ORI GIN AL PA PER

Examining complexities of forest cover changeduring armed conflict on Nicaragua’s Atlantic Coast

Kara Stevens • Lindsay Campbell • Gerald Urquhart • Dan Kramer •

Jiaguo Qi

Received: 2 February 2011 / Accepted: 15 June 2011� Springer Science+Business Media B.V. 2011

Abstract The effects of armed conflict on biodiversity are an emerging concern in

conservation due in part to the occurrence of war in biodiversity hotspots, though few

studies have addressed it. We investigate this topic by examining changes in forest cover

on the Atlantic Coast of Nicaragua from 1978 to 1993, a period covering their civil war.

We predict an increase in forest cover between pre- and post-conflict periods as residents

abandoned agriculture plots and migrated from conflict areas. We used a remote sensing

approach to detect changes in forest cover area and fragmentation at two study sites.

Results confirmed that in the first 5–7 years of the conflict, reforestation was greater than

deforestation, but in the latter years of the conflict deforested land almost doubled that

which was reforested. Although some forest loss was due to Category 4 Hurricane Joan,

several conflict-related factors were partially responsible for these results, such as mass

human migration and land reform. Understanding how and why forest cover changes

during periods of conflict can help conservationists protect resources both during war and

in the tumultuous period following the cessation of violence when nascent governments

lack the power to effectively govern and community institutions are fractured by war. In

areas where the livelihoods of people are directly dependent on local resources, antic-

ipating ecological and social impacts can help improve future conservation efforts.

Keywords Conflict � Deforestation � Land-cover change � Nicaragua � War

K. Stevens (&) � G. Urquhart � D. KramerDepartment of Fisheries and Wildlife, Michigan State University, 13 Natural Resources,East Lansing, MI 48828, USAe-mail: steve492@msu.edu

L. Campbell � J. QiCenter for Global Change and Earth Observation, Michigan State University, East Lansing, MI, USA

G. UrquhartLyman Briggs College, Michigan State University, East Lansing, MI, USA

D. KramerJames Madison College, Michigan State University, East Lansing, MI, USA

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Biodivers ConservDOI 10.1007/s10531-011-0093-1

AbbreviationsAI Aggregation index

CA Class area

FSLN Sandinista National Liberation Front

MSS Multispectral scanner

NIR Near infra-red

R.A.A.N. Northern Atlantic Autonomous Region

R.A.A.S. Southern Atlantic Autonomous Region

RMS Root mean square

TM Thematic mapper

UTM Universal transverse mercator

Introduction

Natural resources and armed conflict are intricately linked, with resource scarcity or

competition cited as a causative agent of conflict (Johannesson 2004; Ross 2004) or natural

resources destroyed during conflict either inadvertently as a side effect of war or delib-

erately to destroy enemy territory and affect local livelihoods (Lamb 1985; United Nations

Environment Program 2007). In addition, increased exploitation of resources can occur

immediately following conflict as weakened governments and communities transition to

peace but lack stability or power to effectively manage and prevent exploitation. Resources

can be used as a tool for peacebuilding and fostering cooperation (Le Billon 2000; Hatton

et al. 2001; Conca and Wallace 2009). Despite the links, few studies have examined

empirically the effects of war on the environment. Understanding the direct and indirect

impacts of armed conflict on biodiversity was highlighted as one of the top 100 questions

of importance to the conservation of global biodiversity (Sutherland et al. 2009). The links

between conflict and deforestation are particularly important—conflict may facilitate or

prevent industrial and subsistence use of forests, such as logging or agriculture, and the

resulting impact has important implications for post-conflict restoration and rehabilitation

of both social and ecological systems.

