Ecological Indicators 6 (2006) 353–368
This article is also available online at:
www.elsevier.com/locate/ecolind
Patterns and causes of deforestation in the Colombian Amazon
Dolors Armenteras a,b,*, Guillermo Rudas c, Nelly Rodriguez a,
Sonia Sua a, Milton Romero a
a
Biological Resources Research Institute Alexander von Humboldt, GIS Unit, Carrera 7#35-20, Bogotá, Colombia (South America)
b
Department of Geography, King’s College London Strand, London WC2R 2LS, UK
c
Department of Economics, Javeriana University, Calle 40 N 6-23, Bogotá, Colombia (South America)
Accepted 29 March 2005
Abstract
Ecosystem information on the Colombian Amazonia is poor in comparison with that on the Brazilian Amazon. We examined
patterns of ecosystem diversity, deforestation and fragmentation and provided an estimate on their possible causes through a
temporal and spatial analysis of biotic and abiotic data using remote sensing and geographical information systems in six pilot
areas covering a total of 4,200,000 ha. Ecological, demographic and socio-economic data were analysed to establish the local
conditions. We used a landscape ecology approach to calculate indicators of ecosystem diversity, cover and forest fragmentation
such as number of patches, mean patch size, mean shape index and mean nearest neighbour distance. Patterns of deforestation
did not run parallel to access roads; instead the typical pattern of unplanned colonization follows the only transportation network
existing in many areas in the Colombian Amazonia: rivers. In addition, we have used indicators of human influence such as
demographic pressure, quality of life and economic activity indicators. Results show that the extent and rate of change varies
between areas depending on population density. Annual deforestation rates were 3.73 and 0.97% in the high population density
growth areas of Alto Putumayo and Macarena respectively, and 0.31, 0.23, and 0.01% in the relatively unpopulated areas of
indigenous population. These changes are related to land use history as well as to environmental and historical socio-economic
factors such as oil extraction, deforestation, cattle ranching or illegal cropping. The current situation in the region suggests that
tropical deforestation rates in the Colombian Amazon are substantially higher than those found in previous studies in the rest of
the Amazon.
# 2005 Elsevier Ltd. All rights reserved.
Keywords: Fragmentation; Satellite imagery; Tropical deforestation; Land use change; Biodiversity; Indicators; Amazonia; Colombia
1. Introduction
* Corresponding author. Tel.: +57 1 6086900x238;
fax: +57 1 6086900.
E-mail addresses: [email protected],
[email protected] (D. Armenteras),
[email protected] (G. Rudas).
The destruction of tropical forests has received
worldwide attention due to the significance of forest
on global climate, carbon sequestration, water cycles,
biodiversity and the potential global effects on climate
1470-160X/$ – see front matter # 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.ecolind.2005.03.014
354
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
change (Fearnside, 1995; Fearnside et al., 2001).
Globally, some estimates suggest that 9 million km2 of
tropical humid forests have been lost in less than 50
years and that current rates of extinctions are not only
high but accelerating (Pimm et al., 2001). Achard et al.
(2002) estimate an annual deforestation rate of 0.38%
of humid tropical forest in Latin America.
Deforestation has led to the fragmentation of
natural ecosystems throughout the world causing
further loss of original forests, reduction of the size of
forest fragments and increasing isolation. Most studies
on ecosystem cover and fragmentation are centred on
the quantification of those changes (Vogelmann, 1995;
Ranta et al., 1998; Sierra, 2000; Steininger et al.,
2001a, 2001b) and on the effects that these can have on
ecological processes (Klein, 1989; Carvalho and
Vasconcelos, 1999; Gascon et al., 1999; Davies and
Margules, 1998; Laurance et al., 1998, 2000; Nepstad
et al., 1999). The Amazon hosts over half of the
world’s remaining tropical forests and it is currently
subject to accelerating deforestation and changing
patterns of ecosystem loss (Laurance, 1998; Whitmore, 1997; Lima and Gascon, 1999). Laurance et al.
(2002) suggest, among others, that Brazilian Amazonia has the world’s highest absolute rates of forest
deforestation and fragmentation.
However, while Brazilian Amazonia deforestation
has been widely analysed (Fearnside, 1990; Fearnside
et al., 1990; Fearnside, 1995; Reis and Margulis, 1991;
Laurance, 1998; Parayil and Tong, 1998; Laurance
et al., 2002) and much emphasis has been placed on
this part of the world, information on other parts of the
Amazon, in particular the Colombian Amazonia, is
scarce or non-existent. While there are a number of
studies on social, demographic and economic determinants of the Amazonian deforestation which
suggest that deforestation is primarily determined
by human population, accessibility, land use and land
tenure issues (Reis and Margulis, 1991; Fearnside,
1993; Wood and Skole, 1998; Laurance, 1998;
Fearnside, 2001; Nepstad et al., 2001; Portela and
Rademacher, 2001; Laurance et al., 2002), these
studies are largely confined to Brazil.
