Protected Areas: Mixed Success in Conserving East
Africa’s Evergreen Forests
*, Neil D. Burgess
, Ruth D. Swetnam
, Philip J. Platts
, Simon Willcock
1 Environment Department, University of York, York, United Kingdom, 2 Center for Macroecology, Evolution and Climate, Department of Biology, University of
Copenhagen, Copenhagen, Denmark, 3 WWF-US Conservation Science, Washington, D.C., United States of America, 4 Department of Zoology, University of Cambridge,
Cambridge, United Kingdom, 5 School of Geography, University of Leeds, Leeds, United Kingdom
In East Africa, human population growth and demands for natural resources cause forest loss contributing to increased
carbon emissions and reduced biodiversity. Protected Areas (PAs) are intended to conserve habitats and species. Variability
in PA effectiveness and ‘leakage’ (here defined as displacement of deforestation) may lead to different trends in forest loss
within, and adjacent to, existing PAs. Here, we quantify spatial variation in trends of evergreen forest coverage in East Africa
between 2001 and 2009, and test for correlations with forest accessibility and environmental drivers. We investigate PA
effectiveness at local, landscape and national scales, comparing rates of deforestation within park boundaries with those
detected in park buffer zones and in unprotected land more generally. Background forest loss (BFL) was estimated at 29.3%
), but varied between countries (range: 20.9% to 285.7%; note: no BFL in South Sudan). We document high
variability in PA effectiveness within and between PA categories. The most successful PAs were National Parks, although
only 26 out of 48 parks increased or maintained their forest area (i.e. Effective parks). Forest Reserves (Ineffective parks, i.e.
parks that lose forest from within boundaries: 204 out of 337), Nature Reserves (six out of 12) and Game Parks (24 out of 26)
were more likely to lose forest cover. Forest loss in buffer zones around PAs exceeded background forest loss, in some areas
indicating leakage driven by Effective National Parks. Human pressure, forest accessibility, protection status, distance to fires
and long-term annual rainfall were highly significant drivers of forest loss in East Africa. Some of these factors can be
addressed by adjusting park management. However, addressing close links between livelihoods, natural capital and poverty
remains a fundamental challenge in East Africa’s forest conservation efforts.
Citation: Pfeifer M, Burgess ND, Swetnam RD, Platts PJ, Willcock S, et al. (2012) Protected Areas: Mixed Success in Conserving East Africa’s Evergreen Forests. PLoS
ONE 7(6): e393 37. doi:10.1371/journal.pone.0039337
Editor: Matt Hayward, Australian Wi ldlife Conservancy, Australia
Received January 25, 2012; Accepted May 21, 2012; Published June 29, 2012
Copyright: ß 2012 Pfeifer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Marion Pfeifer was supported by the Marie Curie Intra-European fellowship IEF Programme (EU FP7-People-IEF-2008 Grant Agreement nu234394). Ruth
Swetnam, Philip Platts and Simon Willcock were funded by the Leverthulme Trust through the Valuing the Arc Programme. The funders had no role in study
design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: marion. email@example.com
Tropical evergreen forests represent around 6% of the
terrestrial surface in Eastern Africa, being found mainly in Eastern
Congo, Rwanda and Burundi and Eastern Tanzania. They
provide goods and services (i.e. natural capital) to rural and urban
communities [1,2,3], are rich in species and local endemics [4,5]
and are vital carbon sinks, storing from 70 to more than 300
tonnes of carbon per ha, depending on structure, climate and
East Africa’s evergreen forests also exhibit marked congruence
with the most densely populated areas of Africa [8,9], and may
therefore be susceptible to habitat conversion. High human
population growth [2,10] coincides with the expansion of
cropland, grazing land and forest plantations at the expense of
natural forests . Remaining forests are known to be degraded
and declining, particularly in easily accessible coastal areas
[11,12], near main cities  and at low altitudes . Towards
the eastern edge of the Congo forests and further towards the
coast, forests within and outside PAs are increasingly accessible
through a network of roads. Additional pressure on forest
resources is exerted by commercial timber trade supplying both
urban expansion and growing demand from abroad ; part of
this logging is illegal and thus unregulated [13,15].