To address the larger topic of the relationship between conservation and conflict, this

study investigates changes in forest cover on the Atlantic Coast of Nicaragua during its

civil war, in which 30,000 Nicaraguans died and hundreds of thousands of Nicaraguan

citizens were displaced in the 1980s (Prevost 1987; Sollis 1989). As with many conflicts

throughout the past century, this one took place in an area of rich biodiversity (Hanson

et al. 2009). Nicaragua’s Atlantic Coast hosts one of the largest remaining tracts of

lowland tropical rainforest in Mesoamerica, which holds populations of five globally

threatened large mammal species (International Union for Conservation of Nature 2011)

and along with one of the most productive fisheries of the Caribbean Sea, supports the

livelihood of the unique indigenous cultures of the semi-autonomous Atlantic Coast. The

armed conflict between the Sandinista National Liberation Front (FSLN) and Contra rebel

groups directly involved or indirectly affected rural areas of eastern Nicaragua from 1979

to 1990.

Nicaragua’s leadership from the 1970s to the 1990s had markedly different approaches

to land use and the environment. A lack of environmental policy characterized the Somoza

family rule from 1937 to 1979, a time when exploitation for economic benefit was the

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norm, and foreign firms exploited the country’s natural resources for the economic benefit

of the political leadership (Rice 1989; Sollis 1989; Vilas 1989). The Sandinista takeover in

1979 sought to nationalize and redistribute land from large landowners to the landless and

smallholders. By 1985, they had succeeded in seizing 1.8 million ha of land, two-thirds of

which was on the Atlantic Coast (Wearne 2002). The Contra War prevented lumber

operations from building access roads to prized timber, thus preventing land use change

typically found along road arteries (Vandermeer 1991). Neither fighting faction inten-

tionally destroyed Nicaragua’s vast tracts of tropical forest on the Atlantic Coast during the

war (Vandermeer 1991). Forced conscription, a depressed economic system, and conflict

between rebel groups and the Sandinista army pushed people from remote rural regions to

city centers for safety, and tens of thousands eventually fled across Nicaragua’s borders to

Honduras and Costa Rica (Basok 1990; Christie et al. 2000). From 1981 to 1989, the major

cities along the Atlantic Coast, including Bluefields, Puerto Cabezas and Siuna, doubled in

population (Sollis 1989). This migration, coupled with the decline in commercial timber

exploitation may have resulted in no reduction in forest cover throughout the period of the

war, and perhaps some forest regrowth in fields left fallow.

Previous work has been mixed on the environmental effects of Nicaragua’s civil war.

Some reports suggest that wildlife and fish populations rebounded during the war due to a

lack of economic growth or exploitation during the conflict (Nietschmann 1990). Hundreds

of thousands of people were displaced in the twelve-year period, and as agricultural fields

and pastures were abandoned, forest regrowth is believed to have taken place (Nietsch-

mann 1990). Others suggest the environment was another victim of the war (Heiner et al.

1989). Reports provide differing accounts of the extent of agriculture and extraction that

occurred and how it affected forest cover, and none have used quantitative methods to

describe the change.

Quantitative assessments of forest fragmentation during periods of conflict coupled with

potential economic and social drivers are a topic scarcely covered in the literature. Periods

of conflict can further deplete or restore forested regions depending on the characteristics,

location and duration of the conflict. Two published studies used a remote sensing

approach to examine the effects of conflict on land cover change, but primarily focused on

changes in agricultural land (Suthakar and Bui 2008; Witmer and O’Loughlin 2009).

Although several studies have demonstrated the capacity to quantify forest cover change

using multitemporal satellite imagery in tropical regions (Skole and Tucker 1993; Moran

et al. 1994; Mertens and Lambin 1997; Geoghegan et al. 2001), to our knowledge, this is

the first study in the literature using a remote sensing approach to examine forest cover

change during a period of conflict (Suthakar and Bui 2008; Witmer and O’Loughlin 2009).

We hypothesize the following changes in forest cover over the period of the civil war in

Nicaragua:

(1) An increase in forest cover along the Atlantic Coast during the armed conflict

compared to pre-conflict forest cover extents due to abandonment of rural areas and

reduction in agricultural expansion as a result of the conflict;

(2) An increase in forest fragmentation, exhibited by more numerous forest patches

between pre- and post-conflict time periods within the study sites based on areas of

settlement. Areas of settlement are found along the eastern coast of the southern site

and along a major roadway in the northern site.

The objective of this study is to quantify forest cover change over the period of

Nicaragua’s civil war and to better understand interactions between conflict, local

communities and the natural environment.

