An analysis of the
habitat of the Greater One-horned Rhinoceros Rhinoceros unicornis(Mammalia: Perissodactyla: Rhinocerotidae) at the Chitwan National Park, Nepal
Vivek Thapa 1,
Miguel F. Acevedo 2 & Kul P. Limbu 3
1 Institute of Applied Sciences,
Department of Biological Sciences, University of North Texas, P.O. Box 310559, Denton, TX 76203 USA
2 Departments of Electrical
Engineering and Geography, University of North Texas, 3940 North Elm Street,
Denton, TX 76207 USA
3 Department of Zoology, Post
Graduate Campus, Tribhuvan University, PO Box: 137, Biratnagar,
Nepal
1 thapaviv@yahoo.com
(corresponding author), 2 miguel.acevedo@unt.edu, 3 limbukp@gmail.com
Abstract: We used Geographic
Information Systems (GIS) and landscape-level data obtained from remote sensing
sources to build a habitat suitability index model (HSI) for the Greater
One-horned Rhinoceros Rhinoceros unicornis. The model was based primarily on
important habitat requisites of the modeled species, especially food and
cover. We extracted food and cover
from land cover map and ran focal statistics to determine their proportion in a
neighborhood of 70x70 pixels that accounts for the animal’s average mean annual
home range, which is ~4km2 = 400 ha = 70x70 pixels x 900 m2= 4410000/10000 = 441 ha. We used
two arbitrarily selected parameters a and Tc to observe the
impacts of food and cover on the HSI. We performed sensitivity analysis by varying values of parameters around
their nominal, which revealed that the HSI value of a pixel is changed with
uncertainty with very low values of a fraction of the food or cover. We identified four habitat types from
the HSI map. We used patch and
class metrics of FRAGSTATS program to estimate the amount and fragmentation of
each habitat type. The metrics
provided composition and configuration status for all four habitat types. We
found a presence of a total of 476 patches with 517.82km2 belonging
to suitable habitat type. These
areas can be targeted for management, monitoring and improvement to provide
habitat for the target and sympatric species.
Keywords: Chitwan National Park, GIS, Greater One-horned Rhinoceros,
habitat fragmentation, habitat suitability index model, remote sensing.
doi: http://dx.doi.org/10.11609/JoTT.o3698.6313-25
Editor: Kees Rookmaaker, Rhino Resource Center, United
Kingdom. Date
of publication: 26 September 2014 (online & print)
Manuscript details: Ms # o3698 | Received 14
August 2013 | Final received 03 September 2014 | Finally accepted 08 September
2014
Citation: Thapa, V., M.F. Acevedo &
K.P. Limbu (2014). An analysis of the habitat of the Greater One-horned
Rhinoceros Rhinoceros unicornis (Mammalia: Perissodactyla:
Rhinocerotidae) at the Chitwan National Park, Nepal. Journal of Threatened
Taxa 6(10): 6313–6325; http://dx.doi.org/10.11609/JoTT.o3698.6313-25
Copyright: © Thapa et al. 2014. Creative Commons
Attribution 4.0 International License. JoTT allows unrestricted use of this
article in any medium, reproduction and distribution by providing adequate
credit to the authors and the source of publication.
Funding: U.S. National Science
Foundation (NSF) for partial funding for this study under the grant CNH
BCS-0216722.
Competing Interest: The authors declare no
competing interests.
Author Contribution: VT designed, proposed and conducted entire research and performed GIS and
remote sensing work. He wrote the paper with intermittent assistance from
Miguel Acevdo. MFA was graduate professor for Vivek Thapa at University of North Texas. He supervised all work of the study and the paper and performed
sensitivity analysis. He edited some sections of the paper. KPL was heavily
involved in the field work at Chitwan National Park, Nepal.
Author Details: Vivek Thapa - an
independent researcher and not affiliated with any organizations. Currently, he
is writing proposal to Texas Parks and Wildlife Department to conduct habitat
analysis and population demography of White-tailed Deer in Hagerman National
Wildlife Refuge, Texas, USA. He aims to use GIS, remote sensing, FRAGSTATS and
statistical analysis to conduct aforementioned project. Miguel
F. Acevedo - in addition to his departmental affiliations he is Faculty
in the Graduate Program in Environmental Sciences, UNT. His work integrates
environmental monitoring and modeling to understand the dynamics of
environmental and ecological systems, and to provide socially relevant results
concerning pollutants, land use change and climate variability. Kul Prasad Limbu - affiliated with
Tribhuvan University, currently, he is involved in venomous snake-bite
epidemiology and their taxonomy. His primary research interests include
wildlife and conservation biology.
Acknowledgements: We thank U.S. National
Science Foundation (NSF) for partial funding for this study under the grant CNH
BCS-0216722. We acknowledge the assistance of several individuals such as the
staff of National Trust for Nature Conservation (formerly known as King
Mahendra Trust for nature Conservation), especially Mr. P. Khanal and the
guides who offered the first author (V.T.) immense help during the adventurous
field trip in the year of 2004–2005. The assistance of Dr. J. Kennedy and
Dr. E. Zimmerman during the project was crucial. We thank O. Sanchez, Editor,
College of Engineering, University of North Texas, for constructive and
extensive editorial comments.