Colombia, having one of the most diverse regions
in flora and fauna in the world, has been identified as a
‘‘mega-diverse’’ country (IAvH, 1998). While tropical
ecosystem transformation is occurring all over the
tropics, the loss of biodiversity and landscape
transformation in Colombia remains largely unknown,
such that entire ecosystems could be under threat of
disappearance. Global conservation prioritisation
(Myers et al., 2000) proposed the northern Andes
and the Choco regions as two hotspots in Colombia,
while the Amazonia was classified merely as a ‘‘major
wilderness area’’. This ‘‘hotspot’’ approach is directed
towards decision-makers, but devalues and detracts
attention away from non-designated areas (Bates and
Demos, 2001).
There is evidence that ecosystems in half of the
Colombian Amazon are experiencing high rates of
deforestation. Ruiz (1989) estimated that 2.5 million ha of forest were lost in the late 1980s in
Colombia. Sierra (2000) analysed the extent and rate
of deforestation and the level of forest fragmentation
in the Napo region of western Amazonia, which
included a small portion of the Colombian Amazon,
and concluded that deforestation was advancing faster
on the Colombian side of his study area (0.9%/year)
due to population growth from the foot of the Andes
towards the Amazon.
Detailed and updated studies are still lacking in
Colombia. There are no regional geographic databases
of current information on the dynamics and patterns of
land cover change and the levels and patterns of
fragmentation and ecosystem integrity for this part of
the world (Sierra, 2000). Hence the aim of this study is
to provide an estimate of both deforestation and
ecosystem transformation and probable causes of
change using an analysis which incorporates both
biotic and anthropogenic data for a portion of the
Colombian Amazon. An approach that incorporates
both factors is critical for understanding of the
consequences of human activities on the natural
environment in the Colombian Amazon. As a first
attempt towards monitoring and analysing the state of
natural ecosystems in the Amazonia, we have chosen
to use remote sensing (RS) and a geographic
information system (GIS). In addition, we have
evaluated possible economic and socio-demographic
determinants of deforestation.
The patterns of ecosystem diversity, the spatial
patterns of deforestation and the resulting forest
fragmentation patterns that occur in Colombian
rainforests are totally different to those documented
in Brazil (Batistella et al., 2000) or even in Ecuador
(Sierra, 2000). In Colombia, pasture-led deforestation
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
for ranching activities occurs, but in the absence of a
clear state policy, spontaneous colonization has also
occurred since the early 1970s and both follow rivers
as the only existing communication network in some
areas.
The more recent – and more significant – threats to
the eastern slopes of the Andes and thus the adjacent
Amazon lowland are the cropping of illicit cultivars.
Coca cultivation in the Andes, in particular in Peru,
Bolivia and Colombia, has been expanding over the
last 20 years, resulting in the destruction of an area of
2.4 million ha of tropical forest (United States
Department of State, 1999). This illegal cropping is
located primarily in remote tropical forest areas and in
mountainous terrain outside governmental control.
Cultivation of illegal crops, therefore, extends beyond
the traditional frontier forests, becoming a serious
threat to the most isolated pristine areas where
terrestrial transport access does not exist. Until today
most parts of the Colombian Amazon have been
passively protected due to their relative inaccessibility.
Understanding the human dimension in the
deforestation of the Colombian Amazonia will be
an important contribution to the knowledge of the
Amazon. The analysis we present here merges
datasets from satellite based estimates of land cover
change and ecosystem fragmentation with demographic and socio-economic indicators and has the
potential to contribute to global environmental
modelling efforts currently underway (International
Geosphere-Biosphere Programme (IGBP), International Human Dimension Programme on Global
Environmental Change (IHDP), etc).
2. Methods
2.1. Study area
Colombia extends between 128260 46 N and
48130 30 S, and 668500 54 E and 798020 33 W. It is
the fourth largest country in South America, after
Brazil, Argentina and Peru, and covers an area of
approximately 1,142,000 km2. Colombia is a geographically diverse country. The western part is mostly
mountainous but major parts of the country are plains
located below 500 m. Colombia embraces 7% of the
Amazonian basin (Domı́nguez, 1987).
355
Due to variation in geology and geomorphology, the
region yields environments with varying drainage
systems and soil qualities. This has led to very
significant differences in ecosystem composition and
structure that supports a high degree of biological
diversity. The region can be divided into five broad
vegetation categories (Kalliola et al., 1993; Domı́nguez, 1987; Prance, 1985; Huber, 1981; Sierra, 1999):
(1) Lowland forests <600 m (Kalliola et al., 1993),
which can be either riparian (Várzea), periodically
flooded forest (Bosques Temporalmente Inundables, moist (Igapó) or permanently flooded forest
(Bosques Inundables);
(2) Upland forests, differentiated into riparian upland
forest complexes (Campinarana, Bosques de
Tierra Firme, Bosques de Colinas) and montane
upland forest complexes (Piedemonte, Sierra);
(3) Isolated summits, occurring in the western Amazon, with Tepui vegetation and montane savannas
with high biological endemism (Tepuis, Pantepui);
(4) Large scale dry and humid savannas also found in
the western Amazon (for example the Llanos of
Colombia and Venezuela);
(5) Various types of aquatic and swamp vegetation
complexes along the major rivers such as the
Amazon.
