37

 

 

 

 

 

CHAPTER – 3

METHODS  

 

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3. METHODS

3.1. The Overall Study Design

This chapter describes both the field methods and analytical methods I have

followed in this study. Plants, birds and large herbivorous mammals are the key

biodiversity components that I investigated. These major biodiversity elements

are indicators of ongoing ecological changes in the study area. Vegetation,

being the primary producers in the ecosystem, is the cardinal component of the

habitat, and is the key determinant of other biodiversity components including

birds and mammals (Kremen 2005, Wilson et al. 2007).

The key objective of my study is to measure impact of human pressures

on the structure and composition of vegetation, abundance and richness of birds

and abundance of large mammals, as I hypothesized human pressures in a

region to generally have direct effects on these biodiversity components. Hence

I chose one single survey design that would enable measuring both human

disturbances and varied biodiversity parameters at the same locations from a set

of field surveys, for an objective assessment.

I first assessed and made use of available digitized maps of the

Nagarahole region, before conducting additional field surveys to incorporate

information not available in these maps using a GPS (Garmin 12 XL). I then

prepared detailed maps for the study area showing clearly the different

39

management and access regimes I was interested in comparing for generating a

single survey design.

Since 1986, scientists from the Centre for Wildlife Studies (CWS) have

been conducting long term monitoring studies in the Nagarahole National Park

region assessing large mammal densities using purposively placed line transects

laid in proportion to different habitat types available. In 2003, after carefully

examining the existing stratified random survey design and the results and

experiences gained from the new systematic sampling survey design used in

three sites in Maharashtra (Karanth and Kumar 2005), the CWS scientists

designed a new transect survey system for sampling animal populations in

Nagarahole (Karanth et al. 2008). Under this transect system, line segments of a

predetermined length are placed systematically across the entire survey area,

with the first line segment being placed truly randomly (Buckland et al. 2004),

using the automated survey design feature in DISTANCE software (Thomas et

al. 2010). The line segment samplers were then transformed to a square

geometry to improve field logistic efficiency and to reduce duplication of

efforts (Karanth and Kumar 2005, Karanth et al. 2008). I chose this survey

design system as the basis for designing survey protocols for sampling target

biodiversity components (vegetation, birds and mammals) and human

disturbance in my study region.

40

A systematic sampling design with a random start was used in program

DISTANCE (Thomas et al. 2010) to generate samplers covering the entire

study area. To maintain uniformity with the transect survey system followed in

the rest of Nagarahole National Park, I retained a transect length of 3.2 km for

sampling in my study area. I marked a new set of transects in Maukal and

Devmachi region (LPA) and added several new transects in MPA and HPA

regions to line transects already designed by CWS in Nagarahole reserve. Each

square sampler was placed at a distance of 3 km from the adjoining one. The

co-ordinates of the corners of the squares geo-referenced and the points were

located on ground using Garmin 12 XL GPS. Each arm of the line was

measured and marked at every hundred meters using red paint and metal plates.

In all, I marked 22 transect lines in the entire study area. Highly

protected area (HPA) management regime, medium protected area (MPA)

management regime each had 8 transects and least protected area (LPA)

management regime had 6 transects. Figure 3.1 shows the schematic map of

the transect survey design used to measure different components of

biodiversity. Since the areas under the three regimes being compared were

found to be similar in terms of key habitat factors (vegetation type, rainfall and

topography) described in Chapter 2, this transect system ensured that all the

different biodiversity parameters measured along these transects were amenable

for comparisons across the study area as well as amongst the three management

regimes.

41

Figure 3.1: Map showing a system of 22 transects that systematically covered

the three management regimes prevalent within the study area. Inset map shows

the location of study area in India.

42

The three management regimes and, consequently the three human

disturbance levels expected in my study area are likely to have variable impacts

on different biodiversity components both in respect of species

composition/species richness and species abundance. Some species of plants,

birds and mammals were chosen as the ‘target biodiversity components’ for

more detailed investigation of the effects of management regimes/human

disturbance.

