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2.1 Due-diligence study for location and species diversity 21

DOCUMENTATION & DATA ANALYSIS

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2.1 DOCUMENTATION AND DATA ANALYSIS

2.1.1 ABSTARCT

Our prime goal was to bioprospect macrofungi on the basis of the ethno-myco-

medicinal traditional knowledge. Due-diligence in terms of the location, have an

idea about the quantum of diversity available and ensure ethnomedicinal

background to get valid practices. Due to lack of any documented investigations

we had to go for a theoretical estimation of possible size of diversity since the

dense forest area is very low. According to our liberal and conservative estimates

there may be 3000 or 1100 to 440 species of macrofungi, respectively in Gujarat.

Substantial sum of these could be unique to this location. We locked on the

Dangs, as it has the maximum very dense forest with maximum cover. Moreover

the tribes of this location were found to be well known for their traditional

etnomidicinal knowledge. In order to test and validate the hypothesis that the

Dangs was an appropriate location a positive control location with opposite

climate and geography was considered as well.

2.1.2 INTRODUCTION (Gujarat:an overview)

On 5/1/1960 Gujarat was established as a state, after being separated from

northern state and Maharashtra in south. It covers the Western part of India and is

surrounded by Pakistan and Rajasthan in the north, Madhya Pradesh in the east,

Maharashtra in the South-East and Arabian Sea in the West. The 1600 km

coastline of Gujarat is 1/3rd of the nation’s coastline and thus is the longest

amongst all the states. Almost 79.8% of 50,671,017 people inhabiting in 1,96,024

sq km of Gujarat are literate. It

witnesses an average rainfall of

93.2 cm and has max-min

temperature of 25-45oC and 15-

35oC in the summers and

winters respectively

(gujaratindia.com).

Gujarat demarcated

within latitude 20°01' to 24°07'N

and longitude 68°04' to 74°04' Figure 2.1.1. Forest cover of Gujarat.



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E, the state enshrines various ecosystems like grasslands, dry and wet deciduous

forests, deserts, scrublands, wetlands, mangroves, coral reefs, estuaries and

gulfs, harboring many rare and endangered species. The state gets rainfall

around 300mm in the north, which is not as scanty in the southern provinces

receiving 2500 mm. The land is divided in to three regions namely Gujarat

Mainland, guarded by mountains like Aravalli, Satpura, Vindhya and Sahyadri.

Secondly, the famous rann of Kutch and finally the Saurashtra peninsula. It

withholds several National Parks, including Gir Forest National Park (Girnar), near

Junagadh, Velavadar National Park in Bhavnagar District, Vandsa National Park

in Bulser District, and Marine National Park by the Gulf of Kutch in Jamnagar

District. There are also a number of wildlife sanctuaries and nature preserves,

including Anjal, Balaram-Ambaji, Barda, Jambughoda, Jessore, Kachchh-desert,

Khavda, Nal-sarovar, Narayan-sarovar, Paniya, Purna, Rampura, Ratanmahal,

and Schoolpaneshwar (gujaratindia.com). Geographic genesis of the state is said

to be marked by the breaking of western continental margin from the Gondwanan

land due to the northward drift.

Gujarat has a total forest cover of 14,946 sq km, of which 114 sq km is very

dense forest, 6,231 sq km is moderate dense forest and 8,601 sq km is open

forest (Fig 2.1.1). According to a 2004 report of Forest survey of India the forest

cover has decreased from 9.8% (Dir. of Econ. and Stat., Govt. of Gujarat, 1995) to

9.6% that surmounts to a total loss of 13% forest cover in 2 years due to human

activities (gisdevelopment.net). Thus loss of habitat would lead to loss of diversity

and disappearance or dilution of traditional knowledge.

2.1.3 Due-diligence study step 1: Identifying the region of research

Of the total number of plants documented from Gujarat, around 353 species occur

in desert zone, 540 in semi-arid zone and 488 in Malabar zone (Gavali and

Sharma, 2004). The dense forest cover in Gujarat is now limited to just three of

the 26 districts, Dangs (78 sq km), Surat (27 sq km) and Junagadh (9 sq km)

(fsi.nic.in). Hence, it was estimated that the Dangs enshrine almost 68.42% of the

states very dense forest cover. Thus this region had to be the prime location of

our research as it being in the Malabar-western Ghat hill zone had better forest



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cover and comparable diversity both of which are necessary for more number of

fungi per plant.

To test the hypothesis that the traditional knowledge and macrofungal

biodiversity and ethnomedicinal diversity in the Dangs will be more or better we

took a positive control area. This was Jessore sloth bear sanctuary, which was

opposite to the Dangs in every sense. It is situated in the North at the opposite

extreme of the Dang and had arid climate with scanty rainfall. Further down the

line, comparison regarding the quantity and quality of traditional knowledge and its

related macrofungal diversity, in between both the communities is done. The

details of both the regions are elaborated below.

2.1.3.1 Jessore community

This community was defined as the sum of those local people inhabiting in and

around the Jessore sloth bear sanctuary (Fig 2.1.2A, 2.1.3 A & B, 2.1.4 A). This

sanctuary is located in the Aravalli hill range in North Gujarat next to the

Rajasthan and harbors, Southern dry mixed deciduous and desert thorn types of

forests. Jessore is the second highest hill of Gujarat. The major floral species

comprise Ber, gando baval, khair, Isaraily baval, dhav, dudhalo, gando baval,

dhavado, saledi, kadaya, siras, gorad etc.

The flagship specie of the area is Sloth bear. The top carnivore, leopard

cohabits the area with other vertebrates. Other important animals include rhesus

macaque, Indian civet cat, Indian porcupine, striped hyena, fox, jackal, bluebull,

wild boar, hare, langur, wolf etc. The reptiles include snakes, tortoises and lizards.

The avifauna includes spurfowls, cuckoos, barbets, woodpeckers, tree pies,

flycatchers, shrikes, spoonbills, storks, cranes, egrets and many raptors.

Prosopis invasion occurring almost in pure composition in certain areas

needs phased replacement with the local floral species. The area being drought

prone, water conservation works need special attention for improvement in

hydrological cycle of the area.



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Figure 2.1.2. Geographical outline of the A. Jessore Sancturay in Banaskantha district Northwards and B. Purna Sanctuary in the Dangs southwards. Cartoon not scaled.



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There are different communities such as 'Thakore', 'Chauhan', 'Koli',

'Kumbhar', 'Mali', 'Rabari', 'Harijan', 'Waghari' and 'Agrawals' in the villages

located near the Sanctuary. The tribal communities of the area are 'Majirana',

'Garasia', 'Gamar' , 'Damor' , 'Khokharia' , 'Dama', 'Dhajora', 'Dhrangi', 'V ansia'

and' Kharadi': Traditionally the people are religiously inclined. Economy of the

people is mainly agrarian. Most of the people are engaged in agriculture. Cattle-

rearing is the other major occupation of the villagers as well as of 'Rabaris'. The

people depend upon forests for fuel-wood, fodder, grazing, and collection of

honey, 'timru' leaves, gums, wild fruits and medicinal constituents of plants. Honey

collection, grazing, lopping practices and fire wood collection are the major

damaging factors, adversely influencing the habitat. Nilgai (Blue bull) invades the

croplands and cause damage to the farmers (gujaratforest.gov.in).

2.1.3.2 Purna community

This community was defined as the sum of those local people inhabiting in and

around the Purna wildlife sanctuary (Fig 2.1.2B, 2.1.3 C & D, 2.1.4 B & C). This

sanctuary is located in the Dangs. It has the thickest forest cover in the Gujarat.

Apart from tall teak trees, other floral species are sadad, timru, bamboos, khair,

kalam, haldu, sisham (rosewood), salai, kadaya, killai, sevan, tanachh etc.

Moreover Bamboo is wide spread and for habitat for specific mushrooms.

The sanctuary area of 160.8 sq. km. is spread over a hilly terrain with

plateaus and small valleys. These hill ranges are the western and northern

extreme of the Western Ghats. The landscape of sanctuary is traversed by river

Purna and Gira along with other and rivulets. It receives moderate to even heavy

rainfalls that averages to around 250 cm annually, which is the highest. The forest

is typically a tropical moist deciduous type. The landscape is lush green, thick

forests, interspersed with rivers, small tribal villages and scattered fields. The

human population is totally tribal represented by Bhils, Warlis, Konkanas, Dubdas,

and Kolchas etc. The forests support a rich tribal culture in the form of houses,

dresses, jewelry, agriculture, fishing, musical instruments and folk dances

(gujaratforest.gov.in).



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The forests of Gujarat are said to be biodiversity rich withholding 13% of

countries plant diversity. Moreover, 15% of 29 tribes nurture traditional knowledge

encompassing 750 medicinal plants. The Tribal population in Gujarat is mainly

concentrated in the eight districts along the Eastern border of the state. 96% live

in the Dangs, Valsad, Surat, Bharuch, Vadodara, Panchmahals, Sabarkantha and

Banaskantha. The tribal region extends from Sabarkantha district through

Panchmahals down to Surat, Valsad and the Dangs. About 92% of the scheduled

tribes are from the rural areas (Patel, 2005).

Figure 2.1.3. The panoramic view of the valley of Jessore (A and B) and Purna (C and D) sanctuaries; B and D have glimpses of Banas and Gira rivers respectively.

A B

C D



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Figure 2.1.4. Locations of TEMP documentation. A is the hill range of Jessor sanctuary, B and C are Berdipada and Mahal range of Purna sanctuary respectively. Copyright Google Earth.