For figures, images, tables -- click here
Introduction
The
Greater One-horned Rhinoceros Rhinoceros unicornis is a Vulnerable
species (Talukdar et al. 2008) (Image 1). They were once widely distributed throughout the Brahmaputra, Indus, and
Ganges plains of South Asia, but indiscriminate poaching and unprecedented
habitat loss nearly pushed them to extinction (Laurie 1982; Cohn 1988;
Dinerstein & Price 1991; Jnawali & Wegge 1993; Dinerstein 2003; Harini
et al. 2008). They are now
restricted to small isolated populations on the Indian subcontinent, mainly in
India and Nepal (Laurie 1982; Rookmaaker 1984; Dinerstein & McCracken 1990;
Dinerstein & Price 1991). In
Nepal, Dinerstein & Price (1991) recognized four distinct subpopulations in
Chitwan National Park (CNP hereafter), based on their isolation. They used physical barriers such as
rivers and low mountains and ecological boundaries such as sal forests and
agricultural lands to identify the four subpopulations of the Sauraha (1), the
Bandarjhola-Narayani River (2), the west (3) and the south (4) (Fig. 1). They also reported frequent movement of
animals from far-east of the park boundary that was initially thought as a
separate population by Laurie (1982). They combined this population with the Sauraha, which historically had
the highest population of rhinos and was most intensively studied due to easier
access from major cities such as Narayanghat and Kathmandu, better
accommodations (the park elephants, hotels) and communications than the rest of
the park (Mishra 1982a; Lekhmul 1989).
Dinerstein
(2003) reported a small mean annual home range of breeding male and female to
be 4.3km2 and 3.5km2 respectively. The reason for such a small home range
is the presence of prime habitats and relatively less competition from
sympatric species such as hog deer and the absence of swamp deer in CNP as
compared to Bardia National Park (Jnawali & Wegge 1993; Jnawali 1995; Odden
et al. 2005; Wegge et al. 2006). The prime habitats include a mosaic of grasslands, oxbow lakes, Sal
forests, and alluvial floodplains. However, most research reports indicate the importance of grassland
habitats mixed with wallows and some cover for their survival. Nonetheless, rhinos do travel far if the
habitat fails to provide enough food and water (Jnawali & Wegge 1993;
Jnawali 1995). The existing
literature on monitoring rhino habitats of Chitwan Valley is scarce since most
papers on rhino focus on biological and ecological aspects of the species
(Laurie 1978; Owen-Smith 1988; Lott & McCoy 1995; Dinerstein 2003). Satellite remote sensing and GIS
(Geographic Information Systems) offer an opportunity to contribute knowledge
about the habitat. These
technologies are increasingly used to model spatial information about wildlife
habitat (Porwal et al. 1996; Sharma et al. 2004). In addition, remote sensing coupled with
field work and GIS has proven effective in deriving much needed data for
habitat monitoring, conservation and management and such data is lacking for
Greater One-horned Rhinoceros (Laperriere et al. 1980; Porwal & Roy 1996;
Innes & Koch 1998; Berlanga-Robles & Ruiz-Luna 2002; Dinerstein 2003).
Satellite
images represent important data sources to develop vegetation maps for large
areas. Particularly, Landsat 7
Enhanced Thematic Mapper-Plus (ETM+) images have been proven useful and
cost-effective for large-scale habitat analysis (McClain & Porter
2000). A vegetation map derived
from remote sensing can be processed to determine habitat suitability by using
habitat evaluation procedures (HEP) leading to the development of a map of
habitat suitability index (HSI) (Allen 1982). HEP involves collection of information
on behavior, food habits, mating season, taxonomy and the animal’s position in
the trophic niche of a target species in order to evaluate its habitat (Porwal
& Roy 1996; Sharma et al. 2004). The resulting HSI map can aid in the understanding and management of habitat
for this species. It could be used
to locate, target and manage areas of suitable habitats and therefore support
conservation and restoration program in CNP. In addition, HSI maps can be further
analyzed to determine spatial structure, particularly fragmentation, of
suitable habitat. The FRAGSTATS
program (McGarigal & Marks 1994) offers the capabilities to calculate
several metrics related to spatial structure and fragmentation.
The
major objective of this paper is to determine the amount and spatial structure
of the remaining rhino habitat in CNP. Our research consisted of three major components: (1) developing a
land-cover map emphasizing the vegetation of the CNP by using a Landsat ETM+
image, (2) developing HEP to generate a HSI map, and (3) determining the
spatial distribution of the current suitable habitat area for rhinoceroses via
fragmentation metrics.
METHODS
Study Area
CNP
is located in the lowlands of Nepal along the northern border of India at an
elevation of 110–850 m and
covers 932km2 of park area (Fig. 1) and 750km2 of buffer
zone in the Chitwan and Makawanpur districts (DNPWC 2009). The buffer zone (community forest) is
managed by the locals and is mainly used for fuel and fodder collection. In addition, it is home to several
rhinos and other wildlife. Although
the area is almost flat, the terrain consists of some depressions and Churia
hills at an elevation of ~300m. This hill, located in between Rapti River to the north and Reu River to
the south, forms a physical barrier to the sub populations of rhinos residing
on their banks. Geographically, the
park lies from 83041’–83049’E longitudes and from
27001’–27041’N. CNP’s bordered in the northeast,
northwest and west are Rapti and Naryani rivers which in turn are bordered by privately
owned land used primarily for agriculture. On the east, it is bordered by Parsa National Park. Climate is subtropical, with
temperatures rising to approximately 370C on a typical summer
day. Mean annual precipitation is
2400mm with 90% of it falling during the period from May to September.
Vegetation
consists of deciduous, mixed, and riverine forests punctuated by grassland
communities. Sal forests are a
prominent forest type, covering 70% of the park area dominated by trees of Shorea
robusta and occur on upland, well-drained slopes rarely used by the
rhinoceroses but frequented by wild elephants (Laurie 1982). The riverine forest association is
composed of Trewia nudiflora, Bombax ceiba, Acacia catechu,
and Dalbergia sisoo frequently used by the rhinoceros and they seek out
fruits of T. nudiflora during summer (Lekhmul 1989; Dinerstein &
Price 1991; Jnawali & Wegge 1993; Dinerstein 2003; Rawat 2005; Subedi
2012).
