forest fragmentation and regrowth in an institutional mosaic ...ifri/publications/r07i-20 nagendra,...

RESEARCH ARTICLE

Forest fragmentation and regrowth in an institutionalmosaic of community, government and private ownershipin Nepal

Harini Nagendra Æ Sajid Pareeth ÆBhawna Sharma Æ Charles M. Schweik ÆKeshav R. Adhikari

Received: 19 March 2007 / Accepted: 25 August 2007

� Springer Science+Business Media B.V. 2007

Abstract This study analyzes forest change in an

area of Nepal that signifies a delicate balance

between sustaining the needs and livelihood of a

sizable human population dependent on forest prod-

ucts, and an effort to protect important wildlife and

other natural resources. The study area, a portion of

the Chitwan valley district of Nepal, represents what

may be becoming a common institutional mosaic in

many countries of the world who have a population

reliant on forest products for their livelihood: (1) a

national park; (2) a designated park buffer involving

participatory forest management programs; (3) scat-

tered patches of designated community forest; and (4)

large areas of adjacent landscape made up of mostly

private landholdings under agricultural practices.

Utilizing Landsat images from 1989 and 2000, we

analyze land cover change in each of these manage-

ment zones using landscape ecology metrics and

quantifying proportional distributions of land cover

categories. Our results show significant differences in

terms of land cover dynamics and landscape spatial

pattern between these land ownership classes. These

findings indicate that community-based institutions

(participatory management programs in the park

buffer and the designated community forests) are

capable of halting or even reversing trends in

deforestation and forest fragmentation.

Keywords Park � Community forestry �Institutions � Land cover change � Fragmentation �Nepal

Introduction

Forest clearing represents a major driver of global

warming and climate change. Yet in recent times

there have been reports of forest regrowth taking

place in multiple regions across the world, and

growing recognition of the potential role that these

secondary forests can play in mitigating some of the

harmful effects of global environmental change (Bray

et al. 2003; Rudel et al. 2005). While the narrative of

deforestation has taken a primary place in land cover

H. Nagendra (&)

Center for the Study of Institutions, Population

and Environmental Change, Indiana University,

408 N. Indiana Avenue, Bloomington, IN 47408, USA

e-mail: [email protected]

H. Nagendra � S. Pareeth � B. Sharma

Ashoka Trust for Research in Ecology

and the Environment, 659 5th A Main,

Hebbal, Bangalore 560024, India

C. M. Schweik

Department of Natural Resource Conservation and Center

for Public Policy and Administration, University

of Massachusetts, Amherst, Holdsworth Hall 217,

Amherst, MA 01003-9275, USA

K. R. Adhikari

Institute of Agriculture and Animal Sciences,

Tribhuvan University, Rampur, Chitwan, Nepal

123

Landscape Ecol

DOI 10.1007/s10980-007-9162-y

change literature (e.g., Geist and Lambin 2002), there

is a growing awareness of the need to go beyond

these linear narratives, and to recognize that land-

scapes are complex shifting mosaics wherein both

forest clearing and regrowth take place, often simul-

taneously (Nagendra 2007; Rudel et al. 2005; Bray

et al. 2003).

Secondary forests can have multiple beneficial

impacts on the global environment, providing crucial

environmental or ecosystem services such as carbon

sequestration, watershed protection, habitat for

endangered species, and support for forest dependent

communities (Durst et al. 2004; Lugo and Helmer

2004; Rudel et al. 2005). The driving forces associ-

ated with forest transitions have been comparatively

well documented in economically developed coun-

tries in the temperate world (Mather and Needle

1998). In contrast, drivers of reforestation in less

wealthy countries in the tropical world have remained

less studied, although there is now increasing

evidence of large-scale reforestation in many of these

regions of the world (e.g., Bray et al. 2003; Ostrom

and Nagendra 2006).

Discussions of tenure are essential to an under-

standing of forest-cover change in such contexts

(Sikor 2006; Nagendra 2007). Yet, a mere documen-

tation of formal tenure regimes is not sufficient, and

practices of forest management within local social,

cultural, and institutional contexts require attention

(Ostrom 2005; Hayes 2006). Forests are also embed-

ded within larger-level socio-economic and political

settings, which also have the capacity to significantly

influence outcomes. Thus, more detailed examina-

tions of land cover change across different tenure

regimes and rule systems are required, and can

significantly assist our ability to understand the

impact of institutions on land cover change and the

recovery of secondary forests (Turner et al. 1996;

Wimberly and Ohmann 2004; Agrawal and Chhatre

2006; Southworth et al. 2006; Ostrom and Nagendra

2006; Hayes 2006; Nagendra 2007).

How do we develop a better understanding of

landscape change in contexts where there are simulta-

neous processes of forest clearing and regrowth?