A

B

C



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2.1.4 Due-diligence study step 2: Identifying tribes with rich traditional

knowledge

As such the Dangs are populated with other tribes like Kunbi and Gamit. The

Kunbhi are said known to be farmers across central and western India. We found

even a mushroom named after them as “Kunbhi kunwar” meaning a farmer’s

prince as it grows in farm borders. Later during the rule of Gond tribe, the

Deshmukhs and Despandias were administrators. One of the Deshmukh

participated in our program and had made significant contribution of

mycoethnomedicine in this region. Deshmuks were considered as leading

kunbhis. The Gamit community is one among the major tribes in Gujarat and are

located in various parts of Gujarat like the Dangs, Valsad, Surat and Bharuch

(indianetzone.com). Gamit means a village dweller and are hard-working people.

Enthoven, a British anthropologist, commented that these people resemble proto-

Australoids and had entered the country 50,000 to 60,000 years back. This

community specifically has earned a name for deep knowledge of ethnomedicine.

They are popular and well known because of its unique mode of treatment. This

community has survived the test of time by passing its knowledge to the next

generation, which may have been diluted lately due to deforestation, afforestation,

climate change or migration of tribes towards urban localities.

Around 15% of total state population is tribal that spread across the forests

and the eastern boundary of the state, and have rich traditional knowledge.

Changing life style, market influence and urban migration are diluting out

traditional knowledge.

2.1.5 Due-diligence study step 3: The estimate of probable species diversity

Approximately 9.6% of the forests of Gujarat have managed to harbor mystic

ranges of fungal biodiversity. Our prime interest resides in the macro-fungi

belonging to Basidiomycotina and Ascomycotina, more commonly known as

mushrooms. Out of a huge number of total estimated species of fungi world wide

only 5% (69,000 to 74,000) are known, out of which only 10,000 are known to be

the fleshy macro fungi and only a handful of these are known to be lethal.

As reported by “Gujarat Ecological Commission” (GEC), 164 fungi out of

23,000 in India were estimated to be in Gujarat. Apart from this, according to the



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study conducted in Jessor, by Gujarat Ecological Education Foundation, the total

number of plant species reported was 400, of which only two were fungi. Wide

varieties of mushrooms are reported from various parts of Gujarat. The tribes in

Dang and all over Gujarat are engaged in gastronomical and medicinal practices

of mushrooms. But there was no proper systematic documentation regarding the

biodiversity of species and ethno-myco-medicinal traditional knowledge.

2.1.5.1 MATERIALS AND METHODS

For calculation of probable species diversity conservative (Mueller et al., 2007)

and liberal (Sankaran, 2003; Hawksworth, 2001) vascular or angiosperm plant to

fungi ratio were employed. The final datasheet was formulated from the well-

known data sheets as, Data sheet of European council of fungi (ECCF), Data

sheet of Biodiversity of fungi (Mueller and Bills, 2004), Data sheet of Rajasthan

University; department of Botany, Data sheet of Madras University; Center for

Advanced Study in Botany and Data sheet of British Mycological Society. We

collected such information and identified the mushrooms up to genus level by their

morphological and anatomical characters (Singer, 1975; Leelavathy, 2000). Data

sheets prepared for exhaustive documentation, were bifurcated as macrofungal

documentation containing its field (Annexure 1) and morphological data and

know-how documentation containing data about the traditional uses of

mushrooms (detailed in chapter 2.2 and 2.3). The colloquial names, as “Vansarta”

(if growing on bamboo), “Kulambhi-kunwar” on soil, and puffballs for “bhupidh”,

coined by the native people are used here for tagging an unidentified species.

2.1.5.2 RESULT AND DISCUSSION

After Insects, fungi withhold the second largest biodiversity. The huge figure of

total estimated species of fungi is 1.5 million (Chang, 1993; Sankaran, 2003;

Hawksworth, 2001). Out of which 74,000 are known that stands around 5%. Out

of this 74,000, only 10,000 are known to be the fleshy macro fungi. This ratio of

number of mushroom from known number of fungi stands to be around 14%. But

the paucity of data is reflected by very few records of mushrooms in Gujarat. Only

a handful are known to be lethal, as 2000 species from 30 genera are known to

be edible, But only 80 of them are grown experimentally, 40 cultivated



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economically, 22 commercially and only 6-8 are produced at industrial scale

(Chang, 1993). In case of mushrooms only 10% of them are known, of which 5%

are rendered useful on utilitarian grounds, vacating a large room to be

accommodated and delved for various applications regarding those yet to be

identified (Hawksworth, 2001). Of 14,000 species reported globally, India

harnesses a prolific fraction of 1200 (8.5%) species of mushrooms, seemingly the

tip of the iceberg (Natrajan, 2005). Ascomycetes are estimated to constitute 40 to

45% of the total Indian fungal population. As such basidiomycetes have received

primary attention since several decades. An estimate extrapolates 10,000

basidiomycetes species within the overall fungal estimates of 1.5 million

(Manoharachary et al., 2005). Mushrooms alone are represented by about 41,000

species, of which approximately 850 species are recorded from India. Of these

round about 60 wild mushrooms, representing 54 species in 36 genera were

recorded around Mumbai that is very close to southern Gujarat (Deshmukh,

2004). Extensive mushroom species collections and surveys were undertaken

extensively in regions like Himalayan region, Punjab, Kerala and Western Ghats.

A paper reports 300 species of mushrooms and toadstools of which nearly 72

species distributed in 15 fungal genera were formed mycorrhiza with Abies

pindrow Royle, Betula utilis D.Don, Cedrus deodara (Roxb.) Loud, Picea

smithiana (Wall.) Boiss, Pinus roxburghii Sarg, Pinus wallichiana A.B. Jackson,

Rhododendron arboreum Smith, Quercus incana Roxb. and Quercus

semicarpifolia Smith. As many as 24 fungal species were found to be associated

with Q. incana alone (Manoharachary et al., 2005).

Of all newly describe fungi during 1981-2000, 60% were discovered from

tropics. Out of this India tops the list with 900 species documented in past 10

years (Sankaran, 2003). In India, 6900 species of fungi are known out of the total

74,000 known species that are found in the world. Thus 9% of fungal biodiversity

is India’s contribution to the world.

2.1.5.2.1 The liberal theoretical estimate of Species diversity

Fungi are known to occur by ratio of 1:10 on plants (Sankaran, 2003;

Hawksworth, 2001). According to which, the total number of species of fungi in

India should be around a ballpark figure of 1,70,000, which is relative to the



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17,000 species of known angiosperms in India. Thus until now only 4.06% of them

(6,900 known out of 1,70,000 theoretically estimated) are reported in India

(Sankaran, 2003). We had calculated above that 14% of known fungi were found

to be mushrooms. So if we abide by this ratio as canon in order to grossly

estimate the quantum of possible mushrooms, then India may withhold around

24,000 of them.

Apart from this, as reported by GUJARAT ECOLOGICAL COMMISSION

(GEC, 1996); in Gujarat total number of fungi estimated was 164, of 24,000 said

to be in India. This barely matches the theoretical estimate of 1,70,000 possible

fungi in India. To estimate the possible number of fungi in Gujarat we have to

know about the number of angiosperms or vascular plants in Gujarat. The

number of angiosperms found in Gujarat is around 2,200 (GEC, 1996). Thus the

number of fungi may be extrapolated to 22,000 species, by the ratio (Sankaran,

2003) of 1:10, in Gujarat. Using the same yardstick of 14% in case of mushrooms

we can say that the number of mushroom in Gujarat could be around “3000”. This

seems to be a mammoth number. Even if we consider a round figure of about 200

species (instead of 164) of fungi identified from Gujarat, the quantum of those

identified, will pigmy to less than 1% of possible 22,000 species of fungi. The state

lacks an impetus in direction of entire fungal biodiversity inventorying and

evaluation due to lesser number of fungal taxonomist and devoted departments

working on it. But if done Gujarat can hit the gold mine hopefully before most of

the endangered ones go extinct. The actual number or fungi or mushrooms in

specific can be more as soil, insect and aquatic borne species are not considered.

Moreover, this may also fall short of estimated numbers due to habitat loss and

climate changes.

2.1.5.2.2 The conservative theoretical estimate of species diversity

A study on flowering plant and macrofungi ratio revealed 21,679 known and

35,000 unknown macrofungal species of which 50% of the known species were

from North America and Western Europe (Mueller et al., 2007). Around 37% of

temperate Asia and 72% of Australasian species were unique. Now 10,000 and

6,827 macrofungal species were found amidst 20,000 and 12,500 flowering plant

species for North America and Europe, respectively. This gave ratio of 2:1 for



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flowering plant over macrofungi species. The ratio produces very high diversity

estimates for macrofungi 85,000-110,000 in totality. This figure became more

realistic (53,000–65,000) when two different plant/macrofungus ratios were

employed, wherein 2:1 was used for temperate regions and 5:1 for tropical

regions. Then the calculations were close to the projected diversity for macrofungi

of 140,000 species postulated by Hawksworth (2001). Assuming that the 21,679

species names that we compiled during this study is an accurate indicator of the

number of ‘‘known’’ macrofungi, 16-41% of macrofungi have been described to

date. Using the 5:1 ratio also generates a realistic figure of macrofungal diversity

for Hawaii (Hemmes and Desjardin 2002). Similarly the 2:1 ratio for temperate

regions yields a prediction of 8,000 species that is close to the 9,000 macrofungal

species estimate in Australasia. This gives a prediction of 3,000 macrofungi for

temperate South America that is 2/3rd of those documented. These estimates are

in line with consistent with a hypothesis of high overall fungal species diversity, of

which macrofungi is 10% (Rossman, 1994). According to this estimate from

53,000–110,000 species of macrofungi, 530,000– 1.1 million species of fungi can

be extrapolated (Mueller et al., 2007).

On this basis the 2:1 and 5:1 ratio, 2,200 species of flowering plants would

emulate 1,100 and 440 species of macrofungi respectively in the state of Gujarat.