The
physiographic distribution of tall grasslands is also noteworthy. The grassland habitat associations
included either monospecific stands of tall grass species of Saccharum
spontaneum (4–6 m); S. benghalensis, Narenga porphyrocoma, andThemeda arundinacea (5–7 m) or mixed with short grass
species of Imperata cylindrica, Chrysopogon aciculatus, Eragrostis spp.
and several others (Lekhmul 1989; Dinerstein 1989; Dinerstein & Price 1991;
Dinerstein 2003). The short grasses
usually occur within tall grasslands and are intensively grazed by the rhinos. According to Dinerstein (2003), the
aforementioned grass species occupied approximately 15% of the total park area
and occurred on the higher terraces of the floodplain.
Another
tall grass species, S. spontaneum, is the first to colonize the major
river banks after the retreat of annual monsoon floods. It often occurs in pure stands and its
thickness ranges from less than 100m to more than 1km in width and this type of
grassland accounts for only 5% of the total park area (Dinerstein 2003). Among the grassland types, the
rhinoceros seeks out S. spontaneum as it is the most nutritious of
available tall grass species of Chitwan Valley (Laurie 1982; Lekhmul 1988;
Dinerstein & Price 1991; Dinerstein 2003; Subedi 2012).
Satellite Data Selection and Processing
We
selected one ETM+ image for analysis based on criteria of desired geographic
coverage, cloud-free conditions, and date (for seasonal considerations). Single-date scene (path 142/ row 41,
collected in 12 April 2003) from early summer or pre-monsoon (mid-February to
mid-June) was considered optimal because the deciduous trees are leafless
enhancing the chances of discriminating deciduous from evergreen forests
(Harini & Gadgil 1999). The
ETM+ images provide multispectral coverage for seven spectral bands in the
visible (TM1, TM2, TM3), near-infrared (NIR TM4), mid infrared (MIR TM5, TM7)
and thermal portions (TM 6) of the electromagnetic spectrum. Vegetation pixels are pronounced in TM4
(0.7–0.9 µm). The spatial
resolution of these bands is 30m except for the thermal which is 60m. Because of this coarse resolution, we
excluded thermal band from analysis. The image also provides a panchromatic spectral band with better spatial
resolution (15m), which is commonly used for producing quality fusion imagery
to obtain richer information in the spatial and spectral domain (Choi et al.
2005).
We
geo-referenced the image using ground control points (GCPs) collected from
topographic maps of 1:25000. We
used locations of park headquarters, small towns, hotels, army posts and
naturally visible features such as lakes to collect GCPs in order to reduce
geometric and location distortion in the image (Hardison 2003). We used the
image as a guide map during our field work in 2004 and 2005. In 2004, we collected more than 800
ground truth reference points using global positioning systems (GPS). Although there is no standard minimum
distance between ground truth points, we separated points by at least 150m in
order to avoid overlap. In 2005, we
made another trip to collect additional GPS points (20) of confused pixels (to
be explained with detail in the results section). Each point was accompanied with notes of
vegetation and soil type, moisture regime and elevation. We used half of the points to conduct
supervised classification of eight land cover types, by employing the maximum
likelihood algorithm (Harini & Gadgil 1999). The eight land cover types were water,
sand, grassland, agriculture, wetland, settlements, mixed and sal forest (Fig.
2). Our clipped image contained
some agricultural lands, settlements and buffer zone adjacent to CNP. Therefore, we defined a separate class
as agriculture and settlement as rhinos do venture out to nearby fields
especially during night and sometimes become a source of wildlife-people
conflict (Nepal & Weber 1995; Studsrod & Wegge 1995).
We
used the other half of the ground-truth points to assess classification
accuracy (Table 1). Generally,
random points are used as a reference class to assess accuracy. However, in our case, we used a set of
the ground-truth points as a reference class and compared with classified image
class values to evaluate accuracy. This set of ground-truth points employed for evaluation was different
from the set employed for classification. We derived error matrix that provide several statistics including
overall accuracy, omission and commission errors and kappa statistics or KHAT
(measures agreement between classified and reference data). KHAT = 1 if agreement is 100%.
Habitat Suitability Index (HSI) and FRAGSTATS
According
to Dettki et al. (2003), two approaches are used to assess wildlife habitat
relationships: process and empirical. Process-oriented models assess plausible causal relationships or
functional processes underlying habitat use and provide a more general
conceptual framework. In contrast,
empirical models analyze data on habitat use and habitat characteristics
collected at specific sites.
Process-oriented
HSI models use habitat requisites or parameters such as food, cover, and
proximity to water as input variables to a function providing a dimensionless
0.0–1.0 index, where 0 and 1 indicates unsuitable and optimum habitat
conditions respectively (Mitchell et al. 2002; Dettki et al. 2003). This function is determined by
assumptions on how each one of the habitat requisites and their combination
affect suitability and evaluate aptness of a habitat (Porwal et al. 1996;
Dettki et al. 2003). We adopted a
process-oriented approach to develop a heuristic HSI model for the Greater
One-horned Rhinoceros inspired by moose habitat analysis of Dettki et al.
(2003). Even though both species
differ in habitat use, the model is applicable to any wildlife as it simply
requires specific habitat use parameters. To estimate these parameters, we derived detailed rhino habitat use
information from several published papers and field observations (Laurie 1982;
Dinerstein & Wemmer 1988; Lekhmul 1988; Owen- Smith 1988; Dinerstein &
Price 1991; Jnawali & Wegge 1993; Dinerstein 2003; Subedi 2012). We made two major assumptions while
constructing the HSI model for the rhino. First, we assumed that existing information on habitat use by the animal
can be translated to data related to model parameter values. Second, we assumed that food and cover
requirements are more important than water requirement because water is
available year round.
We
used presence of grass, and forest in a neighborhood of 70x70 grid of a target
pixel as input variables to calculate the HSI value for that pixel. The neighborhood size was determined
according to the animal’s average mean annual home range, which is ~4km2= 400 ha = 70x70 pixels x 900 m2 = 4410000/10000 = 441 ha. We selected focal over block statistics
to calculate the HSI value of each pixel. Focal functions assign a value for each processing cell and allow
overlap while block statistics do not allow overlap (all cells within the block
receive identical value). We
extracted food (grass and agriculture) and cover (sal forest and mixed forest)
from the land cover map and ran focal statistics to calculate proportion of
food and cover in a neighborhood, denoted by xf and xc respectively.
