Chapter 6
The Landscape of Uttara Kannada


6.1 Introduction


The landscape of UK, at present, is a mosaic of different
landscape elements, habitats or vegetation types. The word landscape
has a physical feature oriented connotation whereas habitat has an
organism oriented connotation. However, these terms are used loosely
in ecological literature. A cursory look at the Survey of India
toposheets of the study area reveals listing of the LSEs or the
habitat types like laterite quarry, salt pan, sheet rock, mud, edged
rocks, mangrove swamp, boulders, rock ribs, open scrub, mud quarry,
minor forest, dense scrub, prawn farm, open mixed jungle, dense mixed
jungle, fairly dense mixed jungle, teak plantation, cashew plantation,
etc. These terms (names of LSEs) were assigned by the surveyors and
have meaning more suited to surveyors' scheme of classification.
However, in ecology, the researchers are supposed/trained to see from
the eyes of objects of their research. Accordingly, Daniels (1989)
looked the landscape of UK from the eyes of birds (his study objects)
and listed following sub-habitats associated with marshes of the UK:
1. Open sea, 2. Rocky-sandy beach, 3. Tidal mangrove vegetation, 4.
Tidal backwaters: deep with grass and sedge emergents, 5. Tidal
mudflats with or without low scattered grass, 6. Tidal deep estuaries,
7. Salt-pans, 8. Stagnant freshwater with floating and emergent
plants, 9. Stagnant, deep open freshwater, 10. Stagnant, deep open
waters with emergent dead trees, 11. Wet rice fields, 12. Dry
cultivation, 13. Dry scrub, 14. Dry fallow land overgrown with grass,
15. Dry bamboo facies, 16. Open dry deciduous forest, 17. Tall moist
forest, 18. Coastal coconut plantations, 19. Betelnut plantations, 20.
Casuarina plantations, 21. Eucalypti/wattle plantations, 22. Other
orchards and groves, and 23. Villages and other smaller human
habitations. Similarly, Nagendra (1994) has made landscape map of
Siddapur area of UK and identified following ten LSE types through
supervised classification of satellite imagery of that area: 1. Paddy
fields, 2. Grasslands, 3. Acacia plantations, 4. Casuarina
plantations, 5. Areca plantations, 6. Coconut plantations, 7. Ponds,
8. Savannas, 9. Scrub jungle, and 10. Degraded forest.
My approach in this study was to note down whatever names have
already been assigned to various LSEs of UK, see if there is any
consistency in it and adopt suitable looking names as such. Otherwise,
I gave some name according to local peoples' or my own judgement after
seeing the LSEs. Later, after sampling the LSEs, these could be
classified objectively based on certain criteria like presence/absence
list of higher plants, vegetation composition (abundance), other
parameters etc. Moreover, plants perceive the environmental
heterogeneity and respond accordingly at finer levels which are
difficult to be perceived by man in a first few encounters. That is
why, while studying habitat transformation, I tried to note down the
habitats that were told by local people. Some of these habitats/LSEs
may be much meaningful from the point of view of local people but may
not be big or suitable enough to be sampled for WRCPs. Some of the
important habitats told by local people are Areca garden, Acacia
auriculiformis plantation, Bena land, Betta, Brackish water, Bund,
Cashew plantation, Coconut garden (new), Casuarina equisetifolia
plantation, Deep river bed, Degraded Forest, Dense Forest, Dense
scrub, Disturbed forest, Evergreen forest, Semi-evergreen forest, Gaon
tada, Homes & homestead, Khuski land, Laterite quarries, Lowland Paddy
field, Mangrove, Mesa, Minor forest, Mulberry field, Mud road, Narrow
stream, Open scrub, Pond (deeper), Prawn farm, RF, Salt pan, Sand
(flat), Sand dune, Shallow river bed, Small Pond, Sugar-cane field,
Banana plantation, Teak plantation, Valley with pipes, Wide stream
etc.
I could study 46 sites for which I have data on abundance of
flowering plants, six places only presence/absence data, a systematic
recording of landscape transformation along two transects, and a wide
experience of watching a major part of UK's landscape. The details of
data collection regarding these studies are given in chapter 2.
6.2 Data Analysis
6.2.1 Classification of sites (habitats)
From present set of data, the sampled LSEs or habitats of UK can
be classified in various ways. One way it was classified based on
presence/absence of 430 species at 46 sampled sites (Chapter 3). Based
on ecological insight one can put cut off point suitably and make
sensible number of habitat types by pooling similar sites. For
example, in chapter 3, I made 14 clusters of sites for reducing it
from 46 sites. However, in this chapter, I classify again the 46 sites
based on presence/absence of only 50 WRCPs which are being discussed
in detail. Using the presence/absence matrix (Table 5.1 of previous
chapter), the 46 sites were classified by complete linkage clustering.
