Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 August 2020 | 12(11): 16478–16493
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
doi: https://doi.org/10.11609/jott.5590.12.11.16478-16493
#5590 | Received 27 November 2019 | Final
received 28 July 2020 | Finally accepted 05 August 2020
Spatial aggregation and
specificity of incidents with wildlife make tea plantations in southern India
potential buffers with protected areas
Tamanna Kalam 1,
Tejesvini A. Puttaveeraswamy 2, Rajeev K. Srivastava 3 , Jean-Philippe
Puyravaud 4 & Priya
Davidar 5
1,2,5 Department of Ecology and
Environmental Sciences, Pondicherry University, Kalapet, Pondicherry 605014,
India.
3 Former Field Director, Mudumalai
Tiger Reserve, Nilgiris, Tamil Nadu 643223 India.
4,5 Sigur Nature Trust, Chadapatti,
Masinagudi PO, Nilgiris, Tamil Nadu 643223, India.
1 Current address: Samudra Dugar,
Apartment 1A, Raja Rangasamy Avenue, Off 4th seaward Road, Valmiki Nagar,
Thiruvanmiyur, Chennai, Tamil Nadu 600041, India.
2 Current address: Nele, 9th Cross,
J.P. Nagar, 7th Phase, Bengaluru, Karnataka 560078, India.
3 Current address: C-1403, TAISHA,
Nateshan Nagar West, Virigambakkam, Chennai, Tamil Nadu 600092, India.
1 tamannakalam5@gmail.com
(corresponding author), 2 tejumath@gmail.com, 3 srivastavaraj3@yahoo.com,
4 jp.puyravaud@gmail.com, 5 pdavidar@gmail.com
Editor: L.A.K. Singh, Bhubaneswar,
Odisha, India. Date of publication: 26 August
2020 (online & print)
Citation: Kalam, T., T.A.
Puttaveerasawamy, R.K. Srivastava, J.P. Puyravaud & P. Davidar (2020). Spatial aggregation and
specificity of incidents with wildlife make tea plantations in southern India
potential buffers with protected areas. Journal of Threatened Taxa 12(11): 16478–16493. https://doi.org/10.11609/jott.5590.12.11.16478-16493
Copyright: © Kalam et al. 2020. Creative Commons Attribution
4.0 International License. JoTT allows
unrestricted use, reproduction, and distribution of this article in any medium
by providing adequate credit to the author(s) and the source of publication.
Funding: This study was part of the project
“Elephant habitats of the Nilgiri Biosphere Reserve: location, threats and
management” (Grant 96200-1-G016) supported by the Asian Elephant Conservation
Fund of the United States Fish and Wildlife Service.
Competing interests: The authors declare no competing
interests.
For Author details &
Author contribution see end of this article.
Acknowledgements: We would like to express our
sincere gratitude for the support we received from the late Mr. D. Hegde,
former chairman, UPASI, and his colleagues. We are especially grateful to Mr.
Hegde who showed keen interest and enthusiasm towards our study and was very
supportive of it. We also thank the plantation managers and staff for
participating in our study and for sharing their data. We thank Dr. R. Singh,
members of the Coffee Board of India-Gudalur, Pratheesh C. Mammen, and M.
Rajkumar for help rendered. We thank Tarsh Thekaekara, Madhusudan, Subhash,
Jobi, and Shikha from the Shola Trust-Gudalur, for logistical and field
support. We also thank our anonymous reviewers who provided valuable insight
towards our paper and helped in its improvement.
Abstract: Many wildlife species survive in
human-modified landscapes and understanding the opinions of those who share
space with wildlife will aid conservation efforts. Using a questionnaire, we assessed the
presence of 12 mammal species in 78 tea plantations in the Nilgiris, southern
India. We obtained data on (i)
plantation size, location, and elevation, (ii) species presence over a year,
(iii) type and number of wildlife incidents caused, (iv) financial cost of
wildlife damage, and (v) support for wildlife conservation. We used a generalized linear model to assess
whether the distance to protected areas, elevation, and plantation size
influenced species presence and the effect of these variables and wildlife
incidents on support for conservation.
Among all species reported, Bonnet Macaque, Wild Boar, and Porcupine
were the most widespread, and the former two and the Gaur reportedly caused
>50% of damages. Crop damage was the
most frequent (74%, n = 244), whereas livestock predation, attacks on people,
and infrastructure damage constituted <10% of incidents reported. The cost of wildlife damage was negligible
for 72 estates and significant for six.
The number of species increased with proximity to protected areas, with
increasing elevation and plantation area. Plantation management (62%) supported
wildlife conservation, and support increased with decreasing plantation size,
increasing distance to protected areas, and with a higher number of species
reported, but decreased with increasing incidents of wildlife damage. Mitigating impacts of a few widely
distributed species that cause disproportionate damage and compensating those
that incur disproportionately high costs could increase support for
conservation. Education and awareness
programs for the plantation community can further help increase support and
participation in wildlife conservation activities. Plantations can thus serve as supplementary
habitats for wildlife in regions where hard boundaries between protected areas
and human settlements prevail.
Keywords: Conservation attitudes,
human-wildlife coexistence, Nilgiri Biosphere Reserve, wildlife damage.
Introduction
The transformation of terrestrial
ecosystems into human use areas has driven global biodiversity loss (Vitousek
et al. 1997; Johnson et al. 2017) and has forced many species into
human-modified landscapes. Although
protected areas (PA) safeguard remnant habitats and wildlife, the current
global PA network which comprises 14.9% of Earth’s land area (UNEP-WCMC et al.