This study focused on six pilot areas (Fig. 1)
covering a total area of 4,200,000 ha (9% of the
colombian Amazonia), with sizes ranging from
approximately 626,786 ha for the smallest pilot area
(Mitu), to 802,047 ha in the Alto Putumayo region.
These areas are slightly different in environmental and
vegetation conditions although all six areas belong to
the northwestern Amazonia. The Alto Putumayo area
has a strong Andean influence and belong to the
Andean-Amazonian region, the Macarena has both
influence of the Serrania de la Macarena and of the
Amazonian lowlands. Pure and Chorrera are typical
Amazonian lowland regions and both Mitu and Inirida
have influence of the Guyana Shield and the
transitional area between the savannahs of the
Orinoquia and the Amazonian forests. These pilot
areas were selected in agreement with different
national, regional and local stakeholders, according
to current local knowledge, institutional interest and
due to the total lack of information in other areas. All
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D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
Fig. 1. Location of the study pilot areas (1–6) and 10 protected areas (national park, PNN and national nature reserve, RNN) in the Colombian
Amazonia.
of the study areas were delimited without considering
any kind of political boundary. We also analysed the
information available in the ten national protected
areas in the Amazon. These areas represent over 65%
of all current protected areas in Colombia (Fig. 1).
2.2. Data collection
2.2.1. Ecosystem mapping
Remote sensing data from satellite imagery for the
period 1985–2001 (Landsat MSS, TM, ETM) were
used to generate ecosystem maps for all the pilot areas.
In the case of La Chorrera, cloud free satellite
information was only available for the year 1985.
Major ecosystem types were determined by a
combination of supervised classification and manual
interpretation of satellite images supplemented with
secondary information on climate and geomorphology, vectorisation and finally ground-truthing. Areas
transformed by human activities were defined using
the spectral characteristics of deforested sites. This
classification included agricultural areas. Standard
methods of accuracy assessment, based on contingency tables, were used. Ground truth data were taken
at 250 points at each of the five different transects, one
for each pilot area. In one of the sites (Macarena) field
work was cancelled due to increased social unrest, so
accuracy value could not be calculated. Extensive field
work was carried out between June and October 2001
in order to verify classes. Misclassified polygons were
identified and corrected manually in a GIS. Overall
accuracy after correction with ground truth data was
93% in the 1980s and 95% in the 2000s. We identify
accuracy of 1980s map based on natural ecosystems
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
that remain untouched in the 2002. We used both
ERDAS Imagine (ERDAS Inc., 2000) remote sensing
processing software and the GIS software Arcview
(ESRI, 2000) to integrate the data using standard GIS
features.
As a result of this interpretation, maps of ecosystem
cover for two different time periods for five of the six
pilot areas were produced at the 1:250,000 scale.
Ecosystems were identified and classified into biomes
adapting Walter’s classification (1985; Walter and
Breckle, 1986) as follows (a biome is defined here as
an assembly of ecosystems with similar structural and
functional characteristics):
Amazonia and Orinoquia Tropical Forests;
Amazonian Orobiomes;
Andean Orobiomes;
Amazonian Helobiomes;
Amazonian Litobiomes;
Peinobiomes;
Transformed ecosystems.
Maps of deforestation were also produced for each
of the pilot areas. In order to facilitate reporting,
ecosystem classes were aggregated into (a) biomes and
(b) three major ecosystem types (natural, transformed
and water). Landscapes dominated by land uses
associated with agriculture, pasture or urban sites were
assigned the category of transformed ecosystems.
2.2.2. Census estimates and other socio-economic
indicators
Demographic and economic structure indicators
were derived from the population and agricultural
census at the municipal level, the only source of this
information for the Amazon in Colombia (CGR, 1951;
DANE, 1964, 1973, 1985 and1993). The population
data is split into ‘rural’ and ‘total’ for each of the
municipal areas in the pilot areas. The information on
population quality of life was obtained directly from
the Colombian Departamento Nacional de Planeación. This provided us with a synthetic index that
includes information on education, family size,
household building quality material, water availability, garbage collection, household density and income.
We used four types of indices to relate levels of these
indicators to levels of deforestation with the municipal
area as the spatial analysis unit:
357
an index of quality of life, with values between 0
and 100 that represent the minimum and maximum
possible level of population quality of life
respectively.
demographic indices expressed as absolute population (number of inhabitants), population density
(inhabitants/km2) as well as annual population
growth rate (%/year).
an economic activity index, or the percentage of
land area devoted to ranching and farming.
a violence index, the annual percentage of deaths
that were violent deaths.
Table 1 presents the results of the demographic and
socio-economic indicators generated for the pilot areas and protected areas analysed in this study.
2.3. Data analysis
Our goal was to offer region specific information to
support decision-making in the Colombian Amazon
with maximum cost effectiveness under budget
restrictions. Furthermore, the data had to be as up
to date as possible and presented in an easily
interpretable way. This paper focuses on quantifying
both ecosystem changes and the changes in the spatial
patterns of ecosystems that have taken place over time
in the Amazon. It also points out how they might be
related to the changes in the demographic and
economic structure of this area. We reported
quantitative data of land cover change over the last
20 years in this part of the world. The measures were
generated as part of the Indicators Project (Armenteras
et al., 2002; Rudas et al., 2002) at the Biological
Resources Research Institute Alexander von Humboldt of Colombia. Biotic indicators such as ecosystem extent (ha), change rates (%) and fragmentation
indices were based on major biomes types map that we
derived from the remote sensing and ground-truthing
studies described earlier. In addition, we also analysed
ecosystem information for the ten national protected
areas obtained from a general ecosystem map of
Colombia (Etter, 1998).