Satellite remote sensing of forest change, when inte-

grated with social science research methods, provides a

particularly effective approach to analyze the driving

forces that give rise to forest change in a variety of

socio-economic, institutional and biophysical contexts

(Rindfuss et al. 2004; Moran and Ostrom 2005). As

the field of landscape sustainability science becomes

increasingly interdisciplinary, society-centered and

ecology-centered views of the landscape are engag-

ing with each other (Tress et al. 2005; Wu 2006;

Ostrom and Nagendra 2006). This study forms part

of a larger project in South Asia aimed at under-

standing the socio-economic, biophysical and

institutional drivers of biodiversity, forest conserva-

tion and regrowth in densely populated dry and

moist tropical or subtropical landscapes (Nagendra

et al. 2002, 2004, 2005, 2006; Ostrom and Nagendra

2006; Nagendra 2007).

Nepal represents a very useful and crucial setting

for engaging in such a study. Following previous

large-scale deforestation, decentralization of forest

management has led to the recovery of forest cover in

much of the middle hills of Nepal, and the country is

now recognized internationally as one of the most

progressive proponents of community forestry (Agra-

wal and Ostrom 2001). Yet, the impact of community

forestry initiatives in the lowland Terai areas is much

in debate (Nagendra 2002). In comparison to the

middle hills, low initial population densities in the

Terai led to the existence of fewer traditional

institutions of forest management. The traditional

inhabitants have been pushed away from the forests

by economically and socially powerful hill migrants,

and the communities living in close proximity to the

forest edge are largely composed of very heteroge-

neous groups of migrants from the middle hills,

without traditional historical, cultural and social ties

to the region. The high timber value of forests in the

Terai provides a perverse incentive for corruption and

illegal harvesting, and acts as an additional bone of

contention between the State and local communities.

Thus, the challenge for the Terai has been to create

and support new institutions of forest management

(Nagendra 2002; Gautam et al. 2004).

In this paper, we analyze the changes in forest

cover in a rapidly changing landscape in the Nepal

Terai plains. Contrary to previous reports of large-

scale deforestation in the Terai (summarized in Ives

2004), we find significant forest regrowth taking

place in this landscape—but only in certain areas,

while other locations continue to degrade. Does the

location of different forest management institutions

and the regional location of this landscape close to a

well-known national park, and its access to tourist

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income, help explain the differential distribution of

forest change in this landscape? Our overarching

objective to understand how different ownership

regimes and policy environments have impacted the

extent and spatial pattern of forest cover change.

Specifically, we approach this by comparing the

extent of forest clearing and regrowth, and differ-

ences in spatial patterns of forest fragmentation under

conditions of state protection, community protection,

co-management and open access in our study

landscape.

Study area

The landscape is located in the inner Terai valley of

the Chitwan district in Nepal, where major losses in

forest cover have occurred in recent decades largely

because of the valley’s important role for the support

of agriculture in the country (Fig. 1). Up to the mid-

20th century, the area was largely occupied by dense

moist sub-tropical deciduous forests, interspersed

with marshy grasslands with low population densities

(Muller-Boker 1999). In the early 1950s, a large-scale

malaria eradication program of the national govern-

ment opened the way for large-scale land occupation

by migrants from the surrounding middle hills. The

district now contains a complex mix of ethnicities,

with people from all over the country (Matthews

et al. 2000). This landscape provides an interesting

opportunity to study the human processes that drive

forest recovery in a one-time frontier forest converted

over the last 40 years into a complex shifting mosaic

of forest, agriculture, settlement, and clearings.

The first national park of Nepal, the Chitwan

National Park (CNP), was established in Chitwan in

1973, and contains some of the largest patches of

lowland forest in Nepal (Smith et al. 1998). Adjacent

to this is located the Parsa Wildlife Reserve (PWR),

established in 1984 to form a spatially contiguous

conservation unit on the eastern side of the CNP.

Hundreds of families living in villages located around

the park depend on these forests to a significant degree

(Nepal and Weber 1994). These forests also harbor a

range of major wildlife species dependent on the forest

habitat within the park as well as forest patches

surrounding the park. Thus, issues of forest cover and

fragmentation in the overall landscape surrounding the

park assume critical significance (Seidensticker 2002).

Due to sustained human pressure, by the early

1990s, several of the forest areas outside the park

were severely degraded due to illegal timber extrac-

tion, grazing, and collection of fuelwood and fodder

(Nepal and Weber 1994; Matthews et al. 2000).

Conflicts with local communities living outside the

park have led to efforts at initiating participatory

forest management through the Community Forestry

and Buffer Zone management programs. Previous

studies of land cover change between 1989 and 2000

using a broad scale classification have demonstrated

that the landscape is beginning to show signs of forest

regrowth in some areas in response to recent efforts at

forest protection (Schweik et al. 2003; Nagendra

et al. 2004, 2005). Through a much more detailed

supervised classification and an analysis of changes

in the extent and quality of forest distribution, we

examine the impact of different forest institutions and

management approaches on forest cover and frag-

mentation in this region.