And the total species of fungi of which macrofungi is 10% delivers the figure of

11,000 to 4,400 probable species of fungi. These figures, which are for temperate

and tropical regions, are not aligned with the fact that Gujarat is arid Northwards

and semi-arid Southwards climatically. Thus being arid prone Gujarat might have

lesser than 770 average macrofungal species. If 40 to 70 % of these fungi are

unique then around 407-792 to 163-317 macrofungal species can be unique to

Gujarat. If the lower most and highest are considered in round figures then there

may be 160 to 800 unique species in Gujarat.

If these extrapolations are done on 17,000 species of angiosperms known

to exist in India then a considerable chunk is yet to be unearthed. Based on

national estimates it can be said that 3,400 species of macrofungi may be present

assuming India to be tropical. But the estimate would be around 8,500 if the

temperate region ratio is used. An average of both lands up at 5950 which if

rounded off would be around 6000 species of macrofungi. This would escalate to



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60,000 species of fungi based on 10% macrofungi principle (Rossman, 1994). Of

these 2,220 to 4,320 species of macrofungi can be expected to be unique to India.

Gujarat can be estimated to harbor around 13% of national macrofungal diversity.

Most of the macrofungi recorded from India are basidiomycetous. Though

around 65% of the known basidiomycetes of the world are documented in India

there is a huge dearth of ethnomycological data regarding all these species. Of all

the ethnomycological data available only a miniscule of data regarding medicinal

use of these species is registered. Above all, possible existence of 160 to 800

unique species in Gujarat can foretell for sure substantial amount of unique

ethnopharmacological interactions. This in turn implies that there can be brighter

prospects for drug discovery. In other words these regions which have unique

species may have unique metabolites as well and this widens the horizon for the

discovery of multiple novel therapeutic products.

2.1.5.3 CONCLUSION

Thus in a 3 step due-diligence analysis few things were decided. The location of

research was to be the Dangs as it had most of the states dense forest cover and

had harbored tribes with rich ethnomedicinal traditional knowledge. Moreover the

substantial macrofungal species diversity was theoretically substantial and as the

Dangs had better forest types the greater macrofungal diversity was assured.

Moreover a substantial amount of species were to be unique and thus

anthropological interactions with them could have credentials for better

biopropspecting. Finally to test and validate all of these assumptions another

location with opposite climate, geography, scarcer forest, less diverse plantations

was selected. There were no assumptions made regarding the ethnomedicinal

traditional knowledge of Jessor sanctuary. But the effect of the available species

diversity on diversity and depth of traditional medicinal knowledge was seriously

considered. Penultimately after the comparison of the traditional ethno-myco-

medicinal practices from Purna community in the Dangs and the Jessor

community in the Banaskantha, the best ones would be studied further for

pharmacological and phytochemical screening.

2.2 Documentation of TEMPs

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2.2 DOCUMENTATION OF TEMPs

2.2.1 ABSTRACT

Traditional ethno-mycomedicinal practices involving macrofungi were documented

using questionnaires from Jessore and Purna communities located in the

Northern and Southern Gujarat. The quality of traditional knowledge within and in

between the communities was compared by informant’s consensus index factor

calculated for each ailment. The possibility of any relation between the traditional

practices and informant’s consensus index factors was assessed as well.

Alternately in order to classify the types of species usages, binary scores were

allotted based on the presence or absence of species storage for later use, open

sharing and specificity of the practices. Followed by this species scores were

subjected to Euclidean distance dissimilarity matrix based hierarchical

agglomerative clustering.

In totality, 23 species were documented addressing various ailments, of

which 9 species were used to treat general aspects like convalescence, whereas

others (14 species) were used for specific ailments. In the Jessore community, 5

of the 7 ailments (71.2%) were related to skin problems, whereas in Purna

community only 7 of 18 ailments (38.9%) had similar usage. The total ailments

addressed and species documented from the Purna community had a greater

diversity and bore higher informant’s consensus index value in comparison to the

Jessore community. Cause and effect of some abnormal informant’s consensus

values, rendering it dubious, are also discussed. Hierarchical agglomerative

clustering revealed the influence of all the three aspects scored as above. The

corner-stone species are more important than other species because they are

necessary for the survival of the traditional know-how and well being of the

communities engaging them and have applications. Moreover, depletion due to

rampant use of such species calls upon there identification and conservation. This

could be achieved to an extent by simple binary scores based clustering. Certain

corner-stone species with closed (absence of) knowledge sharing were found to

withhold specific practices. Those with general medicinal applications can be

useful as nutraceuticals, whereas those with specific claims can be screened

further in order to identify pharmaceutical potentials. In addition to this the present


35


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work mentions the documentation of traditional ethno-myco-medicinal practices of

several species for the first time ever.

2.2.2 INTRODUCTION

The pre-recorded and existing anthropological interaction with macrofungal

biodiversity on the medicinal axis calls for bioprospecting, which is yet to meet its

zenith. Medicinal and edible uses of macrofungi in India is quiet prevalent, some

of which dates back to 1700-1100 BC (Wasson, 1967). Based on plant-fungi

associations (Hawksworth, 2001), theoretical estimates revealed that the state of

Gujarat may possibly harbour around 2000 such species (Lahiri et al., 2008).

Evolving from some of the seminal studies on the cultural significance of the

mushrooms (Garibay-Orijel et al., 2007; Pieroni, 2001), the present work

emphasizes on the species as the centre of study rather than the community

withholding the traditional ethno-myco-medicinal practices (TEMPS). Preservation

of the knowledge pool, identification, conservation and characterization of the

species are the primary factors that necessitated such a paradigm shift facilitating

species-centric anthropological interaction. The communities studied reside miles

away from modern day state-of-the-art medical facilities and depend largely on

traditional and natural medicinal resources, in absence of which or during

emergency they walk down several miles to the nearest city, with high fever for

diagnosis and treatment. Apart from studying the medical practices (entailing

macrofungi) of the communities, pharmacological validation of the claims was also

desired to be followed by. In order to do so, from the pool of practices important

ones were to be identified for further analysis. Thus the present study is an effort

to synchronize the analysis of traditional practices and bioprospect them as well.

The concept of key-stone species is well-known in ecology, wherein such species

are important for the survival of other dependent species. Similarly, vital

macrofungal species that are used to tackle important or critical ailments suffered

by the subjects of a community can be called as corner-stone species.

Identification of such corner-stone species was a prioritized as it was integral to

both ethnomycological and bioprospecting spheres. The use of such species was

often not shared by traditional know-how practitioners, which will be addressed

here as “closed knowledge pool”. Contrarily some of the know-how were very

common to all and were openly shared hence addressed as “open knowledge


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Figure 2.2.1. The schematic location of study undertaken (not to scale). A. Map of

India with the state of Gujarat in black; B. The state of Gujarat with Jessore in the

north and the Dangs in the south in grey; C and D are schematic and actual

photographs of the location of Jessore Community; E and F are schematic and

actual photographs of the location of Purna Community;


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pool”. The sampled community contained people from both kind of knowledge

pools and hence are mixed.

This is the first report of medicinal use of macrofungi from the state of

Gujarat in Western-India. Moreover, the TEMPs documented practices claimed for

several species are also novel. Of all the 23 species documented those with

specific uses will be studied further for their candidature regarding

pharmacological evaluation.

2.2.3 METHODS

2.2.3.1 Location and data collection

Gujarat harbours rich biodiversity withholding 13% of countries plant diversity,

with a 15% of 29 tribes nurturing traditional knowledge regarding 750 medicinal

plants of 1200 general plants. The present study was contained within the Jessore

Sanctuary, in Banaskantha, in the Northern part of the state and the Purna Wild

life sanctuary in the Southern part (Fig 2.2.1). The details were furnished by the

local tribes, of which, the Ghanashia and Rabaris were from Jessore Community

(JC) and Dangi people from Purna Community (PC). The present report deals

only with the medicinal aspects of the ethnomycology, with respect to its

ecological niche.

2.2.3.2 Documentation of tribal mycomedicinal practices

The collection of materials was done by S. K. Lahiri, with prior permission of the

Department of Forest, and the documentation of which are endorsed under the

Biodiversity Act. The samples were identified with the help of literature (Singer,

1975; Leelavathy, 2000; Lincoff, 1989; Pegler, 1986), and an eminent taxonomist,

Dr. A. B. De, Raj College, Burdhwan, West Bengal (Lahiri and De, 2007). Total 40

people were considered for study of which some were excluded if information was

vague. The informants were asked to respond to the queries of the questionnaires

prepared in accordance to the study.

2.2.3.3 Consensus analysis

Consensus analysis of TEMPs in terms of Factor of informant consensus (Fic) was

done to test the reliability, homogeneity and the extent of selection of certain


38


Chapter-2

plants for treating an ailment. Fic was calculated as reported earlier

(Subramanyam and Newmaster, 2009), Fic = Nur – Nt /(Nur – 1). Wherein, Nur is

total number of reports within the community for a particular ailment and Nt is the

total number species used for that particular ailment in the community.

2.2.3.4 Higherarchical agglomerative clustering

The hierarchical agglomerative clustering of species was done from Euclidean

distances in turn calculated from binary scores of the 3 factors elaborated below.

In order to characterize the practices and the involved species, 3 factors

influencing it were devised. The presence of the desired traits in all the 3 factors

were ascribed the value of one, whereas for the undesired traits, zero. Based on

the binary data, a dissimilarity matrix of Euclidean distances was subjected to

hierarchical agglomerative clustering based on with group-average linking rule

(Hintze, 2007). Use of species for general or specific ailments, open or restricted

sharing of the know-how within the community and storage of species for later use

are the 3 factors that were used to classify the species.

The ailments addressed were found to be either general (convalescence,

good health, strengthening) or specific (for a particular disease/ailment). Species

used for general practices were scored as 0 and specific as 1, because the latter

was more important over the other.