In view of the fact that diverse taxonomic groups as well as human

impacts are to be measured, I decided to rely on non-invasive sampling

methods which did not involve physical capture or tagging of individuals. I also

did not employ non-invasive field survey methods that involved expensive

equipment or advanced laboratories, such as photographic sampling (Karanth

and Nichols 1998) or genetic sampling (Mondol et al. 2009). My primary

method of field surveys revolved around the use of a system of line transects

(Buckland et al. 2001, 2004, Williams et al. 2002) that systematically covered

all three areas under different management/human access regimes. These

transect-based field surveys were chosen to measure the following parameters:

(1) Species composition, diversity and richness of different biodiversity

components.

43

(2) Abundance/densities of select individual species and/or guilds or

categories of plants, birds and mammals.

The same set of line transects were used to measure two explanatory

variables, which were hypothesized to influence both animal abundance and

species composition: Human Disturbance Index (HDI) an anthropogenic

variable summarizing the human impacts, which is a result of a particular

management/access regime prevailing over the area as well as Normalized

Differential Vegetation Index (NDVI), an ecological variable that is a good

surrogate of forest vegetation type and climate.

3.2. Assessment of habitat similarity

To obtain a baseline comparison of vegetation characteristics among the three

management regimes, I relied on remotely sensed vegetation indices, which

make use of reflectance bands sensitive to chlorophyll absorption and cell wall

reflectance. These indices are calculated as ratios of individual bands or of band

sums or differences to facilitate recognition of and variation within types and

densities of growing forests, plantations and crops. The most commonly used

index is the Normalized Differential Vegetation Index (NDVI), which is

considered as a good surrogate for the measure of vegetation cover type and

green biomass (Bawa et al. 2002, Krishnaswamy et al. 2009).

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NDVI typically varies between -1 and +1, and values of NDVI for forest

vegetation generally range from 0.1 to 0.7, with values greater than 0.5

indicating dense vegetation (Wang et al. 2001, Krishnaswamy et al. 2009).

NDVI is derived using the following algorithm:

NDVI = (C2 – C1)/( C2 + C1);

Where C2 and C1 are near infra red and visible red channels.

I used NDVI values to ascertain uniformity in vegetation type in the

study region. I used LISS IV data (30-11-2005) for computing NDVI values for

all 22 lines transects in the study region and computed average NDVI values for

each of the three management regimes. For each transect NDVI values for 4

points on each arm of transect were extracted and the mean NDVI of the 16

points was computed to reflect the vegetation status.

I also used NDVI value computed for each of the 22 line transects as an

ecological variable to independently assess its influence on both species

richness and abundance of target biodiversity components.

3.3. Field Survey Methods

3.3.1. Overall Approach to Field Surveys

Species richness and species abundance was measured for each of the following

three biodiversity components: Plants, Birds and Mammals. Plants were

45

classified into 3 categories based on girth; Bird abundance was measured for 19

foraging guilds of birds; Mammals were categorized as terrestrial or arboreal

mammals for measuring abundance. Based on principles of replication,

randomization and stratification, I used a single overarching survey design to

quantify abundance of plants, birds, mammals and human impacts/disturbance.

I used vegetation survey plots placed along transects to measure plant

diversity and abundance, point transect surveys for bird richness and abundance

and line transect surveys for measuring abundance of herbivorous mammals. In

the following sub-sections of this chapter, I describe various survey design

aspects, basis for their choice, as well as the data collection and analysis

protocols followed for each of the biodiversity components targeted in my

study.

3.3.2. Plants

Vegetation sampling was carried out on twenty-two permanent line transects,

used for sampling mammals, birds and human disturbance, in order to capture

subtle vegetation structure and compositional changes happening at the level of

each sampling site (transect). The vegetation structure and composition is prime

determinant of variations in richness and abundance of other biodiversity forms.

