ORIGINAL ARTICLE

The recovery of the lower montane cloud forest in the Mucujunwatershed, Merida, Venezuela

Nestor Gutierrez B. • Stefanie Gartner •

Juan Y. Lopez H. • Carlos E. Pacheco •

Albert Reif

Received: 14 April 2012 / Accepted: 19 January 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract High deforestation rates in tropical countries

continue to reduce forest cover and thereby habitat quantity

and quality. However, in some places the forest is recov-

ering and expanding thus offsetting the biodiversity and

ecosystem service losses. In order to characterize the forest

recovery, land use and land cover (LUC) changes were

analyzed using aerial photographs, taken between 1952 and

2009, of a peri-urban watershed in the Andes region of

Venezuela. The qualities of the changes were assessed

using landscape indices and hemeroby indicators. In that

period, the forest cover increased about 18 %, mainly due

to abandoned pastures on steep slopes. At the same time,

the urban area expanded about 4 % on valley bottoms,

while pastures and crop fields were reduced about 20 %.

The results also showed that forest patches were aggre-

gating, whereas pastures were fragmenting. A reduction in

direct human impacts on forests growing on abandoned

pastures resulted in a slight recovery of the lower montane

cloud forest structure and plant composition. But non-

native species were found in all LUC categories. During

the study period, we documented not only forest recovery,

but also urban area growth, intensified land use and inva-

sions by non-native species all of which could partially

counterbalance the positives of forest recovery.

Keywords Andean cloud forest recovery � Land use and

cover change � Hemeroby � Forest transition � Peri-urban

Introduction

Throughout history, humans have replaced forests with

fields, pastures, and settlements (Houghton 1994; Millen-

nium Ecosystem Assessment 2005). Ongoing deforestation

and land use changes are particularly serious in tropical

regions where the rates are higher than in other biomes

(FAO 2011). Although there has been a reduction in

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10113-013-0413-y) contains supplementarymaterial, which is available to authorized users.

N. Gutierrez B. (&)

Instituto de Investigaciones para el Desarrollo Forestal, Facultad

de Ciencias Forestales y Ambientales, Universidad de Los

Andes, Vıa Chorros de Milla, Conjunto Forestal, Merida 5101,

Venezuela

e-mail: nestguti@gmail.com

N. Gutierrez B. � S. Gartner � A. Reif

Chair of Vegetation Science and Site Classification, Institute of

Forest Sciences, Faculty of Environment and Natural Resources,

University of Freiburg, Tennenbacher Str. 4, 79106 Freiburg,

Germany

S. Gartner

e-mail: stefanie.gaertner@waldbau.uni-freiburg.de

A. Reif

e-mail: albert.reif@waldbau.unifreiburg.de

J. Y. Lopez H.

Laboraorio de Fotogrametria y Sensores Remotos, Escuela de

Ingenierıa, Facultad de Ciencias Forestales y Ambientales,

Universidad de Los Andes, Vıa Chorros de Milla,

Conjunto Forestal, Merida 5101, Venezuela

e-mail: jlopez@ula.ve

C. E. Pacheco

Escuela Tecnica Superior Forestal, Facultad de Ciencias

Forestales y Ambientales, Universidad de Los Andes, Vıa

Chorros de Milla, Conjunto Forestal, Merida 5101,

Venezuela

e-mail: carlosa@ula.ve

123

Reg Environ Change

DOI 10.1007/s10113-013-0413-y

deforestation in the last decades, at least 5 million hectares

of tropical forest continue to be lost every year (FAO

2011). Venezuela ranks as one of the tropical countries

with the highest deforestation rates. According to the

FAO, the annual rate of deforestation is about 0.6 %,

(ca. 288 9 103 ha year-1) (FAO 2011).

The change in land use and cover (LUC) type is a dynamic

process with highly variable outcomes within ecosystems and

landscapes (Foster 1992; Geist and Lambin 2001; Lambin

et al. 2001). LUC change is a very complex process involving

geophysical and biological properties of ecosystems as well

as socioeconomic and political conditions in a particular time

period (Geist and Lambin 2001; Lambin et al. 2001). The

interaction of these factors is the force driving the changes in

the LUC configuration (Lambin et al. 2001).

Under specific socioeconomic conditions, deforested

landscapes can recover and the forest area can be stabilized.