Global and regional analyses suggest that protected areas may
be able to stop land clearing and to mitigate logging, hunting, fire
and grazing [16–19]. Thereby, mixed-used PAs may be as
effective, or more effective, than strict PAs in preventing forest
fires and forest loss, especially in less remote areas , but see
Burgess et al . In East Africa, National Parks have firm
restrictions on resource use and strong law enforcement ,
although there are exceptions (e.g. Mago National Park in
Ethiopia; ). Nature Reserves are intended to protect biodi-
versity, but law enforcement is sporadic and they are often
understaffed . Game Parks are largely designed for conserva-
tion of large mammals  and sport hunting, and are only
occasionally patrolled (predominantly during the hunting season).
Forest Reserves (commonly gazetted as multi-resource use areas)
are often located in areas with valuable timber stocks and used for
extractive forestry. They may allow extractive resource use by
adjacent communities; this extraction may be permit-regulated,
but law enforcement is typically weak .
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Analyses of PA effectiveness stress the importance of park
management [18,20] and accessibility [24,25]. Processes outside
PAs (i.e. encroachment of invasive species, regional and local
pollution, and socio-economic pressures) may shape processes
within PAs [26,27]. A widespread lack of integration of PAs with
local development and community needs can lead to land
alienation . Fear of conservation-related ‘land grabs’ may
accelerate ‘defensive farming’, as local communities struggling to
meet their resource needs expand the land under cultivation to
formalize land tenure and gain land use security . Also,
‘leakage’ may offset forest protection within parks by elevating
forest loss in areas nearby, as demands for food and fuel still need
to be met [30,31].
In our analyses, we evaluate the success of East Africa’s PA
network for the conservation of evergreen forests (classified as
broad-leaved evergreen tropical forest; International Geosphere
Biosphere Programme classification) between 2001 and 2009. We
quantify forest trends within PAs, in three buffer zones (B01:0–
1 km from park edge, B15:1–5 km from park edge, B510:5–10 km
from park edge) and in the unprotected surrounding matrix. By
focusing our buffer trend analyses on distances of up to 10 km
from park boundaries, we minimize confounding problems of
Figure 1. National forest trends in East Africa. Shown are overall forest trends independent of protection status (a) and forest trends depending
on protection status (b). Note that only Kenya, Tanzania, Uganda, Rwanda and Burundi are fully covered by the study area.
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overlap between neighboring PAs. Unlike global scale analyses,
which are typically restricted to IUCN category parks , our
analyses cover all kinds of state managed reserves in East Africa
(Forest Reserves, Game Parks, National Parks and Nature
Reserves); however, we do not have accurate spatial data for
community based management approaches (e.g. Wildlife Man-
agement Areas and Village Land Forest Reserves in Tanzania), so
these reserves are excluded from analyses. To interpret our
findings for forest management, we test for significant dependen-
cies of forest trends on protection status and indicators of human
pressure, i.e. population density, road networks, and distance to
major towns and fire events.
Joppa et al.  have previously evaluated changes in natural
vegetation and forest fragmentation across four moist tropical
forests: the Amazon and Congo ‘wilderness forests’ and the high-
biodiversity forests at the Atlantic coast and in West Africa. They
find large geographic variation, however their analyses are static
(using a 1 km spatial resolution dataset from 2002) and ignore
effects of the matrix at larger spatial scales. In our study, we
quantify forest trends at higher spatial resolution (500 m), to
produce an assessment of park effectiveness over eight years, based
on temporal evidence of forest change within and outside park
boundaries, whilst also comparing forest trends within parks and
buffers to regional background forest loss (BFL).
Between 2001 and 2009, forest cover in East Africa decreased in
all countries, except the Southern Sudan region (Figure 1), and
most severely in the (previously) forest-rich countries of Uganda
and Rwanda. Forest decrease was strongest outside protected
Table 1. Number of parks per category and country.