Methods

Identification of management zones

Based on initial field work in March 2001, we

identified three main types of forest protection

regimes operating in the landscape (Schweik et al.

2003). The first, and most apparent, was the protec-

tion provided by the spatially contiguous boundaries

of the Government-owned and managed CNP and

Parsa Wildlife Reserve (PWR). We focused our

attention on areas close to the protected area bound-

aries (hereafter, ‘‘Park Periphery’’, identified in Fig. 2

in red), as these are the areas where there is

maximum human pressure on the park (Heinen and

Mehta 1999). Boundaries of the CNP and PWR were

digitized from topographic maps using ArcInfoTM.

Second, areas managed by buffer zone forest

management communities (hereafter referred to as

‘‘Buffer Forests’’, and identified in Fig. 2 in red) were

identified in the landscape during additional field

visits in April and May 2002. The buffer zone

program represents a form of co-management

between the state and communities (Nepal 2002;

Nagendra et al. 2004), and is based on legislation

passed in 1993. The program is active along the park

boundary, and was sponsored by the United Nations

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Development Programme (UNDP) until 2005. Third,

we identified and digitized the boundaries of forest

patches managed by community forestry user groups

(hereafter, ‘‘Community Forests’’, identified in Fig. 2

in black). The community forestry program, opera-

tional in the Terai since the early 1990s, enables user

groups to conserve and manage these forests, and sell

and distribute products including forest timber

(Shreshtha 1998).

Information on the spatial boundary of the Buffer

and Community Forests was collected using

Geographical Positioning System (GPS) units and

information provided by local communities and forest

officials. Finally, the area within the study landscape

not covered by the Park Periphery, Buffer Forests or

Community Forests was demarcated as the ‘‘Sur-

rounding Landscape’’. This area represents a region

with some open access forests and a larger section of

more strongly protected and surveyed private land

holdings, where pressures for agricultural and urban

land use have been high (Matthews et al. 2000;

Schweik et al. 2003).

Fig. 1 Study area

Fig. 2 Distribution of land

cover change from 1989–

2000 across management

zones

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123

Description of management zones

A total of nine formally registered and handed over

community forests and 14 formally registered buffer

zone forests were located in the study area. In

addition to the formal management category, an

understanding of the accepted rules of forest man-

agement is critical to understand the de facto rules

that impact forest change (Hayes 2006; Ostrom and

Nagendra 2006). We conducted interviews in 2002

with each forest user group, based on methodology

developed by the International Forestry Resources

and Institutions (IFRI) program currently coordinated

by Indiana University and Michigan University

(Ostrom and Nagendra 2006). These forms provided

us with information on selected variables believed to

be crucial in impacting the effectiveness of local

institutions and rules-in-use (Nagendra et al. 2005;

Nagendra 2007). Detailed information on the differ-

ences in institutional rules and structure of the

Community Forests and Buffer Forests is provided

in Nagendra et al. (2005) and Ostrom and Nagendra

(2006).

Processing and classification of satellite images

A Landsat TM image of January 1989 and an ETM

image of March 2000, from the Nepali winter (dry)

season, were used for analysis. Images were sub-

jected to atmospheric correction, radiometric

calibration and radiometric rectification procedures

to facilitate comparability across dates (Jensen 2000).

The 1989 image was geometrically registered to

1:25,000 scale topographic maps, and the 2000 image

was geometrically registered to the 1989 base image.

RMS errors of registration were maintained at levels

below 0.5 pixels and registration was verified visually

by overlaying and swiping registered images (Jensen

2000).

Field training data for classification of the 2000

image was collected between May–June 1999 and

February–May 2001. Information from 177 locations

was used for a supervised classification of the 2000

image into open forest, dense forest, and non-forest

categories. The 1989 image was classified into the

same categories based on 1992 aerial photographs of

the region obtained from the Government of Nepal,

along with 1:25,000 scale topographic survey maps

developed from these photographs (Schweik et al.

1997). Classification accuracy was evaluated for both

classifications using a random sample of test points

distributed across the landscape that was not used in

the original classification procedure.

Assessment of forest status—land cover change

Land cover change and forest fragmentation both

provide important indicators of the impact of man-

agement zones and consequent accessibility on forest

change (Turner et al. 1996; Mertens and Lambin

2000; Wimberly and Ohmann 2004). For assessing

land cover change, individual classifications for 1989

and 2000 were combined using ARC/INFOTM soft-

ware to provide a change image that identifies

sequences of land cover classes for both observation

dates (Petit et al. 2001). Since there were three land

cover classes in each date, this recoding resulted in a

total of nine change classes. These were grouped into

6 change categories depending on the nature of

change in forest cover that they represent (Table 1).