2018) is inadequate for the long-term conservation of several species,
particularly those that are wide-ranging (Woodroffe & Ginsberg 1998;
Jenkins & Joppa 2009; Di Minin et al. 2016). On the other hand, certain human-modified
landscapes such as coffee and tea plantations can provide refuge, foraging
grounds, and enable wildlife movement between reserves (Bal et al. 2011; Rathod
& Rathod 2013; Guzmán et al. 2016; Kumar et al. 2018). In landscapes that lack intact or protected forests,
such plantations can provide supplemental habitats for wild animals (Bhagwat et
al. 2008; Krishnan et al. 2019). The
survival of many species, however, will ultimately depend on their ability to
persist and be tolerated in human-modified landscapes.
Wild animals that are displaced
by habitat loss and fragmentation may harm humans, their properties, and their
livelihoods (Torres et al. 2018). For
instance, in Cameroon, 12 different mammal species damaged cocoa pods in cocoa
plantations (Arlet & Molleman 2010).
In India, damage by Asian Elephants Elephas maximus to a variety
of crops causes economic loss to farmers (Ramkumar et al. 2014; Govind &
Jayson 2018); and Leopards Panthera pardus reportedly attack people and
livestock in tea plantations (Sidhu et al. 2017; Kshettry et al. 2020). Such incidents can reduce tolerance for
wildlife, lead to retaliatory killing of wild animals, and can also affect
ongoing conservation efforts (Nyhus et al. 2000; Marchal & Hill 2009; Kalam
et al. 2018); however, under certain circumstances, humans are tolerant of wild
animals. For instance, in Africa,
farmers tolerated Chimpanzees Pan troglodytes verus as they would eat
the fruit of the cashew nut and pile the nuts, thereby facilitating harvest by
farmers (Hockings & Sousa 2012). In
Indonesia, farmers tolerated Orangutans Pongo abelii in oil palm
plantations and agricultural farms as they were considered harmless
(Campbell-Smith et al. 2010); and Islamic religious beliefs protected
crop-raiding macaques (Macaca tonkeana and M. ochreata brunnescens)
(Riley & Priston 2010).
Identifying the extent of human
tolerance for wildlife, and the factors that reduce and promote tolerance, is
crucial for the conservation of wildlife in human-modified landscapes (Treves
& Bruskotter 2014). Interviews and
surveys are widely employed to assess tolerance to wildlife presence among
local communities. For instance, they
have been used to assess tolerance towards (i) wildlife presence, (ii) economic
loss to wildlife, and (iii) responses towards conservation initiatives (Fulton
et al. 1996; Arjunan et al. 2006; Kansky & Knight 2014). In this study, we used questionnaire surveys
to assess wildlife presence and support for wildlife conservation in tea
plantations in the Nilgiri Biosphere Reserve (NBR), which is part of the Western
Ghats (a global biodiversity hotspot) of India.
The NBR comprises of six critical
PAs and is an important region globally for the conservation of the Asian
Elephants, Bengal Tiger Panthera tigris, Nilgiri Tahr Nilgiritragus
hylocrius, and the Critically Endangered White-rumped Vulture Gyps
bengalensis. Since the British
colonization in the 19th century, however, montane evergreen forests
(known locally as ‘sholas’) and montane grasslands in the NBR have been
transformed into agricultural fields, monoculture plantations, and other land
uses (Prabhakar & Gadgil 1995). As a
result, many monoculture plantations adjoin PAs and include open grassy
expanses, swamps, patches of forest along streams, fuel-wood plantations, and
degraded forest fragments that support rich flora and fauna (Shankar &
Mudappa 2003; Kumara et al. 2004). A
critical shortcoming of the NBR is that it has been designed without a
transition zone, which is mandatory as per UNESCO guidelines for biosphere
reserves (Daniels 1996; Puyravaud & Davidar 2013; UNESCO 2019). Hard boundaries affect both humans and
wildlife. Therefore, a transition zone,
where human activities are more compatible with conservation, may help reduce
these impacts. Assessing wildlife
presence in tea plantations and human tolerance of wildlife in the NBR would
help understand whether plantations can act as transition zones in this
region. Moreover, tea is a non-edible
crop and can thus reduce economic losses caused by wildlife.
We conducted our survey in the
Nilgiris District (henceforth Nilgiris) in the NBR. We surveyed 78 small and large tea
plantations to assess (i) wildlife presence in each plantation, (ii) estimate
damages caused by wildlife and its financial costs, and (iii) assess support
for wildlife conservation among plantation managers. We tested the hypotheses that support for
wildlife conservation would be positively associated with increasing (a)
plantation size, and (b) distance to PA, and negatively associated with (c)
higher incidents of damage, and (d) their increasing costs.
Methods
and Materials
Study area
The Nilgiris (2,452km2)
lies between 11.6–11.91 0N and 76.21–77.03 0E in the
state of Tamil Nadu (Figure 1). This
region is mountainous with elevations ranging from 900–2,500 m. The heterogeneous landscape and climate (von
Lengerke 1977) support diverse vegetation types including lowland tropical
rainforests, deciduous forests, thorny scrub vegetation, upper montane shola
forests, and grasslands (Prabhakar & Pascal 1996). Forests cover 1,426km²
(Department of Economics and Statistics 2016) constituting 58% of the total
area and several important PAs such as Mudumalai Tiger Reserve (321km²) and
Mukurthi National Park (78km²) are located here.