Number of Parks (all parks and
parks with forests) Number of parks with forests and IUCN
Country All Forests 1b II III IV V VI
Burundi 3 3 0 0 0 3 0 0
3 3 030000
1 1 010000
Kenya 17 10 0 10 0 0 0 0
1 1 000000
3 2 020000
Rwanda 3 3 0 2 0 1 0 0
1 1 000000
Tanzania 17 13 0 8 0 2 0 0
Uganda 7 7 0 7 0 0 0 0
10 4 040000
Burundi 4 3 0 0 0 3 0 0
Congo 3 3 0 0 0 0 0 0
Tanzania 6 6 1 0 0 0 0 0
Ethiopia 3 1 000001
Kenya 1 0 000000
Mozambique 7 3 0 0 0 1 0 0
Tanzania 29 14 0 0 0 8 0 1
Uganda 5 2 0 0 0 0 0 2
Zambia 15 7 0 0 0 0 0 7
Kenya 129 72 0 4 0 0 0 0
Mozambique 5 2 0 0 0 1 1 0
Malawi 26 18 0 0 0 0 0 0
Rwanda 2 2 0 0 0 2 0 0
Tanzania 515 220 6 0 0 45 0 7
Zambia 232 24 0 0 0 0 0 0
country only partially covered in the East African study area.
Countries differ strongly with regard to presence and abundance of parks in the different protection categories. For example, Nature Reserves (NR) are only present in
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Figure 2. Forest trends within individual parks of four different protection categories between 2001 and 2009 as function of initial
forest size in 2001 (log10-scale). For graphical display of forest trends we excluded (very small) PAs that increased their forests by more than
300%. Thus, we excluded five Forest Reserves: Mukugodo FR in Kenya (3.9 km
, 822%; forest cover in 2001 and forest change), Ngaia FR in Kenya
, 900%), Geita FR in Tanzania (0.2 km
, 600%), Vumari FR in Tanzania (0.6 km
, 433%), Mwalugulu FR in Tanzania (0.4 km
, 450%). On this
basis, we also excluded four National Parks: Rubondo NP in Tanzania (0.4 km
, 800%), Murchison Falls NP in Uganda (14.5 km
, 391%), Mago NP in
Ethiopia (0.4 km
, 3550%) and Ruma NP in Kenya (1.1 km
Table 2. Trends in forest cover across protection categories between 2001 and 2009.
National Park Nature Reserve Forest Reserve Game Park
22 (96.4655.3) 6 (1113.06919.6) 204 (28.665.3) 24 (85.5664.6)
50% loss 14 (7.063.2) 2 108 (7.762.1) 16 (13.164.7)
50% loss 7 (288.96156.8) 6 (1113.06919.6) 91 (54.9611.0) 8 (230.46191.2)
26 (521.46276.0) 6 (334.76190.3) 133 (41.469.7) 2 (12.863.6)
23 5 116 2
Shown are the number of parks experiencing forest loss (Ineffective PAs) and the number of parks experiencing no change or an increase in forest cover (Effective PAs).
Values in brackets show the mean area of forests (6 standard error; in km
). Ineffective PAs were further divided into parks that lost more than or less than half their
forest cover between 2001 and 2009.
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areas (background forest loss, hereafter BFL) with a decrease of
(29.3%) over the eight year period. Quantifying forest
trends separately for ‘2001 to 2004’ (Period 1) and ‘2004 to 2009’
(Period 2) shows that areal forest loss slowed during Period 2 in
some countries (Kenya, Ethiopia, Somalia, Zambia), while in
others forest was gained during Period 2 following loss during
Period 1 (Rwanda, Burundi, Congo), a pattern broadly consistent
inside and outside protected areas (Table S1).