This process is known as forest transition (Mather 1992;

Mather and Needle 2000; Perz 2007). Forest transition is

driven by several factors including: (1) the intensification of

agriculture on the best sites (more production on less area),

(2) the abandonment of some marginal unproductive agri-

cultural areas, (3) a loss of incentives to cut down forest for

agricultural purposes, (4) the development of new income

sources from economic sectors other than agriculture, (5) the

migration of people into urban and industrialized areas to

take advantage of the economic opportunities (Grau and

Aide 2008; Mather and Needle 2000). These tendencies,

coinciding with forest recovery, have been reported from

several tropical areas in the past decade (Aide et al. 2012;

Grau and Aide 2008; Redo et al. 2012), including Puerto

Rico (Pares-Ramos et al. 2008; Thomlinson et al. 1996),

Costa Rica and Panama (Redo et al. 2012), Dominican

Republic (Grau et al. 2007b), Argentina (Grau et al. 2007a),

and Brazil (Baptista 2008). However, examples from South

American tropical mountain regions are still scarce.

The few studies done on LUC dynamics in Venezuela

indicate that deforestation is still the dominant process

(e.g., Aldana and Bosque 2008; Briceno 2003; Hernandez

and Pozzobon 2002; Pacheco et al. 2011a; Pozzobon et al.

2004). But there are some counteracting tendencies such as

the rapid transition from an agricultural to an industrial

economy fueled by oil, the migration of people from rural

to urban areas with improved economic and social condi-

tions, and an increased environmental awareness that helps

reduce anthropogenic pressures on natural ecosystems

(Wunder 2003). These tendencies could explain the recent

increase in forest area within Venezuela (Aide et al. 2012;

Pacheco et al. 2011a). The ongoing forest transition seems

to be occurring with higher frequency in the Venezuelan

Andes and Coast Cordillera (Pacheco et al. 2011a), which

is supported by some unpublished local examples (e.g.,

Gutierrez 1999, Rodriguez 2005 and Lozano 2006).

The increase in forest area can be an opportunity to

offset ecosystem degradation and diversity losses (Aide

and Grau 2004; Grau et al. 2003; Wright and Muller-

Landau 2006). However, one aspect commonly missing in

the LUC analysis is an assessment of the quality of the

changes in the recovering ecosystems (Grau et al. 2008).

This is probably because of the absence of indicators that

can objectively and practically be used to discriminate

changes in the LUC dynamic. The spatial properties of the

landscape (patch size, diversity, dominance, etc.) are

commonly used in conservation ecology, as they directly

relate to biological fluxes, habitat quality, biodiversity,

productivity, and ecosystem functioning (Biswas and

Wagner 2012; Chen et al. 2008; Turner 1989). However,

assessing the quality of changes in landscape dynamics

requires the linking of biological properties with the spatial

characteristics of the landscape units (Chen et al. 2008).

The current forest transition is dominated by human

activity (Lugo 2002, 2009); therefore, the measurement of

human influence in the ecosystems is essential. The

hemeroby concept was developed to assess a gradient of

human influences on flora and ecosystems (Jalas 1955) and

was later adapted to evaluate human impacts on ecosys-

tems, land use types, plant communities, soils, and forests

(e.g., Brentrup et al. 2002; Grabherr et al. 1998; McRoberts

et al. 2012; Reif and Walentowski 2008; Stoll 2007; Tasser

et al. 2008). We used the hemeroby index to integrate

different structural and compositional vegetation properties

related to human activities in order to assess the quality of

changes in the LUC analysis together with quantitative

metrics describing the change in landscape features.

In this study, we did a detailed local case study of a peri-

urban area in the Andes of Venezuela (1,800–2,500 m a.s.l.),

originally covered by Andean cloud forest, a threatened forest

type. We analyzed and evaluated the characteristics of the

LUC changes over a period of 57 years. The scope of this

study was: (1) to help fill the land use change and forest

transition knowledge gap in tropical South American moun-

tainous areas; (2) to assess a long time period using historical

aerial photographs (not frequently studied) dating back to

before the availability of satellite images; (3) lastly, this study

goes one step further than most of the forest transition studies

by evaluating the quality of the LUC changes through the

quantification of the anthropogenic influences on landscape

units by analyzing floristic and structural attributes based on

our field assessments.

Study area

The research was conducted in the Mucujun watershed of

the Venezuelan Andes. The watershed consists of a valley

about 6 km wide and 30 km long located 8 km north of the

N. Gutierrez B. et al.

123

city of Merida (8.65N; 71.12W), the state capital (Fig. 1).

The watershed ranges from 1,500 to 4,500 m above sea

level (a.s.l.), the mean annual temperature ranges between

12 and 19 �C, and the mean annual precipitation lies

between 1,600 and 1,965 mm, with two peaks in April and

October (Ataroff and Sarmiento 2003; CIDIAT unpub-

lished data).