On every transect line at an interval of 200 m distance, 25 X 4 m rectangular

plots were laid perpendicular to the line on either side making it a rectangular

46

plot of 50 X 4 m for sampling plants with > 30 cm Girth at Breast Height

(GBH), here-after I refer to these plants as tress throughout my thesis. I placed

a 4 X 4 m plot at either end of this primary plot to record plants with < 30 cm

GBH and > 10 cm Girth at Ground Height (GGH), here-after referred to as

shrubs. For measuring plants with < 10 cm at GGH (here-after referred to as

herbs in the remainder of the thesis), two 1 X 1 m plots were nested at the

diagonal end of each secondary plot (4 X 4 m). Figure 3.2 shows the schematic

diagram of survey design used for measuring vegetation structure, composition

and abundance of plants in the three management regimes. Vegetation sampling

surveys were carried out during March to May 2005.

Sampling was done at 337 points in all on 22 line transects for trees

covering an area of 6.74 ha. All the trees measuring > 30 cm of GBH were

identified to species level, height was measured using Ravi’s multimeter and

GBH was recorded at 1.3 m from ground using a measuring tape. The tree

canopy density was measured, using convex densitometer. The tree canopy

density was measured at three points in each of the rectangular plot, one in the

middle of the rectangular plot placed exactly on the transect line, two points

one on either side at the end of the rectangular plot. At each point the total grids

on the convex mirror occupied were counted on all four directions and average

computed as canopy density.

47

Figure 3.2: A schematic diagram of vegetation sampling plots used to measure

plant biodiversity component across the three management regimes in the study

area.

48

Nested within the tree plots, at opposite ends of the rectangular tree plot,

two plots of 4X4 m size were laid for shrub sampling. All the individual shrub

species were identified, GGH measured using calipers and each individual

species height was measured using marked measuring rod. The shrub cover %

was assessed qualitatively. In all sampling was done at 674 plots covering an

area of 1.078 ha.

For sampling herbs, 1 X 1 m two plots were placed diagonally opposite

ends nested within the shrub plots were used. In all herbs were sampled at 1348

plots covering an area of 1348 sq m. All herbs were identified to the species

level, with the help of local tribes and floristic experts. Ocular assessment of

percentage grass cover, presence of exposed soil, litter and dead wood was

made.

Taxonomic identification of all plant specimens encountered in

vegetation survey plots was ascertained by (a) tallying local vernacular names

with scientific names, (b) using floral guides, and (c) consulting floristic experts

from local universities.

3.3.3. Bird Species and Guilds (Non-Gallinaceous)

Transect counts, point counts, territory mapping are some of the standard

techniques predominantly used for bird density estimates (Bibby et al. 1992,

Lloyd et al 2000). In closed forest habitats point counts are more preferred over

49

line transect (Lloyd et al. 2000). It is mainly because the observer has more

chance to cover long distances, observe birds while standing at one place, rather

than walking through the habitat. I also conducted field trials to check the

suitability of line transects and point transects. Based on the field trials I chose

point transect (variable circular plot) method for sampling. Standard point

transects protocol as described in Buckland et al. 2001 was used for sampling

the data. Permanently marked line transects were used as base for choosing

points and sampling was done at every 200 m distance. Point counts were

centered on rectangular plots of vegetation. There were 16 points on a 3.2 km

length of transect (see Figure 3.3 for a schematic diagram of the survey design

for birds).

I sampled all 22 transects four times and entire sampling was completed

within one season (December 2004 to January 2005). On each occasion all the

22 lines were covered once, before taking up the second round of sampling. I

trained twenty-three qualified bird-watchers in field protocols including using

of Laser range finders. Only trained survey personnel were used to collect point

transect data. The field sampling was carried out from 06:15-09:30 hrs, and

each point transect was covered 4 times. At each sampling point, sampling was

done for five minutes, without any wait period. In all the surveys were done at

1324 points, using 88 man-days of fieldwork.