BFL differed among countries (Table S2), being high in
Rwanda (279.3%; 24,159 km
) and Burundi (282.9%;
). Relative BFL was moderate in Uganda (236.3%),
although areal BFL was higher than in any of the other countries
). Relative BFL was very low in the Eastern Congo
(20.9%), although that still translated to 1,325 km
of forest lost
between 2001 and 2009. Note that relative BFL was also high or
moderate in Somalia (85%), Zambia (82%), Ethiopia (56%) and
Mozambique (45%). However, our analyses covered only small
parts of these countries and areal BFL was low in Ethiopia and
Countries differ in the presence and abundance of reserves
within each protection category, as well as in the proportion of
IUCN parks (Table 1). Overall, PAs lost comparatively little of
their forest cover (378 km
; 20.6%), and in Uganda, Congo and
Malawi there was an increase in forest cover within protected area
boundaries (Figure 1). Game Parks were least effective, losing
24.4% of their 2,078 km
forest cover present in 2001. Nature
Reserves and Forest Reserves lost 5.3% and 3.5% of their
respective 8,686 km
and 11,337 km
forest area. Only National
Parks, often the best protected and funded PAs, increased their
forest area, by 3.2% from 15,679 km
, although this is primarily
driven by the success of National Parks in Tanzania (Figure 2).
PAs differed in their effectiveness depending on protection
status, initial forest size and location of the park (Figure 2). The
Wilcoxon rank-sum test with Bonferroni adjustment of P values
(hereafter referred to as P
) indicates significantly higher areal
forest loss from Game Parks compared to Forest Reserves and
National Parks (P
,0.01), and significantly higher relative forest
loss compared to National Parks, Nature Reserves and Forest
Forest conservation success differed considerably within protec-
tion categories: nearly 50% of National Parks, 50% of Nature
Reserves, 61% of Forest Reserves and 92% of Game Parks were
Ineffective (Table 2), some of them losing more than 50% of their
forests, especially when initial forest extent was small (Table 2).
Parks encompassing smaller forest patches experienced stronger
relative forest loss (linear model of relative change in forest cover
as a function of initial forest size, including only Ineffective parks:
National Parks: p,0.01; Forest Reserves: p,0.001; Game Parks:
p = 0.066), some losing their forests entirely (61 of 204 Ineffective
Figure 3. Satellite-derived estimates of forest trend within and around three PAs in East Africa. Forest cover increased (green),
decreased (red) or remained constant (orange). Some parks show significant loss in forest cover within their three buffer zones (0–1 km, 1 to 5 km,
and 5–10 km). Other land cover transitions are white.
Table 3. Percentage of forest change in buffer zones around
PAs between 2001 and 2009.
Reserve Game Park
100.6646.4 (20) 8.167.6 (5) 90.3622.0 (64) 44.0626.9 (3)
B01 62.5632.9 (18) 210.866.4 (5) 31.4614.1 (58) 8.861.7 (2)
B15 19.4621.4 (17) 0.0614.8 (5) 91.8660.4 (56) 7.466.6 (2)
B510 24.2617.7 (17) 215.1610.3 (5) 19625.0 (54) 6.461.3 (2)
263.468.3 (18) 216.466.8 (6) 257.264.0 (83) 256.467.4 (12)
B01 257.8615.1 (14) 229.8614.1 (6) 243.6611.5 (57) 260.9610.2 (9)
B15 211.3648.2 (14) 235.8615.7 (6) 256.865.9 (68) 252.2612.6 (10)
B510 218.1625.6 (15) 240.9617.1 (6) 240.1610.7 (62) 244.7615.7 (9)
Buffer zones: B01: zero to one km from park boundary, B15: one to five km
from park boundary, B510: five to 10 km from park boundary. Cell entries:
Mean values (6 standard error) of forest change rates across parks within
protection categories are shown (Number of parks in brackets). Note that parks
were merged if they were located closer than 10 km from one another.
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Forest Reserves, 8 of 22 Ineffective National Parks, 5 of 24 Ineffective
Game Parks) (Table 2).