Same or different practitioners shared the use and identity of some species

with others people of that community (scored as 0), whereas for some species the

practice and identity was kept secret (scored as 1). It was hypothesized that the

use of the latter is more critical and such species may be important.

It was also noticed that some species were preserved for later use. The

questionnaire prepared was made to accommodate this query. If the practitioner

desired to store the species for later use, then the score of 1 was assigned, or 0 if

not stored, as storage would mean that they were important for latter use.

2.2.4 RESULTS AND DISCUSSION

Since ages mankind has bioprospected the biodiversity that was available in their

vicinity. The traditional medicinal practices established by tribal people, entirely

dependent on natural resources, tend to retained ancient knowledge as survival of


39


Chapter-2

the race is a crucial issue. Habitat erosion, deforestation and ever shrinking

traditional knowledge pool can negatively influence the furtherance of such

practices that are essential for the dependent communities. Moreover, they are an

integral part of the future arsenal for drug discovery. Thus identification and

documentation of such practices are of prime importance as of now. The ethno-

myco-medicinal traditional practices of the tribal from two communities were

documented and analyzed to establish the quality of various practices and

characterize and segregate them.

2.2.4.1 Documentation of ethnomycomedicinal practices

Of 20 informants consented in each community (total 40) 15 were short-listed and

interviewed. Those presenting vague or ambiguous practices were excluded. For

the sake of homogeneity of the study the types of informants were equi-

proportional between both the communities. Of total 15 candidates of a

community, 3 were traditional medicine practitioners, 10 were farmers and 2 were

housewives. The TEMPs were observed to be widely different between both the

communities. The types of species also varied with respect to their taxonomic

origin and ecological niches. The Traditional ethnomycomedicinal practices

revealed around 23 total species which were recognized based on the rigorous

cross-checking of answers to the questionnaires. The number of TEMPs found in

JC was much lesser to that in PC. The JC reported 5 TEMPs in comparison to the

18 TEMPs from PC (Table 2.2.1). All macrofungi are arranged with respect to

their local names, taxonomy, hosts if any, their respective uses and their

ecological niches.


40


Chapter-2

Table 2.2.1 . TEMPs from Jessore and Purna community.

Botanical Name (Acronym)/(Family)

Native Name

Preparation and Usage

TEMPs from Jessore Community (JC) 1 Xylaria (Xy)

unknown species (Clavariaceae)

More-pankh (peacock feather)

It is used in diseases like pneumonia, constipation, and eczema. It is also used to treat children suffering pneumonia or pneumonia like symptoms. Around 5 to 6 entire fruiting bodies are triturated fresh with water for several times to form a uniform paste. The paste is consumed orally for pneumonia or constipation but is applied topically for eczema. Generally for pneumonia 1 tablespoon is administered for 3-5 times on different days. For sever constipation same dose is given after dinner for 2 to 3 days. In case eczema the affected area is covered with the paste and bandaged. This is repeated till recovery is evident.

2 Coprinus comatus (Cc) (Coprinaceae)

Ajjio It is mostly used for skin related diseases like lesions, bruised and infected skin, or for wound healing. The part of the fruiting body used in these cases is the cap and not the stipe. To be more specific the cap is opened and the spore bearing gills are applied to the affected area directly, which is then bandaged. It can be said that the spores are given the prime importance. The spores are applied till the healing is considerable.

3 Phallus (Ph) unknown species (Phallaceae)

Datto (button)

The cap is crushed in mortar with water and applied on wound, skin infections, boils, or lesions but is not bandaged. The application is repeated twice everyday for 3 days or longer if needed.

4 Lepiota cristata (Lp) (Clavariaceae)

Chatri (umbrella)

The entire mushroom is crushed in mortar and applied on the boils to speed its maturation and the regression. One or two applications are sufficient to treat.

5 Scleroderma (Sc) Unknown species (Sclerodermatales)

Dado (ball)

One or 2 fruit bodies are applied topically only for physical afflicted wounds. The gleba is triturated in water and applied over wounds and


41


Chapter-2

bandaged. The same is repeated for 2 to 3 times or till total recovery is confirmed.

TEMPs from Purna Community (PC) 6 Termitomyces

tyleranus (Tt) (Pleuteaceae)

Adim It is dried and used for prophylaxis throughout the year for general protection against diseases or with special reference to chicken pox. Also burned and inhaled to speed up the occurrence, maturation and scaling of chicken pox. Cooked or raw mushrooms are consumed orally.

7 Phellinus durissimus (Pd)

Galpacodio (addressing mumps)

Generally they (7 to 11) are triturated in water and applied on mumps affected edema, fruiting body heated and padded on simple edema or inflammation; used for mastitis or initial boils; also used to pace up the full occurrence of chicken pox by inhaling heated or partially burned fruiting bodies on fire. Application for 1-2 times is said to be sufficient.

8 P. linteus (PL) 9 P. rudis (Pr) 10 P. merrillii (Pm) 11 P. robinea (Po)

12 Unidentified macrofungi (Rd)

Lal bhuko (red powder)

Applied on lesions, edema, boils, mastitis, rashes, bruised skin infection, pimples, impetigo, pustules, fissures, any inflammation or wound healing purpose. Spore mass applied in water or oil base 1-2 times.

13 Bovista (Bv) unknown species (Lycoperdacea)

Bhupid The spore mass used as topical application on bruised skin infections directly till recovery.

14 Dictiophora (Dp) unknown species (Phallaceae)

Jaadi (net)

Topical application of spores on edema or any kind of inflammation as a sure shot is claimed. Practitioners have so much faith that in its absence soil bearing mycelia is also used.

15 Pleurotus (Pt) unknown species (Pleurotaceae)

- It is said to impart strengthening effect when consumed orally after cooking.

16 Lentinus squarrulosus (L1 or TV) (Lentinaceae)

Vansarta (growing on bamboo)

It is a prized edible mushroom in the locality and is consumed without the stipe. It is consumed after proper cooking as it is believed to be hard to digest. It is stored through the year as powder and added in soups for better health. It is also used specifically during convalescence.

17 Lentinus (L2) - It is harder to chew and hence is less


42


Chapter-2

unknown species (Lentinaceae)

preferred. Properly cooked stipeless dried powdered mushrooms are orally consumed for strength. It is also sometime known to cause nausea and stomach ache.

18 Termitomyces (Tu) unknown species (Tricholomataceae

Adim It is consumed orally for general good health.

19 Termitomyces microcarpus (Tm) (Tricholomataceae)

Sita-adim (small adim)

20 Unidentified (Kk) Kunbi-kunwar (Farmers-prince)

They (No. 19 to 23) are consumed to attain good health specifically during the monsoon or during convalescence.

21 Unidentified (Rk) Raj-kunwar (royal prince)

22 Termitomyces (Rb) unknown species (Tricholomataceae)

Rombadia

23 Termitomyces (Ra) unknown species (Tricholomataceae)

Rombadia-adim

2.2.4.2 Types of ailments treated with macrofungi

JC engaged macrofungi entailing treatment regimes for pneumonia, constipation,

eczema, bruised skin infection, lesion, boils and wound healing. Whereas, PC

employed macrofungi species for immune enhancement required in specific

diseases apart from addressing ailments like bruised skin infection, edema,

mumps, measles, chickenpox, mastitis, boils, wound healing, rashes, lesions,

pimples, impetigo, pustules, fissure and inflammation in general, employing

macrofungi. The latter community was found to address a wider range of ailments

countered by TEMPs.

Relevance of Species and its TEMPs

Cc (Ershova et al., 2001; Efremenkova et al., 2003; Yang et al., 2003; Fan

et al., 2006; Luo et al., 2004; Badalyan et al., 2003) and PL (Chen et al., 2006;

Kim et al., 2005; Yeon et al., 2008) are the only species for which substantial

amount of bioprospecting and bioactivity is already done. Two separate species

belonging to the genus of Tt are reported to have antioxidant (Puttaraju et al.,

2006) and neuritogenic (Qi et al., 2001) activities, which are not directly related to


43


Chapter-2

the TEMPs recorded by us. TEMP related activity of Dictyophora indusiata, a

species dissimilar to local variety Dp, is reported to posses anti-inflammatory

effects attributed to (1→6)-branched (1→3)-β-D-glucan (Hara et al., 1982). This

corroborates well with the local TEMP (Table 2.2.1, No. 14) thought it seems to be

a different species. TEMP of Bv was reported for other species used for healing

abscess and other wounds in animals (Lans et al., 2007). Pm is known to possess

antioxidant activity that may be an integral part underlying its TEMP (Chang et al.,

2007). Rest of the TEMPs and the species are reported here for the first time to

the best of our knowledge.

2.2.4.2 Informant Consensus of TEMPs

Consensus indexing has been widely practiced for evaluating the reliability of the

traditional knowledge. Most of the ailments treated by the Purna community had a

better informant’s consensus index than the Jessore community. PC used a total

of 18 species for various ailments but JC employed only 5 species. Jessore is in

the North of the state at the border of Rajasthan and has many dunes with

patches of sparse forest on hills as it receives very scanty rainfall. Purna is

located in the southern region of the state, which has dense dry deciduous forests

and receives heavy rainfall. This geo-climatic difference can influence the density

and the diversity of macrofungi available in a particular geographical location and

can leave the traditional practitioner with meagre resources than others with

access to a greater biodiversity.

Any-how, such factors may not be the final arbitrators because the quality of

know-how may have fewer but rarer significant practices within the limited

resources with respect to the varying ecological niches. Good consensus values

should be a help in identifying the important species. Thus, such a marker that

can attach a range of values to evaluate the reliability of the traditional knowledge

can be of great importance for pharmacological research as well. The credibility of

the consensus analysis regarding both the aspects in TEMPs was assessed for

the current small and mixed knowledge pool.

