Predictable waves of sequential forest degradation
and biodiversity loss spreading from an African city
Antje Ahrendsa,b, Neil D. Burgessc,d,e, Simon A. H. Milledgef, Mark T. Bullingg, Brendan Fisherh, James C. R. Smarti,
G. Philip Clarkej, Boniface E. Mhorok, and Simon L. Lewisl
a
Royal Botanic Garden, Edinburgh EH3 5LR, United Kingdom; bEnvironment Department, University of York, York YO10 5DD, United Kingdom; cZoology
Department, University of Cambridge, Cambridge CB2 3EJ, United Kingdom; dWorld Wildlife Fund–United States, Washington, DC 20037; eCenter for
Macroecology, Evolution, and Climate, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark; fTRAFFIC, The Wildlife Trade
Monitoring Organization, P.O. Box 106060, Dar es Salaam, Tanzania; gOceanlab, University of Aberdeen, Newburgh, Aberdeenshire AB41 6AA, United
Kingdom; hProgram in Science, Technology and Environmental Policy, Woodrow Wilson School of Public and International Affairs, Princeton University,
Princeton, NJ 08540; iEnvironment Department, University of York, York YO10 5DD, United Kingdom; jThe Hermitage, Crewkerne, Somerset TA18 8HA,
United Kingdom; kDepartment of Botany, University of Dar es Salaam, P.O. Box 35091, Dar es Salaam, Tanzania; and lEarth and Biosphere Institute, School of
Geography, University of Leeds, West Yorkshire LS2 9JT, United Kingdom
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biodiversity conservation carbon emissions reducing emissions from
deforestation and forest degradation sustainability tropical forest
degradation
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A
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pproximately one-third of remaining tropical forest has been
degraded through selective logging (1), a practice that adds
carbon at a rate of ~0.5 Pg·y−1 to the atmosphere (2). Forest
degradation is broadly defined as the long-term reduction of the
overall potential supply of goods and services, including carbon
storage, wood production, and biodiversity conservation. The
impacts of individual forms of tropical forest degradation are
understood in outline; for example, industrial logging reduces
carbon stocks (3–5) and changes biodiversity, often reducing it (5–
8). Deforestation and degradation patterns have been related to
road access, road density, topography, and biophysical characteristics such as soil fertility (9–12). Yet, forest degradation dynamics driven by the sequential exploitation of a series of goods of
various qualities, with attendant losses in public goods, have not
been tested systematically. This knowledge is critical if optimal
policies are to be implemented to manage forests to enhance
delivery of public goods such as climate regulation and biodiversity retention. In particular, the policy instrument entitled
www.pnas.org/cgi/doi/10.1073/pnas.0914471107
“Reducing Emissions from Deforestation and forest Degradation” (REDD), which is currently being negotiated within the
United Nations Framework Convention on Climate Change (2,
13), focuses overwhelmingly on deforestation. Forest degradation
is receiving less attention because of difficulties in monitoring
degradation as well as understanding its drivers, leading to uncertainty in formulating possible policy interventions (14–16).
Economic theory (von Thünen model) (17) provides a general
model to predict patterns of forest degradation. This asserts that
land is allocated to the activity that provides the maximum net
value (“rent” in economic terms), which, in turn, is the largest gain
from the land minus the costs incurred to obtain that gain (17–19).
This translates to a prediction that waves of forest degradation will
emanate from major demand centers and expand into nearby
forested areas, targeting resources in sequence, starting from
those of highest value (19). Such a sequence of demand, linked to
resource utilization, has been demonstrated for unmanaged fisheries, where it is termed “fishing down the food web” (20–22), but
has not been shown for the exploitation of differently valued
tropical forest products, nor has it been linked to impacts of forest
degradation on public good provision. Here, we test economic resource use theory predictions against ground observations of forest degradation along a 210-km transect running south from the
demand center Dar es Salaam (DES), Tanzania, over a period of
14 years (1991–2005).
Results
Changes Between 1991 and 2005. We found three distinct degradation waves emanating from DES in accordance with the von
Thünen model (Fig. 1 and Tables S1 and S2). In 1991, the innermost degradation wave, which comprised the extraction of
low-value wood for charcoal production, extended up to 50 km
from DES and was dominant within a 20-km distance from the
city. This largely provided cooking fuel for DES residents. A
second degradation wave extracted low- and medium-value timber for local and DES consumption in construction and for export. This middle wave extended 20–100 km from DES and was
dominant at distances of 20–50 km from DES. Beyond 50 km, an
Author contributions: A.A., N.D.B., S.A.H.M., and S.L.L. designed research; A.A., G.P.C.,
and B.E.M. performed research; A.A. and M.T.B. analyzed data; and A.A., N.D.B., M.T.B.,
B.F., J.C.R.S., and S.L.L. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
To whom correspondence should be addressed at: Royal Botanic Garden, 20A Inverleith
Row, Edinburgh EH3 5LR, United Kingdom. E-mail: [email protected].
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.0914471107/-/DCSupplemental and http://www.rbge.org.uk/science/geneticsand-conservation/antje-ahrends-homepage.
PNAS Early Edition | 1 of 6
ECOLOGY
Tropical forest degradation emits carbon at a rate of ~0.5 Pg·y−1,
reduces biodiversity, and facilitates forest clearance. Understanding degradation drivers and patterns is therefore crucial to managing forests to mitigate climate change and reduce biodiversity loss.
Putative patterns of degradation affecting forest stocks, carbon,
and biodiversity have variously been described previously, but
these have not been quantitatively assessed together or tested
systematically. Economic theory predicts a systematic allocation
of land to its highest use value in response to distance from centers
of demand. We tested this theory to see if forest exploitation
would expand through time and space as concentric waves, with
each wave targeting lower value products. We used forest data
along a transect from 10 to 220 km from Dar es Salaam (DES),
Tanzania, collected at two points in time (1991 and 2005). Our predictions were confirmed: high-value logging expanded 9 km·y−1,
and an inner wave of lower value charcoal production 2 km·y−1.
This resource utilization is shown to reduce the public goods of carbon
storage and species richness, which significantly increased with each
kilometer from DES [carbon, 0.2 Mg·ha−1; 0.1 species per sample area
(0.4 ha)]. Our study suggests that tropical forest degradation can be
modeled and predicted, with its attendant loss of some public goods.