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Study site

Two sites with a total area of approximately 160,000 ha were selected for analysis based

on our ability to locate forested areas with relatively cloud-free satellite imagery along

Nicaragua’s Atlantic Coast for three time periods, during similar phenological stages, set at

5 year minimum intervals between 1978 and 1993 (Fig. 1; Table 1). We based minimum

temporal periods on previous studies that determined that a 5-year interval was sufficient

for establishment of pioneer species after disturbance from hurricane or agricultural

abandonment (Uhl and Jordan 1984; Boucher et al. 2001; Guariguata and Ostertag 2001).

The sites host two types of forest ecoregions: Central American Atlantic moist forest and

Meso-American Gulf Caribbean mangroves (Olson et al. 2001). Atlantic moist forests

predominate along the Atlantic Coast. Common species include Dipteryx panamensis,

Cordia alliodora, Cecropia obtusifolia, Galipea granulosa, Dendropanax arboreus, Mi-conia spp., Pentaclethra macroloba, Qualea spp., Ceiba pentandra and Ficus spp. (Taylor

1963; Christie et al. 2000). Mangrove forests reside at the confluence of the terrestrial and

aquatic ecosystems of Pearl Lagoon and much of the Atlantic Coast. Species include red

(Rhizophora mangle), white (Languncularia racemosa), and black (Avicennia germinans)

mangrove as well as buttonwood mangrove (Conocarpus erectus) (Roth 1992).

The Atlantic Coast has the lowest population density in Nicaragua, estimated at 4

people/km2 in 1989 (Campbell and Hammond 1989). The main ethnic groups depending

upon these forests for their livelihoods are the Rama, Miskitu, Sumu, Garifuna, Creole and

Fig. 1 Study areas from the R.A.A.S. and R.A.A.N. provinces of Nicaragua

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Mestizo people, who practice subsistence fishing and farming in the autonomous regions

that comprise the northern [Region Autonoma del Atlantico Norte (Northern Atlantic

Autonomous Region) or R.A.A.N.] and southern [Region Autonoma del Atlantico Sur

(Southern Atlantic Autonomous Region) or R.A.A.S.] provinces of the Atlantic Coast. The

Mestizo population in this area immigrated from the western region of the country and has

different patterns of resource use and political affiliations compared to indigenous popu-

lations in the rest of the study area. The site in the R.A.A.N. will be referred to as the

northern site, and the site in the R.A.A.S. as the southern site, hereafter.

Methods

This study used post-classification change detection methods to quantify conversion from

forest to non-forest or from non-forest to forest based on multitemporal satellite images.

We generated a ‘‘from–to’’ matrix to quantify trends from the change detection analyses,

and these temporal trends were then analyzed in conjunction with previously reported data

on land tenure, human migration and funding for the conflict to explain potential causes of

forest change.

Landsat satellite images with a universal transverse mercator (UTM) Zone 17N pro-

jection were obtained from three different time periods (Table 1). We acquired Landsat 3

multispectral scanner (MSS) images with a 60 m resolution for the southern site and we

geometrically corrected the 1978 and 1992 images to the 1985 image with root mean square

(RMS) values\0.5 pixels (Bannari et al. 1995). We acquired Landsat 5 MSS and thematic

mapper (TM) images for the northern site and resampled the 1978 MSS image to a 30 m

resolution before geometrically correcting it to the 1988 and 1993 TM images with an

RMS \ 0.05 pixels. Resampling did not improve the MSS image resolution, but provided

consistency in pixel size comparison across time intervals. We atmospherically corrected all

images from both sites using a CosT atmospheric correction model (Chavez 1996).

We selected regions experiencing minimal cloud cover in the satellite imagery across all

time periods. We partitioned images into individual classes based on variations in spectral

signatures using an ISODATA unsupervised classification method (Lillesand et al. 2004).

Parameters were set on the principal axis with two standard deviations, approximate true

color, and 10 iterations or 95% convergence.