The analysis of Fic (Informant’s consensus index factor) revealed varied

outputs (Table 2.2.2). The values were high for most of the ailments ranging from

0.9 to 0.928, except three ailments, which gave erroneous results due to only 1

report causing the denominator to be 0. In PC, 11 diseases scored the maximum


44


Chapter-2

indicating no options to the species employed, but 2 ailments were low scoring

with several options. Certain scores ran into negative figures because they had

more number of species (Nt) and lesser number of reports (Nur). The multiplicity

of species is due to superficially indistinguishable closely related macrofungal

species, which is common for polypores of the same genus. Such may not be the

case with plants. Some times several species of a genus may be closely

associated chemotaxons. Thus either of them may serve the purpose as the

active compound may be conserved in most of them. Pharmacologists screening

for such active molecules can isolate it from either of them, but in such case Fic

can be misleading as it remains unattended by the consensus paradigm.

The negative values also show closed knowledge pool causing lesser or single

reports for Nt > Nur (number of species > reports), which is also true for Nur = 1

and hence Fic = infinity. Thus all values below zero belong to closed knowledge

pool, but for Nur > Nt closed pools are also possible. Fic is impaired for smaller

studies, as Nur has to be > 1 and Nt has to be > Nur to get only positive values.

Moreover, due to ecological challenges like habitat erosion and species dilution,

the effective species might become rare, which will stress the practices and can

have two possibilities. If no alternates are available then traditional knowledge

might become extinct as well because of the lack of practice over generations. In

such cases of rare practices, Nur may be 1 and Fic will be ∞. If several alternates

to the species are available then another possibility arises, wherein Nt may be

high due to dilution of know-how. The Fic value is indifferent to such cases.


45


Chapter-2

Table 2.2.2. Informant consensus analysis (Higher values of the Informants

consensus index factor (Fic) between 0-1 signify better consensus).

Ailment Nur Nt Fic JC

Pneumonia 1 1 ∞ Constipation 1 1 ∞ Eczema 1 1 ∞ Bruised skin infection

14 2 0.923

Lesion 12 2 0.909 Boils 11 2 0.9 wound healing 15 2 0.928

PC

Immune-stimulation 14 1 1

Bruised skin infection 15 2 0.923

Edema 3 6 -1.5 Mumps 3 6 -1.5 Measles 3 5 -1 Chickenpox 3 6 -1.5 Mastitis 1 6 ∞ Boils 3 1 1 Wound healing 3 1 1 Rashes 3 1 1 Lesions 3 1 1 Pimples 3 1 1 Impetigo 3 1 1 Pustules 3 1 1 Fissure 3 1 1 Inflammation 15 7 0.57 Convalescence 15 1 1 Good-health / Strength

15 11 0.286

Use of each species for multiple symptoms of the same ailment may cause

higher Nt which can be deceptive as Fic will be low. The Consensus value is rigid

as all the plants used for the illness are treated equally as they may have lower or

higher efficacy. Thus dynamic treatment is needed which treat all the species as

case-to-case basis. The identification of the most effective species of several

options for a particular ailment is the target for molecular investigations.


46


Chapter-2

For objectives like drug discovery consensus value may be deceptive as

one of the several species may be very effective, but will be enshrouded by other

species causing low consensus value. Thus, preference given to one species over

others can mark its greater efficacy over others and has to be incorporated in the

estimates.

Moreover, if one species is used in different illnesses which are alike then

the community knowledge may not have discrepancy but will have lower richness

as lesser numbers of species are used for more diseases. Thus, not only the

health but the wealth of the community knowledge is important as well but this is

not addressed by Fic. Hence, this analysis is not very successful in the current

scenario and yet other need based analysis addressing other variables is

required. Such factors can be used to segregate the practices or species.

Hierarchical agglomerative clustering can be one such simple tool.

2.2.4.3 Clustering analysis

Some excellent studies on the cultural significance of the mushrooms (Garibay-

Orijel et al., 2007; Pieroni, 2001) seemed to be from open knowledge pool as the

know-how of species usage was common. Moreover, the application of such

species was mostly addressing general health remedies. The present study

differed in both the aspects. The knowledge pool and the practices or application

of species within a particular community is found to be an admixture of both

situations. In addition to this, the aim of the study also differed as identifying the

corner-stone species was essential as explained above. The tailored observations

addressing the current requirements were made to segregate the species based

on three aspects of usage and know-how, which could easily isolate the corner-

stone species.

The species used for specific practices attending a particular critical-

ailment is more precious than those used for general (non-critical) purposes as

strength giving and recovery from illness. It was observed during the study that

the know-how of such species employed for specific ailments were known by few

practitioners, some of them being traditional medical practitioners, immobilized the

knowledge. This caused the existence of the closed knowledge pool co-existing

with the open knowledge pool. Thus such species can said to be more important


47


Chapter-2

regarding medicinal values than the general ones. Species stored for later use

were found to be important, and hence needed to have preeminence over others.

Figure 2.2.2 . Higherarchical agglomerative clustering of species. The clusters of

species are separated at various dissimilarity distances. The brackets are labelled

as per the categorizing factors. Under the storage factor, the not stored group

includes cluster-6 marked *, and stored group includes cluster-2 marked ▲.

Hierarchical agglomerative clustering of species based on the presence or

absence of three factors mentioned above revealed a typical clustering pattern

(Fig 2.2.2) with group-average linking rule generating best cophenetic correlation

coefficient (0.9968) and delta value (at 0.5, 0.035; at1.0, 0.052). The 1st cluster


48


Chapter-2

(Rk, Kk, Tm, Tu, RA, R and Pt) separated at distance value of 1 of total 6 minor

clusters.

The 2nd cluster had L2 and L1 (distance value 0.9) as outliers to the 3rd

cluster. Sc, Lp, Ph and Cc separated from the rest at 0.77, where 3 minor clusters

were observed. Tt (0.65) was outlying a cluster of Rd, Po, Pr, Pm, Pl and Pd in

turn separating from the last cluster (0.57) of Dp, Bv and Xy. L1 and L2 have

TEMPs alike 1st cluster marked by non-specific usage (aiding recovery from

illness or strengthening), but are outlying to the rest (with ailment specific TEMPs)

because they are stored for later use.

The 3rd cluster includes TEMPS for specific ailments, yet it is an outlier to

the rest as it is not stored being prone to quick decay. Although Tt has specific

TEMP and is stored like those in the following clusters it is a singleton because it

is in open knowledge pool. The 6th cluster is separated from 5th as the later is

stored and former isn’t. The 5th cluster was mostly comprised of polypores

because all of them do not decay easily unlike those in the 6th cluster, which are

soft fleshy and perishable species.

Thus storage of the species is directly related to its perishable nature and

not necessarily to its medicinal value alone. Moreover, the storage parameter

gave intermingled clusters and hence is not a potent factor to segregate the

species. At the same time species involved in general (clusters 1 and 2) and

ailment specific (clusters 3 to 6) treatments were segregated.

This is a significant attribute as further investigations on medicinal

potentials are to be done on those species which have ailment specific

applications. It is also observed that the species were well segregated along the

knowledge pool paradigm.

TEMPs which were not shared in the community were found to address

important and specific ailments, though some specific TEMP related species were

in open knowledge pool. Species with specific TEMPs in open knowledge pool

were largely related with skin related disorders, which were less serious that those

specific TEMPs in closed knowledge pool.


49


Chapter-2

2.2.5 CONCLUSIONS

In comparison with the Jessore the Purna community revealed several significant

TEMPs, though both the communities contribute to usages reported for the first

time. The mention of TEMPS involving species like Xy, Ph, Lp and Sc from JC

and Tt, Rd, Bv, Pd, Pr, Po, Pm and Dp from PC are novel.

Preliminary bioprospecting by simple binary clustering could characterize

the species-practice data matrix, wherein the edible mushrooms used for non-

specific ailments can be potential sources of nutraceuticals. Other species

addressing specific ailments will be further studied for their pharmaceutical

potential. Fic gave ambiguous results which can be attributed to several reasons

like non-plant study, heterogeneous knowledge pool (closed and open) and

comparatively smaller subject size. Herein, the health (reliability) of the community

knowledge was better depicted by multiple factors/markers, rather than single Fic

value, which was useful in capturing the wealth (richness) of community

knowledge as well. Fishing of promising practices from the pool of vague

practices could be grossly achieved to an extent, which for fine tuning at the level

of individual species will need more number of markers. For a detailed

pharmacological evaluation such an analysis, will be useful in identifying few

potent species from a big database.

2.3 Isolation of potent species by TEMPLE

50


Chapter-2

2.3 ISOLATION OF POTENT SPECIES BY TEMPLE

2.3.1 ABSTRACT

The colossal scale of biodiversity can challenge bio-prospecting if each species

has to be addressed one by one. Traditional ethnomedical practices can be a

potent yardstick for narrowing down to species engaged in practice for

approachable and tangible out puts. Reverse pharmacology led identification of

active fraction and pharmacophore was the intended frame of research. In order

to prioritize on few of many species as lead sources addressing drug discovery,

Traditional Ethno-Mycomedicinal Practice Scores (TEMPS) were ascribe to

analyze practices within and between two communities from Purna and Jessore

region in the Northern and Southern Gujarat. Thus the evaluation of the sum total

of the pattern of influences of various factors involved in shaping the TEMP data

matrix was addressed as Traditional Ethno-Mycomedicinal Practice Lattice

Evaluation (TEMPLE). Of both the communities, Purna community, was found to

be richer in traditional know-how. Phellinus durrisimus and an unidentified species

surfaced as potent contenders.