Community benefits programs and resourcing are among the
weakest points in protected area management effectiveness .
Detailed information on local management of selected PAs in our
study area (see Tables S2 and S3) suggests that involving local
communities in forest management improves forest conservation
outcomes. Mukogodo FR in Kenya, for example, has largely been
managed and conserved by the local indigenous community, with
little interference of the government following initial monitoring
and training . Participatory forest management is also used in
Vumari FR in Tanzania, whose legal status is listed as ‘excellent’,
and benefits from regular council-funded patrols and conservation
interventions such as licensing charcoal burning and pole-cutting.
The extent to which PAs can conserve their forests are also likely
to be governed by the trade-off between benefits associated with
conservation and opportunity costs resulting from forsaken access
to forest resources . Economic value (e.g. presence of
commercial timber, firewood and charcoal) and local use (e.g.
charcoal, honey, grazing, thatching) of forests in Mukogodo FR for
example, are low .
Ideally, information on forest cover before and after imposing
land use restrictions would be used to determine the extent to
which gazetting a new PA affects deforestation rates . These
analyses are beyond the scope of this study, but we can show that
forest loss in the vicinity of some PAs exceeds the 9.3% BFL in
East Africa (Figure 3, Table 3). In the B01 buffer, this is the case
for 16 National Parks, 7 Nature Reserves, 67 Forest Reserves and
9 Game Parks; in the B15 buffer it is true for 13, 7, 85 and 8 and in
the B510 buffer for 16, 7, 78 and 7 National Parks, Nature
Reserves, Forest Reserves and Game Parks, respectively. Forest
loss in buffer zones of some Effective National Parks is higher than
BFL in East Africa (Table 4). Country-specific BFL is more severe
in most countries than the overall BFL in East Africa (Table S4).
There are still some PAs for each protection category and country,
where forest loss within buffer zones exceeds country-specific BFL
Drivers of Forest Loss
Our analyses show that, on average, human density peaks at 1
to 2 km distance from park boundaries for Nature Reserves,
Forest Reserves and Game Parks, but increases with distances
from the boundaries of National Parks (Figure 4). Nature Reserves,
Forest Reserves and Game Parks may be perceived by local
communities to represent more viable resources for exploitation
(legal or illegally), leading to population clustering in their
immediate vicinity, although assessments of perceptions over
access to resources need to be carried out within communities to
support such conclusion.
As elsewhere [25,38], human population growth and forest
accessibility are significant drivers of the observed forest trends in
East Africa. Logit models of the binary response variable ‘no forest
loss’/‘forest loss’ show significant correlations with human
population density, but also vegetation burning, slope and distance
to road networks and towns (Table 5).
Steeper slopes are associated with lower deforestation pressures,
presumably because they are less suitable for agriculture and also
less accessible compared to gentler slopes [39,40], which has been
linked to lower opportunity costs and lower necessary spend for
effective forest protection [40,41]. Steepness (mean and minimum
of slope) was significantly less in the B01 buffer zones of Ineffective
PAs compared to slope in the B01 buffer of Effective PAs, but only
in the case of Forest Reserves (Wilcoxon rank sum test: P,0.005
and P,0.01) and Game Parks (Wilcoxon rank sum test: P,0.05
and P,0.05). Thus, hampered forest accessibility appears to play a
role in reducing deforestation in parks that allow extractive
resource use and are generally less well-protected, but is less
important in well-protected parks.
Table 4. Forest trends in buffer zones (B01, B15, and B510) around Effective National Parks (i.e. parks that increased or maintained
their forest area between 2001 and 2009).