The valley bottom is dominated by agriculture and

increasingly by urbanization, while the foothills are mostly

covered with pastures for cattle grazing. The steepest

slopes are forested, and above ca. 3,500 m a.s.l. paramo

vegetation is dominating. At the beginning of the 20th

century, the watershed was predominantly covered with

small agricultural fields (corn, potatoes, beans, sugarcane,

wheat) for local consumption. But the amount of pasture-

land has increased during the last century (Lujan 2007

unpublished) onto even steeper slopes and reaching up to

2,600 m a.s.l. Several factors including the Venezuelan

agrarian reform of 1960 and a higher demand for agricul-

tural goods (Pacheco et al. 2011b; Velazquez 2001) pro-

moted a new wave of agricultural colonization in the

Mucujun watershed lasting from the late 1960s into the

early 1970s (Vilanova et al. 2008). The reforms involved

the redistribution of farm lands to peasants coming from

other rural areas of Merida. The Mucujun watershed has

experienced a steady increase in population and a corre-

sponding housing construction boom, especially evident in

recent decades. The population of the state of Merida

changed from mainly rural in 1950 (82 % of the population

living in rural areas) to mainly urban in 2001 (88 % living

in cities over 2,500 inhabitants) (INE 2004; Ramos 1995).

The Mucujun watershed has been protected since 1985

because it supplies about 70 % of Merida’s fresh water. In

1989, the upper part of the watershed (above ca.

2,500 m a.s.l. Fig. 1) was included in the Sierra de La

Culata National Park (Vilanova et al. 2008). The protection

status does not totally exclude land use but differentiates

between areas with a priority on conservation (e.g., forest

on slopes over 30 %) and other areas (e.g., valley bottoms)

where traditional land uses can continue.

A previous analysis of the LUC showed that an increase

in forest and shrubland cover over the total watershed

between 1988 and 1996 had occurred (Gutierrez 1999

unpublished). This increase coincides with a reduction in

Fig. 1 The study area in the Mucujun watershed, the Sierra de La Culata National Park, its relative location to the city of Merida, and within

Andean cordillera of Venezuela

The recovery of the lower montane cloud forest

123

the area used for agriculture (MAC 2000; MAT 2008).

Although people living in the Mucujun watershed earn

money from farming potatoes, other vegetables and tour-

ism, most of the inhabitants earn incomes from jobs in

Merida city (INE 2004).

The LUC analysis described here focused on the lower

montane cloud forest (LMCF), which in the Venezuelan

Andes is generally located between 1,800 and 2,400 m a.s.l.

(Ataroff 2001). This forest ecosystem has important con-

servation values because of its high biodiversity and its role

in maintaining a hydrological balance (Ataroff and Rada

2000). Yet this forest type continues to be converted to

agricultural land and is considered an endangered ecosystem

(Ataroff 2001; Oliveira-Miranda et al. 2010). The study area

was delineated between 1,800 and about 2,500 m a.s.l, about

where the forest borders were located in our 1952 photo-

graphs. In the Mucujun watershed, this altitudinal belt cor-

responds to the area where major anthropogenic changes

have taken place. This watershed makes an interesting case

study because it shares common characteristics with other

montane regions of Venezuela due to the urban transition

and its consequences to the LUC change, as well as the

duality in the use versus conservation pressure brought about

by the peri-urban areas.

Methods

Analysis of land use and cover change

The land use and land cover changes were analyzed using

panchromatic aerial photographs from four different time

periods, for 1952 (mission A32) with a 1:40,000 scale; for

1987 (mission 010479) with a 1:25,000 scale; for 1996

(mission 010493) with a 1:65,000 scale, and for 2009, the

panchromatic bands of the SPOT50s sensor 2HRGs were

combined in an image with 2.5 m of resolution and

geometrically corrected (donated by Laboratorio de Pro-

cesamiento Avanzado de Imagenes Satelitales (LPAIS:

http://lpais.fii.gob.ve/) of Venezuela). The photographs

available did not offer complete coverage of the watershed;

therefore, the study area was determined by the 1987

photograph which covered the smallest area.

The aerial photographs were scanned at 800 dpi and

geometrically corrected using the orthorectification module

of the open source software GRASS-GIS (GRASS Devel-

opment Team 2010; Neteler and Mitasova 2008), adjusting

the final pixel resolution to 2 m for all aerial photographs

because it was the minimum common resolution of the data

source. For the orthorectification, a 2 9 2 m resolution

digital elevation model (DEM) was produced using the

regularized spline tension method in GRASS-GIS (Neteler

and Mitasova 2008). The DEM was developed on

topographic maps at a scale of 1:25,000 produced by the

Venezuelan cartography services (Direccion de cartografıa

nacional 1975) and digitalized by Pacheco and Barrios

(2004). Twenty ground control points were established in

the field using a differential GPS (Magellan Promark) to

georeference and co-register the photographs in the

orthorectification process. Finally, orthophoto mosaics

were produced for the times considered.