The district has a human
population of around 700,000 (Census of India 2011). There are six administrative subdivisions
called taluks, of which we surveyed three: Gudalur (726km²), Kotagiri (397km²),
and Coonoor (229km²). Gudalur lies on
the western side of the Nilgiri Plateau at a lower elevation (≈1,000m) and
receives an annual rainfall of around 2,300mm.
Kotagiri and Coonoor lie on the upper plateau (>1500m). Kotagiri is situated along the northern
slopes and receives an annual rainfall of 800–1,500 mm, whereas Coonoor lies
east of the plateau and receives 1,200–1,500 mm annual rainfall.
The Nilgiris District is also an
important tea growing region in southern India, and plantations of tea and
coffee have replaced a high proportion of native grasslands and montane forests
(Kumar & Bhagavanulu 2008). Today,
the plantations range from smallholdings (<10ha) to over 400ha (Tea Board
India 2003) and cover about 23% (560km²) of the district area (Department of
Economics and Statistics 2016). Several
tea plantations in the Western Ghats are also next to PAs, and they provide a
permanent or transitory habitat for many species, including those that are
endangered (Shankar & Mudappa 2003; Kumara et al. 2004).
Methods
We surveyed 78 small and large
tea plantations in the three regions mentioned earlier, from January to March
2011. We first obtained a list of tea
plantations from the offices of the United Planters Association of Southern
India (UPASI) in Gudalur and Coonoor.
During our survey, we came across many plantations that were not members
of UPASI, that we also included. We categorized
all reserved forests that have a lower level of protection, tiger reserves, and
national parks that are strict nature reserves, as PAs in this study.
Questionnaire Survey
We used a structured
questionnaire (Appendix A1 in supplementary data) which focused on: (i)
location of the plantation office, (ii) size of plantation, and (iii) distance
to PAs. Using a global positioning
system (GPS), we recorded the location of each plantation office and used this
as the point for geo-referencing. We
then calculated the distance to PAs using a GRASS geographic information system
(GRASS GIS). Further, we asked about the
(iv) sighting frequency of 12 mammalian species, (v) incidents of crop and
infrastructure damage, livestock depredation, and attacks on humans, and (vi)
financial costs of wildlife damage over one year (January 2010 to January
2011). Last, we inquired about (vii) the
management’s support for wildlife conservation (positive/negative).
We selected 12 species that could
cause different types of damage: Asian Elephant, Gaur, Wild Boar Sus scrofa,
Sambhar Rusa unicolor, Muntjak Muntiacus muntjak, Sloth Bear Ursus
ursinus, Bonnet Macaque Macaca radiata, Crested Porcupine Hystrix
indicus, and Indian Giant Squirrel Ratufa indica that could raid
crops and cause infrastructure damage; Bengal Tiger, Leopard, and Dhole or
Asiatic Wild Dog Cuon alpinus that could prey on livestock. Photographs of these mammals were shown to
interviewees to reduce error in identifying the wildlife in question. We did not carry out any independent field
survey to verify the presence or absence of these species.
We initiated the survey by first
contacting and interviewing plantation managers to ascertain wildlife present
on their premises and to gauge whether their company supported wildlife
conservation or not. We then interviewed
one ground-level supervisor to corroborate wildlife presence and damages. Wherever possible, we verified wildlife
presence by going through records of wildlife sightings maintained by
plantation staff under the Rainforest Alliance Certification. We also interacted with villagers living
around the periphery of the plantations to crosscheck and verify the data
collected from the plantations we surveyed.
Wildlife presence and species
richness
When a species was reported to be
present in a plantation, we coded it as 1 and its absence as 0. All the species presence were summed up in a
plantation, to get an estimate of the total number of species (species
richness) reported. If present, we asked
for sighting frequency, which was also coded: never = 0, daily/weekly = 1,
regular monthly = 2, occasionally once a year = 3.
Wildlife incidents
We categorized the reported crop
and infrastructure damage, livestock depredation, and attacks on humans, as
‘wildlife caused incidents’ and not as ‘human-wildlife conflict’ for reasons mentioned
by Davidar (2018). We used a binary
score for each type of incident reported in a plantation, 1 if reported and 0
if not reported. We summed up all the
incidents reported over the year, by species and for each plantation.
Financial costs of wildlife
damages
Plantation managers provided
financial data on wildlife damage over a year (January 2010 to January
2011). If the cost of wildlife damage
was negligible, they were not recorded by the management team and hence not provided
to us. Besides documenting the financial
cost of wildlife damage, comparing them with other components can help
determine the actual cost incurred and how significant the financial loss can
be to those affected. We used the cost
of preventing insect pest damage (pesticide usage) in tea plantations as a
baseline of financial cost control to compare the damage caused by
wildlife. The estimated cost of wildlife
damage and pesticide usage per hectare over the year in each plantation was
noted in Indian Rupees and converted to United States Dollar (USD) using the
rates prevalent during the study period.
Support for wildlife conservation
We coded the responses towards
support for wildlife conservation as 0 if negative and 1 if positive, however,
many plantation managers did not provide a response, which we recorded as ‘no
response’. Hence during the analysis, we
recoded the responses as 0 if negative, 1 if positive, and 2 if ‘no
response’. We ran two sets of analysis,
one with the negative responses and another where we merged the ‘no response’
category with negative response category.
We did so because negative opinions may have repercussions if the
results of the survey were placed in the public domain (Newmark et al. 1993;
Gillingham & Lee 1999; Liu et al. 2011).
Data analyses
We used the software R 3.2.3 (R
Core Team 2016) for statistical analysis.