In sub-Saharan Africa, an area experiencing the highest rate of urban
migration worldwide, coupled with a high dependence on forestbased resources, predicting the spatiotemporal patterns of degradation can inform policies designed to extract resources without unsustainably reducing carbon storage and biodiversity.
SUSTAINABILITY
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Edited by Partha Sarathi Dasgupta, University of Cambridge, Cambridge, United Kingdom, and approved May 26, 2010 (received for review December
15, 2009)
1991
1991
2005
2005
N
Charcoal
wave
1
DES
DES
2
4
6
3
5
Low/medium-value
timber wave
Charcoal
wave
7
High-value timber
wave
Low/medium-value
timber wave
8
9
10
High-value timber
wave
11
12
Dominant forest use charcoal burning
Dominant forest use logging of low/medium-value timber
Dominant forest use logging of high-value timber
0 5 10 20 30 40
Kilometer
1 Ruvu North FR; 2 Pande GR; 3 Ruvu South FR; 4 Pugu FR; 5 Kazimzumbwi FR; 6 Vikindu FR;
7 Kisiju; 8 Mchungu FR; 9 Ngulakula FR; 10 Ngumburuni FR; 11 Namakutwa; 12 Kiwengoma
Fig. 1. Map of the degradation waves of dominant forest use in the study area in 1991 and 2005. The numbered forests were those used in the study (with
the exception of no. 9 because of dangerous field conditions). Charcoal burning has moved a road distance of 30 km from DES in this time period, and
medium-value timber logging has moved 160 km. The outer boundary of high-value timber logging was already outside the study area in 1991. Muhoro and
Nyamwagne, the two forests south of the Rufiji River (green), do not follow the general degradation pattern because they are plantation forests.
outermost wave extracted high-value timber for DES consumption in construction and for export.
The order of concentric degradation waves remained the same
in 2005 (Fig. 1 and Tables S1 and S2), but each had expanded
significantly. Charcoal production had become the dominant use
up to 50 km from DES but extended to 170 km from DES, with
the outer boundary of this wave having moved 120 km since 1991,
and the outer boundary of the area where charcoal production is
the dominant use having moved 30 km (2 km·y−1). Charcoal production sites (pits or earth mound kilns, mean size 8 × 8 m) covered
~8% of the forest area within a 20-km distance of DES. At a distance of 170 km, they covered 0.3%, and beyond 210 km, no
charcoal production was found (Fig. 2A). The sharp drop in
charcoal production sites at an increasing distance from DES is
likely to have been driven by the cost of transporting the charcoal
to DES, making the marginal gain from production small to negative at greater distances from the city. However, as nearby forests
are exhausted, charcoal prices increase and charcoal production
further from DES becomes more attractive, as shown by DES
charcoal prices, which increased from US $0.18 kg−1 in 1997 to
US $0.27 kg−1 in 2007 (23, 24) (US $ 2009, calculated using a gross
domestic product deflator).
In 2005, medium-value timber logging [round wood export
value up to US $250 per cubic meter at the time (25)] dominated at
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50–170 km from DES, and the outer bound of this second wave had
moved 110 km, reaching the forests south of the Rufiji River
(Fig. 1). High-value timber logging [round wood export value ~US
$330 per cubic meter at the time (25)] started at 170 km (the inner
boundary of high-value timber logging wave having moved
∼9 km·y−1), was dominant starting from 210 km and continued to
at least 220 km (the maximum extent of our sampling from DES)
(Fig. 1). Timber logging removed tree species in sequence. Two
high-value timber species [Milicia excelsa (Welw.) C. C. Berg and
Brachylaena huillensis O. Hoffm.] have been entirely depleted, and
stocks of two others [Pterocarpus angolensis D.C. and Khaya
anthotheca (Welw.) C.D.C.] have almost been exhausted (Table
S2). Mean timber value per tree stump increased US $0.1 per
kilometer from DES (US $ in the year 2005) (Fig. 2B). Furthermore, the proportion of trees logged below their legal minimum
harvestable diameter dropped from 100% at ≤20 km from DES
to 50% at distances ≥200 km. The observed increase in both
mean harvested tree size and mean timber value with increasing
distance from DES and the high proportion of undersized harvested trees far from DES are likely to be indicative of unsustainable harvesting.
Thus, in line with resource use theory, forest degradation
waves are expanding rapidly from DES; the charcoal burning and
medium-value timber “wave fronts” have expanded by 120 and 110
Ahrends et al.
A
B
[
]
D
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C
E
ECOLOGY
Jaccard
Fig. 2. Patterns in forest use and condition at increasing distance from DES. (A) Forest use at increasing distance from DES, quantified as the numbers of stumps
of trees used for charcoal burning (R2 = 0.73) or as low-, medium-, or high-value timber (R2 = 0.01, 0.41, and 0.74, respectively), presented as interpolated lines
using a Loess function (span = 2). (B) Forest condition with distance from DES measured using forest structure (basal area, average stump diameter, standing
timber, and above-ground live tree or standing carbon) and economic (average stump value) variables. (C) Estimated tree species richness at differing distances
from DES using observed species richness (Mao Tau Index) and total species richness (Chao 1 and Jaccard 2 Indices) estimators, each randomized 50 times over
eight transect sections (total sample area = 0.4 ha). (D) Kisiju forest at a distance of 90 km from DES has almost been destroyed since 1991, and all woody
resources have been converted to charcoal. (E) Illegally logged high-value timber harvested at a distance of 200 km from DES. For the production of A, B, and C,
n = 8 independent data points (forests), respectively.
km, respectively, between 1991 and 2005. The inner boundary of
the high-value timber logging wave expanded 120 km, close to the
edge of the study area. Multiple regression models showed that of
the seven (eight in the case of species diversity) tested factors that
may predict levels of degradation in the 10 forests studied in 2005
(Table S3), distance from DES, with one exception, was the sole
significant predictor and explained between 60 and 80% of variation (Table S4). Only the average stump value was better explained
by the accessibility of the forests to lorries, which may indicate that
accessibility is a stronger factor in high-value timber logging than
distance; however, the two variables were also strongly inversely
correlated (Pearson correlation = −0.72; Table S3). Importantly,
we found no relationship between the level of degradation within
a forest and management policies or institutions governing that
forest (Table S4).