2.3.2 INTRODUCTION

Over 50 million years termites and ants of the tribe Attini rearing and consuming

macrofungi (fruiting body visible with naked eye is addressed as macrofungi

(Chang, 1992)) are known to be the frontiers of myco-bioprospecting (Muller et al.,

1998). The advent of Ethnomycology was witnessed long before the very term

being coined by R. Gordon Wasson (Wasson, 1967), who also unleashed the

depiction of “Soma” as a mushroom summed up in 120 solkas. Only 10% of

macrofungi are known and 5% are rendered useful (Hawksworth, 2001), of which

8.5% macrofungi are reported from India (Natarajan, 2008). Around thousands of

species are theoretically estimated (Lahiri, 2008) in Gujarat as per the yard sticks

defined earlier (Hawksworth, 2001), if so, a rendezvous between biodiversity and

bioprospectors remains uninitiated.

Failures witnessed earlier in bioprospecting (McClatchey, 2005) can be

attributed to the chasm between non-specific practices and highly specific target

screening programs. Through practices spanning within various multivariate

dimensions, the present endeavor, by employing TEMPLE, targets at filtering


51


Chapter-2

outliers and clusters as feasible lead-sources is indispensable in drug discovery

programs. The semi-quantitative clustering aided approach helped us in

identifying the pharmaceutical and nutraceutical groups. Further in the direction of

isolating the working group of species and identifying the primary contender

species the following evaluations are done. In addition to this the quality and

quantity of the TEMP are used to gauge the merits of the respective communities.

2.3.3 MATERIALS AND METHODS

2.3.3.1 Location, data collection and documentation of tribal mycomedicinal

practices

The details were furnished by the local practitioners

(Fig 2.3.1), of which the Ghanashia and Rabaris were

from Jessore Community (JC) and Dangi people from

Purna Community (PC). Collection was done by S. K.

Lahiri, followed by identification aided by literature

(Singer, 1975; Leelavathy, 2000; Lincoff, 1989;

Pegler, 1986), and an eminent taxonomist, Dr. A. B.

De, Raj College, Burdhwan, W. B. Macrofungi

documented (Fig 2.3.2), are given with their

acronyms and herbarium numbers (specimens

submitted to Dept. of Plant pathology, Indian Agriculture Research Institute, Delhi

or L. M. College, Ahmedabad). Termitomyces tyleranus Oteino, (Tt –

LM/Fu/Mcr/01); Xylaria sp., (Xy – LM/Fu/Mcr/02); Coprinus comatus (Mull.:Fr),

(Cc – LM/Fu/Mcr/03); Bovista sp., (Bv – LM/Fu/Mcr/04); Phallus sp., (Ph –

LM/Fu/Mcr/05); Lepiota cristata (A. & S.) FR., (Lp – LM/Fu/Mcr/06); Dictiophora

sp., (Dp – LM/Fu/Mcr/07); Scleroderma sp., (Sc – LM/Fu/Mcr/08); Phellinus

durissimus (Lloyd) Roy, (Pd - HC10-46882); Phellinus linteus (Berk. Et. Curt)

Teng, (PL - HC10-46883); Phellinus merrillii (Murr.) Ryv., (Pm - HC10-46884);

Phellinus rudis (Berk.) Zeng, (Pr - HC10-46885); Phellinus robiniae (Murr.) Ames,

(Po – LM/Fu/Mcr/09); unidentified (Rd – LM/Fu/Mcr/10).

Figure 2.3.1. Locations of study


52


Chapter-2

Figure 2.3.2. The photographs of macrofungi reported to have medicinal use from

Jessore or Purna. In numerical order the macrofungi are, Termitomyces tyleranus

(Tt), Xylaria sp. (Xy), Coprinus comatus (Cc), Phallus sp.(Ph), Phellinus

durissimus (Pd), Bovista sp. (Bv), Unknown (Rd), Dictyophora sp. (Dp), Lepiota

cristata (Lp), Scleroderma sp. (Sc), Phellinus linteus (PL), Phellinus merrillii (Pm),

Phellinus rudis (Pr), Phellinus robiniae (Pd).


53


Chapter-2

2.3.3.2 Traditional Ethno-Mycomedicinal Practice Lattice Evaluation

(TEMPLE)

The answers to queries were scored as per the scoring system given below

(Annexure 2, Table 1) and the sub indices were calculated from the minor indices,

which in turn gave the main indices used to derive the final compound index

called as Traditional Ethno-Mycomedicinal Practice Lattice Evaluation Score

(TEMPLES) as given below,

The same can also be better contained as given below.

Activity Index = (Core Index) x (Shell Index)

Core Index = AII x AMI x MSI x WHI

Shell Index = CPI x MAI

Non-activity index = (Decisive Index) x (Positive Critical Index) (Negative Critical Index)

Decisive Index = EI x FI x KTI x NGSI

Positive Critical Index = CKR X DLI

Negative Critical Index = DSI X TI

Anti-inflammatory (AII), antimicrobial (AMI), immunostimulatory (MSI) and wound

healing Indices (WHI) were scored by multiple facets of treatment of ailments. Co-

species priority index (CPI) is used to highlight importance of one species over

another. Mode of Application Index (MAI) stressed upon the mode of application

or consumption. Economic index (EI) is adapted to the necessary modifications

needed (Garibay-Orijel, 2007) for scoring those practitioners who rendered

TEMPLES= √ KTI xFI xEI xNGSI xCPI xWHI xAII xMSI xAMI xMAI xCKR xDLI

(TI x DSI)

4

TEMPLES = √ (Activity Index) x (Decisive Index) x (Positive Critical Index)

(Negative Critical Index)

4

TEMPLES = √ (Activity Index) x (Non-Activity Index) 4


54


Chapter-2

services for free or left it to the discretion of the beneficiaries, which had to be

scored lower than those done only in lieu of prime economic favours. Which

species would be the subject of research is the matter of prime interest?

Addressing it Feasibility index (FI) scales the factors as perceived abundance

index, PAI (Garibay-Orijel, 2007). To compare the biomass available the dry

weight of each fruiting body (Weight Index, WI). The possibility of culture isolation

(Culturability Index, CI) for fermentation studies and DNA isolation from the

retained samples for pharmacophore gene-fishing (Genomic Index, GI) were also

addressed. From these sub indices, the main index FI was calculated as FI = (PAI

+ CI + GI) x WI. Knowledge Transfer index (KTI) was modified (Garibay-Orijel,

2007) as transfer can be horizontal (KTI-h) , i.e. from one family or practitioner to

others of same community, or vertical (KTI-v), which is the transfer of knowledge

from one generation to another, essentially within the same family. The main

index was calculated from the sub indices as KTI = KTI-h x KTI-v. Novel Genus

or Species Index (NGSI) advocates it to have a better scope of novel molecules

yet unattended, rather than those which are commonly known. Dereplication from

literature index (DLI) is an index to refurbish the TEMP by its present status with

respect to the amount of work done based on literature review. DLI was calculated

as, DLI = DLIss x DLIos X DLIsub ; where DLIss is DLI for same species. DLIos

is DLI for other species of the same genus, intended to relate between the extent

of diversity of activities and strength of chemo-resemblance within the genus.

DLIsub stands for DLI of similarity or difference of growth substrate for same

species as different geographical locations may influence chemotype diversity.

Doctrine of signature index (DSI) is used to avert any irrational correlation

between the species and TEMP based on false beliefs. Toxicity index (TI)

watches over toxic or adverse drug reactions.

Community knowledge richness index (CKR) of TEMP from Jessore

Community (JC) or Purna Community (PC) was added respectively to imbibe the

integrity of community know-how level. Some communities recognize only

superficial noticeable physical changes, whereas others recognize a greater

diversity of diseases, further classified into several coexisting symptoms, attended

with or without rituals. The fact that a community with better score may have

advanced know-how, can hopefully resolve discrepancies between the similar or

different practices of two unrelated communities using similar species. In addition

2.3 Isolation of potent species by

DOCUMENTATION & DATA

to the total number of disease targets known

sampled community

macrofungi and the total number of species used for treatment of all the disease

(St) are markers of community knowledge richness.

was opined to emphasize high

a desirable trait for any particular community, and it was derived as

SDR = Total number of Diseases or targets treated using macrofungi (Dt)

Figure 2.3.3. Concept

macrofungi used for diseases D1, D2, D3, D4 and D5. The occurrence of same

number of diseases is in three different cases, which are arranged in decreasing

order of importance Case II > Case III > Case

(Case II) are better than overlapping diseases (Case I and Case III) between

given number of species.

It can be said that higher the multiplicity of the disease targets treated with

a particular macrofungi, greater could be

target common for many macrofungi is denoted as

as non-overlapping

assessing the ratio of disease targets to macrofungi can be dece

number of species may cluster around one disease leaving behind an overlapping

pattern. Disease with

sum-total value of all the macrofungi from the individual values in turn calculate

from the values assigned to the diseases they are used in. The value assigned for

a particular disease or target is,

{Total number of events (species) the disease can theoretically appear in (

Isolation of potent species by TEMPLE


to the total number of disease targets known (Tk) by all the informants within the

ed community, the total number of disease targets


are markers of community knowledge richness. Species

was opined to emphasize higher quantum of disease targets for each species as


Total number of Diseases or targets treated using macrofungi (Dt) Total number of Macrofungi (St)

Concept of overlapping diseases. Species A, B, C, D and E are



order of importance Case II > Case III > Case I, as non –


given number of species.


a particular macrofungi, greater could be the prospects of lead hits. A disease

target common for many macrofungi is denoted as overlapping

(Fig 2.3.3). For a given number of reported macrofungi,

assessing the ratio of disease targets to macrofungi can be dece


isease with Overlapping macrofungi Species index

total value of all the macrofungi from the individual values in turn calculate


a particular disease or target is, DVn = (St / Sd n), from JC or PC,

{Total number of events (species) the disease can theoretically appear in (

55

Chapter-2

by all the informants within the

the total number of disease targets (Dt) treated using


pecies Disease Ratio (SDR)

er quantum of disease targets for each species as


Total number of Diseases or targets treated using macrofungi (Dt)

of overlapping diseases. Species A, B, C, D and E are



overlapping diseases



the prospects of lead hits. A disease

overlapping, and those unique

3). For a given number of reported macrofungi,

assessing the ratio of disease targets to macrofungi can be deceptive, as more


pecies index (DOS) calculates the

total value of all the macrofungi from the individual values in turn calculated


from JC or PC, where, St =

{Total number of events (species) the disease can theoretically appear in (total


56


Chapter-2

number of macrofungi Species reported to be used in a particular community JC

or PC; in other words, the total number of macrofungi species practiced in a

community is theoretically the maximum possible value for any overlapping

disease)}. Sdn = {Total number of macrofungi Species used to treat that particular

disease (for first disease n=1) tagged JCD1 or PCD1}. For TEMPs of a sampled

community engaging more that one species, a totally non-overlapping disease will

be addressed by only one macrofungi and will have maximum disease value

(DVn), as its Sdn will be one. But if the same is overlapping, then the disease

value (DVn) is addressed by multiple macrofungi, and thus its partial value will be

lesser for higher Sdn. The sum of DV1 to DV n yields the penultimate index species

value Ma, calculated from the disease values, only for those diseases/targets in

which the particular macrofungi species is used.