Nine LCU categories (Table 1) were discriminated and

plotted using on-screen visual digitalization. The smallest

cartography unit (SCA) considered for digitalization was

ca. 1.5 ha (2 9 2 mm) based on the 1996 photograph. The

categories defined represent the most frequent LUC of the

Table 1 Land cover and land use categories identified on the visual

interpretation of aerial photographs

Land use and land cover

categories

Description

Closed forest (CF) Old-growth LMCF or advanced

secondary forest characterized

by canopy cover of 75–100 %

and trees with different height

sizes, also dense riparian forest.

Seen as dark tones in the

photographs and irregular

texture

Advanced secondary forest

(ASF) (sensu (Gutierrez et al.

2012)

Secondary forest of about 25–40

recovering from other land uses

or disturbed forest, 50–75 %

canopy cover. Also narrow

riparian forest. Seen as lighter

tones and regular canopy cover

Shrubland Dense stands of low bushes and

small trees, 20 to a maximum of

50 % tree cover. Often fallow

fields

Pasture Anthropogenic grassland or

savanna like vegetation, tree and

shrub cover up to 20 %

Crop field Cash crops differentiated by even

texture, light color, and regular

forms

Agroforest Forest-like canopy with 50–70 %

tree cover with homogeneous

texture and color, usually coffee

plantations or backyards

Plantation Forest plantations of Pine (Pinusspp.) and tropical ash (Fraxinusuhdei (Wenz.) Lingelsh.). Tree

cover 75–90 %, homogenous

texture, color, and forms

Urban Scattered to dense buildings,

mainly for residential,

educational and small industry

purposes

Water Bodies of water (e.g., lakes), but

not creeks

N. Gutierrez B. et al.

123

study area. The photograph scale, for the most part, showed

a clear differentiation of the LUC categories. There were,

however, slight difficulties in discriminating between pas-

tures, crop fields, some low shrubland areas and fallow fields.

In these cases, texture, hue, polygon forms, and field checks

complemented the visual interpretation. The urban areas

were plotted when the density of the built-up areas permitted

it. However, the majority of dispersed houses could not be

plotted because they were smaller than the SCA.

The vector layers resulting from the visual interpretation

were transformed into raster data and overlaid on the

GRASS-GIS to determine the changes over a span of four

time periods: (1) between 1952 and 1987, (2) between 1987

and 1996, (3) between 1996 and 2009, and (4) between

1952 and 2009. The analysis of changes followed the

approach proposed by Pontius et al. (2004) using the cross-

tabulation matrix to account for the persistence, gains,

losses, and swaps of cover types between the LUC cate-

gories. The cross-tabulation matrix was used to analyze

systematic transitions in the landscape. A systematic tran-

sition was when a certain LUC category gained more area

from another category than would be expected randomly

and the category loosing area lost more than expected (Alo

and Pontius 2008). The expected transitions (gains and

losses) of cover for each category were calculated using the

off-diagonal analysis proposed by Pontius et al. (2004) and

Alo and Pontius (2008). The cross-product of the 1952,

1987, and 2009 periods was reclassified to produce a map

showing the persistence, the main LUC units, and the

patterns of change (gain, loss, and swap).

The annual rates of change for the LUC categories were

calculated using the formula developed by Puyravaud

(2003):

r ¼ 1=t2 � t1ð Þ � ln A2=A1ð Þ ð1Þ

where A1 is the cover at the initial time (t1) and A2 is the

cover at the next time (t2). For the categories that were not

recorded in the older photographs, an area equal to the

SCU (1.5 ha) was used as A1 for calculating the rate of

change.

Accuracy assessment

The accuracy of the visual interpretation for each date was

assessed using expert knowledge (Congalton and Green

2009). It was done through an expert familiar to the study

area, who did a visual interpretation of the orthophotos at

random points in each time period (138 for 1952, 206 for

1987, 196 for 1996, and 205 for 2009) distributed

according to the area cover by the LUC categories, using

GIS tools and following the principles of Congalton

(1988). The expert classification was compared with our

visual interpretation by means of confusion matrices. The

overall accuracy and Cohe’s Kappa index of Agreement

(KIA) (Cohen 1960) were then calculated for each time

period.