Consequences of Forest Degradation. Moving beyond a simple von
Thünen model, we couple the extraction results with the loss of
public goods (carbon storage and biodiversity), an inherent tradeoff. The systematic depletion of forest resources resulted in declines in carbon storage and biodiversity (Fig. 2 B–D). In 2005,
within a 20-km radius of DES, forests had 25 trees per hectare
Ahrends et al.
(bootstrapped SE = ±3.46), compared with 99 trees per hectare
(bootstrapped SE = ±6.35) within a radius of >20 and ≤50 km and
193 trees per hectare (bootstrapped SE = ±15.11) at a radius ≥200
km (Fig. 2B). Thus, at ≤50 km from DES, forest canopies were no
longer closed, and at ≤20 km, the forests were practically removed.
Generally, between 1991 and 2005, there were major declines in
tree density (trees per hectare), above-ground carbon (Mg·ha−1),
and mean tree diameter in a given forest, further illustrating how
forest conditions have deteriorated (Figs. S1 and S2).
Estimates of total species richness increased from 8–13 tree
species (depending on the index used: Chao 1 Index or Jaccard 2
Index) per sample area (0.4 ha) in the forest closest to DES to 41–52
tree species in the forest furthest from DES (Fig. 2C). On average,
forests had 0.1 more species per sample area with each kilometer
from DES. Similarly, above-ground carbon storage in live trees
increased from 4 Mg·ha−1 (bootstrapped SE = ±2.84) in the forest
nearest to DES to 52 Mg·ha−1 (bootstrapped SE = ±4.99) in the
forest furthest from DES (Fig. 2B), increasing, on average, by 0.2
Mg·ha−1 with each kilometer from DES. A first-order estimate of
the above-ground carbon lost from the forests in the study area
(258,000 ha) between 1991 and 2005 is ~0.2 Tg·y−1, equivalent to
PNAS Early Edition | 3 of 6
over a quarter of the annual emissions of carbon from fossil fuel use
in Tanzania over the same period (26).
Discussion
The progressive decline in the value of harvested woody resources
at a given distance from DES over the past decade and increasing
distance of transport for equivalent-value products over time
suggest a likely unsustainable “logging down the profit margin”
scenario akin to the sequential “fishing down the food web” resource utilization patterns seen in unmanaged marine habitats
(21). At current levels of demand and continued outward expansion of the exploitation waves, we predict that there will be no
high-value timber species remaining in Tanzanian coastal forests
up to 220 km from DES in 2010 (Fig. 2E) and up to the southern
Tanzanian border within 37 y.* A recently opened bridge across
the Ruvuma River at the southern Tanzanian border is likely to
facilitate encroachment of the degradation wave into Mozambique. Charcoal burning is predicted to continue to expand in line
with urban demand and a lack of affordable alternatives, and the
inner wave of charcoal extraction is very likely to continue traveling outward (27). It is probable that these trends will be accompanied by further reductions in public goods such as carbon
storage, biodiversity retention, and supply of water. With raw
material exports to generate foreign currency revenue for subSaharan governments, alongside 73% of the urban population
across sub-Saharan Africa [currently experiencing the world’s
fastest rate of urbanization (28)] reliant on biomass fuels, mainly
charcoal (29), the implications derived from our analysis extend
beyond Tanzania. An ability to predict the future spatiotemporal
dynamics of forest degradation across sub-Saharan Africa may
provide a vital tool for targeted policy interventions for biodiversity preservation, climate change mitigation, and human
development, particularly within the context of REDD.
To date, modest capacity in the forestry sector, typical of the
vast majority of African countries (30), and high demand for
natural resources have meant that governance in the forest
reserves analyzed in this study has been weak (25). This is despite
a strict forest resource extraction licensing system clearly stated
in Tanzania’s Forest Act (no. 14 of 2002) and a ban on roundwood exports (Forest Regulations of 2004, Government Notice
no. 153). For example, in 2005, records from China show it
imported 10 times more timber from Tanzania than Tanzania’s
total declared exports, with the Tanzanian government losing
estimated revenue of US $58 million (25). Consequently, degradation patterns from 1991 to 2005 followed the predictions of
economic resource use theory, irrespective of the forests’ management or protected area type. However, the only forest in 1991
in our study area that did not have legal reserve status, Kisiju,
had been cleared entirely by 2005, a typical fate of nongazetted
forests in Tanzania (31), showing that although government
restrictions do not currently prevent degradation, they can successfully prevent complete deforestation. It is also important to
note that the ratio of management types to the number of forests
in this study was high. It is therefore possible that we have
underestimated the effects of particular management types on
reducing degradation. For example, another study focusing
mostly on less accessible mountain areas of Tanzania suggests
that participatory forest management (PFM) has a positive effect
on forest condition (32). Furthermore, as is common across subSaharan Africa, all the forests in this study were government
owned, whereas private or collective ownership or stronger
governance could alter the balance of economic incentives, and
therefore lead to different dynamic patterns of degradation (19).
*The inner wave boundary was at 170 km in 2005 and moves 9 km·y−1. The road distance
between DES and the southern Tanzanian border is ~500 km.
4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.0914471107
Our Tanzania study provides three insights of importance to
the debate on using payments to ensure that the decisions of
economic actors affecting a given piece of land favor carbon
storage and high biodiversity rather than leading to net carbon
release to the atmosphere and reduced biodiversity. First, deforestation and degradation waves should be represented in
state-of-the-art models of the “opportunity costs” of avoiding
deforestation and degradation (i.e., the foregone economic
benefits from alternative land uses) if these models are to depict
the complex real-world spatiotemporal patterns of carbon or
biodiversity losses from forests. This may allow discrimination
between models that predict 3-fold differences in the costs of
reducing deforestation by 10% by 2030 (33) and would help to
reduce the uncertainty associated with the costs of post-Kyoto
REDD schemes proposed as an important mechanism within the
United Nations Framework Convention on Climate Change to
mitigate carbon dioxide emissions through conserving tropical
forests (13). Second, carbon fluxes from degradation are significant, suggesting that mitigating degradation, rather than merely
avoiding deforestation, should feature more strongly in ecosystem service payment schemes such as those proposed in REDD.
Third, models of degradation dynamics may enable the identification of areas that are differentially vulnerable to carbon loss,
enabling spatially targeted REDD policies and incentives to be
applied to prevent the relevant type of degradation activity (e.g.,
extraction of charcoal vs. low-value timber vs. high-value timber).
Addressing these issues is made even more urgent because
achieving carbon dioxide emission reductions in this way is timelimited and economically irreversible: once degradation has occurred, it cannot be avoided in the future.