With the exception of fragmentation indicators,
which were calculated using the software Fragstats
(McGarigal and Marks, 1995), the other measures
were calculated using standard GIS functions in
Arcview and ERDAS Imagine and statistical analytical tools such as SPSS v.10.
358
Table 1
Socio-economic, demographic and biological indicators: pilot and protected areas in the Amazon
Alto Putumayo (PL)
Macarena (PL)
Inirida-Mataven (PL)
La Chorrera (PL)
Mitú (PL)
Puré (PL)
Amacayacu (PT)
La Paya (PT)
Nukak (PT)
Sumapaz (PT)d
Chiribiquete (PT)
La Macarena (PT)
Los Picachos (PT)
Tinigua (PT)
Puinawal (PT)
Cahuinari (PT)
Quality of
life index
Population (1993)
(number of
inhabitants)
Population density
(inhabitants/km2)b
Annual population growth (%)
Rural
Total
Rural
Total
Rural
Total
1973–1985
1985–1993
1973–1993
42.9
33.4
n.a.
n.a.
n.a.
n.a.
n.a.
78.6
n.a.
44.3
n.a.
36.0
35.2
34.5
n.a.
n.a.
62.4
50.5
n.a.
n.a.
n.a.
n.a.
71.7
64.7
61.8
61.0
57.5
42.4
42.1
40.1
n.a.
n.a.
213549
96690
13942
20146
9768
3028
14539
32778
28628
78597
8280
91162
89963
61275
22422
3322
388062
127306
18367
22963
13896
5227
35083
54740
33424
132308
10078
116369
115084
76874
26847
4489
9.98
2.45
0.76
0.30
0.47
0.13
1.90
2.42
1.18
2.76
0.15
2.85
2.19
2.45
0.33
0.06
18.11
3.16
1.00
0.32
0.67
0.22
5.10
4.03
1.36
4.23
0.18
3.63
2.77
3.08
0.39
0.08
3.7
8.2
2.6
3.6
7.7
8.3
8.8
12.2
0.0
3.8
6.6
6.6
8.3
7.6
1.7
19.7
4.0
6.0
6.8
7.9
4.5
4.7
5.3
6.6
0.0
2.0
8.4
2.8
1.8
1.0
12.7
10.0
3.8
7.4
4.3
5.3
2.8
6.9
7.4
4.7
0.0
1.5
7.3
5.1
5.7
5.0
4.1
15.8
n.a.: not available.
a
Total indicators for municipal areas with territory in selected pilot and protected areas.
b
Weighted mean by municipal territory participation in total pilot or protected area.
c
1980s data (no information available for the 2000s).
d
Without Bogotá’s indicators.
Pasture area
(% total area)
Violent
deaths (%)
Natural ecosystems
remaining
(% total area) 2000s
56.6
26.1
0.0
0.0
0.0
0.0
0.4
1.5
13.4
16.3
1.4
24.8
14.1
9.2
0.0
0.0
27.9
38.0
0.0
0.0
0.0
0.0
0.0
15.1
65.3
24.9
31.2
26.8
62.2
42.4
0.0
0.0
27.9
68.5
94.3
98.3c
91.2
99.2
99.7
86.6
96.8
99.1
100.0
79.9
97.0
82.4
100.0
100.0
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
Pilot (PL) and
protected areas (PT)a
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
The rate and extent of natural ecosystem loss and
fragmentation was calculated in five of the pilot areas,
because for one (La Chorrera) cloud free satellite
information was not available. Matrix information was
generated to obtain total area of each ecosystem type
and estimates of patterns and average rates of
deforestation throughout the study period (1985–
2001) for each pilot area. In order to calculate the
average annual deforestation rate, we assumed that
this rate is not constant and the following formula was
employed:
½LnðAt1 Þ LnðAt0 Þ 100
;
t1 t0
where A equals the ecosystem area (ha), t1 final year
and t0 initial.
Forest fragmentation was analysed for the period
1985–2001 using the following landscape metrics:
fragment number, size (mean patch size, patch size
standard deviation), shape (mean shape index, mean
perimeter/area ratio and mean patch fractal dimension)
and edge (total edge, edge density and mean patch edge).
The results for each index were grouped using 0.5
standard deviation limits into three classes (high,
average, and low). We constructed maps showing the
degree of transformation and the fragmentation of
natural ecosystems for each pilot area using these three
categories and combining the results for each index into a
single fragmentation level that could be illustrated in a
map. Fragstats softwarewas also used to studylandscape
diversity within the five sites, and three different indices
of landscape diversity were used in order to compare the
pilot areas: the number of ecosystem types, the Shannon
diversity and the evenness index.