50

Figure 3.3: A schematic diagram of survey design used for measuring bird

biodiversity component across the three management regimes in the study area.

51

All visually detected birds were recorded after ascertaining their species

identity. Birds whose calls were only heard were excluded from bird count data.

Distance was measured using the Laser finder or assessed visually at very close

quarters to the cluster center if it was a flock of birds. Bird species were

identified using binoculars. Birds flying far too high (raptors, swifts and

swallows) and/or fly-by birds were not included in the sampling. I also

excluded predominantly ground-foraging gallinaceous birds from point transect

data.

The bird count data was then pooled across species to estimate

abundance of guilds defined a priori. The species specific detections also

formed the basis for constructing ‘detection history’ matrices for estimating

species richness of birds.

3.3.4. Mammals and Gallinaceous Birds

I used standard line transect method (Buckland et al. 2001, Karanth et al. 2002)

for estimating densities of large herbivores (including both terrestrial and

arboreal) and predominantly ground-foraging gallinaceous birds. Line transects

sampling, for mammals and gallinaceous birds, was conducted during the

months April 2004 to May 2004. Figure 3.4 shows the schematic diagram of

line transect survey design used in this study.

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I followed field protocols prescribed by Karanth et al. 2002 for line

transects surveys of large mammals. To reduce foot-fall noise and to improve

detection efficiency, only two trained volunteers walked on each transect line to

collect the data once in the morning from 6:15 to 8:15 and once in the evening

from 15:45 to 18:00. All the large herbivores (12 species including arboreal

mammals) sighted were recorded; the sighting angle and angular distance to

individual or to the center of the cluster were recorded. The animals present

within a radius of 30 m were considered a cluster (Karanth et al. 2002). At each

detection event: species, size of the animal cluster, sighting angle with

reference to the line transect walked and the sighting distance from the observer

to the individual animal or to the group center was measured using the LASER

ranger finder. The sighting angles were measured using liquid filled compass.

The sighting angle and the sighting distance were used to calculate the

perpendicular distance of the animal location site on transect.

Each of the 22 line transects was walked six times covering total length

of 392.5 km. Data from the temporal replicates were pooled and treated as a

single sample. The encounter data was used to calculate the densities, by

multiplying cluster density with cluster size (Karanth et al. 2002).

53

Figure 3.4: A schematic diagram of the line transect survey design used to

measure the abundance of terrestrial mammals, arboreal mammals and

gallinaceous birds across the three management regimes in the study area.

54

3.3.5. Human Disturbances and Impacts

One of the major constraints in measuring the human induced disturbances is

lack of inexpensive (without involving complex technology), single standard

method to capture all the disturbances comprehensively in an area. I measured

human disturbance all along the transect lines on which mammalian, birds and

vegetation sampling was carried out, so that I can correlate the human

disturbance with changes in: vegetation structure and composition; bird species

richness and abundance; and mammalian abundance.

I observed the local people dependency on forest for their livelihood and

identified thirteen various forms of human disturbance and their associated

‘signs’ that could be readily counted and/or presence recorded. Two observers

walked each transect line once and recorded “signs” of human activities such

as: cut stems, cut bamboo, lopped trees, logged trees, notches cut on trees,

presence of cattle dung/tracks and poaching signs approximately with in 30 m

distance ‘visible’ on either side of the line transect. Given the deciduous nature

of study area with relatively open undergrowth and an average strip-width of 50

m of visibility in the study area, it was reasonable to expect near-certain

detection of all human-activity signs within 30 m distance from transect line.

Also, because I was primarily interested in assessing overall human disturbance

prevalent in the study area, both old and new ‘signs’ of human activities were

recorded. Tally counts of these signs were made for each 100 meter segment of

55

Figure 3.5: A schematic diagram of the survey design used to assess the extent

and magnitude of human disturbances across the three management regimes in

the study area.

56

the transect line to record the extent of human disturbance (see Figure 3.5).