These were then used in the following way.
We
define the suitability Sf and Sc for food and cover as
xf / Tf when xf < Tf
Sf = {
1 when xf ≥ Tf
and
xc / Tc when c < Tc
Sc = {
1 when c ≥ Tc
whereTf and Tc are parameters defining a threshold of each factor to
obtain maximum suitability. These
threshold parameters are determined in the following manner.
For
food, a large rhino eats between 60–80 kg a day fresh weight and spends
most of its time browsing or grazing (Dinerstein 2003). In addition, the food primarily consists
of wild sugarcane Saccharum species. According to Coombs & Vlitos
(1978) estimation, the standing biomass of sugarcane is 100 MT/ha fresh weight
or 35 MT/ha dry weight, which gives a fresh/dry ratio of 100/35. So, one rhino eats approximately
21–28 kg/day of dry weight. Assuming the mid value of this range, 24.5 kg/day, the annual food
requirement of one animal is 24.5 kg/day x 365 day/year = 8.9 Tons/year.
Furthermore,
Dinerstein (2003) calculated 0.34 kg/ m2 dry weight of Saccharumin Chitwan. Thus in the home range of 400ha, we would have a total of 3400 kg/ha
x 400 ha = 1,360 Tons of dry food if it is covered entirely by grass. According to Dinerstein & Price
(1991) about 39 animals have been reported to use 3.2km2 (320ha)
area of prime habitats mostly composed of Saccharum and riverine forests. Therefore, we assume 40 animals would
use the 400ha of home range area. Then, 8.9 tons/year per animal x 40 animals = 356 Tons of food per year
which represents a fraction Tf = 356 /1360 = 0.26. However, there are no data available to
estimate parameter Tc. We
assumed that 12ha out of 400ha would be sufficient cover, and therefore Tc =
12/400= 0.03. Because of its
uncertainty we will use sensitivity analysis as described below.
Sensitivity Analysis
The
impact of cover on habitat suitability must take into account its seasonal
importance. During monsoon season
(4 months), we factor in the need for cover by using a geometric mean of food Sfand cover Sc suitability with a weight factor a. However, during the rest of the year (8
months), the animals do not need cover and food suitability accounts for
HSI. The final HSI model is then a
weighted arithmetic mean of the monsoon and off-monsoon suitability.
4 8
HSI = - — ( Sfa x Sc(1-a) )+ — Sf
12 12
For
parameter a, we estimated that food is much more important than cover
and assigned a value of a=0.8 (4 times more important). In order to study the potential error that
the arbitrary selections of a and Tc may produce, we conducted
sensitivity analysis by varying a and Tc by ±20% around their
nominal values (a=0.8, Tc= 0.03).
Figure
3 shows the effect of varying parameter a. As expected, for values of xf, xcaround or exceeding the corresponding threshold values the HSI is insensitive
to changes in a. Only for
very low values of fraction of food the HSI is more sensitive to a,
reaching values of up to 25–30 % change in HSI. Figure 4 shows the effect of varying
parameter Tc. As before, for values of xf, xc exceeding
the corresponding threshold the HSI is insensitive to changes in Tc. Only for very low values of fraction of
cover the HSI is more sensitive to Tc and represents less than 1–2
% change. Therefore, the uncertainty
with respect to Tc is very small.
From
this sensitivity analysis we conclude that the HSI value of a pixel could
change with uncertainty in parameter a for very low values of fraction
of food. Moreover, it will not vary
due to uncertainty in this parameter as long as the neighborhood around that
pixel has fractions of food larger than the threshold Tf=0.26 or 10ha in
400ha. Therefore, the HSI map will
only be relatively uncertain for outside or edge grassland patches. This uncertainty is further reduced by
the following classification of pixels into four classes (as explained below) -
highly unsuitable, unsuitable, moderately suitable and suitable habitat types.
Since all areas of low HSI values (which are relatively uncertain values) will
be classified as either highly unsuitable, unsuitable or moderately
suitable. The resultant HSI map
pixels had values ranging from 0 to 1. We needed to group the values between 0 and 1 into different classes
that represented different habitat types and that in turn reflected habitat
conditions. For example, pixels
with low values such as 0.01, 0.10, 0.14, 0.23 etc reflected poor habitat
conditions (these pixels either had less cover or food or vice versa) as
compared to pixel values of 0.5, 0.8 and so on. Thus, higher pixel values indicated
favorable habitat conditions. And
to assign the pixels into different habitat types, we selected natural breaks
(Jenks) as classification method in ArcMap and derived four classes as shown in
Fig. 5.
We
selected this method primarily because we wanted to find the breaks (high
jumps) in the pixel data values of 0 and 1. The break serves as a boundary while
delineating classes. For example,
pixels with values from 0–0.22 were grouped in one class and pixels with
values from 0.22–0.48 were grouped in another class and so forth. In this case, the boundary lies at
pixels with 0.22 values. In order
to use these classes in FRAGSTATS (decimals are not accepted), we further
assigned integer values of 1,2,3 and 4 to represent the four habitat categories
(1 = highly unsuitable, 2 = unsuitable, 3 = moderately suitable and 4 =
suitable habitat pixels) (Fig. 5). We converted HSI map into ASCII text file and calculated metrics. We carefully selected metrics relevant
to the habitat requirements of the rhinoceros and that met our objectives
(Table 2). Our primary interests
were (1) to find the size of the individual patch (2) to find the amount and
distribution of a particular patch type and (3) to find whether particular
patch types are contiguous or fragmented. And to meet our first objective, we selected AREA metric at patch
level. This is a useful metric as
many vertebrates including rhino require suitable habitat patches larger than
some minimum size. For example, the
average home range of a rhino is ~400ha. We selected CA/TA (Total Area), PLAND (Percentage of Landscape) and NP
(Number of Patches) at class level to satisfy the second objective. These metrics quantify composition of
the patches. And to meet the third objective, we selected COHESION (Patch
Cohesion Index) to find physical connectedness of particular patch type. This metric measures configuration of a
patch to its neighbors in the landscape.