The overall estimated accuracy for the visual interpre-

tation was quite good, ranging between 74.6 and 83.4 % of

agreement (see online resource 1). The KIA was 0.69, 0.76,

078, and 0.81 for 1952, 1987, 1996 and 2009, respectively,

which indicates a substantial agreement of the classifica-

tion with the expert evaluation.

Analysis of landscape patterns and ecosystem recovery

We calculated common descriptive landscape measures

(Chen et al. 2008; O’Neill et al. 1988; Turner 1989) to

evaluate changes in landscape structure. The measures

selected were as follows: patch size and density, mean area

by the LUC, mean shape by category [using the corrected

perimeter/area index = (0.282 9 perimeter)/(area)1/2 (Baker

and Cai 1992)], landscape diversity [Shannon (H’) and

inverse Simpson (1/S)], and dominance [D = ln (number of

categories)-(H’)] indexes (Baker and Cai 1992; Csorba and

Szabo 2012; O’Neill et al. 1988; Turner 1989). The landscape

variables were calculated using the landscape structure

analysis (r.le) on GRASS-GIS (Baker and Cai 1992).

The degree of human impact on the ecosystems due to

land use change was assessed using the hemeroby index.

It is an integrative measure for the impact of all human

interventions on ecosystems as suggested by Sukopp

(1969) and further developed and applied in Central

Europe and Asia (i.e., Grabherr et al. 1998; Kim et al.

2002; Tasser et al. 2008; Zebisch et al. 2004). The

hemeroby values were ranked according to the ordinal

scale of the index 1–9 adapted from Brentrup et al.

(2002), the higher the value, the lower the human influ-

ence (Table 2). The old-growth cloud forest represents

the lowest level of human influence and therefore has the

highest hemeroby value (9 ahemerob = no direct human

influence).

The hemeroby index was obtained for each LUC category

by comparing five indicators of structure and plant species

composition (Table 2) with the old-growth LMCF as a ref-

erence for natural conditions in the region (Schneider 2001).

In the LUC analysis, the LMCF corresponds to the forest

that persisted during the entire time period. Because the

original natural vegetation was forest, relatively high values

were given to forest structure indicators and tree species

cover values (Table 2). For the evaluation of the ground

vegetation, the proportion of grass cover and the species

composition were integrated as anthropogenic disturbance

indicators. The major threats to the lower montane cloud

forest have been clear-cuts, subsequently converted to cattle

pasture usually with non-native grass species (e.g., Hueck

1961; Sarmiento et al. 1971). The assessment of the

The recovery of the lower montane cloud forest

123

hemeroby for most of the LUC categories was based on 78

(200 m2) plots sampled during 2007–2008, comprising of 27

plots in pastures (which were persistent during the entire

analysis period), 21 plots in shrublands, and 30 in the sec-

ondary forests (i.e., all the areas covered by pastures in 1952

that were subsequently abandoned and were classified as

such). The cover of all vascular plants was estimated in each

plot using the phytosociological approach. Details about the

methods used to assess and analyze vegetation data can be

found in Gutierrez et al. (2012). The hemeroby values for

plantations and agroforests were derived from data extracted

from inventories done in adjacent areas with similar site

conditions (Gutierrez and Gaviria 2009). The most common

non-native species recorded in the inventories were as

follows: Fraxinus uhdei (Wenz.) Lingelsh., Pinus spp.,

Syzygium jambos (L.) Alston, Eriobotrya japonica (Thunb.)

Lindl., Melinis minutiflora P. Beauv., Conyza canadensis

(L.) Cronquist, and Fragaria vesca L.

Based on the ‘‘side by side’’ approach used for succes-

sional studies (Mueller-Dombois and Ellenberg 1974),

comparisons of hemeroby among the four periods (1952,

1987, 1996, and 2009) were conducted. This was possible

due to the relative homogeneity in the environmental fac-

tors and plant composition in this area (Gutierrez et al.

2012). The most dominant non-native species considered

for the hemeroby indicators were already established by the

time of the first aerial photograph (Parsons 1972; Vilanova

et al. 2008).

The indicators selected were first transformed to a

weighting factor according to cover range (Table 2). Based

on the transformation, the hemeroby values for all LUC

categories, except crop fields and urban areas, were

obtained using the formula:

h ¼ CF � SpF � SpNð Þ þ SpNG � CGð Þ þ 2 ð2Þ

where h is the hemeroby value and the following terms are

indicators for different variables describing the degree of

human influence on the vegetation: CF represents the tree

species cover ([5 m in height); SpF is the proportion of

native tree species cover ([5 m) occurring in the reference

cloud forest; SpN is the proportion of non-native species

cover in the tree layer; SpNG is the proportion of non-native

vascular plants in the ground layer (\1 m tall); and CG is

the cover of graminoid species in the ground layer. The

crop fields and urban areas were excluded from the cal-

culation as they were not assessed during the field survey.