Pixels forested both in 1989 and in 2000 (forest–

forest) represented a ‘‘stable forest’’ category, while

pixels that were not forested in either date repre-

sented ‘‘stable non-forest’’ (Table 1). Land cover

conversion occurs when a land cover type is

completely replaced by another, completely different

type (Turner and Meyer 1994). ‘‘Deforestation’’

comprised of pixels that changed from a forest class

(open forest or dense forest) to non-forest, while

‘‘reforestation’’ comprised of pixels that were

Table 1 Land cover change categories for the 1989–2000

change image

1989 2000 Land cover change

categories, 1989–2000

Non-forest Non-forest Stable non-forest

Non-forest Open forest Reforestation

Non-forest Dense forest Reforestation

Open forest Non-forest Deforestation

Open forest Open forest Stable forest

Open forest Dense forest Regrowth

Dense forest Non-forest Deforestation

Dense forest Open forest Degradation

Dense forest Dense forest Stable forest

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non-forest in 1989 and changed to either open or

dense forest in 2000. The remaining categories

represent land cover modification—changes that

affect the quality or density of forest cover without

changing the nature of the land cover class (forest).

Pixels that changed from open forest in 1989 to dense

forest in 2000 were categorized as ‘‘regrowth’’, while

pixels that changed from dense forest in 1989 to open

forest in 2000 were categorized as ‘‘degradation’’ (as

opposed to reforestation and deforestation, respec-

tively). This approach enables us to expand our

understanding of land use, by using information from

interviews with local communities to differentiate

drivers of land cover change such as agricultural

expansion (associated with deforestation) from driv-

ers of modification such as extraction of fuelwood

(more likely to be associated with forest degradation).

Assessment of forest status—fragmentation

Landscape metrics were calculated using the software

FRAGSTATS 2.0 (McGarigal et al. 2002). To sim-

plify interpretation, we selected a set of indices that

enabled us to quantify distinct aspects of spatial

pattern at the class level (Haines-Young and Chop-

ping 1996; McGarigal et al. 2002). Mean Patch Size

(MPS) and Patch Density (PD) provide indications of

the degree of fragmentation for different land cover

types and change images. Mean Shape Index (MSI),

Euclidean Nearest Neighbor distance (ENN), Clum-

piness (CLUMPY) and Interspersion Juxtaposition

Index (IJI) describe attributes of shape, isolation/

proximity, and contagion/interspersion. Complete

descriptions of these metrics, and equations for their

calculation, are provided in McGarigal et al. (2002).

At the patch level, one-tailed Mann–Whitney U

Tests (Sokal and Rohlf 1981) were used to assess

whether area, shape index and the Euclidean Nearest

Neighbor distance differed significantly between

categories of land cover change for each management

zone. Tests of significance for differences in patch

shape parallel those using the shape index and so are

not reported separately here.

Results and interpretation

Classification accuracy assessment

The accuracy of the 2000 classification was estimated

at 90.7%, with a kappa statistic of 0.86 (Table 2), and

the accuracy of the 1989 classification was estimated

at 90.7%, with a kappa statistic of 0.85 (Table 3).

These accuracies are well above the generally

accepted 85% standard for image classifications

(Foody 2002).

Institutional arrangements

User groups indicated different reasons for joining

the Community Forestry and Buffer Zone manage-

ment programs. Located next to the Rapti river, the

Buffer Forest user groups experienced frequent

flooding of their crops and households. In several of

these forests, protection was initiated with the aim of

regenerating degraded forests to create a protective

forest buffer, and mitigate the severity of future

flooding events. In contrast, many Community For-

estry user groups indicated that they were disturbed

by the degradation of their forest cover, and inspired

Table 2 Accuracy assessment for the 2000 classified image

Reference class Satellite map class

Non-forest Open forest Closed forest Column Total Producer’s accuracy (%)

Non-forest 13 0 1 14 92.9

Open forest 0 10 1 11 90.9

Closed forest 1 1 16 18 88.9

Row total 14 11 18 N = 43

User’s accuracy 92.9 90.9 88.9

Overall accuracy = 90.7%; Kappa = 0.86

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to participate in community forestry by the success of

community forestry programs in the Nepal hills.

Buffer Forests received substantial technical and

financial inputs from international and national NGOs

(Bookbinder et al. 1998), which the Community

Forests lacked. In 2002, Buffer Forest user commit-

tees earned an average annual income of 228,000

NRS (at the time of writing, approximately $3,500/

year), largely from tourism, and were able to utilize

some of this for forest maintenance and monitoring.

There was however also substantial variability

between user groups, with the forests located closer

to the park main entrance receiving greater revenues

from tourist visits. In contrast, users of all of the

Community Forests and of the Buffer Forests located

at a distance from the park gate lack the option of

earning substantial incomes from tourism, and thus

lack the finances to invest substantially in forest

plantation or development.