Buffer zones around Effective National Parks
Trends in buffers of
Parks (N – Number of Parks) B01 (0 to 1 km) B15 (1 to 5 km) B510 (5 to 10 km)
N with forest 18 17 17
N (FL) 8 7 10
N (FL . FLBG) 4 3 7
Parks (FL . FLBG) 922 [66.3: 212.2%] NP6 [2474.6: 212.0%] NP6 [2329.8: 215.7%]
NP1 [13.3: 233.9%] NP5 [952.3: 276.0%] 926 [1.3: 233.3%]
NP5 [342.7: 231.5%] 9162 [1.1: 2100%] 9162 [1.7: 275.0%]
9162 [3.4: 287.5%] NP5 [851.3:82.4%]
779 [0.4: 2100%]
2296 [0.2: 2100%]
756 [339.9: 2 23.3%]
Numbers in bold represent the WDPA Identifier. 756: Aberdare, Kenya (est. 1950), 779: Nyika, Malawi (est. 1965), 922: Kilimanjaro, Tanzania (est. 1973), 926:
Gombe, Tanzania (est. 1968), 2296: Ruma, Kenya (est. 1983), 9162: Rusizi, Burundi (est. 1980), NP1: merged parks 925 (Arusha, Tanzania, est. 1960) and 303328 (Meru,
Tanzania, est. 1951), NP5: merged parks 9148 (Nyungwe, Rwanda, est. 1933) and 9161 (Kibira, Burundi, est. 1934), NP6: merged parks 863 (Volcans, Rwanda, est. 1929),
18438 (Rwenzori Mountains, Uganda, est. 1991), 40002 (Kibale, Uganda), 40042 (Semuliki, Uganda, est. 1993), 313109 (Mgahinga Gorilla, Uganda, est. 1930), 166889 (Parc
National des Virunga, Congo) and 957 (Queen Elizabeth, est. 1952).
N with forest – Number of parks that encompassed evergreen forests in this buffer zone; N (FL) – Number of parks with forest loss in that buffer zone; N (FL . FLBG) –
Number of parks with forest loss (FL; in %) that was higher than background forest loss (FLBG; in %) outside protected areas in East Africa; Parks (FL . FLBG) – name of
parks with FL . FLBG). See Table 3 for further details on buffer zones.
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We also found a significant effect of long-term rainfall (Table 5),
with forests in drier regions appearing more susceptible to habitat
conversion. Possible explanations are that dry climates may affect
the ability of forests to regenerate after disturbance, that dry forests
burn more readily and more extensively  and are more
suitable for production of charcoal and extraction of firewood.
Also, remaining moist forests are often found on challenging
Large areas of evergreen forests have been lost from East Africa
during the 20th century [43,44,45] resulting in carbon emissions
, reduced habitat for forest dependent biodiversity [5,15], and
reduced availability of essential ecosystem services [2,46]. Initial
conservation efforts in East Africa, like elsewhere, focused on
creating PAs . However, PAs - worldwide - have faced
challenges imposed by inadequate park budgets, varying public
and political support and development pressures beyond park
The mandate for PAs in some East African countries (e.g.
Tanzania, Kenya) has changed dramatically in past decades from
prioritizing areas for large mammal conservation, to protecting
biodiversity in the 1980s and 1990s, and more recently to alleviate
poverty and support livelihoods, both key objectives in Tanzania’s
Participatory Forest Management (PFM) policy . The pro-
liferation of PFM in Tanzania, legally underpinned by the 1998
National Forest Policy and the 2002 Forest Act, increases decision-
making powers of villages on the use of forest resources and
empowers them to declare, own and manage their forests .
As demonstrated in the case of Mukogodo FR as well as other
Effective Nature Reserves and Forest Reserves (S2), PFM can
significantly improve forest conservation outcomes. However,
human pressure is also shown to lead to deforestation encroach-
ment to within park boundaries, especially among less well-
protected parks, and some countries (i.e. Uganda, Burundi,
Zambia) fare worse than others. Forest conservation success varies
Figure 4. Changes in human population densities with increasing distance from parks across the study area in East Africa. Patterns of
human population densities within buffer zones of protected areas differ between protection categories.
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considerably within and between protection categories and within
and between countries. And, National Parks perform better than
other protection categories in terms of protecting forests.