The hemeroby values for them were set at 2 (euhemerob)

and 1 (polyhemerob), respectively, due to the high influ-

ence that human activities had on them (Brentrup et al.

2002).

Table 2 Hemeroby indicators and weighting factors for estimating human influences and the recovery of the LMCF

Structural

features

Range

(%)

Weighting

factor

Composition features Range

(%)

Weighting

factor

Indicators of the structure and floristic composition of the tree layer (max. value 5)

CF: tree cover

[5 m

[50 2.5 SpF: proportion of native species in the tree layer (%)

(where LUC is composed of native species but with less

than 5 species, the factor 0.5 will be assigned)

[90 2

60–90 1.5

25–50 1.5 30–60 1

10–25 1 15–30 0.5

\15 0

5–10 0.5 SpN: proportion of non-native species in the tree layer. \5 1

05–20 0.5

\5 0 [20 0

Range (%) Weighting factor Range (%) Weighting factor

Indicators of the floristic composition

of the ground vegetation (GV)

(max. value 2)

SpNG: cover of non-native vascular

plants in the GV

\5 2 CG: proportion of graminoid

species in the GV

\10 1

5–50 0.5 10–50 0.5

[50 0 [50 0

References for floristic structure and composition where taken from Schneider (2001) and Gutierrez et al. (2012)

Hemeroby scale (adapted from (Brentrup et al. 2002): 9 = ahemerob: non direct human influence (HI); 8 = oligohemrob: small HI;

7 = mesohemerob: moderate HI; 6 = meso to b-euhemerob: Moderate to strong HI; 5 = b-euhemerob: Strong HI; 4 = b-eu to a-euhemerob:

Strong to very strong HI; 3 = a-euhemerob: Very strong HI; 2 = a-eu to polyhemerob: Very strong to mainly artificial; 1 = polyhemerob:

Mainly artificial

N. Gutierrez B. et al.

123

To evaluate the changing human influence over time, a

hemeroby value was calculated for the whole landscape

using the formula:

Ht ¼Xi

1ðhi � a1Þ=A ð3Þ

where Ht: hemeroby value for the total landscape; h:

hemeroby value for the LUC category i; a: area of the

category i, and A: total area of the landscape.

Results

Land use and cover (LUC) dynamics

The proportions of the LUC types in the Mucujun water-

shed have changed dramatically during the 57-year period

we looked at (Fig. 2). About 45 % (1,655 ha) of the ori-

ginal LMCF area in 1952 was covered by pastures and only

Fig. 2 Land use and cover maps from 1952, 1987, 1996, and 2009 in the lower montane cloud forest belt of Mucujun watershed

The recovery of the lower montane cloud forest

123

Ta

ble

3L

and

scap

ep

rop

erti

esfo

rth

eM

ucu

jun

wat

ersh

edin

the

dif

fere

nt

per

iod

san

aly

zed

LU

Cca

teg

ori

esT

ota

lco

ver

(ha/

%)

Nu

mb

ero

fp

atch

esM

ean

pat

chsi

ze/(

S.D

)[h

a]M

ean

shap

ein

dex

(=(0

.28

2X

per

imet

er)/

(are

a)1/2

)

19

52

19

87

19

96

20

09

19

52

19

87

19

96

20

09

19

52

19

87

19

96

20

09

19

52

19

87

19

96

20

09

Clo

sed

Fo

rest

1,0

88

.0

29

.7

1,0

72

.4

29

.3

1,2

25

.2

33

.4

1,4

12

.7

38

.6

9.0

10

.08

.01

4.0

12

0.9

(±1

62

.4)

10

7.2

(±1

33

.6)

15

3.2

(±2

08

.9)

10

0.9

(±1

83

.2)

3.5

3.1

3.4

3.7

Ad

van

cese

con

dar

y

fore

st

26

4.3

7.2

59

5.2

16

.2

70

0.9

19

.1

60

4.2

16

.5

60

.03

7.0

31

.05

3.0

4.4

(±4

.8)

16

.1

(±2

4.4

)

22

.6

(±5

1.3

)

11

.4

(±2

1.9

)

2.7

2.9

3.2

3.1

Sh

rub

lan

d3

58

.0

9.8

27

6.7

7.6

38

2.8

10

.4

22

5.5

6.2

37

.03

2.0

39

.04

4.0

9.7

(±1

2.3

)

8.6

(±1

3.6

)