Although there are no significant differences in

terms of forest size or user group size, the user group:

forest ratio in Buffer Forests is significantly higher

than in Community Forests. This can indicate a

higher pool of users potentially available to partic-

ipate in management and monitoring per unit forest

area (Nagendra 2007). The substantial proportion of

forest monitoring appears to be contributed by the

communities in both management regimes. Results

from field visits in May 2005 indicate that these

communities have been able to protect their forests in

the face of some very difficult and insecure situations

following the intense conflicts within the country,

signifying the resilience of their efforts.

Forest change

Figure 2 depicts the distribution of land cover change

categories for different management regimes between

1989 and 2000. Clear differences in the proportional

distribution of the stable forest and stable non-forest

categories were observed across management

Table 3 Accuracy assessment for the 1989 classified image

Reference class Satellite map class

Non-forest Open forest Closed forest Column total Producer’s accuracy (%)

Non-forest 19 1 0 20 95.0

Open forest 0 10 0 10 100.0

Closed forest 2 1 10 13 76.9

Row total 21 13 10 N = 43

User’s accuracy 90.5 83.3 100.0

Overall accuracy = 90.7%; Kappa = 0.85

Fig. 3 Percentage of area

occupied by different land

cover change categories

across management zones

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regimes (Fig. 3). Community Forests contained the

highest proportion of stable forest area (59%),

followed by the Park Periphery (38%) and Buffer

Forests (34%), while the Surrounding Landscape had

the least area in stable forest (only 23%). Stable non-

forest distribution followed an opposite trend, occu-

pying only 2% of the Community Forests, but 43% of

the Surrounding Landscape.

These distributions have been shaped by past

histories of land use and by differences in the

biophysical location of these forests. Community

Forests in 1992 were largely located at higher

elevations and steeper slopes compared to all other

zones. Thus, this zone has much more area in stable

forest and much less area in stable non-forest

compared to the Buffer Forests, which experienced

significant land conversion to agriculture and urban

uses. The Chitwan National Park contains a rela-

tively high proportion of stable forest due to its

early designation as a park. At the Park Periphery,

there is frequent and extensive flooding from the

Rapti River leading to the formation of large open

floodplain grasslands, as well as burning for tradi-

tional harvest of thatch grass during specific times

of the year (Heinen and Mehta 1999). Hence we

also find a significant proportion of the Park

Periphery zone in stable non-forest. Finally, in the

Surrounding Landscape, largely comprised of areas

in the fertile Chitwan agricultural valley, forests had

been cleared in large sections by the late 1980s to

give way for agriculture, housing and road expan-

sion (Schweik et al. 1997; Matthews et al. 2000),

giving rise to a high proportion of area in stable

non-forest.

Community Forests were dominated by land cover

modification categories (over 30% in degradation and

regrowth), and contained a lower proportion of area

in land cover change categories (only 9% in defor-

estation and reforestation). The dominant direction of

land cover modification was towards regrowth (22%).

Buffer forests contained roughly similar distributions

of modification (31%) and change categories (26%),

but in contrast to Community Forests, were domi-

nated by reforestation (22%) and degradation (20%)

occurring simultaneously. Clearly, both these pro-

grams provide some protection to forests in this

landscape (Fig. 2). These differences indicate the

impact of land use history, management approaches

and the biophysical location of these two zones.

Buffer Forests are located along the Rapti river,

where large sections were cleared for agricultural use

up to the early 1990s. Following heavy flooding in

1993, and the establishment of the park buffer zone

program, there was extensive tree planting with the

aim of creating natural barriers to the river and

encouraging the return of wildlife to these areas,

thereby providing income to communities through

ecotourism. National and international aid agencies

including the King Mahendra Trust for Nature

Conservation (KMTNC), the Biodiversity Conserva-

tion Prioritization Project (BCPP), and the United

Nations Development Program (UNDP) provided

financial and technical support for tree planting

(Bookbinder et al. 1998; Nepal 2002). The increased

soil fertility in this riverine region encourages the

rapid growth of these trees. A combination of these

factors has resulted in a dramatic increase in forest

cover (reforestation) within a relatively short period

of 10 years in the Buffer Forests.