The Jaccard index was used as a measure of similarity between all
pairs of sites.
6.2.2 Characterisation of clusters of sites (habitats)
Based on site/habitat classification, 13 site/habitat-clusters
were identified (site-clusters A to M in Figure 6.1). The constituent
sites of these site-clusters were taken together and data on 11
parameters from all the quadrats of constituent sites were extracted.
Mean and standard deviation values of these parameters for each
site-cluster were computed and the results are presented in table 6.1.
6.2.3 Logical approach
A presence/absence matrix of 50 WRCPs from 12 clusters of sites
was prepared (Table 6.2). Program testall.c (Chapter 3, Appendix 3.3)
was used to find out which combination of clusters of sites would give
maximum number of WRCPs. Similar to chapter 3, minimum and average
values were also computed.
6.2.4 Habitat transformation
Data on habitat transformation was collected as stated in chapter
2. These data were summarized in the form of a table of
landscape/habitat transformation (Table 6.3) to show the ongoing
habitat changes in UK.
6.3 Results
6.3.1 Classification of sites (habitats)
The result of classification of sites is given in figure 6.1.
Thirteen site-clusters (A-M) are marked on the figure. Site-cluster M
(site 3) has none of our WRCPs. Therefore, it will not be discussed.

Table 6.1 Mean and standard deviation values of 11 community parameters for 12
site-clusters (Figure 6.1).
+----------+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|SITE CL |PEX |P3M |PEVG |PROP |PTSL |CC |USP |WRCP |TOTA |TOTB |TOTC |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|A |AVG |0.003 |0.79 |0.79 |0.78 |0.4 |61.1 |11.8 |3 |11.4 |18.7 |6.28 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.041 |0.2 |0.29 |0.17 |0.31 |37.5 |3.66 |1.83 |8.4 |18.1 |4.88 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|B |AVG |0 |0.6 |0.86 |0.78 |0.58 |89.2 |14.5 |3.41 |13.9 |14.4 |6.95 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0 |0.16 |0.14 |0.17 |0.28 |19.9 |4.12 |1.57 |5.89 |12.4 |4.88 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|C |AVG |0.000 |0.7 |0.76 |0.78 |0.47 |64 |12.9 |2.63 |9.89 |14.7 |5.77 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.008 |0.21 |0.28 |0.19 |0.27 |36.6 |5.47 |1.9 |5.39 |9.16 |5.02 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|D |AVG |0.261 |0.93 |0.14 |0.95 |0.28 |38.1 |5.46 |0.56 |5.79 |8.91 |0.67 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.4 |0.14 |0.26 |0.12 |0.31 |34.1 |2.6 |0.78 |3.15 |6.98 |1.42 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|E |AVG |0.604 |0.98 |0.02 |0.83 |0.27 |29.2 |3.57 |0.18 |9.51 |4.75 |0.44 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.451 |0.06 |0.07 |0.35 |0.36 |24.7 |2.22 |0.43 |4.98 |8 |0.87 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|F |AVG |0.213 |0.89 |0.19 |0.9 |0.24 |16.1 |7.65 |1.1 |6.97 |10.9 |2.25 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.347 |0.14 |0.24 |0.2 |0.29 |17.7 |3.64 |1.3 |3.9 |9.62 |3.32 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|G |AVG |0.118 |0.89 |0.16 |0.76 |0.11 |15.7 |9.06 |1.21 |5.05 |23.2 |2.63 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.25 |0.16 |0.28 |0.28 |0.17 |25.7 |4.5 |1.17 |3.65 |24.5 |3.77 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|H |AVG |0.599 |0.98 |0.08 |0.81 |0.37 |15.2 |6.15 |0.91 |7.24 |16.4 |1.63 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.442 |0.07 |0.19 |0.26 |0.33 |16.7 |4.59 |1.1 |5.41 |18.3 |3.43 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|I |AVG |0 |1 |0 |0.68 |0.11 |6.01 |4.02 |0.02 |4.62 |0.42 |4.24 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0 |0.02 |0 |0.47 |0.28 |6.79 |1.42 |0.14 |1.6 |1.28 |1.78 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|J |AVG |0.847 |1 |0.91 |0.93 |0.04 |23 |6.09 |1.95 |13.6 |10.8 |7.52 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.25 |0 |0.1 |0.2 |0.16 |11.2 |2.17 |1.29 |2.69 |22.3 |4.18 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|K |AVG |0.241 |1 |0.24 |0.45 |0.09 |4.12 |2.25 |0.49 |3.49 |3.77 |2.3 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0.422 |0 |0.42 |0.48 |0.21 |8.49 |2.76 |0.72 |6.23 |7.5 |4.54 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
|L |AVG |0 |1 |0 |0.91 |0.00 |0 |4.26 |0.72 |0 |33.3 |0 |
| +----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+
| |STD |0 |0 |0 |0.08 |0.02 |0 |1.09 |0.45 |0 |21.1 |0 |
+-----+----+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+-----+

Abbreviations as in Table 5.2.