RESULTS
Vegetation Classification
The
overall accuracy was about 70% with more than 80% of water, sand, sal forest
and mixed forest pixels accurately classified (Table 1). KHAT index for water and sand exhibited
more than 90% agreement between reference and classified pixels. Whereas it was less than 40% for
agriculture indicating 60% of the pixels were incorrectly classified. The classification of sand, sal, and
mixed forest was satisfactory. It
was easy to create an area of interest (AOI) for sal and sand pixels as 70% of
the park area is covered by sal forest and sand pixels were easy to select
based on the location and spectral profile. The three major rivers had sandy banks
of relative thickness running parallel to them that made easier to create
AOI. Once a group of pixels
belonging to the same class is identified, ERDAS automatically select areas
with similar pixels under the supervision of the user - a tool commonly
employed during supervised classification. On the other hand, wetland, grassland and agriculture were classified
with 75% and 50% accuracy. The
reason for low accuracy of grassland category may have resulted from confusion
of agriculture and grass pixels, a typical situation in such studies. The growth stage or the height of the
crops and the grass could have been similar when this image was taken. The agricultural land contains some of
the settlement and grassland pixels. Similarly, wetland pixels could be confused with other water
bodies. The largest wetland area is
actually the outlet of one of the major lakes, Tamar Tal (Tamar Lake). It is located closer to the confluence
of the three rivers; and when the lake is inundated during the monsoon season,
water overflows to this area. The
purpose of the second visit to the field was to confirm vegetation and location
of this area. The fieldwork
revealed a secondary succession of grassland in the outlet. While one side of
the outlet was covered with marshy vegetation, the other side flourished with
grassland. The marshy vegetation
would eventually convert into grassland that would serve as additional habitat
for the Greater One-horned Rhinoceros. The area or the lake could have overflowed when this image was taken in
2003. The spectral profiles of
these pixels also exhibit the nature of vegetation that is submerged in
water. New classification routines
are needed that can tease apart detailed reflectance patterns that are
essential to distinguish agriculture, grassland, and settlement pixels. This increases need in field
identification of these classes, and the use of higher-resolution imagery is
another promising alternative.
HSI and Habitat Fragmentation
For
One-horned Rhinoceros, food is considered to be the most important factor of
their habitat components with some seasonal cover. Thus, we assigned a value of 0.8 for
parameter a. We calculated
threshold values for food to be Tf = 0.26. And for cover threshold, we assumed 12ha
out of 400ha (mean annual home range) would be sufficient to hold one animal
and Tc = 0.03. We performed
sensitivity analysis by varying the nominal values of a and Tc by
± 20%. Interestingly, we found HSI
is only sensitive to a with low values of fractions of food (xf)
and cover (xc). In other
words, the uncertainty of parameter a is minimum if food and cover
availability is more than their respective thresholds. The small uncertainty of parameter Tcsuggested that forests are used less frequently or used seasonally but it is a
critical component of habitat requisites. The HSI map showed distinct spatial pattern, with high values of HSI
along the areas near water bodies and adjacent grasslands and edges of sal
forests with low values in the inland areas (Fig. 5).
The
metric result showed presence of 476 patches that belong to four different
habitat types (Table 3 and 4). AREA
metric revealed the largest patch (51,065.73 ha) belonged to suitable habitat
type (Table 3). The total area of
suitable habitat is 51,781.95 ha or 517.82 km2 that include some
buffer zone and agricultural areas outside the park boundary. Moreover, the lowest number of patches
(NP = 18) and highest value of connectivity metrics (99.84) for suitable
habitat to support its homogeneity across the landscape especially along the
river boundaries (Table 4). This
indicates that rhinos inhabiting habitats near the three major rivers can
travel undeterred using river banks as a migrating route. This is corroborated by the findings of
Dinerstein & Price (1991) that discovered frequent rhino movement from
far-east to the Sauraha subpopulation. However, resident rhinos of Reu and Rapti may be permanently separated
by contiguous blocks of highly unsuitable habitat of sal forest and so are the
subpopulation of the Bandarjhola- Narayani River and the Sauraha by extensive
blocks of agriculture and settlements. The rhinos can use river banks but previous works have indicated that
they do not travel far if prime habitats are nearby, i.e., abundant productive
floodplains. Other metrics such as
PLAND indicated highly unsuitable habitat (mostly sal forests) occupied the
largest percentage of the landscape (39%) followed by suitable habitat patches
(36%). Moderately suitable habitat
patches are the most fragmented and the least in numbers as indicated by NP and
COHESION metrics and occurred adjacent to the suitable habitat patches (Table
4).
DISCUSSION
Vegetation classification
The most valuable
data—land-cover types, soil profile, plant composition, and density of
canopy cover—were determined by the field study. Field verification of land cover types proved
to be the most effective method and best suited to using a GPS unit as the
device of choice. Ground-truth data
along with pictures of field activities compensated the paucity of previously
tested data such as aerial photos and assisted in classifying the vegetation
types represented in CNP. We used
the supervised method to classify the image into eight land cover classes -
water, sand, settlement, wetland, grassland, agriculture, mixed and sal forest. We employed a new technique to assess
classification accuracy. Generally,
random points created in ERDAS or aerial photos or previous data are used to
assess the accuracy of a thematic map (Anderson et al. 1976; Congalton &
Green 1999; Salovaara et al. 2005). For this project, we used ground-truth points to classify, test and
enhance accuracy. The overall
classification accuracy was 70%. Most categories including water, sand, sal and mixed forest, and wetland
were classified with more than 80% accuracy. The low accuracy value for grassland and
agriculture could be attributed to the confusion of their pixels. Depending on growth stage and season,
agricultural pixels are easily mistaken for grass and it is a common and
persisting problem in remote sensing. Moreover, grasslands near Tamar Lake are classified as mixed or sal
forest; this classification may be due to the tall nature of these
grasslands. With a height ranging
from 6–8 m, sometimes they grow with small trees and shrubs and can
completely overshadow them. To
achieve more accuracy for the grassland, we recommend the use of
higher-resolution imagery.