Anderson’s Level I classification scheme acted as a framework from which to divide the

initial classes into specific land cover categories (Anderson et al. 1976). Pixels classified as

Table 1 Selected satellite imag-ery for land cover changeanalysis

Satellite Resolution(m)

WRS path/row

Date

Southern study site

Landsat MSS 60 p16r51 1 April 1978

Landsat MSS 60 p16r51 8 April 1985

Landsat MSS 60 p16r51 5 May 1992

Northern study site

Landsat MSS 60 p16r50 1 April 1978

Landsat TM 30 p15r50 8 April 1988

Landsat TM 30 p15r50 9 February 1993

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forest included deciduous, mixed forested land, and secondary forest. Pixels classified as

non-forest included those identified as agriculture, rangeland, and barren land. Urban and

built-up land were not present in this study area, and we used a mask to eliminate other

non-relevant categories (water, wetland, cloud, or cloud shadow). Execution of a 3 9 3

statistical majority filter across each image eliminated classification noise (Mather 2004).

Output images contained only forest and non-forest pixels that were never classified as

water, wetland, cloud, or cloud shadow to ensure an equal area comparison across all time

periods.

Our ability to accurately associate land cover types to spectral signatures was limited

due to a lack of ground truth data from historical time periods. Therefore, we relied on

visual interpretation to determine appropriate land cover types (Martin and Howarth 1989;

Skole and Tucker 1993; Mas and Ramirez 1996). Reduced land cover categories (forest,

non-forest) made class differentiation using visual interpretation relatively straightforward

and fairly accurate, although differentiation between land cover categories with similar

spectral signatures is a common challenge when working with multi-spectral imagery. For

example, distinguishing primary from secondary forest or grassland from shrubland,

agriculture, or savanna proves challenging when training data does not exist. In this study,

we created a forest class that included both primary and secondary forest to eliminate

spectral confusion. Therefore, we needed only to distinguish these two classes from

alternative, non-forest land cover types. While still a formidable task, one advantage was

that forest regeneration in tropical regions saturates the near infra-red (NIR) band in multi-

spectral imagery quickly due to the presence of dense, green biomass, and this phenom-

enon is most prominent during dry season months when crop foliage is least dense, pro-

viding a tool to differentiate secondary forest from non-forest categories (Steininger 1996).

Despite class aggregation, the advantage of high NIR reflectance during forest regeneration

stages, and image acquisition during dry season months, classification uncertainty

remained with misclassification potential existing between young pioneer tree saplings and

shrubs, forbs or grasses.

Because ground truth data for each time period and region did not exist, we generated

50 points each located within classified forest and non-forest pixels from each image using

a stratified random sampling approach (Jensen 1996). Points located at the perimeter

between classes or No Data values were not included in the accuracy assessment because

of 3 9 3 filter effects in these regions. The random points were reclassified and then

compared to the existing classification. Producer’s accuracy values ranged from 84.31 to

95.65% across forest and non-forest classes in the southern site, while user’s accuracy

values ranged from 84 to 96%. The overall Kappa coefficient values ranged from 0.71 to

0.84. The northern site exhibited producer’s accuracy values ranging from 81.36 to

95.12%, user’s accuracy values ranging from 78 to 96%, and overall Kappa coefficient

values ranging from 0.69 to 0.90.

Change detection

Forest and non-forest pixel areas were measured for each time period in each site and older

values were subtracted from newer values to quantify change. Pixel values equal to zero

corresponded to no change from either forest or non-forest classes; pixel values equal to 1

indicated a change from non-forest to forest; and pixel values equal to -1 indicated a

change from forest to non-forest between two time periods. We calculated the deforestation

rate using Eq. 1.

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R ¼ 1=ðt2 � t1ÞðlnðA2 � A1ÞÞ ð1Þ

where R is the deforestation rate and A1 and A2 are the amount of forested area at time 1

and time 2, respectively (Puyravaud 2003).