Table 6.2 The presence/absence matrix of 50 WRCPs from 12
site-clusters.
+----+------------------------+------------------------------------+
| | | Site-clusters |
| | +------------------------------------+
|Sl. |Species |1 2 3 4 5 6 7 8 9 10 11 12 |
|No. | |A B C D E F G H I J K L |
+----+------------------------+------------------------------------+
|1 |Ipomoea pes-caprae |0 0 0 0 0 0 0 0 0 0 0 1 |
|2 |Musa sp w |0 0 0 0 0 0 0 0 0 1 0 0 |
|3 |Colocasia sp |0 0 0 0 0 0 0 1 0 1 1 0 |
|4 |Curcuma neilgherrensis |0 0 0 0 0 0 1 0 0 0 0 0 |
|5 |Dioscorea sp bl |0 0 0 0 0 1 1 0 0 1 0 0 |
|6 |Amorphophallus sp |1 0 0 0 0 0 1 1 0 1 1 0 |
|7 |Dioscorea sp sl |0 0 0 0 0 1 1 0 0 1 0 0 |
|8 |Dioscorea pentaphylla |0 0 0 0 0 1 1 0 0 1 0 0 |
|9 |Acacia catechu |0 0 0 0 1 0 0 1 0 0 0 0 |
|10 |Crotalaria prostrata |0 0 1 0 0 0 1 1 1 0 0 0 |
|11 |Zizyphus oenoplia |0 0 1 1 0 1 1 1 0 1 1 0 |
|12 |Carissa carandas |0 1 0 0 0 1 1 1 0 0 0 0 |
|13 |Syzygium caryophyllatum |0 1 1 0 1 1 1 1 0 0 0 0 |
|14 |Vigna sp |0 0 0 0 0 1 1 1 0 0 0 0 |
|15 |Curcuma sp |1 1 1 0 1 1 1 1 0 1 1 0 |
|16 |Crotalaria sp |0 1 1 0 1 1 1 1 0 0 0 1 |
|17 |Piper nigrum |1 0 1 0 0 1 0 0 0 1 1 0 |
|18 |Dioscorea sp |1 1 1 0 1 1 1 1 0 1 0 0 |
|19 |Syzygium laetum |0 1 0 0 1 0 1 1 0 0 1 0 |
|20 |Syzygium cumini |0 1 1 1 0 1 1 1 0 0 0 0 |
|21 |Jasminum sp |1 1 1 1 1 1 1 1 0 1 0 0 |
|22 |Ipomoea sp |1 1 1 0 0 1 1 1 0 0 0 0 |
|23 |Murraya koenigii |0 0 1 0 0 0 1 0 0 0 0 0 |
|24 |Ziziphus rugosa |1 0 1 1 1 1 1 0 0 0 0 0 |
|25 |Bambusa arundinacea |0 0 1 1 0 1 0 0 0 0 0 0 |
|26 |Emblica officinalis |0 0 1 1 0 1 0 0 0 0 0 0 |
|27 |Citrus sp |1 1 1 0 0 0 0 0 0 1 0 0 |
|28 |Mangifera indica |1 1 1 1 0 1 1 0 0 0 0 0 |
|29 |Garcinia indica |1 1 1 1 0 0 1 1 0 0 0 0 |
|30 |Sapindus laurifolius |1 1 1 1 1 0 1 0 0 0 0 0 |
|31 |Artocarpus hirsutus |0 1 1 1 0 0 1 0 0 0 0 0 |
|32 |Cinnamomum verum |1 1 1 0 0 0 1 0 0 0 0 0 |
|33 |Piper sp nl |0 1 1 1 0 0 0 0 0 0 0 0 |
|34 |Garcinia gummi-gutta |1 1 1 1 0 1 0 0 0 0 0 0 |
|35 |Garcinia talbotii |0 1 1 0 0 0 0 0 0 0 0 0 |
|36 |Garcinia morella |0 1 1 0 0 0 0 0 0 0 0 0 |
|37 |Knema attenuata |1 1 1 1 0 0 0 0 0 0 0 0 |
|38 |Piper sp bl |1 1 1 1 0 0 0 0 0 0 0 0 |
|39 |Piper sp |1 1 1 1 0 0 0 0 0 0 0 0 |
|40 |Piper sp tssl |0 0 1 1 0 0 0 0 0 0 0 0 |
|41 |Zingiber sp |1 1 0 1 0 0 1 0 0 0 0 0 |
|42 |Syzygium hemisphericum |1 1 1 1 1 1 0 0 0 0 0 0 |
|43 |Myristica dactyloides |0 1 1 0 0 0 0 0 0 0 0 0 |
|44 |Cinnamomum malabathrum |1 1 1 0 0 0 0 0 0 0 0 0 |
|45 |Syzygium gardneri |1 1 1 0 0 0 0 0 0 0 0 0 |
|46 |Myristica malabarica |1 1 1 0 0 0 0 0 0 0 0 0 |
|47 |Gymnacranthera canarica |1 0 1 0 0 0 0 0 0 0 0 0 |
|48 |Myristica fatua |1 0 0 0 0 0 0 0 0 0 0 0 |
|49 |Pinanga dicksonii |1 0 0 0 0 0 0 0 0 0 0 0 |
|50 |Piper hookeri |1 0 0 0 0 0 0 0 0 0 0 0 |
+----+------------------------+------------------------------------+
| |Total |25 28 34 18 10 20 25 16 1 12 6 2 |
+----+------------------------+------------------------------------+
Table 6.3 Ongoing habitat changes in Uttara Kannada.