9.8

(±1

1.9

)

5.1

(±6

.8)

2.7

2.7

2.5

2.4

Pas

ture

1,6

54

.9

45

.2

1,2

59

.2

34

.4

93

3.7

25

.5

1,0

94

.1

29

.9

29

.03

8.0

60

.05

7.0

57

.1

(±1

28

.2)

33

.1

(±7

5.0

)

15

.6

(±2

5.9

)

19

.2

(±4

5.4

)

2.9

3.0

2.5

3.2

Cro

pfi

eld

28

6.7

7.8

21

2.7

5.8

14

8.8

4.1

90

.0

2.5

6.0

10

.08

.02

2.0

47

.8

(±6

7.7

)

21

.3

(±4

6.3

)

18

.6

(±2

5.5

)

4.1

(±6

.6)

2.1

2.3

2.5

2.1

Ag

rofo

rest

11

.4

0.3

62

.7

1.7

54

.7

1.5

29

.8

0.8

1.0

6.0

3.0

13

.01

1.4

(±0

.0)

10

.4

(±1

0.7

)

18

.1

(±7

.5)

2.3

(±1

.8)

2.6

2.4

2.9

2.2

Pla

nta

tio

n0

.0

0.0

95

.3

2.6

11

3.2

3.1

76

.0

2.1

0.0

7.0

7.0

5.0

0.0

(±0

.0)

13

.6

(±1

2.5

)

16

.2

(±6

.0)

8.4

(±5

.6)

02

.82

.82

.3

Urb

an0

.0

0.0

89

.3

2.4

10

3.8

2.8

13

0.8

3.6

0.0

22

.02

6.0

40

.00

.0

(±0

.0)

4.1

(±5

.1)

4.0

(±4

.6)

3.3

(±5

.0)

02

.82

.22

.7

N. Gutierrez B. et al.

123

29.7 % (1,088 ha) was forested. Since then forest cover

(closed forest and advanced secondary forest) experienced

a remarkable comeback with a net recovery of about 18 %

(665 ha) of the total area (Table 3). Closed forest (CF) and

advanced secondary forest (ASF) increased at an annual

rate of 0.5 and 1.5 % over the period analyzed (Fig. 3).

However, the land use categories with even higher rates

of change during this period were plantations and urban

areas (7.2 and 8.2 % per annum, respectively, Fig. 3).

These categories made up only 5.6 % (ca. 207 ha) of the

total area in 2009. The categories with a net area loss were

shrublands, pastures, and crop fields (Fig. 3). Altogether

they lost about 890 ha (24.3 %) with annual change rates

of -0.8, -0.7, and -2.0 %, respectively.

The rate of change of the LUC categories was not

constant during the two periods (Fig. 3). The CF had a low

annual net loss rate of 0.04 % between 1952 and 1987;

thereafter, during 1987–1996, the area of dense forest

increased at a rate of 1.3 and 0.3 % between 1996 and 2009

(Fig. 3). The ASF increased continuously from 1952 to

1996 and was nearly stabilized (-0.003 %) in the last

period. The pastures decreased constantly in the first two

periods, but between 1996 and 2009 they increased at a rate

of 0.4 %. The urban area is the only use type that increased

during the whole time period we analyzed, whereas during

the same time period, crop field area decreased. In general,

the rate of change for all LUC categories slowed down in

the last period (Fig. 3).

Pattern of land use and cover changes and systematic

transitions

The forest and pasture categories showed the most

remarkable net cover changes in the study area (Fig. 4, see

Fig. 3 Annual rates of change the LUC categories of the Mucujun

watershed in the lower montane cloud forest belt. The numbers in the

bars indicate the percent of the area that change yearly calculated with

the formula (1) developed by Puyravaud (2003), and the numbers in

the table values indicate the percent of change in the analyzed period

The recovery of the lower montane cloud forest

123

Online resource 2). The area of CF increased due to the

succession of shrublands to ASF and ASF to CF, and this

tendency was constant during the whole period (1952–2009,

Fig. 4, columns 2, 3 and 4). Although CF had some swapping

changes with other categories, the tendency was to gain area

from other categories, mainly secondary forest. In the first

period, there was a higher expansion of ASF area on former

pastures (5.6 % net gain) and CF (1.6 %) (Fig. 4). The pas-

tures during 1952–1996 were continuously being replaced by

all the other uses, but mainly by shrublands, ASF, farm crops,

and urban areas. In the last period (1996–2008), ASF and

shrublands reverted to pastures, accounting for about 57 % of

the pasture area that was gained.