In contrast, the Community Forests were mostly

protected from land cover clearing for agriculture and

urban construction due to their location on higher

elevations and steeper slopes (Schweik 2000). Nev-

ertheless, these forests were heavily used by local

communities prior to the 1990s for grazing, extrac-

tion of timber, fuelwood, and non-timber forest

products, leading to the creation of highly degraded

forests in many instances (Matthews 2000; Nepal

2002). Following initiation of community forest

activities, user groups in this landscape have largely

managed their degraded forests by providing protec-

tion from grazing, fuelwood, and timber extraction

with minimal planting (Nagendra et al. 2005; Ostrom

and Nagendra 2006). Consequently, there has been an

increase in vegetation density (22% regrowth) in the

Community Forests, in contrast to the substantial

change from non-forest area to forested area (22%

reforestation) in the Buffer Forests (Fig. 2). While the

impact of protection has made it difficult to clear

forested land for other, agricultural and urban land

uses, it is interesting to note that there is more

degradation than deforestation in both zones. The

Buffer Forests exhibit greater degradation than the

Community Forests, indicating the continued pres-

ence of unauthorized extraction activities in this area.

The Park Periphery shows significant degradation

(21%), indicating the impact of extraction of fuel-

wood and burning when this area is opened to local

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communities during certain days of the year (Heinen

and Mehta 1999). Finally, in the Surrounding Land-

scape, both deforestation (8%) and degradation

(10%) are taking place, indicating the continued

human demand for land conversion and forest

resource extraction in this region. Alongside, there

is an increase in forest cover and density (9%

reforestation, 7% regrowth). This can be largely

traced to tree planting programs by the East-Rapti

Irrigation Project of the Department of Irrigation

along irrigation canals, small patches of private

plantations, and limited forest department planting

and maintenance activities in national forest patches

(Schweik et al. 2003).

Patterns of landscape and forest fragmentation

Table 4 describes differences in spatial pattern for

land cover change categories for each of the four

management zones. Overall, all management zones

exhibited larger and more irregular shaped patches of

stable forest and stable non-forests than forest change

categories. Community Forests had the largest

patches of stable forest, while the Surrounding

Landscape had the largest patches of stable non-

forest (Table 4). Overall patch density for all land

cover change categories was highest in the Surround-

ing Landscape, indicating the high degree of

landscape fragmentation in this region containing

mostly private land holdings, and lowest in the

community-managed Buffer Forests and Community

Forests.

In Community Forests, patches of reforestation

were more fragmented (smaller in size, located

farther apart, and more regular in shape) compared

to regrowth. This finding is substantiated by analysis

at the patch level (Table 5). Reforestation patches

also had lower patch density, lowered clumpiness and

greater interspersion-juxtaposition. But the opposite

is found in the Buffer Forests, where patches of

reforestation are less fragmented than patches of

regrowth, being larger, more irregular in shape, and

closer together, as well as more clumpy and more

interspersed. These differences in patch area and

nearest neighbor distance are not, however, signifi-

cant at the patch level (Table 6). These trends largely

parallel the differences in percentage area occupied

by the corresponding land cover change categories in

these zones. In Community Forests, where there is

increased regrowth, the reforestation category shows

significantly greater fragmentation. In the Buffer

Forests, where there is greater reforestation, the

regrowth category is more fragmented, although

these differences do not appear to be statistically

significant.

Both zones also contain more degradation as a

percentage of total area than deforestation. However,

although patches of degradation are correspondingly

large in Buffer Forests (Table 6), this is not the case

in the Community Forests, where degradation repre-

sents the more fragmented category, and patches of

deforestation are significantly larger—and also

located significantly farther apart—compared to

patches of degradation (Table 5). This indicates the

need for further investigation in the field.

In the Park Periphery, the reforestation and

regrowth categories are more fragmented compared

to deforestation and degradation (Tables 4 and 7).

These findings parallel the relative distributions of

these change categories and highlight the impact of

fire and clearing for thatch grass and fuelwood

extraction in the Park Periphery during certain days

of the year (Heinen and Mehta 1999; Nepal 2002). In

the Surrounding Landscape, degradation represents

the category of land cover modification or change

with the largest patch area, located closest together,

and with maximum clumpiness (Tables 4 and 8). The

Surrounding Landscape appears to be headed towards

a trajectory of increased degradation. Despite refor-

estation occupying a reasonably high proportion of

this zone (Fig. 3), this category is dominated by

relatively small patches of private forests and tree

planting activities in narrow strips alongside roadside

canals following the East-Rapti Irrigation Project

(Schweik et al. 2003). Thus, reforestation remains

fragmented in the Surrounding Landscape, with a

small patch size and large inter-patch distance

(Tables 4 and 8).

Discussion

There has been significant debate about the establish-

ment of different formal tenure mechanisms—

government, private, or community—for conservation

(Ostrom and Nagendra 2006). In this dynamic, forested

landscape in the Nepal Terai plains, different forest

Landscape Ecol

123

management regimes and policy environments have

impacted the extent and spatial pattern of forest cover

change over the past decade. However, formal tenure is

no indication of success in and of itself. Our results

show significant differences in the extent and spatial

pattern of forest change when comparing the four

primary forest management zones in this landscape.