+-------------+----------------------------------+----------------------------------+-
|Reference |Present |Past |P
+-------------+----------------------------------+----------------------------------+-
| 111093 1 |Pond |Paddy field |
| 2 |Sand (flat) |Sand dune |
| 3 |Paddy field |Paddy field |
| 4 |Sand dune |Sand dune |
| 5 |Salt pan |Paddy field |P
| 6 |Bund, Mud road |Brackish water |
| 7 |Shallow river bed |Deep river bed |
| 8 |Brackish water |Paddy field |P
| 9a |Paddy field |Sand dune |C
| b |Paddy field |Mangrove |C
| 10 |6 ft wide stream |60 ft wide stream |
| 11 |Ca eq plantation on eroded hill |Minor forest |
| 12 |Ac au plantation |Minor forest |
| 13a |Paddy field | |P
| b |Brackish water | |P
| 14a |Paddy field | |P
| b |Brackish water | |P
| |Paddy field |Paddy field |C
| |Paddy field |Sand dune |C
| 121093 1a |Brackish water |Brackish water |P
| b |Paddy field |Brackish water |P
| c |Coconut cum Ar garden |Paddy field |C
| 2a |Mangrove | |P
| b |Brackish water |Paddy field |P
| c |Mangrove |Paddy field |P
| d |Paddy field |Brackish water |P
| e |Mangrove |Mangrove |M
| 3 |Upland Paddy field |Lowland Paddy field |
| |Lowland Paddy field | |P
| |Brackish water | |P
| 4 |Ac au plantation |Open scrub |
| 5 |Paddy field |Paddy field |C
| 6 |Eroded laterite hill |Eroded laterite hill |
| 7 |Ac au & Ca eq plantation |Open scrub |
| 8 |Paddy & Sc field |Paddy field |P
| 9 |Coconut cum Ar garden &houses |Coconut cum Ar garden &houses |
| |more lqs now |3 laterite quarries |
| 131093 1 |Paddy & sc field |Paddy & sc field |C
| 2 |Coconut cum Ar garden |Coconut cum Ar garden, |C
| 3 |Khuski land |Khuski, Bena land |A
| 4 |Bena, Khuski land |Khuski land |
| 5 |Areca garden |Areca garden |A
| 6 |Pipes in dried stream bed |More flow |L
| 8 |Khuski land |Mesa, Bena, Betta |K
| 10 |Mesa |Bena, Betta type veg |
| 11 |Paddy field |Paddy field |P
| 12 |Paddy field |Paddy field |P
| 141093 1 |Pond |Pond (deeper) |
| 2 |Bena ->Areca garden |Bena land |A
| 3 |Khuski land |Bena, Khuski land |A
| 4 |Valley with pipes |Valley with more pipes |
| 5 |Betta+Cashew pl |Betta+Cashew pl |C
| 6 |Open scrub |Dense scrub |M
| 7 |Ac au plantation |Betta, scrub |C
| 8 |Mesa |Mesa |M
| 9 |Mesa+Ac au plantation |Open scrub |M
| 10 |Khuski land on Mesa |Khuski land on Mesa |K
| 11a |Areca garden |Paddy field |A
| 11b |Paddy field (tari) |Paddy field (tari) |P
| 11c |Khuski land |Khuski land |K
| 12a |Areca garden |Paddy field |A
| 12b |Paddy field (tari) |Paddy field (tari) |P
| 12c |Khuski land |Khuski land |K
| 13 |Ca eq plantation |Open scrub |
| 14 |Ca eq plantation |Open scrub |
| 151093 1 |Areca garden |Areca garden |A
| 1a |Mulberry field |Khuski land |M
| 2 |Paddy field |Paddy field |P
| 3 |Khuski, Bena land |Khuski, Bena, Betta |K
| 4 |Areca garden |Paddy field |A
| 5 |Paddy field |Paddy field, Khuski |P
| 6 |Areca garden |Paddy field |A
| 7 |Cashew in Khuski |Khuski |M
| 8 |Betta |Dense forest |
| 9 |Better vegetation |Dense forest |
| 10 |Sc +banana plantation |Khuski, Paddy field |M
| 11a |Bena, Betta |Kumri |B
| 11b |Bena |Kumri |b
| 11c |Khuski,Cashew, Paddy |Kumri, Bena,Betta |K
| 171093 1.1 |Paddy field |Paddy field |P
| 1.2 |Khuski, Betta |Kumri |K
| 1.3 |Areca garden |Paddy field |A
| 2.1 |Paddy field |Paddy field |P
| 2.2 |Areca, banana etc garden |Khuski |A
| 2.3 |Disturbed forest |Better vegetation |B