We conducted exploratory analysis on the size, distribution of
plantations, elevation, and proximity to a PA.
We calculated the distance from the point of geo-referencing (plantation
office) to the nearest PA using the v.distance module of GRASS-GIS 7.2 (GRASS
Development Team 2017).
We used a generalized linear
model (GLiM) with Poisson link to analyze whether the distance to a PA,
elevation, and size of a plantation influenced species richness. One assumption of the GLiM is the
independence of observations, and since plantations that are close to each
other may have the same issues, we tested whether the response variable was
spatially autocorrelated. The Moran’s I
indicated no spatial autocorrelation (p = 0.18) of the response variable. We also used a GLiM but this time with
quasi-Poisson distribution (due to overdispersion of data), with the same
explanatory variables to analyze their effects on wildlife damage incidents. In both cases, we included all variables and
interactions and then simplified by stepwise deletion comparing models with the
AIC and ANOVA. We stopped the model
simplification when the AIC was lowest, or the ANOVA became significant. We eliminated two estates, one for which we
could not obtain geographic coordinates and another with an exceptionally large
area (8,000ha).
We examined a few potential
causes that could prompt individuals to approve or disapprove of wildlife
conservation efforts. We named the
dependent variable as “Attitude,” and our explanatory variables were (i)
distance to PA from plantation office, (ii) size of the plantation, (iii)
species richness (of the studied species), and (iv) number of incidents of
wildlife damage.
We used a GLiM to determine the
association between the four explanatory variables and support for wildlife
conservation. We used the binomial link
function as the dependent variable was binomial. We conducted two logistic regression analysis
using two sets of variables. The first
set excluded all the ‘no response’ answers and included only positive and
negative responses. The second set
combined ‘no response’ answers with the negative responses. The first logistic regression started with
all variables but no interactions, due to lack of power. The second logistic regression started with
all variables and interactions. Both
were simplified by stepwise deletion as above.
We analyzed data of those estates
that reported costs of pesticide usage and those that also reported wildlife
damage. We first used log-transformation
to obtain a normal distribution. We then performed a Shapiro-Wilk normality
test to confirm normality. Because one sample
was small, we compared the log-transformed arithmetic means with a t-test to
verify whether wildlife damage costs were similar to insect pest-control costs.
Results
Location, plantation size, and
distance to protected areas
The 78 plantations surveyed
ranged in area from 5 to 8,094 ha and occurred at elevations between 700 to
2,300 m (Figure 1). Of these, 20 were in
Gudalur, 22 in Kotagiri, and 36 in Coonoor (Appendix A2 in supplementary
data). Tea was the primary crop in all
plantations: 57 cultivated only tea, 21 grew coffee in addition, and 23 grew
spices. The average distance to a PA was
2.4km, and the maximum distance was 10 km. Twenty-one plantations were situated
less than one kilometer from different PAs and 56 further away (Appendix A2 in
supplementary data). We were unable to
obtain the GPS coordinates for one plantation.
Wildlife presence and species
richness
There was a median of eight
species reported per plantation with a range from 0 to 12. The most widely distributed species were the
Bonnet Macaque (across 91% of the plantations), followed by Wild Boar (85%) and
Porcupine (78%) (Figure 2). On the other
hand, the Tiger (33%), Dhole (32%), and Muntjak (13%) were rarely reported
(Figure 2). There was a significant
positive correlation between the total number of species in a plantation and
proportion of charismatic species, such as the Tiger and Dhole (Spearman rank
correlation Sr = 0.350116, p = <0.01).
GLiM simplification produced the
most parsimonious model with three variables that were correlated with species
richness (Table 1): distance to a PA, elevation, and interaction between
distance to a PA and plantation size.
Species richness was significantly and negatively correlated with
distance to a PA (p = 0.00104) (Table 1), tended to increase with increasing
elevation, and was weakly and positively associated with the interaction
between increasing distance to a PA and larger area, therefore larger
plantations further away tended to have more species (Table 1).
Wildlife incidents
A total of 244 wildlife-related
incidents were reported over one year, with an average of three incidents per
year per plantation (Appendix A2 in supplementary data). There was no significant effect of distance
to a PA, elevation, or plantation size on the number of wildlife incidents reported. Overall, the Bonnet Macaque, Wild Boar, and
Gaur were implicated in over 50% of the total incidents. Crop damage, such as uprooting tea bushes,
damage to trees, and raiding vegetable gardens, caused mostly by the Gaur
(20%), Bonnet Macaque (19.4%), and Wild Boar (18.3%) were reported in 74% of
plantations (Figure 3). The other
incidents were less frequent: livestock predation by Leopard or Tiger
constituted 9%; infrastructure damage mostly by Bonnet Macaques and
occasionally by Elephants was 8.5%, and attacks on people mostly by the Sloth
Bear and Gaur was 8.5% (Figure 3).
Financial costs
A total of 37 estates provided
financial data on pesticide usage and had an average (exponentiated
log-transformed average) INR 1,682 ha-1yr-1 (1 USD= 45
INR during the study period; USD 37.4) (Appendix A2 in supplementary
data). On the other hand, the cost of
wildlife damage was nil or negligible for 72 estates (Appendix A2 in
supplementary data). The six estates
that reported a loss due to wildlife had an average (exponentiated
log-transformed average) cost of INR 243 ha-1yr-1 (USD
5.4) (Appendix A2 in supplementary data).