The Park Periphery exhibited the highest proportion of

degraded forest and deforestation. This demonstrates

the susceptibility of the areas located just within the

park boundary to human impact from the villages

located outside the CNP, despite frequent monitoring

by the well-staffed Department of National Parks and

Wildlife Conservation, and the Nepal Army.

Table 4 Metrics of spatial pattern summarized for change categories in the four management zones

Change

categories

Community

forests

Buffer

forests

Park

periphery

Surrounding

landscape

Mean patch area (ha) Stable forest 9.00 1.373 3.43 2.05

Stable non-forest 0.70 0.68 5.14 9.08

Reforestation 0.17 0.68 0.35 0.40

Regrowth 0.74 0.31 0.37 0.49

Deforestation 0.65 0.28 0.64 0.38

Degradation 0.27 1.47 0.83 0.65

Mean patch nearest neighbor distance (m) Stable forest 63.35 70.29 68.69 76.63


Reforestation 96.88 76.95 97.39 85.01

Regrowth 70.16 78.82 79.03 89.64

Deforestation 127.56 113.95 94.10 88.44

Degradation 82.13 84.05 68.92 80.21

Mean patch shape index Stable forest 1.50 1.34 1.35 1.32


Reforestation 1.07 1.20 1.16 1.19

Regrowth 1.32 1.19 1.21 1.25

Deforestation 1.22 1.16 1.21 1.16

Degradation 1.16 1.29 1.29 1.22

Patch density (ha–1) Stable forest 0.05 0.15 2.65 7.78


Reforestation 0.19 0.20 3.37 116.00

Regrowth 0.22 0.21 5.43 9.45

Deforestation 0.06 0.09 2.96 13.40

Degradation 0.25 0.08 6.30 10.88

Clumpy Stable forest 0.71 0.63 0.56 0.57


Reforestation 0.20 0.54 0.39 0.37

Regrowth 0.47 0.32 0.34 0.40

Deforestation 0.56 0.34 0.54 0.41

Degradation 0.30 0.67 0.47 0.50

IJI Stable forest 69.31 81.56 69.34 86.19


Reforestation 74.87 90.88 87.70 83.70

Regrowth 37.37 66.38 47.27 59.03

Deforestation 85.70 96.63 81.22 77.48

Degradation 59.15 64.39 47.47 69.77

Landscape Ecol

123

Table 5 Results of a one-tailed Mann–Whitney analysis of differences in mean patch area (Area) and mean patch nearest neighbor

distance (NND), for the Community Forests

AreaNND

Stableforest (SF)

Stable non-forest (SNF)

Reforestation(RF)

Regrowth(RG)

Deforestation(DF)

Degradation(DG)

Stable Forest (SF) SNF>SF**

p < 0.001SF>RF**p < 0.001

RG>SFp = 0.13

DF>SF**p = 0.008

SF>DGp = 0.88

Stable Non-forest (SNF) SNF>SF**

p < 0.001SNF>RF**p < 0.001

SNF>RG**p = 0.005

SNF>DFp = 0.13

SNF>DG**p = 0.001

Reforestation(RF) RF>SF**

p < 0.001SNF>RF**p = 0.008

RG>RF**p < 0.001

DF>RF**p < 0.001

DG>RF**p < 0.001

Regrowth(RG) RG>SF**

p < 0.001SNF>RG**p < 0.001

RF>RG**p < 0.001

DF>RGp = 0.08

RG>DG**p = 0.003

Deforestation(DF) DF>SF**

p < 0.001SNF>DFp = 0.09

DF>RFp = 0.20

DF>RG**p < 0.001

DF>DG**p < 0.001

Degradation(DG) DG>SF**

p < 0.001SNF>DG**p < 0.001

RF>DG**p < 0.001

DG>RG**p < 0.001

DF>DG**p < 0.001

** Significant at P \ 0.01


distance (NND), for the Buffer Forests

AreaNND

Stableforest (SF)

Stable Non-forest (SNF)

Reforestation(RF)

Regrowth(RG)

Deforestation(DF)

Degradation(DG)

Stable Forest (SF) SF>SNF

p = 0.65SF>RF*p = 0.013

SF>RG*p = 0.02

SF>DF p = 0.47

DG>SFp = 0.84


p < 0.001SNF>RFp = 0.11

SNF>RGp = 0.15

SNF>DFp = 0.0.83

DG>SNFp = 0.57


p < 0.001SNF>RF**p < 0.001

RG>RFp = 0.81

DF>RFp = 0.16

DG>RF*p = 0.03


p < 0.001SNF>RF** p < 0.001

RF>RGp = 0.80

DF>RGp = 0.20

DG>RG*p = 0.03


p < 0.001SNF>DFp = 0.89

DF>RF**p < 0.001

DF>RG**p < 0.001

DG>DFp = 0.40


p < 0.001SNF>RF** p = 0.001

DG>RFp = 0.08

DG>RGp = 0.05

DF>DG**p = 0.002

* Significant at P \ 0.05; ** Significant at P \ 0.01

Landscape Ecol

123


distance (NND), for the Park Periphery

AreaNND

Stableforest (SF)