| 3.1 |Paddy field |Paddy field |P
| 3.2 |Khuski |Better vegetation |K
| 4.1 |Paddy field |Paddy field |A
| 4.2 |Areca garden |Khuski |A
| 4.3 |Areca garden |Paddy field |A
| 4.4 |Paddy field |Bena |A
| 5.1 |Paddy field |Paddy field |P
| 5.2 |Khuski |Better vegetation |K
| 5.3 |Khuski |Kumri |K
| 5.4 |Areca garden |Paddy field |A
| 6.1 |Areca garden |Paddy field |A
| 6.2 |Paddy field |Paddy field |P
| 6.3 |Paddy field |Paddy field |A
| 6.4 |D Forest, but improved |Kumri |
| 6.5 |Areca garden |Paddy field |A
| 7.1 |Paddy field |Paddy field |P
| 7.2 |Areca garden |Paddy field |A
| 7.3 |S_cane field |Bena |A
| 7.4 |Areca garden |Paddy field |A
| 181093 1.1 |Paddy field |Paddy field |P
| 1.2 |Areca garden |Areca garden |A
| 1.3 |Teak plantation |Teak plantation |T
| 1.4 |Bena, Cashew |Better vegetation |C
| 1.5 |Areca garden |Paddy field |A
| 2.1 |Paddy field |Paddy field |P
| 2.2 |S_cane field |Paddy field |A
| 2.3 |Pond |Smaller Pond |B
| 2.4 |Ca eq plantation |Ca eq plantation |
| 2.5 |Cashew |Bena |C
| 2.6 |Better vegetation |Kumri |
| 2.7 |Areca garden |Paddy field |A
| 3.1 |Paddy field |Paddy field |P
| 3.2 |Areca garden |Paddy field |A
| 3.3 |Cashew in Khuski |Better vegetation |C
| 3.4 |Cashew in Khuski |Kumri |C
| 4.1 |Areca garden |Degraded Bena |A
| 4.2 |Paddy field |Paddy field |P
| 4.3 |S_cane field |Paddy field |P
| 4.4 |Cashew, Khuski |Kumri |C
| 4.5 |Paddy field |Kumri, Khuski |C
| 4.6 |Paddy field |Paddy field |P
| 191093 1.1 |Paddy field |Paddy field |P
| 1.2 |Areca garden |Paddy field |A
| 1.3 |Bena, Cashew |Betta, Better vegetation |C
| 1.4 |Areca garden |Paddy field |A
| 1.5 |Bena |Kumri, Betta |D
| 1.6 |Pond |Small Pond |B
| 2.1 |Areca garden |Paddy field |A
| 2.2 |Paddy field |Paddy field |P
| 2.3 |Forest (less bamboo) |Forest (more bamboo) |
| 3.1 |Paddy field |Bena, Betta |A
| 3.2 |Bena |Betta,Bena, Kumri |
| 3.3 |Areca garden |Paddy field |A
| 3.4 |Cashew, Khuski |Khuski |C
| 4.1 |Coconut cum Ar garden |Bena, Betta |A
| 4.2 |Paddy field |Paddy field |P
| 5.1 |Paddy field |Paddy field |P
| 5.2 |Areca garden |Paddy field |A
| 5.3 |Cashew in Khuski |Betta, Kumri |C
| 5.4 |Forest (less bamboo) |Forest (more bamboo) |
| 211093 1.1 |Areca garden |Paddy field |A
| 1.2 |Paddy field |Paddy field |P
| 1.3 |Paddy field |Paddy field |P
| 1.4 |Teak plantation |Forest evg to s_evg |T
| 2.1 |Paddy field |Paddy field |P
| 2.2 |Abandoned Paddy field |Paddy field |P
| 2.3 |Paddy field |Betta |P
| 2.4 |Areca garden |Paddy field |
| 3.1 |Paddy field |Forest |A
| 3.2 |Paddy field |Betta |A
| 3.3 |Abandoned Paddy field |Paddy field, Betta |
| 4.1 |Paddy field |Paddy field |P
| 4.2 |Areca garden |Paddy field |A
| 4.3 |S_cane field |Paddy field |P
| 5.1 |Paddy field |Betta |P
| 5.2 |Areca garden |Paddy field |A
| 5.3 |Bena |Dense Forest |C
| 5.4 |Paddy field |Betta |P
| 5.5 |Bena |Betta |C
| 6.1 |Paddy field |Paddy field |P
| 6.2 |Khuski |Better vegetation |K
| 221093 1.1 |Paddy field |Paddy field |P
| 1.2 |Teak plantation |Kumri |T
| 1.3 |Khuski, Bena |Better vegetation |C
| 1.4 |Paddy field |Forest |
| 1.5 |Areca garden |Paddy field |
| 2.1 |Paddy field |Paddy field |P
| 2.2 |S_cane field |Paddy field |P
| 2.3 |Areca garden |Paddy field |A
| 2.4 |Cashew in Bena, Khuski |Bena with more bamboo |C
| 2.5 |Teak plantation |Kumri |
| 3.1 |Paddy field |Bena, Better vegetation |P
| 3.2 |Coconut cum Ar garden |Bena |A