Presence or absence of other signs such as: fire, dead wood clearance, weed

infestation and exposed soil were also recorded in each 100 m segment. Degree

of weed infestation, extent of exposed soil, and, presence of dead and old

growth trees were also recorded. These human impact surveys were carried out

in May 2005.

3.4. Analytical Methods

3.4.1. Assessing Species Richness

Species richness is the number of species present and is the simplest form of

describing ecological community diversity (Magurran 1988). The species

richness estimates have similar problems of detection and spatial sampling

(Yoccoz et al. 2001). For example, detection probabilities of birds are different

across species (Burnham and Overton 1979). Various methods proposed to

estimate species richness are based on species detection probability which itself

is based on the number of individuals present in the region and how hard it is to

detect an individual. Various capture-recapture models are available based on

the detection probability incorporating heterogeneity and detectability (Otis et

al. 1978). In such analyses, detection of each species is analogous to detection

of an individual in a capture study of population abundance. I used capture-

recapture models to estimate bird species richness by using ‘species detection

57

data’ that I had already collected during bird transect counts. However, since

the vegetation survey plots are relatively small and of fixed area, I used

conventional approaches to estimate species richness of plants. The analytical

method followed for estimating species richness in each target biodiversity

component is described below.

Assessment of Species Richness in Plants

Species richness and evenness index was computed using EstimateS program

(Colwell 2009) for each category of plants surveyed: trees, shrubs and herbs.

Basic statistics like the number of species, and diversity indices such as

Shannon-Weiner Index, Simpson index and Fisher’s alpha etc. were estimated

at each transect level and also across management regimes using program

EstimateS (version 7.0) software (Colwell 2009). Diversity indices are single

statistic derived from combination of summary of species richness and

evenness. There are different ways in which the indices, like Shannon-Wiener

index, Simpson index and Fisher’s alpha index are computed (Magurran 2004).

These indices are considered indication of α diversity. I computed all these

indices using EstimateS statistical software (Colwell 2009). The three indices

represent key aspects that include species rarity, species evenness and species

diversity.

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Shannon-Wiener Index is a measure of species diversity which is

represented by stability of the habitat. Shannon-Wiener index is biased towards

rare species. Simpson diversity index is also called species diversity index.

Simpson diversity is calculated based on number of species present as well as

relative abundance of species. There is more emphasis on evenness. Fisher’s

alpha index is calculated based on the assumption that abundance of species

follows log series distribution.

Assessment of Species Richness in Birds

Bird species richness at the transect level and also at each management regime

level were estimated using program SPECRICH (Hines 1996) that specifically

incorporates detection probability into species richness estimates (expected

number of species present). This is in direct contrast to the number of plant

species counted within relatively small fixed area vegetation survey plots,

where there is no uncertainty in the counting and identification of plant species

encountered within the survey plots. Number of species detected in each point

count within a transect formed the basis for computing transect-level species

richness, while cumulative number of species detected at each transect level

formed the basis for computing species richness at each management regime

level. Program SPECRICH uses frequencies and total number of species

detected during field surveys. It is based on modified heterogeneity ‘Mh’ model,

which accounts for variable capture probabilities of bird species, there is no

59

impact of time variation and trap response (Boulineir 1998, Williams et al.

2002).

Assessment of Species Richness in Mammals

Only 11 species of herbivorous mammals (that included both terrestraila and

arboreal) were encountered during transect surveys and hence I did not carry

out any detailed assessment of species richness for mammals.

3.4.2. Assessing Species abundance

Forest structure is its diversity, which is depended on its individual components

(Zenner and Hibbs 2000). The plant community determines the ecosystem

processes (Loreau et.al. 2003, Krement 2005) and the structural characteristics

provide niche for wildlife (MacArthur and MacArthur 1961). Hence estimating

and understanding forest structural variables across management regimes in the

study region, is one of the key objectives of the study. I used the following

methods for estimating abundance of biodiversity components.