The cost of pesticide usage was significantly higher than the cost
incurred due to wildlife damage (Welch two-sample T-test = 3.6, df = 7.3, p
< 0.01).
Support for wildlife conservation
Overall, 62% of respondents
supported conservation, 6.5% did not, and 31.5% did not respond (Table 2). There was no significant difference between
the responses across the three regions, possibly because there were too few
negative responses (log-likelihood chi-square = 6.592, df = 4, p = 0.159). Plantation managers in Gudalur, however, had
the lowest percentage of positive and no responses among the three taluks,
indicating ambiguous attitudes towards conservation.
The first GLiM, which included
only negative and positive responses, indicated that plantation managers
supported wildlife conservation when there were more species present on their
premises (p = 0.0401). The second GLiM
where the ‘no response’ answers were merged with the negative responses
increased the significance of this relationship (p = 0.00201, Table 3, also
Appendix A3 in supplementary data).
Conservation support increased
with an increasing number of species reported in a plantation; with increasing
distance from PA, and among larger plantations situated further away (Table
3). Although incidents generally
decreased support, it was modulated by greater wildlife presence in larger
plantations further away (Table 3). Plantations opposed to, or ambiguous about
conservation were generally larger, and/or with a higher number of incidents
reported (Table 3). The last three
interactions between (i) area and incidents, (ii) distance, area and incidents,
and (iii) distance, species, and incidents were marginally significant and/or complex.
Discussion
Human-wildlife ‘conflict’ is a
global issue that encompasses a wide range of species, events, and settings,
many of which have the potential to harm both humans and wildlife (Dickman
2010). Incidents with wildlife are often
presented with synthetic variables such as economic loss to farmers and
livestock owners, human injuries and mortalities, and loss of human livelihoods
(e.g., Acharya et al. 2016; Acha et al. 2018; Govind & Jayson 2018). Although these variables help us understand
the intensity and extent of incidents with wildlife, it would be incorrect to
infer or depict human-wildlife conflict as a uniform and pervasive threat, from
which anyone and everyone may suffer.
Moreover, such views can diminish support for wildlife conservation and
make conflict management even harder.
On the other hand, several
studies reveal key patterns/differences in human-wildlife conflict events. For instance, human-wildlife interactions are
limited in developed countries due to lower dependency on forest ecosystems but
are far greater in developing countries because there is a higher dependency on
forests, particularly for rural livelihoods, agriculture production and
development (Anand & Radhakrishna 2017). Similarly, only a few species are
known to cause extensive damage. For
instance, 32 species caused damage across 11 protected regions in India, but
only six were responsible for most incidents (Karanth & Kudalkar 2017). In Zimbabwe, of five carnivorous species, the
Lion Panthera leo and Spotted Hyaena Crocuta crocuta were held
responsible for most livestock depredation events (Loveridge et al. 2017). In Nepal, four (out of 12 species) caused
maximum damage to human property and life (Lamichhane et al. 2018). Similarly, in our study, we show that (i)
most of the damages are created by species that are not dangerous, (ii)
incidents of damage to human property and life are spatially clustered and can
probably be avoided, (iii) economic cost due to wildlife damage is in general
low when compared to other costs such as that of preventing insect pest damage,
and (iv) support for conservation is relatively high.
About 50% of wildlife-related
incidents, mostly crop damage, were caused by a few species such as the Bonnet
Macaque, Wild Boar, and Gaur. Whereas
counter-intuitively, increased diversity of wildlife increased support for
conservation. This could be because
plantations supporting a higher proportion of the 12 species selected for this
survey, significantly reported the presence of charismatic species such as the
Tiger and Dhole. Moreover, economic costs were disproportionately borne by a
few plantations and higher costs were mostly because of wild Elephants
destroying fences and infrastructure.
Therefore, reducing impacts of a few pest species, and perhaps
mitigation of Elephant damages in a few plantations, could have
disproportionate effects on conservation attitudes in this region.
Many plantations with significant
wildlife species were not adjacent to PAs, indicating that these plantations
support resident populations of widespread generalist species such as Bonnet
Macaques and Wild Boar. These species
were also considered chronic pests. The
abundance of Bonnet Macaques in forests in peninsular India is very low, and
the species is fast disappearing from its original habitats owing to expanding
ranges of the Rhesus Macaque Macaca mulatta (Erinjery et al. 2017); however,
it is ubiquitous in human settlements due to its adaptability to human food and
refuse (Pillay et al. 2011).
The presence of charismatic
species such as the Tiger and Dhole were reported in estates with more
wildlife. The aesthetic value of several
wildlife species could elicit favorable responses. For instance, de Pinho et al. (2014) reported
that several species perceived as beautiful garnered more conservation support
by agro-pastoralist communities living around Amboseli National Park, in
southern Kenya.
There was considerable support
for wildlife conservation among plantation managers. Surprisingly, support was lower in
larger-sized plantations, especially those located closer to PAs. Studies have however shown that in general,
wealthy farmers with larger agricultural holdings are better able to buffer the
economic costs of wildlife damage (Naughton-Treves & Treves 2005;
Zimmermann et al. 2005). In this case,
however, large industrial plantations were less tolerant of wildlife. The reason for this is not clear. Perhaps surveillance by protected area
managers creates resentment among more powerful plantation groups, or as in
some cases, they have encroached upon reserved forests.
Although non-significant across
regions, a higher proportion of plantations in Gudalur preferred not to state
whether or not they supported wildlife conservation. Gudalur is an important region for wildlife,
as it lies between major PAs, and is an important Elephant corridor connecting
Mudumalai Tiger Reserve and Wynaad Wildlife Sanctuary that run through this
region (Puyravaud et al. 2017). There
are, however, many conflicts over forest leases and land tenure in this region
(Krishnan 2009).