The smallest spatial unit for which economic and
demographic data is available is the municipio, which
has a purely administrative boundary. The census data
were aggregated into a single record for each pilot area
and protected area by weighting the indices by the
percentage of the total area of interest that is covered
by the municipio. This aggregated record data
therefore refers to the characteristics of all the
municipios within these pilot and protected areas.
In order to analyse the impact of human pressures
on natural ecosystems and find the possible determinants of forest ecosystem loss we undertook simple
ordinary least squares (OLS) regression analysis.
Demographic and socio-economic data were treated as
359
independent variables. The percentage of ecosystem
loss (NED, natural ecosystem degradation, includes
only changes from natural to anthropogenic) was used
as the dependent variable. We analysed the correlation
between variables and undertook complementary
regression analysis to clarify the levels of statistical
confidence in the relationships between some of the
analysed variables. Further, we analysed the most
significant determinant of deforestation and projected
the ecosystem changes over 50 years from the present,
assuming that the same tendencies will prevail.
Loss rate ¼
3. Results and discussion
The most representative biomes of the six pilot areas,
are tropical humid forests (61.81%) and helobiomes of
the Amazonia (11.57%). The pilot areas with the
highest percentage of natural ecosystems over the 15year period of analysis (1985–2000) are Puré (99.23%),
Inı́rida-Matavén (94.37%) and Mitú (91.23%)
(Table 2). The area of greatest transformation is the
zone of Alto-Putumayo near the Andes, with only 28%
of natural ecosystems left in 2001, followed by
Macarena with 68.57% (Table 2). These areas also
had the highest average annual rate of natural
ecosystem loss: the highest rate corresponds to the
Putumayo (3.73%), followed by Macarena (0.97%) and
followed by Mitú (0.31%), Inı́rida-Matavén (0.23%)
and finally Puré with 0.01% annual loss rate (Table 2,
Fig. 2).
In general, the relative degree of fragmentation of
each site follows the same order as the above-mentioned
deforestation rates, with the highly fragmented natural
ecosystems in Putumayo and Macarena pilot areas and
less fragmentation in the other three. The pattern of
fragmentation follows the colonization and development associated with the rivers, the only transportation
network in Amazonia (Fig. 3). This pattern of
fragmentation and deforestation is clearly very different
to the ‘‘fishbone’’ patterns in areas of the Brazilian
Amazonia and some parts of Ecuador (Sierra, 2000),
where the construction of roads is one of the main
drivers of deforestation (e.g. in Rondonia, Batistella
et al., 2000), a determinant that is not apparent yet in the
Colombian Amazonia. These differences may reflect
different periods in the evolution of fragmentation that
in Colombia may be in an earlier stage than some areas
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D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
Table 2
Extent, natural ecosystem (NE) cover and landscape level metrics for natural ecosystems in six pilot areas of the Colombian Amazonia
Pilot areas
Study
area (ha)
Natural ecosystems
remaining (ha)
(% of study area)
NEa annual
loss rate (%)
La Macarena
Mataven-Inirida
Mitu
Pure
Putumayo
Chorrerab
713,386
640,887
626,786
705,056
802,477
712,116
560,658 (78.6%)
628,026 (97.9%)
595,480 (95.0%)
700,770 (99.4%)
336, 912 (42.2%)
700,057 (98.3%)
489179
604826
571384
699638
224799
–
a
b
1980s
(68.5%)
(94.3%)
(91.2%)
(99.2%)
(27.9%)
Number of
NE a
Shannon’s
diversity index
Shannon’s
evenness index
2000s
1980s
2000s
1980s
2000s
1980s
2000s
0.97%
0.23%
0.31%
0.01%
3.74%
–
41
41
39
40
45
30
41
41
39
40
45
–
3.32
3.01
2.96
2.80
3.34
2.66
3.28
3
2.97
2.80
3.28
–
0.89
0.81
0.81
0.78
0.88
0.77
0.88
0.81
0.81
0.76
0.77
–
NE, natural ecosystems.
No information available for the 2000s.
Fig. 2. Deforestation patterns and natural ecosystem loss in five pilot areas of the Colombian Amazonia for a period of 15–20 years (between the
1980s and 2000s).
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
361
Fig. 3. Degree of natural ecosystem fragmentation in the Macarena Pilot Area for the year 2000.
in Brazil. Further, there are several small, scattered and
isolated deforestation patches usually related to coca
growing in the Amazon, present in some of the areas but
to a major degree present in Alto Putumayo and
Macarena areas.
Table 2 also summarizes the indices of landscape
diversity for all pilot areas: number of ecosystem types,
Shannon’s diversity (SD) and Shannońs evenness (SE)
index. The areas all have similar ecosystem richness,
but ecosystem diversity and evenness are high in the
more threatened Putumayo and Macarena areas with an
SD ranging between 3.28 and 3.34 for the two areas, and
an SE of between 0.88 and 0.89 for the Macarena (the
highest), and between 0.77 and 0.88 for the Putumayo
area. On the contrary, the less degraded areas have
lower SD and SE: (a) Mataven-Inirida has an SD of 3
and an SE of 0.81, (b) Mitu has an SD of 2.97 and SE of
0.81 and (c) Pure, the most preserved pilot area has an
SD of 2.80 and SE of 0.76.