HSI model and habitat fragmentation
We
constructed two maps (food and cover) from the land cover map. We used two land
cover categories, i.e., agriculture and grassland to make food map and used sal
forest cover type to make cover map. And we ran focal neighborhood statistics to calculate the proportion of
food and cover in 70x70 cell neighborhood that represented mean annual home
range of an adult rhino. In this
way, we developed a heuristic HSI model for the Greater One-horned Rhinoceros
based on a process-oriented approach. In other words, we merely attempted to develop HSI using habitat use
parameters that were collected from extensive literature reviews of spacing
behavior, biology, and daily routine of a rhino (Laurie 1982; Dinerstein &
Wemmer 1988; Lekhmul 1988; Dinerstein 1989; Dinerstein & McCracken 1990;
Dinerstein & Price 1991; Jnawali & Wegge 1993; Dinerstein 2003). We used this information to conduct
neighborhood analysis, to select, construct and build HSI and to assign
threshold to the parameters. Our
sensitivity analysis confirmed about 0.03 cover and 0.26 of food in ~ 400ha is
sufficient to sustain a rhino crash (a group of rhinos). As noted above, this research applied
habitat use to generate an HSI model in an attempt to examine whether such HSIs
can be developed for the mega herbivore; and after conducting these procedures,
we determined that the development of the indices was feasible. However, validation of the results of
any HSIs require rigorous field study involving home range, telemetric studies
for years, and habitat use (Dettki et al. 2003). We think this method will be effective
and useful if used with data that are acquired in the manner explained above
and if such data are available we can apply these procedures to study the
habitat suitability of other large ungulates in the study region or elsewhere.
We
demonstrated that the careful selection of FRAGSTATS metrics yields interesting
and useful results. We quantified the four habitat patches with respect to
patch size, number of patches and connectivity of corresponding patch
types. This would provide much
needed data on the habitat of the rhinoceros (especially on the remaining
suitable habitat patches). COHESION
metrics revealed the contiguous nature of suitable habitat patches that is
vital to the survival of rhinos and other sympatric species. In the case of
rhinos, this means CNP landscape facilitates ecological flows, i.e., there is
constant movement of animals among habitat patches thus reducing the threat of
extinction threshold. As noted
above, the rhinos seek out the most nutritious of all grass species (Saccharum)
that are available year round. These grass species thrive on the floodplains
and are maintained by periodic monsoon floods. It would be interesting to find core
habitats, number of core habitats, edge density, diversity and several others
and FRAGSTATS produces several metrics to study these aspects of a
landscape. However, lack of fine scale
data such as edge effect on rhino population or individual or how a rhino
perceives its habitat and the actual size of the habitat etc limited our study.
We relied heavily on the studies that were conducted mostly on the ecological
and biological aspects of a group of rhinoceros, not on individuals. Laurie (1982), Dinerstein & Price
(1991), and Subedi (2012) did use conventional radio-telemetry and GPS collars
to study habitat use but they focused mainly in finding mean annual home range,
habitat preference, seasonal distribution and feeding habits. Thus, study of effect of edge on a rhino
movement is still lacking and such studies could provide vital information
about distribution and movement of a rhino within its suitable habitat and
influence on its behavior due to adjoining unsuitable habitat - an open area
for future study. Further, we
examined process-oriented approach to model the rhino habitat and found it can
be ecologically meaningful if selection of the used environmental variables is
based on the habitat requirements of the target species. And if the variables
are correctly identified, they enable us to understand the effects of changes
in the landscape on the model outcome.
Management Implications
After a decade long political unrest from 1995 to
2006, Nepal regained peace and stability in 2007 when the Maoists gave up arms
in April of 2006 (Martin et al. 2008; Martin & Martin 2010). Since then, conservation efforts resumed
and poaching was reduced drastically with only one rhino poached in 2011 and
one in 2012 (Martin et al. 2013). Currently, CNP has 503 rhinos and other strongholds such as Bardia
National Park and Shuklaphanta Wildlife Reserve saw increase as well (WWF 2012;
Martin et al. 2013). These areas,
especially CNP, also saw increase in tourist visits that in turn led to
increase in revenues (Martin et al. 2008, 2013; Martin & Martin 2010). Thus, Nepal has once again proved these
mega-vertebrates can be brought back from the brink of extinction with stable
government and relative peace. However, the increase in rhino numbers warrants
more vigorous conservation efforts in addition to existing ones. Therefore, we suggest following
recommendations:
(1) We found approximately 517.82km2 suitable habitats
available. Fragmentation metrics
such as lowest number of patches (NP = 18) and highest connectivity value of
99.84 indicates they are less fragmented and highly contiguous as compared to
other habitat types - crucial feature to reduce extinction thresholds and to
facilitate ecological energy flows. The largest patch size is 510.63km2 and is mostly located
near floodplains. Similarly, Kafley et al. (2009) reported the presence of
614km2 of suitable habitats in similar areas, which is more than
100km2 to what we found. This increase is attributed to the addition of 171km2 of
suitable Sal forest habitats (rhinos could use these areas) to 443km2that are already inhabited by rhinos (Kafley et al. 2009). We conducted our fieldwork in the
summers of 2004– 2005 and used GIS, remote sensing and GPS locations of
food and cover to build our suitability maps. Kafley et al. (2009) used GIS, remote
sensing and presence only data (obtained from rhino census of 2008) to build
and MAXENT modeling technique to validate suitability maps. Our methods varied but produced similar
results. There was an interval of
three years in the above studies. We suggest similar studies can be conducted using rhino census of 2011,
GIS and remote sensing. Further, a
regular (5 or 10 year period) multi-temporal change detection analysis using
our results as base maps, could reveal dynamism of the floodplain
habitats. Such studies could
provide continuity in monitoring of suitable and target moderately suitable
habitats for restoration.