Land tenure insecurity is widely
observed in tropical and developing regions and often overlaps with areas that
have high conservation value (Bruce et al. 2010). There was a distinct land tenure system
called the ‘janmum’ tenure in Gudalur which the Tamil Nadu State Government
sought to abolish in 1969 through the “Gudalur Janmum Estates” (Abolition and
Conversion into Ryotwari) Act, 1969.
Litigation over implementing this Act has been dragging on, and this
uncertainty has resulted in large scale encroachment of forest land (Davidar et
al. 2012). Out of the 32,375ha of
disputed land in the taluk that falls under janmum system of hereditary
proprietary rights, 11,736ha have been identified as forests, and 6,475ha have
been leased to local communities (Ravichandran 2019a). Among the remaining 14,164 unsettled
hectares, 12,140ha has been encroached upon by plantations (Ravichandran
2019b).
Land tenure insecurity can create
resentment towards conservation. For
instance, Romañach et al. (2007) found that land “squatters” were not as
positive towards the presence of carnivores when compared to those who held a
title deed to communal land. Similarly,
Guinness (2016) also found that land ownership significantly influenced local
perceptions of crop-raiding. Hence, it
is possible, this could be among the reasons for antagonism towards
conservation among many plantation managers in Gudalur. Targeted education and awareness programs for
the plantation community in general are thus necessary, as they can help
increase support for wildlife conservation and encourage participation in
ongoing conservation efforts in the region.
Our study shows that plantations
provide a supplementary habitat for many endangered and iconic species. Support for conservation was high, although
the ubiquitous presence of some species such as the Bonnet Macaque and Wild
Boar, considered ‘pests’ by the respondents, caused a high proportion of
damages. Overall, a few species caused
most of the problems, and a few plantations suffered high costs. Mitigation attempts should, therefore, focus
on these species and plantations to increase conservation support. With adequate mitigation of negative impacts,
plantations can serve as a ‘transition’ zone for the Nilgiri Biosphere Reserve,
to soften the hard boundaries between protected areas and the human-dominated
mosaic, and to facilitate the movement of wildlife between reserves.
Table 1. Results from GLiM
analysis of variables associated with species richness across 76 plantations.
|
Coefficients |
Estimate |
Std. Error |
z value |
Pr(>|z|) |
|
Intercept |
1.5220585 |
0.2295756 |
6.630 |
3.36e-11 |
|
Distance |
-0.0842639 |
0.0257034 |
-3.278 |
0.00104 |
|
Elevation |
0.0003574 |
0.0001399 |
2.555 |
0.01061 |
|
Distance: Area |
0.0001418 |
0.0000453 |
3.130 |
0.00175 |
|
Null deviance: 70.896 on 75
degrees of freedom Residual deviance: 55.537 on 72
degrees of freedom AIC: 345.97 |
||||
Table 2. Support for wildlife
conservation across 76 tea plantations in the Nilgiris.
|
Region |
Responses n (%) |
Total |
||
|
Negative |
Positive |
No Response |
||
|
Gudalur |
1 (5.2) |
9 (47.4) |
9 (47.4) |
19 |
|
Kotagiri |
2 (9) |
13 (59) |
7 (32) |
22 |
|
Coonoor |
2 (5.7) |
25 (71.4) |
8 (22.9) |
35 |
|
Total |
5 (6.5) |
47 (62) |
24 (31.5) |
76 |
The differences between the three
regions were not significant (Log likelihood chi square = 6.592, df = 4, p =
0.159).
Table 3. Results of GLiM analyses
on variables associated with support for wildlife conservation among 76