Ma = Σ DV1 to n = (DV1 + DV2 + - - - - + DVn), where DV1 to n is the disease

value of the individual diseases and a of Ma is any species of 1 to n, all from the

same community.

Disease Overlapping per macrofungal Species (DOS) can be calculated by

summing each of the species value of a community as given under,

DOS = Σ Mx = (M1 + M2 + - - - - + Mn), where, x = sum of 1 to n number of

macrofungi from JC or PC.

Finally the Community Knowledge Richness was calculated as,

CKR = [(DOS x SDR) + St + Dt + Tk ]

Higher value of DOS index would indicate more number of non-overlapping

disease targets and lower would signify grater amount of overlapping disease

targets per macrofungal species. In totality the higher value of index can be

interpreted as wider number of targets, thus withholding appreciable potential as a

richer database for lead search.

These indices can be broadly categorized into multifaceted first and second

order sub-groups of indices. The main indices can be divided into two first order

sub-groups the Activity group (CPI, WHI, AII, MSI, AMI and MAI) and Non –

activity group of indices. The activity group can be further divided into second

order sub-groups as Core group (WHI, AII, MSI and AMI) and Shell group (CPI


57


Chapter-2

and MAI) of indices. Non – activity group can also be bifurcated into Decisive

group (KTI, FI, EI and NGSI) and the Critical group (CKR, TI, DSI and DLI) of

indices. In the TEMPLE equation given above the Critical group has been divided

to third order sub-group of positive (CKR and DLI) and negative critical indices (TI

and DSI) with respect to their good or indifferent and bad effects on the practices

made evident in the analysis.

2.3.3.3 Data Interpretation and Statistical analysis

The data interpretation and statistical analysis were done as reported earlier

(Garibay-Orijel, 2007) using NCSS (Hintze, 2007). Normal TEMPEL scores were

used for analysis but were scale down by 4√ for legitimate representation.

2.3.4 RESULTS AND DISCUSSION

Drug discovery being time consuming and finance intense, advocates the catch

‘fail first, fail cheap’, reducing the multiplicity of leads failing to through various

phases. Idiosyncratic leads identified by screening non-human host can be

effectively replaced by validated TEMPs in order to increases the chance of the

leads to succeed in the human trials by several folds.

2.3.4.1 Documentation of ethnomycomedicinal practices

The closed knowledge pool marked by reluctance of the practitioners to share

their traditional knowledge afforded only few know-how sharers. Of two, the first

category included species with general and non-specific medicinal practices (in

diet, for convalescence or said to have general strengthening and energizing

effects). Second category encompassed macrofungi (14 species) with specific use

against ailments or diseases. The practices engaging these species are

communicated elsewhere.

2.3.4.2 Traditional Ethno-Mycomedicinal Practice Lattice Evaluation

(TEMPLE)

Of all the 14 TEMPs reported, nine were from PC and five from JC. Higher

numbers of TEMPs reported from PC couldn’t be the only valid reason to adjudge

it as the better community, because other community richness analysis had to be

harnessed in order to make it robust. The summary of practices reflected by the


58


Chapter-2

scores of macrofungi is given below (Annexure-2, Table-2) for all indices

calculated as per the specification given above (Annexure-2, Table-1).

TEMPLES (Fig 2.3.4A) revealed Rd (3394.65) and Pd (1920.31) as the

high scorers, of which the former without NGSI was (1073.5) 1.8 times lower to

Pd. Thus, analysis of the underlying factors causing so rather than direct

numerical interpretation seems to be safer for decision making. The score of

mainand sub indices are given below (Annexure-2-Table 5 and 6).

Figure 2.3.4. Scores of main and sub indices. A. TEMPELS of all the macrofungi,

B. Scores of the sub-groups as Core, Shell, Activity, Positive Critical, Negative

Critical indices, the latter three are Non-activity indices.


59


Chapter-2

Figure 2.3.5. Scores of the indices from both the communities. A. main and B.

sub indices of the species from Jessore community (JC), C. main and D. sub

indices of the species from and Purna community (PC).

The activity group formed the crux of the study as it addresses the

pharmacological activity of the species underlying the TEMP, rest of the indices

were employed to rationalize a doable task within the given frame of resources

and objectives. In the activity group, the second order core group was directly

linked to the biological activity unlike the shell group. Core index (Fig 2.3.4B)

delineated characteristic scores for Phellinus complex being the highest. The

Shell index, an adjunct of Core index, had Tt and Xy with highest, progressing Xy

beyond Pd and Rd in Activity index. The species that are favoured are often used

up soon being rare, thus other species are opted, though the most coveted ones

may have better efficacy than others. In such cases frequency of use may be

misleading, if not coupled with factors as CPI. Xy from Jc and Pd from PC were

some of most favoured. MAI is an index that can deliver tangible outputs as the

mode of consumption not only correlates with the toxicity but also with the

possible effect of the drug. From both communities, MAI elevated Xy and Tt orally


60


Chapter-2

consumed followed by Pd and Rd inhaled after heating, correlate to presence of

anthraquinones that sublimate on heating.

In non-activity sub-group, for decisive index, Rd scored highest because of

NGSI, barring which Pd surfaced above all species followed by the Phellinus

complex paralleled by Tt but left behind by Cc from JC. The values of sub index

KTI-v were entirely reflected in the score of KTI, rather than KTI-h as there was no

seepage of knowledge outside. Xy alone from JC and all species from PC all

macrofungi were age old practices, advocating for community knowledge

richness. As hypothesized, pre-eminence was imparted to the species, wherein

costlier services were rendered for critical and serious diseases, ailments or

disorders, reflected by EI. This notion didn’t fall in place for Tt that had higher

prices because of its edibility and the practice of storing it for later

immunostimulating and diseases resistance use. Practitioners charged for

collection and treatment involving Cc, Sc from JC and Tt from PC, but non-

professionals rendered services free (Xy and Lp) or left it to the beneficiary (Ph,

Bv, Dp, Rd, Pd, PL, Po, Pr and Pm) with barter options. FI is set in course with the

classical track of drug discovery harnessing reverse pharmacology by bioactivity

guided fractionation of active extracts, requiring a sizeable source of biomass.

The sub indices PAI, CI and GI, were multiplied by WI to amplify the importance of

larger biomass as an alternate to macrofungi that are nor small neither abundant.

On the other end if a species is abundantly available, but weighs only few

milligrams, then a large quantum would be needed for active principle isolation. If

this is not feasible then, studies can be made on its fermentation extracts

engaging pure culture isolates. This stands secondary to direct isolation of leads,

as the diversity of secondary metabolites are greater in fruiting body phase,

especially in situ, than the asexual mycellial stage in artificial milieu. If the modus

operandi would entail identification of genetic markers, then GI and CI would

stand for prominence. If isolation of metabolites from solid or liquid state

fermentations were intended then CI would have been of greatest value. In such

case, unlike the frame of this research CI and/or GI would have been valued

higher than PAI and WI. Moreover, the Decisive index apparently favoured

species from PC, which seemed to be under the influence of FI. Geographical and

climatic conditions may be one of the limiting factors regulating FI in terms of

abundance and size of fruiting body. Factors like lower rainfall in JC as compared


61


Chapter-2

to PC may partially weaken the stand of contenders from JC. Xy and Ph,

advocated to have potent activity, were now turned down by FI fine tuning the

Decisive index. The Negative Critical index was lacked any presence. Positive

Critical index down-weighed JC by CKR and DLI, but PC.

The CKR score of Tt, Dp and Rd seemed to offer lot of scope in line with

Bv, Pd, Pr and Po, which were ahead of Pm and PL. Other than Cc rest of the

species from JC vouched by DLI were not visible in Positive Critical index due to

lower CKR than PC. Comparison between two communities showed PC (283.30)

scored 4.64 times (Fig 2.3.5A) better for CKR than JC (61.0, Fig 2.3.5C). The total

knowledge of both the communities (Tk) about all the kinds of diseases/targets

addressed could not be estimated due to conservative approach of the

practitioners, because of which CKR had to be calculated without it. The total

number of diseases (Dt) and macrofungal species (St) was higher for PC (16 and

9) than JC (7 and 5), respectively.

Species disease ratio (SDR), calculated from disease values (Annexure-2-

Table 3 and 4), greater than one is desirable, which was true for both the

communities, though SDR of JC (1.4) was lower than PC (1.78). Finally, DOS

nailed the end of conflict for PC (145.11) that was much higher than JC (61). The

predicament of DOS was dictated not only by the higher number of species and

diseases from a community, but also by the quantum of non-overlapping

diseases, which being 42.85% for JC (3 of total 7 diseases) in comparison to the

56.25% for PC (9 of 16 diseases), seemingly played its role. Moreover, although

JC had more overlapping diseases, yet within the smaller fraction of PC the extent

of overlapping was greater. Evolving such TEMPLE data matrix for lesser

generalization (too much overlapping) by exclusion of such practitioners can be

rewarding, but is only feasible when a sizable scoop of community (practitioners)

is sampled.