There is evidence for ‘leakage’ around protected areas (Tables 3,
4, S4), which needs to be analyzed in more detail using higher-
spatial resolution satellite images going back to the 1970s,
combined with targeted fieldwork for parks established after
1980. Our approach could easily be used to identify ‘sorrow parks’
(i.e. parks that show high deforestation within boundaries and/or
within their immediate buffer zones) that should be prioritized for
management adjustment. We acknowledge that each of these
Ineffective parks will require a slightly different, individual manage-
ment approach. But, comparing management and surrounding
matrix traits of these parks to those of Effective parks in future
analyses can be used to reveal the presence of general mechanisms
controlling park effectiveness across geographical scales.
Pragmatic (human-centered) approaches to forest conservation
emphasize the importance of conservation in human-modified
landscapes . Managing the human-forest interaction across the
landscape is politically more feasible than excluding communities
from forest resources. However, it may not provide wanted
outcomes in regions of rapid human population growth
[2,17,51,52] and in regions where the relationship between ‘poor’
and ‘forest use’ is transformed by further interventions (i.e. impact
of logging or mining companies, influx of newcomers interested in
land for crops and livestock) .
While there is an ongoing need to assess ‘fitness-for purpose’
across the PA network, increasing and enforcing existing pro-
tection status is likely to remain best-practice on the ground for a
while to come. This forest-centered approach to slow or reverse
forest loss in East Africa could be combined with (i) establishing
and managing multiple-use buffers (e.g. foster tree planting for
firewood extraction) around existing PAs [3,27], (ii) reconnecting
local communities to their forests , (iii) establishing payments
for ecosystem services schemes managed to provide local benefits,
and (iv) to identify motivating factors driving resource extraction
locally to subsequently provide sustainable and feasible alternatives
for services provided by forests [3,55].
Materials and Methods
Our study area in Eastern Africa (3,882,887 km
; bounded by
N6, S-15, W27.5, E42.5), covers Uganda, Kenya, Tanzania,
Rwanda and Burundi and extends to partially cover neighboring
countries including Somalia, Ethiopia, South Sudan, Congo,
Zambia, Malawi and Mozambique.
Classification of Pas
We defined five land management categories of decreasing
protection status: National Parks . Nature Reserves . Forest
Reserves . Game Parks . Unprotected land . Boundaries of
PAs were derived from the World Database on Protected Areas
. Parks were reclassified into National Parks, Nature Reserves,
Game Parks (includes Game Reserves, Game Management Areas,
Game Controlled Areas, Game Sanctuary, Hunting Reserve and
Controlled Hunting Area), and Forest Reserves (includes Village,
District and Nationally Managed Forest Reserves). Buffer zones
were created around individual PAs after merging parks within
protection categories that were located #10 km from one another.
Extraction of Forest Distribution Data
The distribution of evergreen broadleaved forests in the study
area was extracted from MODIS Type 1 land cover grids
(discussed in Pfeifer et al. ; downloaded from https://wist.
echo.nasa.gov/,wist/api/imswelcome/), which provide informa-
Table 5. Significant drivers of forest trends (0: no forest loss, 1: forest loss) modelled using general linear models with logit link
Model 1: East Africa Model 2: Tanzania, Kenya, Rwanda, Burundi, Congo
Model Population Density (0.021, 0.001)*** Population Density (0.018, 0.001)***
Distance to Fire (214.650, 0.649)*** Distance to Fire (213.940, 0.684)***
Slope (0.077, 0.009)*** Slope (0.098, 0.009)***
Game Parks (2.170, 0.252)*** Game Parks (1.704, 0.262)***
Not Protected (0.840, 0.107)*** Not Protected (0.652, 0.112)***
National Parks (20.468, 0.149)** National Parks (20.613, 0.159)***
Nature Reserves (0.041, 0.176)* Other Protection (20.376, 0.163)*
Other Protection (0.219, 0.152) Distance to Road (21.302, 0.289)***
Mean Annual Rain (20.003, 0.000)*** Distance to Towns (3.878, 0.288)***
Mean Annual Rain (20.003, 0.000)***
LR 4296.7 3070.8
Pseudo-R 0.31 0.22
AIC 9586.2 8354
Numbers in brackets give the mean and standard error of the coefficient; associated P values are given at *P,0.05,
We computed deforestation models for East Africa (Model 1) and for a subset of the study area (Model 2) because geographic data on the spatial location of towns and
roads were available for the countries listed in Model 2 only . ‘Protection Status’ is treated as categorical variable with the terms: Game Parks, Not Protected, National
Parks, Nature Reserves, Other Protection). A subsequent Wald Chi-Squared test indicates that the overall effect of Protection Status is statistically significant (P,0.0001).