| 271093 1.1 |Areca garden (new) |Betta |A
|SNS>H 1.2 |Coconut garden (new) |Betta, M. Forest, RF |C
| 1.3 |S_cane field |Betta, MF |C
| 1.4 |Betta |Open scrub jungle |
| 1.5 |Paddy field |Paddy field |P
| 1.6 |Areca garden |Areca garden |A
| 2.1 |Areca garden |Areca garden |A
| 2.2 |Forest, Betta |Better vegetation |
| 281093 1.1 |Areca garden |Areca garden (bad) |A
| | |(Pada jamin) |
| 1.2 |Betta |Betta, dense Forest |B
| 1.3 |Bena |Dense Forest |B
| 1.4 |Homes & homestead |Betta |H
|DMBhat's 2.1 |Areca garden |Dense Forest (>1939) |A
| 2.2 |Paddy field |Paddy field (bad) |P
| | |Pada jamin, or hadbittu gadde |
| 2.3 |Betta |Better vegetation, Dense forest |B
| 2.4 |Khuski |Bena, Betta, Forest |K
|GTHegde 3.1 |Areca garden |Areca garden |A
| 3.2 |Paddy field |Bena (malki Bena) |P
| 3.3 |New Areca garden |Gaon tada |A
| 3.4 |Gaon tada |Gaon tada |
| 3.5 |Betta |Forest |B
| 291093 1.1 |Areca garden |Areca garden |A
| 1.2 |Betta |Better vegetation |
| 1.3 |Minor Forest |Better vegetation |
|SGHEGDE 2.1 |Areca garden |Areca garden, Forest |A
| 2.2 |New Areca garden |Betta, Dense Forest |A
| 2.3 |Betta |Dense forest |
| 2.4 |Paddy & S_cane field |Paddy field, Forest |A
| 2.5 |Bena, Betta |Forest |
| 3.1 |Areca garden |Areca garden, Forest |A
| 3.2 |Betta |Betta, Better vegetation |B
| 3.3 |Paddy field |Paddy field |A
| 3.4 |Ac au plantation |Open scrub |
| 301093 1.1 |Areca garden |Areca garden |A
|GNHEGDE 1.2 |New Areca garden |Paddy & S_cane field |A
| 1.3 |Paddy field |Paddy & S_cane field |C
| 1.4 |Paddy field(makki) |Paddy field, Bena,Betta,Forest |P
| 1.5 |Bena, Betta |Forest |B
| 2.1 |Areca garden |Areca garden |A
| 2.2 |Paddy field |Paddy field |P
| 2.3 |Betta, Bena |Betta, Open scrub |B
| 2.4 |Ac au plantation |Open scrub, Forest |
| 3.1 |Areca garden |Areca garden |A
| 3.2 |Paddy field |Paddy field |P
| 3.3 |Paddy field, Bena, Betta |Bena, Betta |C
| 3.4 |Bena, Betta |Bena, Betta |
| 3.5 |New Areca garden |Paddy field |A
| 311093 1.1 |Paddy field |Paddy field, Bena, Betta, Forest |P
| 1.2 |New Areca garden |Paddy field, Forest |A
| 1.3 |Forest |Dense forest |B
| 2.1 |New Areca garden |Paddy field, Forest, Dense forest |A
| 2.2 |Paddy field |Paddy field, Forest, Dense forest |P
| 2.3 |Bena |Betta, Forest |C
| 2.4 |Areca garden |Areca garden, Forest |A
| 3.1 |Areca garden |Areca garden, Forest |A
| 3.2 |Coconut cum Ar garden |Forest (RF) |C
| 3.3 |Paddy field |Paddy field |P
| 3.4 |Pond |Pond |I
+-------------+----------------------------------+----------------------------------+-

Abbreviations used in Table 6.3:
lqs = laterite quarries
Ac au plantation = Acacia auriculiformis plantation
Ca eq plantation = Casuarina equisetifolia plantation
Ar garden = Areca garden
pl = plantation
MF = Minor Forest
RF = Reserved Forest
Forest evg to s_evg = Forest evergreen to semi-evergreen
Sc = Sugar-cane
S_cane field = Sugar-cane field


6.3.2 Characterisation of clusters of sites (habitats)
The mean and standard deviation of 11 community parameters for 12
site-clusters are given in table 6.1. Based on two most distinguishing
characters (PEVG and CC) these site-clusters were further grouped into
following divisions of site-clusters: 1. [A, B, C], 2. [D, E, F, G, H],
3. [I], 4. [J], 5. [K], and 6. [L]. These six groups of site-clusters
represent following habitats:
1. [A, B, C]: This group of site-clusters represents Myristica swamp,