The JC depicted diseases like pneumonia, constipation, eczema, which

were high scorers (5) with bruised skin infection, lesion, boils and wounds, scoring

its half (2.5). Whereas PC exposed diseases like immunostimulation

(prophylaxis), boils, wounds, rashes, lesions (psoriatic), pimples, impetigo,

pustules and fissure scored most (9) for DOS, followed by bruised skin infection

with half of the high score (4.5). Edema (on physical insult), mumps, measles,


62


Chapter-2

chickenpox, and mastitis were low scorers (1.5), unlike inflammation (ailments

which could not be classified under any specific inflammatory disease, but said to

have all five hallmarks of inflammation, were also considered), which scored the

lowest (1.23). AMI was not only the highest recruiter of species (71.43%), but also

a leading source of overlapping targets. Though MSI followed close-by with 64.3%

species covered, yet its contribution to overlapping diseases was minor, because

the targets were opined to be different for the diseases. AII captured 57.15%

species which is more considerable than WHI containing only 42.87%. DLI is a

temporal index subjected to change in scores with respect to the increase in

numbers of reports on various species. In JC except Cc (5) all the other species

secured a substantial DLI score of 100 (Xy, Ph, Lp and Sc). Whereas in PC, Tt,

Rd and Dp were high scorers (100) closely followed by Bv, Pd, Pr and Po (80). To

some extent Pm (60) was a distant bystander unlike PL (5) that was swept away

(Fig 2.3.5A). A low DLI score would only mean that it has lesser scope of work,

which has to be exploited accordingly. The influence of DOS, DLI related sub-

indices and WI are clearly visible (Fig. 2.3.5B and 2.3.5D) on species from JC and

PC. NGSI, DSI and TI are excluded as they are indifferent to all except Rd

favored by NGSI.

2.3.4.3 Multivariate analysis

From heat-map high and low scoring outliers could be easily identified in

both the dimensions. Hierarchical agglomerative clustering of species based on

the main indices (Fig 2.3.6A) revealed three major groups, as Group A at 155.16

(Pd), Group B (Cc, Lp, Sc, Ph and Xy) and Group C (Rd, Po, Pr, Pm, PL, Bv, Dp

and Tt) at 80.58 dissimilarity distances. Group B and Group C had Cc and Rd as

outliers to the co-subgroup contained Lp and Xy independent of Sc and Ph co-

lying. In Phellinus complex (excluding Pd) of Group C, PL separated as a

secondary outlier followed by Pm, inturn separated form Po and Pr with a

dissimilarity distance below the cutoff value of 1.0. The other subgroup in Group C

had Bv outlying the co-lying Tt and Dp. In heat map of the indices clustered by the

species, (Fig 2.3.6A), CKR separated initially as an outlier, followed by FI, DLI and

NGSI. Rest of the indices separated as two closely related clusters (CPI, WHI,

MAI and EI; AMI, MSI, AII and KTI), which were further separated from each other

with minor dissimilarities.

2.3 Isolation of potent species by

DOCUMENTATION & DATA

Figure 2.3. 6. Heat map with double clustering by complete linkage rule. Clustering of all the species by the following A. all main indices, B. activity, C. non-activity group of indices.

Isolation of potent species by TEMPLE


Heat map with double clustering by complete linkage rule. Clustering of all the species by the following A. all main indices, B. activity,

activity group of indices.

Figure 2.3 .7. Ordination clusspecies from TEMPELS. A. Multidimensional scaling of species, B. Principal components analysis of species, C. Principal components analysis of all main indices.

63

Chapter-2

Ordination clustering of species from TEMPELS. A. Multidimensional scaling of species, B. Principal components analysis of species, C. Principal components analysis of all main indices.


64


Chapter-2

The influence of activity indices (Fig 2.3.6B) was suppressed by non-activity

indices. Clustering of species by only activity group of indices exhibited early

trajectory of Phellinus complex, within which Pd was found to be an outlier. Of

other three groups Tt was the most dissimilar outlier and Sc and Lp were co-liers

with dissimilarity higher than other groups. The third group had Xy as a singleton

along with Bv and Cc as co-liers, dissimilarity of which was greater than the fourth

group having Ph as an outlier and Dp plus Rd as co-phenons. The pattern of

clustering of species (Fig 2.3.6C) done with non-activity group of indices was

found to be similar to clustering of main indices.

`Amongst the second order of indices, the basic clustering materialized by

Core group of Activity Indices was influenced by Shell group of indices by

triggering cluster shift of Pd and Rd apart from several other contenders (data not

shown). The clustering of Critical group of Non-activity indices was unique

emphasizing its importance, but was later influenced by Decisive group leaving its

strong imprint on the final TEMPELS clustering (data not shown). Thus it is

important to cluster at all levels realizing the cluster shift with respect to high and

low scorers for studying the influence of indices made to assist decision making.

Multidimensional scaling (Fig 2.3.7A) by non-metric multidimensional

scaling (NMMDS) yielded 1st (65.92%) and 2nd (94.08%) dimension with 2.04 and

0.87 Eigen values supported by a stress of 0.0236. Pd and Rd are again outliers

to two clusters, of which one had Lp, Sc, Ph and Xy and the other cluster had

three sub-groups with Cc separated from rest of the Phellinus complex followed

by Bv, Dp and Tt. Major transition of Cc is in 2nd dimension close to PL implying

DLI as a major role player in the 2nd dimension. Unlike Cc, Lp is observed to differ

for EI and core group of indices.

In Principal components analysis (PCA) of the species (Fig 2.3.7 B) first

two components that entailed total variability of 53.81% and 87.26% cumulatively,

with Eigen values of 11.04 and 2.01 respectively, declaring FI and CKR as major

contributors of the variations in 1st component and DLI in 2nd. Resembling

NMMDS, PCA with rotation placed Pd and Rd far from other two groups,

pronouncing them as prominent outliers. Of the two oppositely poled, one group

composed of Cc, Dp, Tt and closely knit rest of the Phellinus clan with PL outlying,

whereas the other encompassed Xy, Sc, Ph and Lp outlying within the group. The


65


Chapter-2

PCA of indices (Fig 2.3.7 C) showed weak clustering unlike hierarchical

clustering.

Spearman’s correlation analysis revealed coincidental positive correlation

of CKR to KTI (p<0.0001; 0.84) and AII (p=0.007; 0.68), contributed by incidence

of species scoring high for all 3 indices from PC. Similar case registered for MSI

(p=0.008; 0.67) and AII (p=0.01, 0.63) scorers being more feasible (FI) by either

abundance or weight if few, vouch for sustenance of such practices assisted by

closed knowledge pool. CPI was found to be almost equally correlated to DLI

(p=0.018, 0.62) and MAI (p=0.022, 0.6) suggesting that the most preferred

species have better efficacy owing to their direct consumption and that they are

not much investigated. This indicates positive outcome of the analysis, and makes

way for the furtherance of such species for pharmacological evaluation and lead

search. Moderated correlation was observed between MSI and AMI (p=0.03, 0.56)

delineating the possible role of immune stimulation against infections, alongwith

KTI and AII (p=0.04, 0.55).

Negative correlation was found to be most significant between MSI and

WHI (p<0.0001; 0.87). It was also observed that sometimes combination of MSI

and AII were also used first to ripen or aggravate and then to reduce a boil. This

was supported by negative correlation between MSI and WHI, which on

backtracking was responded as not used because of aggravating a wound rather

than healing it. It is well known that immune stimulation may cause tissue damage

by reactive oxygen species (ROS) and other enzymes released by the

neutrophils, macrophages and other cells involved in immune-surveillance

(Kirkham & Rahman, 2006). Negative correlation between AMI and DLI (p=0.019,

0.614) and FI and WHI (p=0.019, 0.618) were also found to be equivocally

prominent. The imprint of Cc being worked out for its antimicrobial activity was

reflected in negative relation of AMI and DLI, and constrains the research in this

direction alone. Contradictory relation between WHI and FI is because such

species are smaller in size and are mostly used only topically, thus required in

less quantity. This also shows that low FI calls upon adaptability for the survival of

a practice. A correlation coefficient of 0.54 depicted minor but reciprocal effect of

CPI and WHI (p=0.045). A weak correlation was sighted for negative impact of

CPI on WHI, which may indicate that the other more preferred species may be


66


Chapter-2

more efficacious due to added antimicrobial and anti-inflammatory activity. All

such facets provided important insights regarding the interactions that might have

been evolving since ages to portray the current pattern of practice woven to

external and internal factors as revealed by TEMPLE.

2.3.5 CONCLUSIONS

Instead of literal interpretation form scores, the dynamics of the species trajectory

under the influence of various layers and groups of indices interestingly helps in

sidelining the lesser important species. The TEMPLE score coroneted Pd and Rd

as top charters. Pd backed by CKR migrated from the Phellinus complex due to

its greater suitability for the reverse pharmacology approach entailing animal

experiments demanding greater biomass to be extracted, pillared by its pre-

eminence over its counterparts and DLI indicating no pharmacological

investigations done. The fact that Rd scored thrice of Pd due to NGSI, though Pd

had better activity profile, indicates the semi-quantitative nature of TEMPLES and

necessitates considerations to be made at the first, second and even at the third

order of indices. CKR used to highlight PC as richer than JC can also be adapted

to analyze and compare several individual practitioners of one or many

communities, school of practices, texts and codices or other distinct sources.

Hence, TEMPLE can be a promising dynamic Swiss-knife in the pre-lead-activity-

screening tool-box.

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