(2)
The availability of large areas of suitable habitats indicates CNP has far
greater carrying capacity for rhinos than it currently holds. However, current studies inform that
above habitats are deteriorating at an alarming rate (Lahkar et al. 2011;
Subedi 2012). Both studies indicate
the slow and steady intrusion of exotic invasive species such as Mikania
micrantha, Mimosa diplotricha, and Chromolaena odorata may
overshadow current conservation success in rhino strongholds including
Kaziranga National Park, Assam (Lahkar et al. 2011; Subedi 2012). In CNP, the imminent impact was observed
on the habitat use as the home range increased from ~4.3km2(Dinerstein 2003) to 20.54±6.06 km2 (Subedi 2012). We recommend the use of our HSI and
vegetation maps, and habitat patch data in addition to ongoing concerted
efforts of the Zoological Society of London, Department of National Parks and
Wildlife Conservation, National Trust for Nature Conservation, and CABI, to
control aforementioned exotic species.
References
Anderson, J.F., E.E. Hardy, J.T. Roach & R.E. Witmer (1976). A land use and land cover
classification system for use with remote sensor data, U.S. Geological Survey
Professional Paper 964, U.S. Geological Survey, Washington, DC, 28pp.
Allen, A.W. (1982). Habitat suitability index models: Beaver. U.S. Dept. Int., Fish
Wildl. Servo FWS/OBS-82/10.30, 20pp.
Berlanga-Robles, C.S.A. & A. Ruiz-Luna (2002). Land use mapping and change
detection in the coastal zone of northwest Mexico using remote sensing
techniques. Journal of Coastal Research 18: 514–522.
Cohn, J.P. (1988). Halting the Rhino’s Demise. Bioscience 38: 740–744.
Congalton, G.R. & K. Green (1999). Assessing the accuracy of
Remotely Sensed Data: Principles and Practices. Lewis Publishers, CRC
Press, New York.
Choi, M., Y.K. Rae, N. Myeong-Ryong & K.H. Oh (2005). Fusion of Multispectral and
Panchromatic Satellite Images Using the Curvelet Transform. IEEE Geoscience
and remote sensing letters - 2.
Coombs, J. & A.J. Vlitos (1978). An assessment of the
potential for biological solar energy utilization using carbohydrates produced
by higher plant photosynthesis as chemical feedstock. Vol. 2. Proceedings of
International Solar Energy Society Congress, New Delhi, India, Pergamon Press,
New York.
Dettki, H., R. Lofstran & L. Edenius (2003). Modeling habitat suitability
for Moose in coastal northern Sweden: Empirical vs. Process-oriented
Approaches. Royal Swedish Academy of Sciences. Ambio 8(32):
549–556.
Dinerstein, E. (1989). The foliage-as-fruit hypothesis and the feeding behavior of South
Asian ungulates. Biotropica 21: 214-218.
Dinerstein, E. (2003). The Return of the Unicornis - The Natural History and
Conservation of the Greater One-horned Rhinoceros. Biology and Resource
Management Series. World Wildlife Fund, Washington D.C., 320pp.
Dinerstein, E. & C. Wemmer (1988). Fruits Rhinoceros eat:
dispersal of Trewia nudiflora in lowland Nepal. Ecology 69:
1768–1774.
Dinerstein, E. & G.F. McCracken (1990). Endangered Greater
one-horned Rhinoceros carry high levels of genetic variability. Conservation
Biology 4: 417–422; http://dx.doi.org/10.1111/j.1523-1739.1990.tb00316.x
Dinerstein, E. & L. Price (1991). Demography and Habitat use
by Greater One-horned Rhinoceros in Nepal. The Journal of Wildlife
Management 55(3): 401–411.
Hardison, T. (2003). Application of remote sensing and GIS to modeling fire for
vegetative restoration in northern Arizona. MS Thesis. Department of Environmental
Science and Geography, University of North Texas, Denton, 57pp.
Harini, N. & M. Gadgil (1999). Biodiversity assessment at
multiple scales: Linking remotely sensed data with field information. Ecology96: 9154–9158.
Harini, N., S. Pareeth, B. Sharma, C.M. Schweik & K.R.
Adhikari (2008). Forest fragmentation and regrowth in an institutional mosaic of
community, government and private ownership in Nepal. Landscape Ecology23: 41–54; http://dx.doi.org/10.1007/s10980-007-9162-y
Innes, J.L. & B. Koch (1998). Forest biodiversity and its assessment by
remote sensing. Global Ecology and Biogeography Letters 7:
397–419; http://dx.doi.org/10.1046/j.1466-822X.1998.00314.x
IUCN (2012). IUCN Red List of Threatened Species. <www.iucnredlist.org>
Downloaded on 11 June 2013.
Jnawali, S.R. & P.W. Wegge (1993). Space and habitat use by a
small reintroduced population of Greater One-horned Rhinoceros (Rhinoceros
unicornis) in Royal Bardia National Park in Nepal - a preliminary report,
pp. 208–217. In: Ryder, A.O. (ed.). Rhinoceros Biology and
Conservation. Proceedings of an international conference. Zoological
Society of San Diego, San Diego, CA.
Jnawali, S.R. (1995). Population ecology of Greater One-horned Rhinoceros (Rhinoceros
unicornis) with particular emphasis on habitat preference, food ecology and
ranging behavior of a reintroduced population in Royal Bardia National Park in
lowland Nepal. PhD Thesis. Agricultural University of Norway.