Materials and Methods
Study Area. DES is a rapidly expanding city of some 3–4 million people on the
Indian Ocean coast of East Africa. Forest product demand is increasing
sharply to meet expanding markets overseas (particularly China) as well as
a rising domestic demand for building materials and cooking fuel (34, 35).
This pattern of increasing consumption, combined with weak resource
management practices, is typical of other tropical regions (36–38), making
the forests around DES a potentially valuable model system for testing forest
degradation theory. The East African coastal forests, thought to have once
formed a belt along the East African coast from southern Somalia to
northern Mozambique, now remain as a series of highly fragmented forest
patches covering less than 10% of their climatically suitable habitat (31).
Because of their exceptionally high levels of palaeo-endemism, multiple
conservation priority schemes have identified them as one of the most important areas for biodiversity conservation worldwide (39–41).
Field Data Collection. In 1990–1994 (median of January 1, 1991), 11 forests
were sampled in coastal Tanzania, noting the type of extractive activities
that were occurring in each forest (42). In each of the forests, one to three
plots of different size ranging from 0.025 to 0.25 ha were located at random
in areas stratified according to the type of forest vegetation (n = 45 plots,
total area sampled = 4 ha), with all trees with a diameter ≥100 mm at reference height [1.3 m along the stem or above buttresses (drh)] measured
and identified to species. In 2004–2005 (median of January 7, 2005), eight of
these forests were resampled and two additional forests were surveyed. The
forests were chosen to span a distance range of 10–220 km from DES and to
have similar climate, topography, soils, and socioeconomic conditions (Fig.
S3 and Table S5). Within each forest, we randomly located transects (10 ×
500–1,500 m in length) to sample 0.1% of the area of each of the 10 forests.
Within each transect, all trees and stumps ≥50 mm drh were recorded as
alive, naturally dead, or cut and were measured and identified to species
(when possible) (n = 12,018). Again, we noted the types of extractive activities occurring, including quantifying the number of charcoal production
pits. All survey data are provided for the purpose of replicating and building
on this work: dataset I for for the 2005 survey and dataset II for the 1991
surveys are presented in Datasets S1 and S2.
Calculation of Variables for the Analysis (2005 Data Only). Timber value. Trees
qualifying as timber were defined as all trees with straight stems at least 3 m
in length and ≥150 mm drh. We categorized each stem as “high-value,”
Ahrends et al.
Modeling Spatial Degradation Patterns in 2005. We fitted linear regression
models for each of eight dependent variables [average stump value (US $
2005); average stump diameter (millimeters); standing timber (n·ha−1); average basal area (m2·ha−1); standing carbon (Mg·ha−1); and Mao Tau, Chao
1, and Jaccard 2 Indices] with seven predictor variables representative of
economic rent: distance from DES (kilometers) and distance from the main
DES road (kilometers) (Fig. S3), forest truck accessibility, population (population density in wards within a distance of 2 km from the focal forest
reserve), management authority (Table S5), protected area type (Table S5),
and presence or absence of PFM initiatives (32). For species diversity only, we
also included the forest altitude range as an additional candidate predictor,
ACKNOWLEDGMENTS. We thank N. Doggart, C. Meshack, and P. Sumbi
for assistance with fieldwork arrangements. A. Balmford, S. A. Bhagwat,
P. M. Hollingsworth, J. C. Lovett, R. Marchant, A. R. Marshall, R. T. Pennington,
D. Raffaelli, and two anonymous reviewers provided helpful comments on the
manuscript. Funding was provided by the Critical Ecosystem Partnership Fund,
World Wildlife Fund, Deutscher Akademischer Austauschdienst, and Marie
Curie Actions (Grant MEXT-CT-2004-517098 to A.A.), the Leverhulme Trust’s
Valuing the Arc programme (B.F. and S.L.L.), the Royal Society (S.L.L.), and UK
Natural Environment Research Council (Grant NE/E006795/1 to M.T.B.), and data
from the 1990/1994 survey were provided by Frontier Tanzania.
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Forest Use Changes Between 1991 and 2005. The estimate of total carbon loss in
the DES and Pwani regions in Tanzania was calculated as follows. The dominant extractive activity (charcoal burning, medium-value timber logging, and
high-value timber logging) was established for all forests (n = 33) in 1991 and
2005 (degradation stages; see dataset I in Table S1). For forests for which no
data were available, we estimated the degradation stage based on the
degradation wave predictions established in this study verified against expert
opinion. The loss of carbon associated with the transition from one degradation stage to another (e.g., from medium-value timber logging to charcoal
burning) was calculated as the average difference in carbon stored among
forests in these different degradation stages in 2005. The rationale for basing
the rates of carbon loss on spatial data in 2005, instead of on temporal
changes since 1991, is the greater data availability for 2005. Total carbon
stored in the area in 1991 and 2005 was computed as the sum of the products
of each forest’s size (hectares) and the average amount of carbon stored per
hectare in a forest of that particular degradation stage. Given that forests
may have remained in the same degradation stage but still have lost considerable amounts of carbon, our estimate is likely to be conservative.
because the range of available habitats is likely to influence the capacity for
species co-occurrence. Distance from DES and distance from the main DES
road were measured using a car odometer, and therefore represent road
distances. Forest truck accessibility was measured as the sum of scores for
three attributes: (i) main road to forest graded, (ii) roads within forest
graded, and (iii) terrain easily accessible (i.e., no steep hills). If an attribute
was fulfilled, a score of 1 was assigned; otherwise, a score of 0 was assigned.
Figures for average population density by ward were based on Tanzania’s
National Bureau of Statistics estimates (50). Forest altitude range was derived from a high-resolution digital elevation model (Shuttle Radar Topography Mission version 4).