Reforestation(RF)

Regrowth(RG)

Deforestation (DF)

Degradation(DG)

Stable Forest (SF) SF>SNF**

p < 0.001SF>RF**p < 0.001

SF>RG*p = 0.02

SF>DF**p = 0.002

DG>SF**p = 0.009


p < 0.001RF>SNFp = 0.25

RG>SNF**p < 0.001

DF>SNF**p < 0.001

DG >SNF**p < 0.001


p < 0.001SNF>RFp = 0.61

RG>RF**p < 0.001

DF>RF**p < 0.001

DG>RF**p < 0.001


p < 0.001SNF>RG**p < 0.001

RF>RG**p < 0.001

RG>DFp = 0.19

DG>RG**p < 0.001


p < 0.001SNF>DF**p < 0.002

RF>DF**p < 0.001

DF>RG**p < 0.001

DG>DF**p < 0.001


p < 0.001SNF>DG**p < 0.002

RF>DG**p < 0.001

RG>DG**p < 0.001

DF>DG**p < 0.001



distance (NND), for the Surrounding Landscape

AreaNND

Stableforest (SF)


Reforestation(RF)

Regrowth(RG)

Deforestation(DF)

Degradation(DG)

Stable Forest (SF) SNF>SF**

p < 0.001SF>RF**p < 0.001

SF>RG*p = 0.04

SF>DF**p < 0.001

SF>DGp = 0.93


p < 0.001SNF>RF**p < 0.001

SNF>RG**p < 0.001

SNF>DF**p < 0.001

SNF>DG**p < 0.001

Reforestation(RF) RF>SNF**

p < 0.001RF>SNF**p = 0.005

RG>RF*p = 0.04

RF>DF**p = 0.001

DG>RF**p < 0.001

Regrowth(RG) RG>SNF**

p < 0.001SNF>RGp = 0.73

RF>RG**p < 0.001

RG>DF**p < 0.001

DG>RG*p = 0.04


p < 0.001DF>SNF**p < 0.001

DF>RF**p < 0.001

DF>RG**p < 0.001

DG>DF**p < 0.001


p < 0.001SNF>DG**p < 0.001

RF>DG**p < 0.001

RG>DG**p < 0.001

DF>DG**p < 0.001


Landscape Ecol

123

In contrast, the Buffer Forest and Community

Forest user groups consider the rules determining

forest access to be legitimate, and are willing and

active participants in monitoring the forests and

sanctioning of offenders. These groups have been

clearly successful in protecting forest cover, limiting

forest fragmentation, and encouraging regrowth. We

also find a relative emphasis towards management by

forest protection in the less wealthy Community

Forests, leading to regrowth, as compared to the

emphasis on tree planting activities that have led to

reforestation in the Buffer Forests (Seidensticker

2002; Nepal 2002). Finally, the large-scale clearing

and fragmentation of forests in the largely privately

owned Surrounding Landscape represents the poten-

tial fate of forest cover in other management zones,

had they not been under some form of government or

community protection.

These forests are embedded within the larger-level

setting of the Nepal Terai plains, and this regional

setting can significantly influence some of the

outcomes seen. Although large-scale deforestation

has been reported in the Terai in recent decades (Ives

2004), and significant concerns have been expressed

about the success of community forestry in the Terai

(Nagendra 2002), we find that participatory manage-

ment in the Buffer Forests and Community Forests

has been effective at encouraging forest regrowth.

This is despite institutional challenges that include

the significant socio-economic and ethnic heteroge-

neity of user groups, the often large and unwieldy

user group sizes, and the high timber value of these

forests which encourages cross-border timber smug-

gling by poachers (Nagendra 2002; Ives 2004).

Part of the reforestation in the Buffer Forests can

be attributed to the location of these forests adjacent

to the CNP. The CNP is a highly prominent park in

Nepal, visited by large numbers of tourists every

year, and some of the communities earn significant

incomes from ecotourism (Nagendra et al. 2005;

Bookbinder et al. 1998). Yet the extent of regrowth

observed in both Buffer Forests and Community

Forests indicates that community forestry can be a

powerful force for forest conservation in the Nepal

Terai, and points to the need to scale up the

implementation of these programs in this area.

Acknowledgments This research was supported by the

National Science Foundation (Grant SBR9521918), and the

Society in Science: Branco Weiss Fellowship to HN. The

authors are grateful to Birendra Karna, Mukunda Karmacharya,

Sudil Acharya and Kanchan Thapa for their valuable assistance

in the field, and to Elinor Ostrom, Ganesh Shivakoti and

George Varughese for insightful discussions.

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