Assessment of Species Abundance in Plants: Stand Density and Basal Area

All plants encountered in vegetation sampling plots were compiled and number

of plants in each unit area (ha) was computed in EXCEL. Density of plants

encountered was computed for each of the three categories of plants (trees,

shrubs and herbs) separately; stand density, the number of trees per ha; stem

60

density, number of shrubs per ha and herb density, the number of herbs per ha

were compiled for comparison across each management category. I also

computed average tree height, overstorey canopy density, average shrub height,

understorey shrub cover density, percent grass, percent leaf litter cover, percent

exposed soil using the Microsoft Excel spreadsheet functions.

Basal area per ha was computed for trees and shrubs to assess structural

variability whether the site had few old tress with large basal area or young

forest with many trees. Basal area (BA) was calculated using the standard

formula BA= π r2. BA of individual plants was aggregated to compute BA per

hectare. Basal area of plants grouped based on girth class was compared across

each management regime to assess vegetation structure both at overstorey and

understorey levels.

Along with herb density I assessed percentage-exposed soil, litter

percent and percentage grass cover to assess crude estimate of biomass turnover

and indirect evidence of fire in the region. Absence of litter is indicative of both

high incidences of fire as well as removal for manure by local residents. Grass

represents first stage in succession, which is indication of presence of biotic

pressures in the region. Grass cover percentage was computed as a surrogate of

biotic pressure.

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Assessment of Species Abundance in Birds: Line Transect and Point Transect

Surveys

I used program DISTANCE 6 (Thomas et al. 2010) for calculating density

estimates. The principles of density estimate for forest birds using the point

transect is similar to line transect. In point transect the observer records radial

distance from point to the object. Point transect also follows other basic

assumptions of line transects: Birds are detected with certainty; there is no

movement of birds before they were being detected; the location of the bird

identified, if it is in a group, centre of the cluster identified and distance from

the observer measured accurately.

Only birds with more than 60 sightings were considered for density

estimates of individual species. Analytical protocols followed Buckland et al.

(2001). All species detected (irrespective of sample sizes achieved) were

grouped into 19 foraging guilds based on food habits reported in Grimmett et

al. 1999 and density estimates were computed for those guilds which met

sample size criterion (> 60 sightings).

Exploratory analysis was carried out in DISTANCE (Thomas et al.

2010) to identify problems in data sets such as evasive movement of

birds/guilds, rounding-off errors, presence of spike in data, etc. as well as to

decide truncation levels to minimize the effect of outliers on detection function

62

(Buckland et al. 2001). All the three probability density functions: half normal,

uniform, hazard rate with cosine functions were tested to examine data fit. Best

models with smallest AIC value as well as based on goodness-of-fit statistics

that inform about the fitness of perpendicular histogram data close to the line

were chosen for computing density estimates (Karanth et al. 2002). Density

estimates at individual transect level or at each management regime could not

be computed for bird species, which had zero or too few sightings (< 40).

Global detection function for each guild of birds was used to compute

probability of detection (‘p’) and encounter rate (n/L). I computed corrected

encounter rates, by multiplying encountered rate with ‘p’. The corrected

encounter rate for individual transect is used as surrogate for abundance for that

particular transect. I also computed density estimates for bird food-guilds across

transects and also across three management regimes.

I also chose three habitat specialist species to examine the impact of

human disturbances on their abundances. I used Hill myna (Gracula indica), an

inhabitant of dense forests, feeding mainly on fruits, insects, and grains; Jungle

babbler (Turdoides striatus), a forest-edge species feeding mainly on insects,

grains, nectar and berries; Red-vented bulbul (Pycnonotus cafer), an

omnivorous bird of open forests, plains and cultivation lands, feeding mainly on

berries, grains, leaves, and insects.