To establish significant predictors for the level of the forest degradation, we
used a linear regression approach. Two predictor pairs, forest truck accessibility
and distance from DES and forest truck accessibility and altitude range, were
strongly collinear (Table S3), a situation that may lead to inflated SEs (51) and
alter the significance levels of predictors and interaction terms (52). We
therefore reduced the predictor set for each dependent variable to the
strongest uncorrelated set according to the predictive power of variables in
univariate tests (53). This elimination procedure left us with six (seven in the
case of species diversity) candidate predictors (Table S3). Because this set was
still impractically large, we used hierarchical partitioning (54) in each case to
identify a smaller subset of the predictors most likely to play a critical role in
determining the value of the dependent variable. In recognition of the fact
that the parsimony approach may lead to the exclusion of driving variables (9),
we also present all predictors and their levels of correlation in Table S3. Using
the reduced set of predictors (Table S3), we then fitted linear regression
models and, where the hierarchical partitioning indicated potentially high
conjoint contributions, their interactions. Model validation procedures followed (55) and indicated no heterogeneity of variance and the presence of
normality in the residuals. To find the minimal adequate model, we applied
a backward stepwise selection using the partial F statistic. Model validation on
the final model was then carried out once more. It is noted that the number of
data points available for the analysis was small. However, the dataset contributing to each of these points was extensive. This, in conjunction with the
strongly emerging pattern and its consistency across all tested variables,
increases our confidence in the reliability of the analysis. All statistical analyses
were performed using “R” statistical and programming environment version
2.9.2 (56) as well as its libraries “boot” (57) and “hierpart” (58). SEs are based
on 1,000 nonparametric bootstrap replicates.
ECOLOGY
“medium-value,” or “low-value” timber or as “suitable only for charcoal
production” based on published use data (43, 44) and calculated the density
of suitable timber trees and the average drh and basal area sum of all trees.
Value of extracted timber. For each forest, we calculated the average stump
diameter and the average “stump value” based on the mean royalties that
the Tanzanian Government collects for felling the respective species (US $ in
the year 2005).
Carbon stocks. Above-ground carbon stocks were computed using the Dry
Forest allometric equation of Chave et al. (45). This computes the carbon
stored in individual trees using drh and the wood specific gravity of each
stem. Wood specific gravity was taken from a global database (46). When
wood specific gravity data were not available for a species, genus-level
values were used (47).
Species diversity. Three area-standardized species richness estimates were
calculated (Mao Tau Index, Chao 1 Index, and Jaccard 2 Index) for all trees
≥150 mm drh, each computed over a subsample of eight plots (corresponding to a total of 0.4 ha) with 50 iterations, using EstimateS (48). Species
richness standardized by area is sometimes referred to as “species density”
(49) as opposed to species richness standardized by number of individuals.
25. Milledge SAH, Gelvas IK, Ahrends A (2007) Forestry, governance and national development: Lessons learned from a logging boom in southern Tanzania (TRAFFIC East/
Southern Africa, Tanzania Development Partners Group, Tanzania Ministry of Natural
Resources and Tourism, Dar es Salaam). Available at http://www.traffic.org/forestry/.
Accessed June 2007.
26. Boden TA, Marland G, Andres RJ (2009) Global, Regional, and National Fossil-Fuel CO2
Emissions (Carbon Dioxide Information Analysis Center, Oak Ridge National
Laboratory, US Department of Energy, Oak Ridge, TN).
27. Kirilenko AP, Sedjo RA (2007) Climate change impacts on forestry. Proc Natl Acad Sci
USA 104:19697–19702.
28. DeFries RS, Rudel T, Uriarte M, Hansen M (2010) Deforestation driven by urban
population growth and agricultural trade in the twenty-first century. Nat Geosci 3:
178–181.
29. Bailis R, Ezzati M, Kammen DM (2005) Mortality and greenhouse gas impacts of
biomass and petroleum energy futures in Africa. Science 308:98–103.
30. FAO (2009) State of the world’s forests 2009. State of the World’s Forests (Food and
Agricultural Organization of the United Nations, Rome).
31. Burgess ND, Clarke GP, eds (2000) Coastal Forests of Eastern Africa (IUCN Publication
Services Unit, Cambridge, U.K).
32. Blomley T, et al. (2008) Seeing the wood for the trees: An assessment of the impact of
participatory forest management on forest condition in Tanzania. Oryx 42:380–391.
33. Kaimowitz D, Angelsen A (1998) Economic Models of Tropical Deforestation: A
Review (Center for International Forestry Research, Bangor, ME).
34. Liu JG, Diamond J (2005) China’s environment in a globalizing world. Nature 435:
1179–1186.
35. UN-HABITAT (2008) The State of African Cities 2008 (UN-HABITAT, Nairobi, Kenya).
36. Ehrlich PR, Pringle RM (2008) Where does biodiversity go from here? A grim businessas-usual forecast and a hopeful portfolio of partial solutions. Proc Natl Acad Sci USA
105:11579–11586.
37. Geist HJ, Lambin EF (2002) Proximate causes and underlying driving forces of tropical
deforestation. Bioscience 52:143–150.
38. Laurance WF (2008) The need to cut China’s illegal timber imports. Science 319:1184.
39. Mittermeier RA, et al. (2005) Hotspots Revisited: Earth’s Biologically Richest and Most
Endangered Ecoregions (Cemex, Mexico City), 2nd Ed.
40. Olson DM, Dinerstein E (1998) The global 200: A representation approach to
conserving the Earth’s most biologically valuable ecoregions. Conserv Biol 12:502–
515.
41. Stattersfield AJ, Crosby MJ, Long AJ, Wege DC (1998) Endemic Bird Areas of the World:
Priorities for Biodiversity Conservation (BirdLife International, Cambridge, U.K.).
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42. Clarke GP, Dickinson A (1995) Status reports for 11 coastal forests in coast region,
Tanzania. Frontier Tanzania Technical Reports (Society for Environmental Exploration and
University of Dar es Salaam, Dar es Salaam, Tanzania). Available at http://coastalforests.
tfcg.org/publications.html. Accessed March 2006.
43. The United Republic of Tanzania (2004) Subsidiary Legislation. Includes: Notice of the
Commencement Date and Regulation of the Forest (Act No. 14 of 2002) (Government
Printer, Dar es Salaam, Tanzania).
44. Bryce JM (2003) The Commercial Timber of Tanzania (Tanzania Forestry Research
Institute, Morogoro, Tanzania), 3rd Ed.
45. Chave J, et al. (2005) Tree allometry and improved estimation of carbon stocks and
balance in tropical forests. Oecologia 145:87–99.
46. Chave J, et al. (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:
351–366.
47. Lewis SL, et al. (2009) Increasing carbon storage in intact African tropical forests.
Nature 457:1003–1007.
48. Colwell RK (2006) EstimateS: Statistical Estimation of Species Richness and Shared
Species from Samples, Version 8, Available at http://purl.oclc.org/estimates. Accessed
January 2009.