Fragmentation

We divided each study site by a north–south or east–west demarcation based on spatial

variation in land use between areas of indigenous/Afro-Caribbean settlement and relatively

remote regions before conducting a fragmentation analysis (Fig. 1). In the southern site,

communities reside along the lagoon edge with relatively remote areas located beyond

10 km from the lagoon edge. The remote area of the northern site began 5 km south of the

roadway. We created subsets in order to make a targeted analysis of changes within regions

likely abandoned or untouched during the conflict and changes within regions where

people concentrated. The purpose was to compare variation over time periods and not

between different regions.

We calculated three landscape metrics to characterize and to quantify forest fragmen-

tation in each area between time periods. We quantified class area (CA), number of patches

(NP), and aggregation index (AI) values using an 8-neighbor rule (Gergel and Turner 2002;

McGarigal et al. 2002). CA summed the total forest CA and the total non-forest CA and

converted the units into hectares (McGarigal et al. 2002). Number of patches calculated the

number of forest and non-forest land cover patches in each site in each time period

(McGarigal et al. 2002). No background or border cells were included in the calculation,

and patch attributes characterizing shape or size were not described in this metric.

The AI is a class-specific measurement that derives land cover aggregation values based

on shared cell edges within a raster grid. AI is the ratio of the number of shared grid cell

edges from a particular class versus the maximum possible number of shared grid cell

edges from that class (He et al. 2000). Values increase from 0 to 1.0 as aggregation

increases (Eq. 2).

AI ¼ gii

max! gii

� �100ð Þ ð2Þ

gii is the number of like adjacencies between pixels of patch type i and max ? gii is equal

to the maximum number of like adjacencies between pixels of patch type i (McGarigal

et al. 2002). This metric ignored boundary edges and used a single-count method. Metric

values from older images were subtracted from newer images to identify changes in forest

fragmentation across time periods.

Results

Change detection

Thirteen percent (13%) of the total study area was deforested between pre-conflict (1978)

and post-conflict (1992/1993). Approximately 10% of the landscape changed from non-

forest to forest cover, and over 75% showed no change. The northern and southern sites

showed a similar trend in that the area of land deforested from the mid-1980s to the end of

the conflict was much higher than in the previous 7–10 years (Table 2). The time period

from 1978 to 1985/1988 will be referred to as the first interval, and the time period from

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1985/1988 to 1992/1993 will be referred to as the second interval, hereafter. The Mestizo

settlement of Pueblo Nuevo in the northwest corner of the southern site experienced

deforestation during the first interval, while reforestation occurred during the second

interval (Fig. 2).

Fragmentation analysis

Settled areas of both sites accounted for numerous patches of deforestation during the

second interval (Table 3; Figs. 2, 3). Remote areas also increased in number of patches

across all time periods. Settled regions of both sites increased in forest cover during the

first interval, but lost forest cover in the second interval, resulting in little net change

between pre- and post-conflict. The remote area of the northern site increased in forest

cover by 0.1% between pre- and post-conflict periods, while the remote area of the

southern site declined by 20%. The total number of patches increased in remote and settled

regions of both sites between pre- and post-conflict, although the number of patches

decreased in the migrant-settled region, with 22% fewer forest patches in 1992 than in

1978. The entire southern site, including the migrant-settled area, decreased in net

aggregation between 1978 and 1992, while the northern site increased in overall

aggregation.

Discussion

Our hypothesis that more reforestation than deforestation occurred between the pre- and

post-conflict periods was accurate for the 160,000 ha area analyzed for this study for the

first interval. Results from the second interval, an increase in deforestation in both study

sites, indicated the complexities of forest cover change during conflict. We also hypoth-

esized an increase in forest fragmentation in settled areas between pre-and post conflict

periods as measured by number of patches and the AI. In fact, both settled and remote areas

increased in fragmentation over the conflict period, and trends between study sites over the

two time intervals demonstrated a lack of uniformity in land-cover change. Several factors

may have contributed to these results, including Hurricane Joan, human migration, land

reform, and the date used as ‘‘post-conflict’’ for the purposes of this analysis. These factors

and the significance of the results will be discussed here.