plantations in the Nilgiris.
|
Coefficients |
Estimate |
Std. Error |
z value |
Pr(>|z|) |
|
Intercept |
-5.955607 |
2.203375 |
-2.703 |
0.00687 |
|
Distance |
1.130861 |
0.560615 |
2.017 |
0.04368 |
|
Area |
-0.013464 |
0.006077 |
-2.216 |
0.02672 |
|
Species richness |
1.174853 |
0.380301 |
3.089 |
0.00201 |
|
Incidents |
-0.982413 |
0.458589 |
-2.142 |
0.03217 |
|
Distance: Area |
0.016226 |
0.007016 |
2.313 |
0.02073 |
|
Distance: Species richness |
-0.382859 |
0.153184 |
-2.499 |
0.01244 |
|
Area: Incidents |
0.004312 |
0.002351 |
1.834 |
0.06660 |
|
Distance: Area: Incidents |
-0.003560 |
0.001859 |
-1.915 |
0.05555 |
|
Distance: Species richness:
Incidents |
0.061742 |
0.024890 |
2.481 |
0.01312 |
|
Null deviance: 101.054 on 75
degrees of freedom Residual deviance: 65.263 on 66
degrees of freedom AIC: 85.263 |
||||
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Appendix 1. Sample questionnaire
Name of plantation
Corporate/family/others
Year of establishment
Total area of plantation
Region
Plantation crops (tick one) tea coffee cardamom rubber others
Total area (if multiple crops)
Geographical coordinates latitude longitude altitude
Presence of forests in your
plantation yes/no type of forest
Area or % of forest cover
Nearest protected area to estate Approximate
distance (km)
|
Wildlife |
Frequency of sightings in plantation |
|||||
|
Species |
Impact ± |
Daily |
Weekly |
Monthly |
Annually |
Not Sighted |
|
Asian Elephant |
|
|
|
|
|
|
|
Bengal Tiger |
|
|
|
|
|
|
|
Leopard |
|
|
|
|
|
|
|
Gaur |
|
|
|
|
|
|
|
Sloth Bear |
|
|
|
|
|
|
|
Wild Dog |
|
|
|
|
|
|
|
Wild Boar |
|
|
|
|
|
|
|
Bonnet Macaque |
|
|
|
|
|
|
|
Sambar Deer |
|
|
|
|
|
|
|
Muntjak |
|
|
|
|
|
|
|
Crested Porcupine |
|
|
|
|
|
|
|
Malabar Giant Squirrel |
|
|
|
|
|
|
|
Wildlife |
Number of damage incidents in
plantation |
|||||
|
Species |
Crop damage |
Infrastructure damage |
Livestock attack |
Human attack |
Financial loss (INR) |
Comments |
|
Asian Elephant |
|
|
|
|
|
|
|
Bengal Tiger |
|
|
|
|
|
|
|
Leopard |
|
|
|
|
|
|
|
Gaur |
|
|
|
|
|
|
|
Sloth Bear |
|
|
|
|
|
|
|
Wild Dog |
|
|
|
|
|
|
|
Wild Boar |
|
|
|
|
|
|
|
Bonnet Macaque |
|
|
|
|
|
|
|
Sambar Deer |
|
|
|
|
|
|
|
Muntjak |
|
|
|
|
|
|
|
Crested Porcupine |
|
|
|
|
|
|
|
Malabar Giant Squirrel |
|
|
|
|
|
|
Amount spent on insect pest
control per year
Do you (as a management) support wildlife
conservation?
Yes/No Why?
How can you help conserve
wildlife?
Appendix 2. General description
of the 78 tea plantations surveyed across Gudalur, Kotagiri, and Coonoor taluks
of Nilgiris District, India.
|
Taluk |
Estate |
Distance to PA (km) |
Elevation (m) |
Area (ha) |
Number of species reported |
Number of incidents over a
year |
Wildlife damage cost (INR) |
Cost of insect pest control
(INR) |
Support for wildlife
conservation |
|
Gudalur (n=20) |
1 |
3.44 |
960 |
61 |
6 |
3 |
0 |
100000 |
0 |
|
2 |
2.83 |
943 |
7 |
0 |
0 |
0 |
24000 |
2 |
|
|
3 |
2.84 |
940 |
7 |
0 |
0 |
0 |
0 |
2 |
|
|
4 |
0.16 |
1257 |
7 |
3 |
3 |
10000 |
10000 |
2 |
|
|
5 |
6.18 |
1939 |
40 |
8 |
5 |
0 |
0 |
1 |
|
|
6 |
4.71 |
943 |
12 |
3 |
2 |
0 |
0 |
2 |
|
|
7 |
3.29 |
1064 |
853 |
9 |
0 |
0 |
0 |
1 |
|
|
8 |
1.73 |
941 |
243 |
7 |
4 |
0 |
0 |
2 |
|
|
9 |
2.76 |
1093 |
61 |
7 |
3 |
0 |
123600 |
2 |
|
|
10 |
1.82 |
1061 |
896 |
4 |
1 |
350000 |
3200000 |
1 |
|
|
11 |
4.42 |
1036 |
648 |
9 |
0 |
0 |
160000 |
1 |
|
|
12 |
0.5 |
1935 |
1457 |
7 |
2 |
0 |
100000 |
2 |
|
|
13 |
0.03 |
1475 |
24 |
5 |
0 |
0 |
10000 |
2 |
|
|
14 |
1.9 |
1230 |
8094 |
9 |
5 |
0 |
0 |