Landscape diversity results are especially surprising considering that although ecosystem richness is
very similar between areas, ecosystem diversity and
evenness are higher in the more threatened areas such
as the Putumayo and Macarena regions. In fact, this
result is logical because these two areas are the closest
to the Andes and, being topographically much more
heterogeneous than the lowlands of the Amazon areas,
they contain a higher number of different environments and ecosystems.
In the Putumayo region there has been a major
decrease in the percentage of natural ecosystems
(from 42 to 28%), coincident with the annual change
rate of 3.73% discussed above. Fragmentation of
natural forest has increased dramatically. Current rural
population density is 9.98 inhabitants/km2 and has
increased from 1.98 (1951) to 2.97 (1964) to 4.63
(1973) to 7.24 (1985). There has also been a
substantial decrease in the percentage of natural
362
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
Fig. 4. Evolution of rural population density around pilot areas over
the last five population censuses in Colombia. Source: Rudas et al.
(2002) and Armenteras et al. (2002).
ecosystems (from 79 to 68%) similar to the Macarena
region, although the annual change rate is 0.86% which
is lower than Macarena. Natural ecosystems are
becoming increasingly fragmented. Rural population
density in the Macarena region is lower than in the
Putumayo pilot area at 2.45 inhabitants/km2, a populations increased from 0.56 in 1973 to 1.51 in 1985.
The results also show that areas that are more
degraded coincide with areas of high population
density pressure (Fig. 4) and low quality of life.
Information analysis results demonstrate a statistically
significant relationship between demographic pressures and the percentage of natural cover lost (Fig. 2).
In fact, as shown in Eqs. (1)–(4) of Table 3, there is a
statistically significant correlation of both absolute
population and population density with natural
ecosystem degradation (NED). Furthermore the result
is significant for total population (TP), rural population (RP) as well as for total and rural population
density (TPD and RPD). It is important to note that for
models with demographic variables the intercept was
not significant, and so these models were re-estimated
ignoring the intercept. This is based on the assumption
that with zero population, ecosystem degradation
would also be zero.
Contrary to the significant results of the abovementioned demographic variables, annual population
growth rates (APG) are not significantly related to
natural ecosystem degradation NED (Eq. (6), Table 4).
This is because high growth rates are present in both
areas with current high population density (e.g. Alto
Putumayo and Macarena), and in areas with low
population and naturally high growth rates (Vaupés,
La Chorrera and Inı́rida-Matavén). In the former case,
the results indicate that not only do the areas have high
populations but also that the growth processes are still
highly dynamic. In the latter case this result reflects
the fact that although important changes in population
are taking place, population density remains low.
The significant effect of economic activity on
natural ecosystems is also reflected in these results.
Eq. (5) (Table 4) shows that the pasture area (PA, main
legal land use activity) has a significant positive
relationship with degraded ecosystems. However, it is
not appropriate, to undertake a multivariate analysis
using this variable and population variables since
these variables are highly correlated (Eqs. (7)–(10),
Table 4). Another significant result is that neither
quality of life nor violence levels are statistically
related with ecosystem degradation (Eqs. (11) and
(12), Table 4).
Overall, population processes and the main
economic activity have a very significant impact on
natural ecosystems degradation in these areas. For
instance, an increment of one inhabitant per square
kilometer would generate a loss of natural ecosystems
of more than 7% (Eq. (4b), Table 3). As a result, a
deforestation simulation model was developed in
order to project future tendencies of natural ecosystem
degradation in the area. Since rural population density
(RPD) was the most significant determinant of natural
ecosystem degradation (NED) with a r2 = 0.86
(Eq. (4a), Table 3), we used this factor to simulate
ecosystem loss. The values were area weighted.
Roughly speaking, NED equals 6.61 RPD. RPD was
linearly projected over the next 10, 20, 30, 40 and 50
years using the minimum (0.33%), maximum
(17.25%) and average (6.8%) RPD of the last five
demographic censuses in the study area. We then
constructed three different future scenarios assuming
that in the 1990s the ecosystem degradation was zero.