Table 2 Forest cover change from 1978 to 1993 on Nicaragua’s Atlantic Coast

Reforestation(%)

Deforestation(%)

Nochange(%)

Net change inforest coverper annum (ha)

Deforestationrate (%)

Southern site

1978–1985 10.8 7.8 81.4 221.9 0.52

1985–1992 6.8 17.8 75.4 -795.9 -1.94

1978–1992 9.1 16.9 74.0 -287.1 -0.72

Northern site

1978–1988 12.4 8.7 78.9 401.4 0.44

1988–1993 7.1 12.0 80.9 -1,098 -1.20

1978–1993 10.1 11.4 78.6 -98.4 -0.11

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A net increase in forest cover in the northern and southern sites in the first interval

support our hypothesis that forest regeneration occurred during the time period that the

conflict intensified as determined by US Congressional funding and refugee movement

(Fig. 4). The United States Congress was the main funding source for the Contra rebel

group and as such, their support, as well as the mass emigration of Nicaraguan refugees,

serves as a proxy for the escalation of the conflict. The number of Nicaraguan refugees

peaked in 1989 two years after a peak in U.S. funding for Contra rebels that included $70

Fig. 2 Forest cover change in southern site: a from 1978 to 1985, b 1985–1992 and c 1978–1992

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million USD in military aid (Sobel 1995; The UN Refugee Agency 2011). Total forest

cover was highest when the conflict was most intense and decreased as the number of

refugees declined and U.S. funding tapered. Both sites experienced a \1% increase in

forested areas during the first interval, which supports previous research findings that

timber and fuel wood extraction were interrupted substantially.

Another potential cause of increased deforestation in the last years of the conflict may

relate to the dates we used as post-conflict for this analysis. Post-conflict is a nebulous

term. By varying accounts, the war ended or de-escalated between 1988 and 1990

(Table 4). We used 1992 (southern site) and 1993 (northern site) as post-conflict, because

Table 3 Analysis of change in fragmentation

Site Total number offorest patches

Total forest area (ha) Forestarea

Number offorestpatches

Aggregationindex

Southern site

1978 1985 1992 1978 1985 1992 Percent change 1978–1985

Remote 40 45 89 20,127 20,203 16,803 0.38 12.50 0.54

Afro-Caribbean/indigenoussettled

116 93 122 11,011 13,440 11,077 22.06 -19.83 1.48

Migrantsettled

11 24 9 10,715 9,780 9,952 -8.73 118.18 -1.10

Percent change 1985–1992

Remote -16.83 97.78 -4.41

Afro-Caribbean/indigenoussettled

-17.58 31.18 -3.77

Migrantsettled

1.76 -62.50 0.04

Northern site

1978 1988 1992 1978 1988 1993 Percent change 1978–1988

Remote 1,364 1,476 1,924 51,806 52,206 51,746 0.77 8.21 0.58

Afro-Caribbean/indigenoussettled

1,241 1,323 2,189 37,561 41,183 36,148 9.64 6.61 2.05

Percent change 1988–1993

Remote -0.88 30.35 0.70

Afro-Caribbean/indigenoussettled

-12.23 65.46 -0.06

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they were the first dates after these events in which we could acquire cloud-free imagery

for the study sites. By 1992, the number of refugees leaving Nicaragua had declined (The

UN Refugee Agency 2011) and many refugees had already resettled along the Atlantic

Coast and begun the process of rebuilding their homes and restoring and expanding

agricultural fields. Of the documented refugees repatriated from 1980 to 1995, approxi-

mately 58% returned between 1989 and 1992 (over 46,000 people), almost all of them

arrived from Honduras and Costa Rica (The UN Refugee Agency 2011). Therefore, our

estimates of forest cover loss between pre- and post-conflict are overstated because the

post-conflict dates of analysis are 3–4 years after the conflict ended.

We also hypothesized that an increase in fragmentation would take place in areas of

human settlement between pre- and post-conflict time periods, quantified by numbers of

patches and aggregation indices. Results provided mixed support for this hypothesis. All

regions, with the exception of the migrant-settled region, became more fragmented

between pre- and post-conflict according to the number of patches. The number of patches

decreased in the Afro-Caribbean and indigenous settled area of the southern site in the first

interval, but then increased substantially in the second interval, resulting in a net gain in

number of patches between pre- and post-conflict, which is partially explained by the

hurricane. Areas around small communities were dominated by a matrix of agriculture and

forest, thus allowing space for forest regrowth and potentially increased connectivity

Fig. 3 Forest cover change in northern site: a from 1978 to 1988, b 1988–1993 and c 1978–1993

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between patches. Trends in the settled area of the northern site supported our hypothesis—

the number of patches almost doubled between pre- and post-conflict periods.