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Assessment of Species Abundance in Mammals and Gallinaceous Birds: Line

Transect Surveys

Program DISTANCE 6 was used to estimate abundance of large mammals and

gallinaceous birds. The analytical protocols followed Buckland et al. (2001)

and Karanth et al. (2002). As the sample sizes were too low for estimating

animal density at individual transect level and the variance associated with

detection probability was too high, all animal detections were pooled for

terrestrial herbivores and arboreal mammals to estimate encounter rates at

transect level and densities at this group level. Truncation methods were used to

eliminate outlier observations and to improve subsequent model fitting as

prescribed by Buckland et al. (2001). The appropriate detection model best

suited for each species as well as for terrestrial and arboreal groups was

selected on the basis of Akaike’s information criterion (AIC) values, and

goodness-of-fit tests generated by program DISTANCE. Differences in the

encounter rates and / or abundances among transect lines and management /

access regimes were investigated by examining scatter plots, and, box and

whisker plots. The density estimates were compared across the three different

management and access regimes to draw inferences.

Since the sample sizes of each species of mammals found to be too low

at transect level or zero sightings on few transects, I chose to study impact of

human pressures on two groups of mammals, rather than any one species in

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particular. The two groups were: terrestrial mammals that included elephant,

gaur, sambar, wild pig, chital and muntjac; arboreal mammals that included

bonnet monkey, langur and Malabar giant squirrel. All the sighting encounters

were pooled to compute terrestrial and arboreal mammal densities across

transects and across management regimes.

Exploratory analysis was carried out to identify evasive movements,

outliers and spikes close to transect line (Buckland et al. 2001. Global detection

function was used to compute the encounter rate and probability of detection

‘p’ at each transect. I computed corrected encounter rates for each species, at

each transect, by multiplying encounter rate with ‘p’. We used corrected

encounter rates as substitute for density estimates, wherever direct computation

of animal densities was not possible due to sample size constraints. The data

filters and model definition components available in the DISTANCE 6.0

(Thomas et al. 2010) were used for detailed analysis. For objectively selecting

the models best suited for each species, Akaike’s information criterion (AIC)

values, and goodness-of-fit (GOF) tests generated by program DISTANCE 6.0

were used. Truncation was done before model selection. The fitness of

detection function close to line was judged based on GOF test results. The

model with lowest AIC values and best fit was selected. Sighting encounter

rate (n/L), average probability of detection (p), cluster density (Ds), estimated

cluster size E (s) and estimated animal density ( D̂ ) was computed for each

65

transect. Two common species chital and Malabar giant squirrel had > 60

sightings. Hence density estimates were computed separately for these two

species. Detections of three species of gallinaceous birds, Indian peafowl, spur

fowl and jungle fowl were grouped together (due to sample size constraints) to

compute densities across line transect and the three management regimes.

3.4.3. Assessing Human Impacts and Disturbance

The frequency of counts of various human activity signs were computed

separately to calculate human disturbance sign encounter rate per kilometer

walked. The overall Human disturbance index (HDI) for each transect is an

aggregate count index of all disturbance or impact signs observed that

represented the gradient of human disturbance regime across the study area.

I categorized ‘signs’ of all human activities encountered into two

distinct groups: signs that could be quantified and signs that would enable

qualitative assessment. Quantifiable human disturbance signs (such as cut

stems, cut bamboo, lopped trees, logged trees, tree notches, cattle dung,

poaching signs) were aggregated and average number of signs encountered per

unit km of walk effort was computed to obtain human disturbance count index

(HDCI). Similarly qualitatively assessed human disturbance signs (presence-

absence of fire, dead wood presence, weed infestation and exposed soil) were

combined to obtain a frequency based human disturbance detection index

66

(HDDI). The overall human disturbance index (HDI) was the sum of HDCI and

HDDI.

3.4.4. Comparisons of Attributes across Management and Access Regimes

Comparison of structure, composition and abundance of species under each of

the above biodiversity component was carried out across the three management

regimes using Box and Whisker plots. Effect of HDI on the structure,

composition and abundance of species after accounting for variations in NDVI

under each biodiversity component was examined through scatter plots, simple

linear regression and partial Mantel tests.