evergreen, semi-evergreen, disturbed- evergreen & semi-evergreen
forests.
2. [D, E, F, G, H]: This group is represented by deciduous to
semi-evergreen forest, riverside vegetation, open scrubs, and
plantations of Areca, teak, Eucalyptus, Cashew, and Casuarina.
3. [I]: represents teak plantation. The site history of this teak
plantation is such that besides teak, very few other tree species are
left here.
4. [J]: Areca gardens.
5. [K]: Mesas and an Areca garden.
6. [L]: Coastal sandy beach and adjoining sand dunes.
6.3.3 Logical approach
The results of this analysis are given in Table 6.4.
6.3.4 Habitat transformation
The summary of habitat transformation is given in Table 6.3. This
table could be understood much better in combination with listening the
recorded interviews of local people and field visits, if possible.
However, the take home lesson from this study is that people are
transforming habitats/LSEs into economically more valuable habitats/LSEs
depending on thier resource endowment, mental and other capabilities.
The main driving forces behind these transformations are the market,
development activities, increasing population, and human aspiration for
more resource consumption.

Table 6.4 Results of testing all possible combinations for 50 X 12
matrix (Table 6.2).

Maximum
+-------+------------+------------------------------------+
|Sl. |No. of saved|Combination of site-clusters |
|No. |species | |
+-------+------------+------------------------------------+
|1 |34 |3 |
|2 |43 |3 7 |
|3 |46 |1 3 7 |
|4 |48 |1 3 7 8 |
|5 |49 |1 3 5 7 10 |
|6 |50 |1 3 5 7 10 12 |
|7 |50 |1 2 3 5 7 10 12 |
|8 |50 |1 2 3 4 5 7 10 12 |
|9 |50 |1 2 3 4 5 6 7 10 12 |
|10 |50 |1 2 3 4 5 6 7 8 10 12 |
|11 |50 |1 2 3 4 5 6 7 8 9 10 12 |
|12 |50 |1 2 3 4 5 6 7 8 9 10 11 12 |
+-------+------------+------------------------------------+

Minimum

+-------+------------+------------------------------------+
|Sl. |No. of saved|Combination of site-clusters |
|No. |species | |
+-------+------------+------------------------------------+
|1 |1 |9 |
|2 |3 |9 12 |
|3 |9 |9 11 12 |
|4 |16 |5 9 11 12 |
|5 |21 |5 8 9 11 12 |
|6 |26 |5 8 9 10 11 12 |
|7 |30 |5 6 8 9 10 11 12 |
|8 |35 |5 6 7 8 9 10 11 12 |
|9 |40 |4 5 6 7 8 9 10 11 12 |
|10 |46 |2 3 4 5 6 7 8 9 10 11 |
|11 |47 |2 3 4 5 6 7 8 9 10 11 12 |
|12 |50 |1 2 3 4 5 6 7 8 9 10 11 12 |
+-------+------------+------------------------------------+


Average
+-------+------------+
|Sl. |No. of saved|
|No. |species |
+-------+------------+
|1 |16.42 |
|2 |26.83 |
|3 |33.71 |
|4 |38.41 |
|5 |41.72 |
|6 |44.1 |
|7 |45.84 |
|8 |47.15 |
|9 |48.13 |
|10 |48.89 |
|11 |49.5 |
|12 |50 |
+-------+------------+
6.4 Discussion
We human beings classify things based on their characteristics for
better comprehension with less effort. Depending on what finer levels of
differences we consider and where do we put a cut off point, the number
of classes/groups could be varied from one to the number of total
objects being classified. Here, I have classified 46 sampled sites and
based on my ecological insight of watching these sites, I have grouped
them into 13 clusters of sites (A-M). However, when other 11 community
parameters were taken into consideration then it was observed that these
clusters of sites could be grouped further into less number of groups of
site-clusters based on certain parameters like PEVG and CC.