1 |
|
|
15 |
4.99 |
959 |
1214 |
8 |
0 |
0 |
0 |
1 |
|
|
16 |
3.36 |
960 |
1012 |
10 |
2 |
0 |
0 |
1 |
|
|
17 |
1.89 |
955 |
360 |
7 |
3 |
0 |
0 |
1 |
|
|
18 |
0.84 |
700 |
81 |
7 |
2 |
28000 |
0 |
2 |
|
|
19 |
4.57 |
971 |
1214 |
9 |
4 |
0 |
0 |
1 |
|
|
20 |
0.69 |
1156 |
1012 |
7 |
5 |
0 |
0 |
1 |
|
|
Kotagiri (n=22) |
21 |
3.43 |
1842 |
243 |
10 |
3 |
35000 |
3000000 |
1 |
|
22 |
6.18 |
1939 |
28 |
4 |
1 |
0 |
0 |
2 |
|
|
23 |
1.17 |
1461 |
61 |
9 |
2 |
0 |
0 |
2 |
|
|
24 |
1.66 |
1924 |
24 |
7 |
2 |
0 |
0 |
0 |
|
|
25 |
3.54 |
1793 |
27 |
6 |
4 |
0 |
0 |
2 |
|
|
26 |
5.51 |
1980 |
36 |
7 |
6 |
0 |
700000 |
1 |
|
|
27 |
1.4 |
1487 |
166 |
8 |
8 |
0 |
500000 |
1 |
|
|
28 |
0.14 |
1487 |
29 |
8 |
6 |
0 |
216000 |
0 |
|
|
29 |
6.56 |
2006 |
89 |
7 |
3 |
0 |
0 |
1 |
|
|
30 |
0.17 |
1302 |
210 |
6 |
3 |
0 |
290625 |
2 |
|
|
31 |
0.66 |
1288 |
625 |
9 |
6 |
25500 |
3437500 |
1 |
|
|
32 |
0.52 |
1718 |
263 |
8 |
4 |
0 |
30000 |
1 |
|
|
33 |
1.67 |
1502 |
202 |
5 |
2 |
0 |
0 |
1 |
|
|
34 |
0.86 |
1415 |
192 |
10 |
6 |
0 |
1000000 |
2 |
|
|
35 |
4.03 |
1966 |
202 |
8 |
4 |
0 |
90000 |
2 |
|
|
36 |
4.62 |
1879 |
133 |
8 |
6 |
0 |
0 |
1 |
|
|
37 |
3.82 |
1585 |
133 |
9 |
6 |
0 |
0 |
1 |
|
|
38 |
4.55 |
1890 |
10 |
7 |
5 |
0 |
30000 |
1 |
|
|
39 |
1.02 |
1837 |
250 |
9 |
5 |
0 |
236000 |
1 |
|
|
40 |
0.46 |
1921 |
359 |
7 |
1 |
0 |
681681 |
1 |
|
|
41 |
0.36 |
1538 |
24 |
6 |
2 |
5000 |
40000 |
2 |
|
|
42 |
2.71 |
1792 |
118 |
6 |
0 |
0 |
0 |
1 |
|
|
Coonoor (n=36) |
43 |
2.84 |
1935 |
49 |
9 |
10 |
0 |
300000 |
1 |
|
44 |
0.8 |
1688 |
185 |
8 |
3 |
0 |
500000 |
1 |
|
|
45 |
1.78 |
2057 |
151 |
6 |
4 |
0 |
150000 |
1 |
|
|
46 |
4.08 |
1663 |
134 |
7 |
2 |
0 |
0 |
1 |
|
|
47 |
3.8 |
1649 |
61 |
8 |
6 |
0 |
0 |
2 |
|
|
48 |
2.62 |
1810 |
20 |
8 |
2 |
0 |
0 |
2 |
|
|
49 |
0.5 |
1005 |
259 |
7 |
0 |
0 |
100000 |
1 |
|
|
50 |
1.08 |
1349 |
384 |
10 |
3 |
0 |
200000 |
1 |
|
|
51 |
1.22 |
1612 |
61 |
9 |
4 |
0 |
0 |
1 |
|
|
52 |
4.64 |
1756 |
61 |
2 |
1 |
0 |
0 |
0 |
|
|
53 |
* |
* |
19 |
9 |
3 |
0 |
0 |
1 |
|
|
54 |
2.62 |
1810 |
5 |
6 |
0 |
0 |
0 |
2 |
|
|
55 |
1.45 |
1679 |
607 |
8 |
5 |
0 |
200000 |
1 |
|
|
56 |
1.08 |
1350 |
1063 |
7 |
6 |
0 |
400000 |
1 |
|
|
57 |
2.45 |
1579 |
18 |
9 |
5 |
0 |
270000 |
1 |
|
|
58 |
4.04 |
1885 |
36 |
7 |
5 |
0 |
50000 |
2 |
|
|
59 |
4.17 |
1840 |
32 |
7 |
4 |
0 |
0 |
2 |
|
|
60 |
1.92 |
1754 |
50 |
6 |
1 |
0 |
0 |
0 |
|
|
61 |
0.1 |
1867 |
1498 |
12 |
3 |
0 |
0 |
1 |
|
|
62 |
10.04 |
2230 |
21 |
3 |
0 |
0 |
0 |
1 |
|
|
63 |
1.06 |
1572 |
101 |
8 |
5 |
0 |
0 |
2 |
|
|
64 |
0.49 |
2050 |
270 |
11 |
4 |
0 |
300000 |
1 |
|
|
65 |
3.56 |
1863 |
101 |
8 |
4 |
0 |
0 |
1 |
|
|
66 |
0.16 |
1599 |
164 |
9 |
3 |
0 |
380000 |
1 |
|
|
67 |
0.4 |
1545 |
69 |
7 |
5 |
0 |
0 |
2 |
|
|
68 |
1.04 |
1856 |
207 |
8 |
5 |
0 |
0 |
1 |
|
|
69 |
0.7 |
2047 |
176 |
8 |
3 |
0 |
200000 |
1 |
|
|
70 |
7.33 |
2132 |
147 |
4 |
3 |
0 |
0 |
1 |
|
|
71 |
3.38 |
1727 |
12 |
9 |
3 |
0 |
120000 |
2 |
|
|
72 |
1.5 |
1936 |
52 |
4 |
0 |
0 |
220000 |
1 |
|
|
73 |
1.4 |
1969 |
48 |
9 |
4 |
0 |
0 |
1 |
|
|
74 |
2.26 |
1624 |
45 |
8 |
3 |
0 |
0 |
1 |
|
|
75 |
1.98 |
1920 |
70 |
8 |
2 |
0 |
500000 |
1 |
|
|
76 |
0.6 |
1739 |
427 |
10 |
0 |
0 |
0 |
1 |
|
|
77 |
0.86 |
1604 |
600 |
10 |
3 |
0 |
500000 |
1 |
|
|
78 |
2.54 |
2074 |
47 |
6 |
1 |
0 |
0 |
1 |
|
|
* Could not obtain data Wildlife incidents, cost of
wildlife damage and insect pest control over a one-year period (January 2010
to January 2011) Support for wildlife
conservation: 0- Negative, 1- Positive and 2- No Response |
|||||||||