Walting 1997), and are essential to the plant for the uptake of nutrients from the soil. Furthermore, fungi, particularly mushroom forming species serve as valuable food sources for numerous invertebrate and vertebrate forest inhabitants (Hawksworth 1991; Walting 1997).

ANOVA analysis revealed that there was significant difference between frequencies of organisms in different seasons and also from post hoc tests frequencies of organisms in summer, pre-monsoon and post-monsoon were homogenous whereas the frequencies of fungal population in winter was entirely different (Figure 1).

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Received on 09.09.2011

Modified on 14.10.2011

Accepted on 28.10.2011

Research J. Science and Tech. 3(6): Nov.-Dec. 2011: 329-334

	N	Mean	Std. Deviation	Std. Error	95% Confidence Interval for Mean		Minimum	Maximum
	N	Mean	Std. Deviation	Std. Error	Lower Bound	Upper Bound	Minimum	Maximum
Site I	597	0.82	1.010	0.041	0.74	0.90	0	7
Site II	597	0.83	1.146	0.047	0.74	0.92	0	9
Site III	597	0.84	1.072	0.044	0.76	0.93	0	9
Site IV	597	0.79	0.985	0.040	0.71	0.87	0	8
Total	2388	0.82	1.055	0.022	0.78	0.86	0	9