If we consider only these chosen 50 WRCPs for in situ conservation,
then we can think of a logical approach (as discussed in Chapter 3) to
maximize the number of WRCPs being saved for full range of options of
choosing conservation sites. Here also, with present available
facilities, the answer could not be given for all groups for 50 X 46
matrix. Therefore, for a matrix of 12 site-clusters (please remember
that 13th site-cluster has no WRCPs) with 50 WRCPs, the whole range of
options are given in Table 6.4. Whole discussion of Chapter 3 is
applicable to this part also.
As stated earlier in chapter 3, if we have enough resources then we
can choose all the 46 sites to save all the 430 species or 50 WRCPs.
However, setting aside some piece of land for conservation purposes is
a land use decision which we will have to make among competing land
uses. Other considerations will have to be taken into account while such
decision making. Studies from landscape ecology, specially the "seven
emerging general principles of landscape ecology" (Forman and Godron
1986) worth consideration. Furthermore, there could be many other
possibilities depending on availability of resources and other
considerations. Suppose we can channelize resources for recruiting 46
forest guards etc to take care of all the 46 sites independently then
there is no problem even if there are some sites contiguous to each
other. However, in case of limited resources we will have to choose
certain sites depending on our objectives. One way could be to select a
few representative sites from each habitat type. Another way could be to
select a few representative sites from each site-clusters. Yet another
way could be to select representative sites from groups of
site-clusters. I am not arguing that these sites, site-clusters or group
of site-clusters only should be selected. My aim is to show the
procedure. I am also not involved in any actual act of in situ
conservation in Uttara Kannada. However, I may be useful in giving my
opinion and knowledge about plants of Uttara Kannada when it comes to
planning in situ conservation there. One can also look at the CODA
(Conservation Options and Decisions Analysis) procedure developed by
Australian workers (Bedward et. al. 1992). This would be much helpful if
we are giving much imphasis on prioritization and other considerations.
Similarly, while deciding forms of conservation that would be
appropriate and viable for Uttara Kannada, I do not think that there is
any alternate except to do extensive field work, read people of Uttara
Kannada, their perception, culture, ethics etc which are essential for
long term survival of any protected area system. My opinion is that if
local people want then only other outsiders can give some input in the
form of knowledge etc. but outsiders' decisions should not be imposed.
Locals will have to be convinced about conservation. All the above
factors will decide the form of conservation.
For giving full details of ongoing habitat changes and recording
them as benchmarks, I give these habitat transformation data in tabular
form. However, much insight is lost since accompanied explanations of
local interviewees are not given because of limitations. Any
conservation strategy should be planned with full consideration of these
ongoing habitat changes. The landscape of Uttara Kannada and its
landscape elements will change over time depending on the kind of
changes people of Uttara Kannada will make and the kind of other
external factors (externalities like market forces, demand of btelnut,
prawn etc). This is what could be predicted and this only is shown in
"probable future" column of table 6.3 as foreseen by local people.
People of Uttara Kannada will also go for land uses that will give them
maximum satisfaction (within the limitations of ecological conditions
etc). Therefore, keeping land use changes in mind, it is necessary to
conserve a few representative sites of each habitat types/landscape
elements that are available now.
Stochastic events usually have a thinning effect -bringing down the
populations to a very low size and make it more vulnerable to further
stochastic events. One way to take care of stochastic events would be to
conserve multiple sites of different habitat types at various locations.
6.5 Correspondence between species-clusters and
site-clusters
There is a correspondence between species-clusters (from previous
chapter) and the site-clusters. This correspondence could be listed as
follows:
-------------------------------------------------------------
Sl. No. species-clusters site-clusters
-------------------------------------------------------------
1. [A, B, C, D] [A, B, C, D, F, G]
2. [E, F, G] [C, D, E, F, G, H]
3. [H] [G]
4. [I] [F, G, H, J, K]
5. [J] [L]
-------------------------------------------------------------
This way we get information about which group of WRCPs are found in
which group of habitat-clusters. For in situ conservation of these WRCPs
we will have to adopt only those land uses that are friendly to
populations of these WRCPs in their preferred habitats.