49. Gotelli NJ, Colwell RK (2001) Quantifying biodiversity: Procedures and pitfalls in the
measurement and comparison of species richness. Ecol Lett 4:379–391.
50. National Bureau of Statistics Tanzania (2002) Population Census (United Republic of
Tanzania Dar es Salaam, Tanzania).
51. Zuur AF, Ieno EN, Elphick CS (2009) A protocol for data exploration to avoid common
statistical problems. Methods in Ecology and Evolution 1:3–14.
52. Sithisarankul P, Weaver VM, Diener-West M, Strickland PT (1997) Multicollinearity
may lead to artificial interaction: An example from a cross sectional study of biomarkers. Southeast Asian J Trop Med Public Health 28:404–409.
53. Quinn QP, Keough MJ (2002) Experimental Design and Data Analysis for Biologists
(Cambridge Univ Press, Cambridge, U.K.).
54. Chevan A, Sutherland M (1991) Hierarchical partitioning. Am Stat 45:90–96.
55. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009) Mixed Effects Models and
Extension in Ecology with R (Springer, New York).
56. R Development Core Team (2009) R: A Language and Environment for Statistical
Computing (R Foundation for Statistical Computing, Vienna, Austria) Available at
http://www.R-project.org. Accessed January 2009.
57. Canty A, Ripley B (2009) Boot: Bootstrap R (S-Plus) Functions, R Package Version 1.2-35,
(R Foundation for Statistical Computing, Vienna, Austria).
58. Walsh C, Mac Nally R (2008) Hier.part: Hierarchical Partitioning, R Package Version
1.0-3. (R Foundation for Statistical Computing, Vienna, Austria).
Ahrends et al.
Supporting Information
Ahrends et al. 10.1073/pnas.0914471107
A
Average drh
400
(mm) for
standing trees 300
>100 mm drh
Survey 1991
200
Survey 2005
100
0
20
30
50
90
150
210
Distance to Dar es Salaam (km)
B
Standing trees
>100 mm drh
ha-1
600
500
400
Survey 1991
300
Survey 2005
200
100
0
30
50
90
210
Distance to Dar es Salaam (km)
C
Standing carbon
75
(Mg ha-1 for
standing trees
>100 mm drh)
50
Survey 1991
Survey 2005
25
0
30
50
90
210
Distance to Dar es Salaam (km)
Fig. S1. Changes in drh (A), stem density per hectare (B), and above-ground live tree or standing carbon per hectare (C) for trees with drh ≥100 mm between
1991 and 2005 in coastal forests located between DES and the Rufiji River, with matched datasets. Apart from in Kisiju, 2005 data for this figure only include
plots from intact forests, ensuring compatibility with the 1991 data. In 1991, drh, standing trees, and carbon differed among forests because of differences in
vegetation type or forest history. In 2005, patterns in these forest structure variables were driven by distance from DES. The forest at a distance of 90 km (Kisiju)
was entirely cleared for agriculture between 1991 and 2005, and that at a distance of 210 km (Namakutwa) was cleared for farming ∼100 y ago [Clarke GP
(1992) Namakutwa-Nyamuete Forest Reserve, Rufiji District, Tanzania: Site description and conservation evaluation. Frontier Tanzania Technical Reports
(Society for Environmental Exploration and University of Dar es Salaam, Dar es Salaam, Tanzania)] and is regenerating.
Percent
100
Percentage change
drh between 1991 and
2005
80
60
Percentage change
Trees ha- 1
40
20
Percentage change
Carbon ha- 1
0
-20
0
100
200
300
Distance from Dar es Salaam (km)
Poly. (Percentage
change drh between
1991 and 2005)
Fig. S2. Percentage change in drh, standing trees per hectare, and standing carbon ha−1 for trees with drh ≥100 mm between 1991 and 2005 in forests with
matched datasets, with a fitted polynomial (Poly.) regression line for percentage change in drh (R2 = 0.56). The greatest percentage change in drh occurred at
a distance of ∼100 km from DES. Closer to DES, the percentage change was lower because the forests had already been degraded in 1991, and at a distance
≥100 km from DES, the percentage change was lower because degradation there has not yet resulted in significant changes in the forest structure. Note that
Kisiju, at a distance of 90 km, is an outlier and has not been included in this analysis. The percentage change in standing trees and carbon per hectare could not
be fitted because of the low number of available data points.
Ahrends et al. www.pnas.org/cgi/content/short/0914471107
1 of 6
Fig. S3. Location of the study area in Tanzania. The study area stretches from DES to south of the Rufiji River. Solid black polygons are sampled forests and
gray polygons are unsampled forests. Gray dots on the Africa map show the locations of remaining related East African coastal forests.
Table S1. Degradation status in 10 study forests in 1991 and 2005, at increasing distance from
DES
Distance from
DES, km
10
20
30
32
40
50
90
150
170
210
220
Selective highvalue logging
Medium- and
low-value
logging
Charcoal
burning
Forest
1991
2005
1991
2005
1991
2005
Vikindu
Pande
Pugu
Kazimzumbwi
Ruvu North
Ruvu South
Kisiju*
Mchungu
Ngumburuni
Namakutwa
Kiwengoma
NO−
NO−
NO−
NO−
NO−
YES
YES
YES
NO−
NO−
NO−
NO−
YES
YES+
YES
YES
YES+
YES+
NO+
NO−
NO−
NO−
YES+
YES+
YES+
YES+
YES+
YES+
YES+
NO+
YES
YES
YES+
YES+
YES+
NO−
NO−
NO−
NO−
YES
YES
NO+
NO+
YES+
NOYES+
YES+
YES+
NO+
NO+
NO+
YES+
NO−
NO+
YES+
NO+
NO+
NO+, abundant resources, not yet exploited; NO−, not exploited because resources are exhausted; YES+,
exploited with abundant resources; YES, exploited with some resources remaining.
*Note that the unprotected Kisiju forest (90 km from DES) was completely destroyed over the study period (Fig. 2D).
Ahrends et al. www.pnas.org/cgi/content/short/0914471107
2 of 6
Table S2. Changes in forest composition since surveys in the 1990s and the repeat survey of this paper in 2005*
Forest
Source
Past findings on timber values
This study’s findings on
timber values
Vikindu
(1, 2)
Hardly any timber species.
Few individuals of Afzelia
quanzensis (II) and
Khaya anthotheca (I).
No timber species.