While increases in numbers of patches often indicate a decrease in aggregation, this is

not always the case. Our results suggested small increases in forest aggregation in all study

areas, except the migrant-settled region, in the first interval. Interestingly, these increases

took place while the total number of forest patches also increased. This phenomenon is

possible because number of patches do not necessarily decrease linearly as aggregation

increases (He et al. 2000), leaving open the potential for different land cover configurations

to produce these results. In this case, the increase in forest patches may be attributed to

several smaller non-forested patches regenerating to forest, thereby increasing the overall

forest area, in addition to increasing the number of forest patches. As forest area increased,

the likelihood for the presence of adjacent forest patches increased, creating higher AI

Fig. 4 Changes in forest cover along Nicaragua’s Atlantic Coast during the civil war, along with U.S.Congressional funding and Nicaraguan refugee movements from 1982 to 1994

Table 4 Timeline of importantconflict-related events

1978 Pre-conflict Landsat images for southern and northernsite

1979 Sandinistas overthrow Somoza regime

1982 U.S. Congress begins funding Contras (Sobel 1995)

1985 Second Landsat image for southern site

1988(April)

Second Landsat image for northern site

1988(Oct)

Hurricane Joan

1988 Sapoa ceasefire

1989 Civil war ended (Centre for the Study of Civil War 2009)

1990 Sandinistas defeated in elections. U.S. Congress endedsupport for Contras (Sobel 1995).

1992/93 Post-conflict Landsat images for southern and northernsite

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values. Overall, small changes in values across all study areas did not provide substantial

results from which to determine whether fragmentation increased or decreased between

pre- and post-conflict, when measured by the AI.

One potential cause of land use change coincided with the change in government and

thus, the conflict. The Sandinistas instituted a policy of land reform in which landless

peasants received title to lands. Agricultural land held under cooperatives increased by

21% between 1978 and 1987 (Heiner et al. 1989) and more than 80,000 families in

Nicaragua received title to their lands (Carman 1988). Presumably, this reduced the

necessity to deforest an area as a means of demonstrating ownership and contributed to a

reduction in forest loss (Rice 1989; Vandermeer 1991; Stanfield 1995). Another factor

contributing to reduced forest loss is a decline in agricultural productivity that occurred

during the war. Reports indicate that agricultural activities were significantly disrupted

during the conflict. In northwest Nicaragua, crop production data from the Nicaraguan

government and Landsat MSS analysis indicated that agricultural land in production at the

beginning of the conflict in 1979–1980 was less than 60% of 1972–1973 levels because

fighting interfered with planting and prevented people from attending to agricultural plots

(Howe 1983). On the Atlantic Coast where agricultural plots are typically located outside

communities and nested in a forest and agricultural matrix, there was a similar withdrawal

from agricultural activities because of fear of violence or assault while distant from human

settlement (Barbee 1997; Christie et al. 2000). Studies from Bosnia and Sri Lanka during

conflict also demonstrate that agricultural fields were abandoned (Suthakar and Bui 2008;

Witmer and O’Loughlin 2009).

There are ten communities in the southern site and all but one is comprised of indig-

enous and Afro-Caribbean groups that rely to varying degrees on farming and fishing for

their livelihood. The only community in the site that is not majority indigenous is Pueblo

Nuevo, a Mestizo community on the Wawashang River. They do not typically fish as a

livelihood and rely to a greater extent on livestock production requiring pasture than

groups like the Miskitu, Garifuna or Creole people. This variation in livelihood strategy

may explain the contrasting trend in forest cover change of the Pueblo Nuevo region during

both time intervals relative to the other areas of the southern site.