Kafley, H, M. Khadka & M. Sharma (2009). Habitat Evaluation and
Suitability Modeling of Rhinoceros unicornis in Chitwan National Park, Nepal: A
Geospatial Approach. XIII World Forestry Congress. Buenos Aires, Argentina.
Lahkar, P.B., B.K. Talukdar & P. Sharma (2011). Invasive species in grassland
habitat: an ecological threat to the Greater One-horned Rhino (Rhinoceros
unicornis). Pachyderm 49: 33–39.
Laperriere, J., P. C. Lent, W.C. Gassaway & F.A. Nodler (1980). Use of landsat data for
moose habitat analyses in Alaska. Journal of Wildlife Management 44:
881–887.
Laurie, A. (1982). Behavioral ecology of the Greater One-horned Rhinoceros (Rhinoceros unicornis). Journal of Zoology 196: 307–341; http://dx.doi.org/10.1111/j.1469-7998.1982.tb03506.x
Lekhmul, J. (1989). The Ecology of South Asian Tall-grass Community. PhD Thesis.
University of Washington, Seattle, 214pp.
Lott, D.F. & M. McCoy (1995). Asian Rhinos Rhinoceros
unicornis on the run - impact of tourist visits on one population. Biological
Conservation 73: 23–26; http://dx.doi.org/10.1016/0006-3207(95)90053-5
Martin, E. & C. Martin (2010). Enhanced community support
reduces rhino poaching in Nepal. Pachyderm 48: 48-56.
Martin, E., C. Martin & L. Vigne (2008). Recent political
disturbances in Nepal threaten rhinos: lessons to be learned. Pachyderm45: 98–107.
Martin, E., C. Martin & L. Vigne (2013). Successful reduction in rhino
poaching in Nepal. Pachyderm 54: 66–73.
McClain, B.J. & W.F. Porter (2000). Using satellite imagery to
assess large-scale habitat characteristics of Adirondack Park, New York, USA. Environmental
Management 26: 553–561.
McGarigal, K. & B.J. Marks (1994). FRAGSTATS: spatial pattern
analysis program for quantifying landscape structure. United States Department
of Agriculture.
Mishra, H.R. (1982a). The Ecology and Behaviour of Chital (Axis axis) in the
Royal Chitwan National Park, Nepal. PhD Thesis. University of Edinburgh,
240pp.
Mitchell, M.S., J.W. Zimmerman & R.A. Powell (2002). Test of habitat suitability
index for black bears in the southern Appalachians. Wildlife Society
Bulletin 30: 794–808.
Nepal, S.K. & K.E. Weber (1995). The quandary of local
people-park relations in Nepal’s Royal Chitwan National Park. Environmental
Management 19: 853–866.
Odden, M., P. Wegge & T. Storaas (2005). Hog Deer (Axis porcinus)
need threatened tallgrass floodplains: a study of habitat selection in lowland
Nepal. Animal Conservation 8: 99–104; http://dx.doi.org/10.1017/S1367943004001854
Owen-Smith, R.N. (1988). Megaherbivores: The Influence of Very Large Body Size on
Ecology. Cambridge University Press, 364pp.
Porwal, M.C., P.S. Roy & V. Chellamuthu (1996). Wildlife habitat analysis
for ‘Sambar’ (Cervus unicolor) in Kanha National Park using remote
sensing. International Journal of Remote Sensing 17: 2683–2697; http://dx.doi.org/10.1080/01431169608949100
Rawat, G.S. (2005). Vegetation dynamics and management of Rhinoceros habitat in Duars
of West Bengal: an ecological review. National Academy Science Letters -
India 28: 177–184.
Rookmaaker, L.C. (1984). The former distribution of the Indian Rhinoceros (Rhinoceros
unicornis) in India and Pakistan. Journal of the Bombay Natural History
Society 80(3): 555–563.
Salovaara, K.J., S. Thessler, R.N. Malik & H. Tuomisto (2005). Classification of Amazonian
primary rain forest vegetation using Landsat ETM plus satellite imagery. Remote
Sensing of Environment 97: 39–51; http://dx.doi.org/10.1016/j.rse.2005.04.013
Sharma, B.D., J. Clevers, R.D. Graaf & N.R. Chapagain (2004). Mapping Equus kiang(Tibetan Wild Ass) Habitat in Surkhang, Upper Mustang, Nepal. Mountain
Research and Development 24: 149–156; http://dx.doi.org/10.1659/0276-4741(2004)024[0149:MEKTWA]2.0.CO;2
Studsrod, J.E. & P. Wegge (1995). Park-people relationships -
the case of damage caused by park animals around the Royal-Bardia-National-Park,
Nepal. Environmental Conservation 22: 133–142; http://dx.doi.org/10.1017/S0376892900010183
Subedi, N. (2012). Effect of Mikania micrantha on the demography, habitat
use, and nutrition of Greater One-horned Rhinoceros in Chitwan National Park,
Nepal. PhD Thesis. Forest Research Institute University, Dehradun, Uttarakhand,
209pp.
Talukdar, B.K., R. Emslie, S.S. Bist, A. Choudhury, S. Ellis, B.S.
Bonal, M.C. Malakar, B.N. Talukdar & M. Barua (2008). Rhinoceros unicornis. The IUCN Red List of
Threatened Species. Version 2014.2. <www.iucnredlist.org>. Downloaded on
16 September 2014.
Wegge, P., A.K. Shrestha & S.R. Moe (2006). Dry season diets of sympatric
ungulates in lowland Nepal: competition and facilitation in alluvial tall
grasslands. Journal of Ecological Restoration 21: 698–706; http://dx.doi.org/10.1007/s11284-006-0177-7
WWF (2012). Nepal rhino census shows increase. Available from http://wwf.panda.org/?200112/Collective-conservation-efforts-boosted- rhino-population-in-Nepal