Box and Whisker Plots

Box and Whisker plots are perhaps the simplest and among the best methods

for representing group of data (in my case, densities or richness in each

management category). These plots are also most appropriate to assess the

impact of management regimes. The smallest sample minimum is represented

as lower quartile (Q1) and largest observation is represented as upper quartile

(Q3), while the central line in the box represents the median. The box plots

also indicate the outliers. The box plots are best way to represent differences

between populations without underlying any statistical assumptions and hence

fall under the family of non parametric tests. If the median value of any box

does not overlap with other boxes indicate that particular group is significantly

67

different. Please note that these plots not only give information on how the

central tendency (median) amongst categories being compared but also the

dispersion of the data about the median in each category, thus enabling

comparisons of differences in median taking into account the variability within

each category. As observational ecological data are often not of similar size

when a category such as management regime is imposed on the study area, and

there are other sources of variability within each category, box plots are the

most appropriate graphical technique to use.

I used box and whisker plots to compare estimates of species richness

and species abundance of different biodiversity components across three

management regimes. Box plots are drawn using the sampled data from each

management regime. From each sample data 25-75 quartiles are used in

drawing the box, the median is shown as thick line in the box, the whiskers

indicate 10th and 90th percentiles. Outliers are shown as dots above the

whiskers.

Scatter Plots and Regressions

I assumed that variability in biodiversity across the landscape is likely to be

influenced by variability in the forest vegetation (an ecological variable) and

disturbance (an artifact of management regime). NDVI is a good surrogate for

quantifying forest vegetation variability across a large landscape

(Krishnaswamy et al. 2006). I used scatter plots with simple linear regression to

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investigate relationship between vegetation index (NDVI) and estimates of

species richness and abundance of various biodiversity components (plants,

birds and mammals). I also used scatter plots with simple linear fits to explore

the relationship between human disturbance (HDI) and estimates of species

richness and abundance of various biodiversity components. These regression

plots are highly informative and do not involve use of complex mathematics or

statistics for interpretations, and are methods that could be easily replicated by

park managers.

I examined the values of coefficient of determination (r2) at 85%

significance level (p < 0.15) to evaluate the goodness of fit to the linear

regression data (Zar 1996).

Partial Mantel Tests

Variability in biodiversity across the landscape (without considering artificial

boundaries of management regimes) will be influenced by variability in both

forest vegetation and human disturbance. In order to assess the independent role

of disturbance on biodiversity, it is essential to first account for the influence of

forest vegetation type. Furthermore, as there is likely to be spatial auto-

correlation in variables across the landscape, the data points cannot be

considered independent and therefore the use of ordinary multiple regressions is

inappropriate in my study. I therefore adopted the partial Mantel’s correlation

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as an appropriate technique as it uses a non-parametric approach to assess p-

values.

The basic question answered by the Mantel’s test is: Is the variability in

biodiversity explained by variability in HDI after accounting for the influence

of NDVI? A dissimilarity matrix using all transects is generated for each of the

independent covariates, NDVI and HDI.

I used partial Mantel tests to examine if dissimilarity in species

composition (based on Bray-Curtis compositional distances), richness or

abundance across transects can be explained by dissimilarity in covariates such

as disturbance (HDI) and NDVI. Simple Mantel tests allow examining the

effect of only one explanatory variable at a time on a given response variable.

Since I was primarily interested in investigating the influence of human

disturbance (often a consequence of a particular type of management regime)

on a given response variable (e.g., abundance of a species or a guild) after

accounting for the influence of habitat type or vegetation cover (e. g., NDVI), I

used partial Mantel tests (Mantel 1967), which is an extension of Mantel test, to

assess the influence of multiple predictor variables (Burgman 1987, Legendre

and Fortin 1989, Goslee and Urban 2007).