The rate and type of forest regeneration after disturbance is influenced by several factors,

including the type of disturbance, landscape configuration, soil type, and species present

(Guariguata and Ostertag 2001; Chazdon et al. 2007). The forests of the southern study site

recovered from two major and distinct disturbance events—that of recovery from agriculture

abandonment and from Hurricane Joan, while the northern site recovered primarily from

agricultural abandonment. What is the potential composition and ecological value of these

regenerated forests? Agricultural land in the study site consists of small plots for subsistence

purposes, using low-intensity techniques of agro-forestry nested within a forest and agri-

culture matrix. This type of land-use is characterized by access to a seed bank, low soil

disturbance, as well as the presence of remnant vegetation, all features that lead to a more

rapid recovery from disturbance (Guariguata and Ostertag 2001; Chazdon 2003; Chazdon

et al. 2007). Category 4 Hurricane Joan devastated the southern Atlantic Coast in late

October 1988, felling large forest tracts in the southern site. An estimated 500,000 ha of

forest were damaged at the eye of the storm with impacts extending 50 km inland (Will 1991;

Yih et al. 1991). Though the eye of the storm was 55–135 km south of the sites, the hurricane

damaged areas as far north as La Fe (Sandra Sambola, personal communication, see Fig. 1).

The resulting forests after these transition stages are not uniform in terms of their

conservation value, which varies widely depending on the species, the metric used for

measuring diversity, and the scale of disturbance preceding regeneration. In a study

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conducted 5-years post-hurricane on the Atlantic Coast of Nicaragua (south of our study

site) comparing succession in post-hurricane forests and post-agriculture forests, Boucher

et al. (2001) found that successional species between these two disturbance areas were

distinct and that post-hurricane forests had greater tree species richness and evenness.

Forests regenerating from agriculture in our study site could be expected to host Cecropia,

Miconia and Vismia species 5 years after abandonment, while hurricane-damaged forests

would be dominated by species present in pre-hurricane forest such as Macrolobium spp.,

Vochysia ferruginea and Cupania glabra (Boucher et al. 2001). There were several tran-

sitions in land-use evident in the study site. Some areas became forested in the first interval

and changed back to non-forest in the second interval. The varying ability of taxa to persist

in secondary forests is an area of ongoing investigation (Barlow et al. 2007; Parry et al.

2007; Gardner et al. 2009; Chazdon et al. 2009; DeClerck et al. 2010) though large

mammals of conservation concern such as jaguar Panthera onca, Baird’s tapir Tapirusbairdii, and white-lipped peccary Tayassu pecari are known to use secondary forests

(Carrillo et al. 2002; Foerster and Vaughan 2002; Foster et al. 2010). As the area of land

covered in secondary forests expands globally [Food, Agriculture Organization (FAO)

2007], the species assemblage of these forests has important conservation implications.

Understanding how forest cover is affected during periods of conflict and the causes of

this change will help conservationists and managers protect resources in the tumultuous

period following the cessation of violence when nascent governments lack power to

effectively govern and community institutions are fractured by war. During this time,

exploitation is often uncontrolled and is a potential opportunity for conservation inter-

vention to buttress community institutions and prevent substantial losses to biodiversity

(Hatton et al. 2001). The effects of the conflict are still felt along the Atlantic Coast today

as refugees escaping violence around small communities never returned from city centers,

livelihoods and gender roles shifted as a result of diminished ability to practice agriculture,

hunt or fish during the war and social dynamics were altered as a result of differing

political affiliations and alliances (Barbee 1997; Christie et al. 2000). This study highlights

heterogeneity in the effects of war on forest cover based on potential variation in liveli-

hoods, policy, land tenure and the manifestation of other external forces. In areas where the

livelihoods of people are directly dependent on local resources, anticipating ecological and

social impacts will help improve economic and natural security for post-conflict

restoration.

Acknowledgments We would like to thank the Center for Global Change and Earth Observation atMichigan State University and the National Science Foundation (CNH-0815966) for their support in con-ducting this research. We also thank Olivia Courant for background and field research that contributed to thedevelopment of the paper. An anonymous reviewer gave valuable comments and suggestions.

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