The results of applying this model can be seen in
Fig. 5. According to the model, the 16 sites of the
Colombia Amazonia studied will suffer significant
natural ecosystem cover loss (Fig. 5). In fact, only
under a scenario of low rural population density will
Amazonian forests be relatively protected. Both
average and high rural population density projections
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
363
Table 3
Statistical results of the regression analysis of population and economic indicators as predictors of natural ecosystem degradation
Equation
Est Var
(1a)
ŷ ¼ 1:75TP
NED
Predictor
Intercept
TP
(1b)
ŷ ¼ 1:66TP
NED
(2a)
ŷ ¼ 2:97RP
NED
TP
Intercept
RP
(2b)
ŷ ¼ 2:65RP
NED
(3a)
ŷ ¼ 3:76TPD
NED
RP
Intercept
TPD
(3b)
ŷ ¼ 3:74TPD
NED
(4a)
ŷ ¼ 7:08RPD
NED
TPD
Intercept
RPD
(4b)
ŷ ¼ 6:61RPD
NED
(5)
ŷ ¼ 1:08PA
NED
RPD
Intercept
PA
Coefficient
1.76
1.75
P > jtj
t
0.65
7.70
1.66
3.42
2.97
9.52
1.10
6.94
2.65
0.17
3.76
8.36
0.06
6.96
3.74
2.25
7.08
6.61
8.81
0.88
8.32
10.10
0.11
1.08
0.04
7.47
n
F
P>F
R2
R2adjusted
16
59.35
0.000
0.81
0.80
16
90.62
0.000
0.86
0.85
16
48.15
0.000
0.77
0.76
16
69.87
0.000
0.82
0.81
16
48.49
0.000
0.78
0.76
16
77.69
0.000
0.84
0.83
16
6.30
0.000
0.83
0.82
16
102.08
0.000
0.87
0.86
16
55.75
0.000
0.80
0.79
0.526
0.000
0.000
0.290
0.000
0.000
0.953
0.000
0.000
0.393
0.000
0.000
0.968
0.000
NED = natural ecosystem degradation (%) in 1990s. TPD = total population density, inhabitants/km2 (1993). TP = total population, 10.000
inhabitants (1993). RPD = rural population density, inhabitants/km2 (1993). RP = rural population, 10.000 inhabitants (1993). PA = pasture
area (%). NED = natural ecosystem degradation which is the percentage of natural land cover converted to anthropogenic land cover; TP = total
population; TPD = total population density; RP = rural population and PA = percentage of the area under pasture. Sample size = 16 (6 study
plots and 10 natural protected areas).
Table 4
Control analysis statistics of regressions between population and economic indicators and natural ecosystems degradation
Equation
Est Var
(6)
ŷ ¼ 0:72APG
NED
Predictor
PA
Intercept
APG
(7a)
ŷ ¼ 1:54TP
P > jtj
t
1.09
9.41
0.000
15.11
0.72
1.65
0.50
0.120
0.623
PA
Intercept
TP
(7b)
ŷ ¼ 1:48TP
Coefficient
1.13
1.54
0.77
12.32
1.48
15.36
F
P>F
R2
R2adjusted
16
0.25
0.623
0.02
0.05
16
151.84
0.000
0.92
0.91
16
236.05
0.000
0.94
0.94
0.968
0.000
PA
TP
n
0.000
364
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
Table 4 (Continued )
Equation
Est Var
(8a)
ŷ ¼ 2:70RP
PA
Predictor
Intercept
RP
(8b)
ŷ ¼ 2:41RP
PA
(9a)
ŷ ¼ 3:02TPD
PA
RP
Intercept
TPD
(9b)
ŷ ¼ 3:15TPD
PA
(10a)
ŷ ¼ 5:87RPD
PA
TPD
Intercept
RPD
(10b)
ŷ ¼ 5:68RPD
PA
(11)
ŷ ¼ 0:18VD
NED
RPD
Intercept
VD
(12)
ŷ ¼ 0:66QLI
1.10
3.02
3.15
0.90
5.87
5.68
7.48
0.18
15.60
0.44
6.26
8.23
0.44
8.51
10.93
1.15
0.83
19.65
0.06
0.48
0.09
87.79
0.98
2.89
1.82
(14b)
ŷ ¼ 1:09TPD
VD
(15a)
ŷ ¼ 2:79RPD
VD
TPD
Intercept
RPD
17.93
0.97
1.09
15.57
2.79
2.53
0.71
9.41
2.15
1.15
6.07
2.91
F
P>F
R2
16
210.97
0.000
0.94
0.93
16
245.58
0.000
0.94
0.94
16
39.15
0.000
0.74
0.72
16
67.70
0.000
0.82
0.81
16
72.35
0.000
0.84
0.83
16
119.38
0.000
0.89
0.88
16
0.69
0.420
0.05
0.02
10
0.01
0.933
0.001
0.12
10
3.32
0.106
0.29
0.21
16
0.51
0.489
0.03
0.03
16
5.29
0.036
0.26
0.21
16
1.33
0.268
0.09
0.02
16
8.49
0.011
0.36
0.32
R2adjusted
0.000
0.668
0.000
0.000
0.670
0.000
0.000
0.269
0.420
0.641
0.933
0.641
0.106
0.024
0.489
0.000
0.050
0.268
VD
RPD
n
0.041
0.000
VD
Intercept
TPD
(15b)
ŷ ¼ 6:07RPD
2.41
2.25
14.52
VD
Intercept
QLI
(14a)
ŷ ¼ 0:97TPD
3.04
2.70
P > jtj
t
NED
Intercept
QLI
(13)
ŷ ¼ 0:98QLI
Coefficient
0.011
NED = natural ecosystem degradation in 1990s (%). APG = anual population growth, 1973–993 (%). TP = total population, 10,000 inhabitants
(1993). PA = pasture area (%). RP = rural population, 10,000 inhabitants (1993). VD = violent death (%). TPD = total population density,
inhabitants/km2 (1993). QLI = quality of life index (0 < QLI < 100; best = 100). RPD = rural population density, inhabitants/km2 (1993).