Pande
(1, 3)
Hardly any timber species.
Only very few individuals
of B. rhodognaphalon (IV).
Pugu
(1, 4)
Few timber species in low densities.
Few individuals of A. quanzensis (II),
Brachylaena huillensis (I), and Bombax
rhodognaphalon (IV) and very few
individuals of Milicia excelsa (I).
Most timber species had already been
removed during colonial era [e.g.,
K. anthotheca (I) and M. excelsa (I)].
Low-value timber species such as
Antiaris toxicaria (V) left.
Ruvu South
(1)
Only few timber species [e.g.,
logging of B. huillensis (I)
occurred and suitable trees
were already scarce].
Kisiju
(2, 5)
Timber species such as A. quanzensis
(II), Baphia kirkii (II), and Hymenaea
verrucosa (V) were abundant.
Mchungu
(1, 6)
Timber species such as A. quanzensis
(II), B. kirkii (II), and H. verrucosa (V)
were abundant.
Ngumburuni
No
assessment
Namakutwa
(1, 7)
No assessment
Past extraction of timber species such as
M. excelsa (I) and P. angolensis (I) had
reduced the economic value of the forest;
however, a few M. excelsa individuals (I)
remained and B. rhodognaphalon (IV)
and H. verrucosa (V) were abundant.
Ahrends et al. www.pnas.org/cgi/content/short/0914471107
Low-value timber species
such as A. toxicaria (V) left
in the vicinity of the District
Forest Office in an area
that is regularly patrolled.
In other parts of the
reserve, most timber trees
removed.
No individuals of
B. huillensis (I) were found.
A number of individuals of
Julbernardia globiflora (II)
had been harvested
relatively recently.
A single forest patch of
0.005 km2 remained.
Extremely poor and
landless squatters have
logged, burned to charcoal,
and cultivated the entire
forest area.
Timber species such as
A. quanzensis (II), B. kirkii
(II), and H. verrucosa (V)
were abundant.
Two stumps of high-value
species were found:
Pterocarpus angolensis (I)
and Diospyros
mespiliformis (I).
Of these species, no
sizeable standing trees
were found. Stumps
indicated that during the
past 10 years, harvesting
had focused on
A. quanzensis (II), B. kirkii
(II), and Pteleopsis
myrtifolia (IV).
No individuals of M. excelsa
(I) and P. angolensis (I) were
found. Stumps indicated
that during the last 10 years
harvesting had focused on
A. quanzensis (II), Albizia
versicolor (II), J. globiflora
(II), Millettia stuhlmannii (II)
and P. angolensis (I).
Of these, hardly any
sizeable trees remained in
the forest.
Past extraction
activities
Clearing and
afforestion with
exotic species, pole
cutting, and charcoal
burning
Logging and
charcoal burning
Present
extraction
activities
Pole cutting and
charcoal burning
Pole cutting and
charcoal burning
(albeit not
recent)
Logging and
charcoal burning
Charcoal burning
Logging and
charcoal burning not
yet perceived as
a threat
Logging and
charcoal burning
Agricultural
encroachment and
pole cutting
Clear-felling,
charcoal burning,
and pole cutting
followed by
agricultural
encroachment
With the exception
of a few trees felled
for local use, none
reported
No assessment
Logging (recent)
Logging
Logging and
charcoal burning
Logging
3 of 6
Table S2. Cont.
Forest
Source
Kiwengoma
(1, 6, 8)
Past findings on timber values
Moist forest with a high proportion of
valuable timber species, notably K.
anthotheca (I), P. tinctorius (I), and M.
excelsa (I).
This study’s findings on
timber values
Past extraction
activities
M. excelsa (I) was
not found. Recent
logging of large individuals
of K. anthotheca (I) and P.
tinctorius (I) in moist forest
and P. angolensis (I) and A.
versicolor (II) in woodlands
had reduced the number of
large and high-value
timber trees. Timber species
with less value such as A.
quanzensis (II) and H.
verrucosa (V) were found in
large quantities.
Logging of highvalue species such as
K. anthotheca (I)
and M. excelsa (I)
Present
extraction
activities
Logging (recent)
of high- and
medium-value
species such as K.
anthotheca (I), P.
tinctorius (I), and
A. versicolor (II)
Throughout the study area, high-value class I timber species such as M. excelsa and B. huillensis, which were present in the 1990s, have been logged to
exhaustion and are no longer recorded; other high-value species such as P. angolensis and K. anthotheca are now rare. Class II timber species such as
A. quanzensis have also been locally depleted. The majority of recently harvested timber species were B. kirkii and H. verrucosa, which are class II and class
V timber species, respectively. We also found changes in the sizes of harvested trees. There was an average decrease of 200 mm drh between old and recent
stumps of class I and class II timber species. These dramatic shifts in harvested species and harvest areas have also been documented with trade data (9, 10).
*Timber classes are placed in parentheses after the species’ names.
1. Clarke GP, Dickinson A (1995) Status Reports for Tanzania: 11 coastal forests in coast region. Frontier Tanzania Technical Reports (Society for Environmental Exploration and University
of Dar es Salaam, Dar es Salaam, Tanzania).
2. Hawthorne WD (1984) Ecological and biogeographical patterns in the coastal forests of East Africa. PhD dissertation (University of Oxford, Oxford).
3. Mwasumbi LB, Burgess ND, Clarke GP (1994) Vegetation of Pande-Kiono coastal forests, Tanzania. Vegetatio 113:71–81.
4. Howell KM (1981) Pugu Forest Reserve—Biological values and development. Afr J Ecol 19:73–81.
5. Stubblefield LK (1992) Preliminary results of a biological value survey of Kisiju—Dendene Forest, Tanzania: November to December 1992. Frontier Tanzania Technical Reports (Society
for Environmental Exploration and University of Dar es Salaam, Dar es Salaam, Tanzania).
6. Waters T, Burgess N (1994) Preliminary results of biological surveys of Mchungu and Kiwegnoma (Matumbi) forests, and short visits to seven other forested sites in coastal Tanzania:
July to September 1990. Frontier Tanzania Technical Reports (Society for Environmental Exploration and University of Dar es Salaam, Dar es Salaam, Tanzania).
7. Clarke GP (1992) Namakutwa-Nyamuete Forest Reserve, Rufiji District, Tanzania: Site description and conservation evaluation. Frontier Tanzania Technical Reports (Society for
Environmental Exploration and University of Dar es Salaam, Dar es Salaam, Tanzania).