Abbreviations: likelihood ratio (LR), McFadden’s pseudo R
(Pseudo-R), Akaike Information Criterion (AIC) and significance of model (P).
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PLoS ONE | www.plosone.org 8 June 2012 | Volume 7 | Issue 6 | e39337
tion on vegetation cover at 500 m spatial resolution. The MODIS
algorithm calculates the probability of class membership (PM) for
each land cover pixel in each year (via boosting using a base
learning algorithm and high spatial resolution Landsat TM
imagery). PM is high for evergreen forests (2001 and 2009:
PMmedian: 96%, PMmajority: 100%). Background forest loss was
calculated as forest cover change between 2001 and 2009 relative
to the amount of forest area present in 2001.
Analysis of Forest Trends
Forest cover trends for the various spatial subsets (PA, PA
buffers, national, regional) were computed from maps of evergreen
forests between 2001 and 2009. We computed background forest
loss for East Africa and separately for each country. We assessed
forest trends within individual PAs and their buffers for each
protection category, and compared forest trends within park
buffers to overall background forest change.
Environmental Variables in Deforestation Models
Total fire frequency was computed from MODIS active fire
hotspot data between 2001 and 2009. We concentrated on fire
locations with a reported accuracy $50%, accepting that this may
result in underestimating fire frequencies. Fire data were
converted to 1 km grids, indicating whether a pixel was burned
or not in a given year. Fire frequency grids were computed as fire
sums between 2001 and 2009 (e.g. fire frequency per pixel could
range from 0 to 9). MODIS Burned Area maps (MCD45A1)
between 2001 and 2009 were downloaded from http://modis-fire.
umd.edu/form.html (discussed in Pfeifer et al. ). These grids
were transformed into annual presence/absence maps indicating
whether a pixel got burned or not. The derived maps were
subsequently used to compute grids of pixel-specific annual
burning probabilities. Road and town distribution data were
derived from the Africover project . Spatial analyses were
carried out using ArcGIS v9.3 software. Statistical models and
graphics were computed using R v2.11.1 statistical software
Logit Models of Deforestation
We modelled forest change in East Africa as a function of
accessibility (distance to roads, distance to towns, protection status
and slope), vegetation burning (annual burning probabilities and
pixel-specific fire frequency at annual resolution), and human
population density (persons per 500 m cell) derived from density
maps at 1 ha spatial resolution . The binary response variable
in the logit models of forest change was derived by selecting 5000
points randomly from pixels with forest loss and 5000 points from
pixels maintaining their forests. The points were placed more than
1 km distance from one another to minimize spatial autocorrela-
Table S1 Comparison of forest loss (in km
and %) within and
outside PAs in two time periods (P1:2001–2004, P2:2004–2009).
Table S2 Information on management of eight randomly
selected effective protected areas in East Africa.
Table S3 Information on nine randomly chosen Ineffective
protected areas in East Africa.
Table S4 Comparison of forest loss within PA and within their
buffer zones to country-specific background forest loss (BFL).
We are grateful to Minnie Wong (University of Maryland) for provision of
MODIS fire hotspots data.
Conceived and designed the experiments: MP NDB PJP RM. Performed
the experiments: MP. Analyzed the data: MP PJP RDS. Wrote the paper:
MP NDB PJP SW RM.
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