NED = natural ecosystem degradation which is the percentage of natural land cover converted to anthropogenic land cover; TP = total
population; TPD = total population density; RP = rural population, PA = percentage of the area under pasture; APG = annual population growth;
QLI = quality of life. Sample size = 16 (6 study plots and 10 natural protected areas).
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
365
Fig. 5. Three possible scenarios of natural ecosystem degradation in the study area under three different projections of rural population density
(area weighted).
are very pessimistic and suggest that in 50 years
between 85 and 100% of natural ecosystems will be
lost.
Furthermore, the model shown in Eq. (5) (Table 3)
confirms the hypothesis that pasture and cattle
ranching are the main cause of ecosystems degradation in the Colombian Amazon. Both variables were
measured by independent procedures, and the model
estimates that an increase of one percent of pasture
areas would produce a decrease of one percent of
natural ecosystem cover. However, statistical information on the area of illicit crops per municipality is not
available and this must be taken into account. It was
impossible to incorporate these data into the analysis
to further explain deforestation rates in the study area.
4. Conclusions
Ecosystem loss rates reported in this project are
much higher than recent estimates which suggest an
annual rate of change of forest cover of between
0.38% (Achard et al., 2002) and 0.4% in tropical
South America (FAO, 2001). This suggests that
greater attention at a national and international level
should be directed towards the Colombian Amazon.
Furthermore, not only the extent and rate of
deforestation is worrying but also the degree of
fragmentation.
Clearly, the Macarena and Alto Putumayo areas
have undergone dramatic changes since the mid-1980s
mainly due to deforestation associated with, cattle
farming and illegal cropping. Both predominant land
uses are located near the Andes where more than half
of the population lives. In the 1950s oil extraction
brought population immigration to the Alto Putumayo
region. Since the 1970s, partly due to a lack of
government policies, illegal activities such as coca
growing have been taking place and violent paramilitary and guerrilla groups have spread. The current
patterns on the landscape are clearly due to forest
extraction associated with cattle farming combined
with illegal cropping. In the 1970s and 1980s there
was an unsuccessful attempt at establishing slash and
burnt agriculture in the Macarena region. This land
slowly transformed into grazing encouraged partly by
a land reform. Recently, these areas have also been
transformed by illegal cropping due to, in part, by
establishment of a peace talk territory with no
government presence that has lasted a few years.
Analysts have applied different approaches to study
change and the causes of change in natural ecosystems. Over the 15-year period covered by this study,
there has been an increase in the magnitude of natural
ecosystem loss. This loss is a function of the change in
pressure on the natural resources in the region. The
population processes and the main economic activity
related to population have very significant impacts on
natural ecosystem degradation in these areas.
We expect that the GIS methods developed during
this project will prove to be a very valuable tool for
policy and decision makers in the Amazon. We expect
that they will assist in both identifying further data and
solving interpretation problems, as well as developing
new indicators according to the area local conditions
and data availability. This study covered less than 10%
366
D. Armenteras et al. / Ecological Indicators 6 (2006) 353–368
of Amazonia. Clearly, many important sites could and
need to be studied further.
Ecosystem loss rates reported in this project are
much higher than recent estimates which suggest an
annual rate of change of forest cover of between
0.38% (Achard et al., 2002) and 0.4% in tropical
South America (FAO, 2001). This suggests that
greater attention at a national and international level
should be directed towards the Colombian Amazon.
Furthermore, not only the extent and rate of
deforestation is worrying but also the degree of
fragmentation. This is critically important due to its
effect on forest degradation and ecosystem functionality.
Hopefully, the results of this work will provide
some much-needed answers in our efforts to understand the spatial pattern and probable causes of
deforestation. At the same time we also hope that
natural resource managers and planners use the
information presented in this paper as to undertake
biodiversity policies in the Colombian Amazonia.
Remote sensing offers rapid and accurate sampling
and is perhaps the only affordable means of looking at
processes over large spatial areas. GIS analysis and
mapping are a good way of informing and warning
managers. It is imperative to keep in mind that
indicators have to be quantitative so that results can be
compared over time and space. It is important to
continue further interpretation, modeling and predictions of the dynamics of our natural resources through
analysis involving both remotely sensed and social
data. Although this research is just a small contribution to the still scarce knowledge regarding the
Colombian Amazonia, we hope that this article will
capture the attention of both public and researchers
towards this part of the world.
Acknowledgements
This work was the result of the project Diseño e
Implementación del Sistema de Indicadores de Seguimiento de la Polı́tica de Biodiversidad en la Amazonia
Colombiana (Instituto Humboldt – Ministerio del
Medio Ambiente, Crédito BID 774 OC/CO). Our
thanks to Fernando Gast, General Director of Instituto
Alexander von Humboldt. We want to specially thank
Juan Carlos Betancourth and Carol Franco and also
Pedro Botero and Jaime Forero for their work in this
project. We also want to thank the following participant
institutions: CDA, Corpoamazonia, Cormacarena,
Instituto Sinchi, Unidad de Parques and the Ministerio
del Medio Ambiente. Finally we want to thank Sophia
Burke and Scott Newey for their corrections and
comments on the manuscript.
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