8. Sheil RD, Burgess N (1990) Preliminary results of biological surveys in Zaraninge (Kiono) and Kierengoma (Matumbi Hills) Coastal Forests, Tanzania: January to March 1990. Frontier
Tanzania Technical Reports (Society for Environmental Exploration and University of Dar es Salaam, Dar es Salaam, Tanzania).
9. Milledge SAH, Gelvas IK, Ahrends A (2007) Forestry, Governance and National Development: Lessons Learned from a Logging Boom in Southern Tanzania (TRAFFIC East/Southern
Africa, Tanzania Development Partners Group, Tanzania Ministry of Natural Resources and Tourism, Dar es Salaam, Tanzania).
10. Milledge SAH, Kaale BK (2003) Bridging the Gap: Linking Timber Trade with Infrastructure Development and Poverty Eradication Efforts in Southern Tanzania (TRAFFIC East/Southern
Africa, Dar es Salaam, Tanzania).
Ahrends et al. www.pnas.org/cgi/content/short/0914471107
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Ahrends et al. www.pnas.org/cgi/content/short/0914471107
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Distance from
DES, km
Distance from DES
main road, km
Forest truck accessibility
Population density
within
wards within 2 km
Management authority
Protected area type
Presence/ absence
of PFM
Forest altitude range
0.5
0.52
5
0.0
6
0.63
7
Stump
value
0.37
0.45
−0.03
0.44
0.42
−0.14
−0.14
0.03
−0.14
0.62
−0.56
−0.34
0.0
0.0
−0.4
−0.78
−0.15
−0.09
×
××
×
×
××
×
4
−0.72
−0.59
0.39
3
×
−0.69
−0.51
2
0.65
1
×
×
××
×
×
×
×
××
×
××
××
××
××
××
Timber
Stump
drh
Forest use
×
×
××
×
××
××
××
Basal
area
Remaining
woody
resources
Candidate predictors for modeling the forests’ levels of degradation and their levels of Pearson correlations
×
×
××
××
×
××
××
Carbon
stock
××
××
××
×
×
×
××
Maotau
Index
××
×
××
×
×
×
××
Chao 1
Index
××
××
××
×
×
×
××
Jaccard 2
Index
Biodiversity
×, strongest uncorrelated predictors selected for the hierarchical partitioning analysis; ××, predictors chosen for the backward stepwise model selection. Bold-face numbers denote levels of correlation ≥0.7,
respectively ≤-0.7).
8
5
6
7
3
4
2
1
Candidate
predictor
Table S3.
Table S4. Multiple regression model results for spatial degradation patterns in 2005
Category
Dependent variable
Forest use
Average stump value,
US $ 2005
Remaining woody resources
Carbon stock
Remaining biodiversity
β*
Model
Average stump
diameter, mm
Standing timber, n·ha−1
Basal area/ha, m2·ha−1
Standing carbon, Mg·ha−1
Mao Tau index
Chao 1 index
Jaccard 2 index
P
F
df
Adjusted R2
y = 59.091–17.273*
Access1†: –26.031*
Access2†: –31.136* Access3†
y = 86.267 + 0.685* DisDES‡
Na
<0.05
7.22
4
0.727
0.898
<0.01
25.03
6
0.774
y = 44.4 + 0.772* DisDES
y = 3.744 + 0.064* DisDES
y = 9.069 + 0.203* DisDES
y = 7.061 + 0.081* DisDES
y = 11.942 + 0.111* DisDES
y = 16.665 + 0.135* DisDES
0.866
0.852
0.804
0.899
0.859
0.839
<0.01
<0.01
<0.05
<0.01
<0.01
<0.01
17.97
15.92
10.93
25.19
16.9
14.32
6
6
6
6
6
6
0.708
0.681
0.587
0.776
0.694
0.656
DisDES, distance from DES in kilometers; Na, not applicable.
*β is the standardized regression coefficient, calculated as coefficient × (SD predictor variable/SD-dependent variable).
†
Forest truck accessibility scores were fitted as a factor with three levels, ranging from extremely difficult access (Access1) to easy access (Access3).
Table S5. General information and sampling intensity across the 10 study forests in coastal Tanzania sampled in 2005
Forests
Vikindu†
District
Size, km2
Reserve type
Management
authority
PFM
Ownership
Altitude range, m
No. 10 × 50-m plots
Percentage of total
area sampled*†‡
Pande
Ruvu South
Kisiju‡
Mkulanga
Rufiji
Rufiji
Rufiji
Rufiji
Rufiji
21.79
Prot FR
FBD
Kisarawe/
Kibaha
350.00
Prot FR
FBD
2.00
General land
NA
10.35
Prot FR
FBD
22.00
Prot FR
Local
40.00
Prot FR
Local
46.34
Prot FR
FBD
20.25
Prot FR
FBD
Yes
Gov
100–309
55
0.13
Yes
Gov
100–314
69
0.15
NA
Gov
∼0–20
14
0.35
No
Gov
4–28
20
0.10
No
Gov
44–148
3
0.01
Yes
Gov
16–56
59
0.15
Yes
Gov
128–385
36
0.15
Yes
Gov
282–677
47
0.12
Pugu
Kisarawe Kinondoni Kisarawe
17.96
Prod FR
FBD
No
Gov
20–88
8
¶
0.02
12.26
GR
Wildlife
Division
No
Gov
90–202
63
0.22
Mchungu Ngulakula Ngumburuni Namakutwa Kiwengoma
FBD, Forest and Bee-Keeping Division (Ministry of Natural Resources and Tourism); FR, forest reserve; Gov, government owned; GR, game reserve; Prod,
productive; Prot, protective; Local, respective district authority.
*In forests with an area larger than 40 km2, sampling was confined to randomly chosen regions within the forest, and these were sampled with the above
intensity.
†
Kisiju and Ngulakua FR were excluded from the analysis.
‡
Kisiju is not a protected area and has almost entirely been converted into agricultural land. Sampling intensity in Ngulakula was too low.
¶
Sampling intensity was low but likely to be representative, because large parts of Vikindu FR were converted into agricultural land and the actual area
covered by forest remnants is small.
Other Supporting Information Files
Dataset S1 (XLS)
Dataset S2 (PDF)
Ahrends et al. www.pnas.org/cgi/content/short/0914471107
6 of 6