Pastures and shrublands were the most dynamic cate-

gories. They were replaced by ASF, plantations, agrofor-

ests, and urban areas especially between 1952–1987 and

1996–2009. When urban areas expanded, they mostly

supplanted crop fields and pastures (Fig. 4). About 50 % of

the total changes were exchanges between categories

(swaps), i.e., pastures became crop fields and vice versa

and CF were replaced by ASF and vice versa.

The spatial distribution of the persistence and transition

of the main categories can be seen in Fig. 5. Closed forests

remained primarily on slopes where they comprised about

21 % (788 ha) of the total forest area. If the areas of the CF

and the ASF were combined, the area permanently covered

by forests would be c.a. 1,115 ha (35 %), making it the

most persistent category on the landscape. The pastures

persisted in 13 % (472 ha) of the study area. The increase

in forest cover was largely seen in the foothills adjacent to

existing forests and also next to rivers amounting to about

790 ha. The non-forest cover was more persistent in the

Fig. 4 Systematic LUC changes in the Mucujun watershed between

1952 and 2009. The four columns represent the four periods. Each

column is divided into rows which represent the land use categories.

The rows (and colors) represent the LUC categories, and the size of

the bars within each row represents the cover percentage in the

landscape for each category/period. The colors representing the LUC

categories that remain within its row indicate persistence, other colorsdifferent than the category show gains from the category

corresponding to that color. The numbers indicate net changes in

percentage of the total landscape. The lines indicate the dominant

systematic transitions (Pontius et al. 2004); these are the changes

occurring in higher rate than expected by chance. For example, the

closed forest lost between 1952 and 1987 more area as expected

becoming ASF (1.6 % of the study area), thereafter expanded

consistent on the cost of previous ASF

N. Gutierrez B. et al.

123

lower foothills and in the valley bottoms; however, the

valley bottoms have largely been transformed into subur-

ban settlements.

Landscape characteristics and forest recovery

More than 50 % of the landscape area, consisting mostly of

forest and pasture, remained unchanged during the study

period (Fig. 5, see Online resource 2). The other half of the

landscape has been rather dynamic.

The CF and ASF showed an aggregation pattern,

increasing in cover and mean patch area, while the number

of patches decreased until 1996 (Table 3; Fig. 5). There-

after, the increase in CF was mainly by inclusion of older

secondary forest patches rather than by the expansion of

the former forest borders (Fig. 2), which account by a

decrease in mean patch size and increase in the number of

forest patches. The pasture, agroforest, and crop field cat-

egories have a higher increase in patch number because

they have been fragmented, mainly by increases in urban

patches and secondary forest.

The ASF beside the major rivers produced the increment

in the shape index value, indicating long and narrow patch

shapes. The decrease in the values in the pasture and urban

settlement categories indicated the tendency toward the

formation of more regular shapes through the subdivision

of patches.

The main characteristics of the LUC categories

according to the floristic and structural indicators are that

(Table 4): the secondary forest had a homogeneous tree

cover dominated mainly by long-lived pioneer and few

shade tolerant species. The secondary forest shared about

40 % of the species with the old-growth LMCF but con-

tained only a few pasture elements. Accordingly, it was

ranked mesohemerob (7—moderate HI). The shrubland

was characterized by an irregular tree cover of long-lived

pioneers, many of them also present in the old LMCF.

There were few pasture elements in this cover type, and its

hemeroby was estimated as meso- to b-euhemerob

(6—moderate to strong HI). The agroforest and plantation

were ranked with a hemeroby value of 3—a-euhemerob

[Very strong HI, Table 4)], due mainly to the high number

of non-native species in the tree layer. The pasture had a

sparse scattering of small trees, and the ground vegetation

was commonly dominated by the African grass Melinis

minutiflora. These indicators resulted in the hemeroby

value of 3.

The main changes in landscape structure were the

increases in patch number, patch density, and the conse-

quent decreases in mean patch size (Table 5); this was even

more striking after 1996. The number of categories (rich-

ness) increased because the urban and plantation areas

became spatially apparent in 1987. These changes were

also observed in the slight increase in the Shannon and

Fig. 5 Main pattern of change

between LUC types in the

Mucujun watershed between

1952 and 2009. The swap

categories in this map describe

areas that changed to other LUC

types in the intermediate period

but had reverted by the last

period

The recovery of the lower montane cloud forest

123

Simpson diversity indexes which showed a diversification

of the landscape structure by the subdivision of pastures

and crop fields up to 1996. However, by 2009, there was a

reduction in diversity while the dominance grew due to the

increases in forest and pasture areas which dominate the

current landscape (Table 5).

At the landscape level, there was a general reduction in

human influence (Table 5). The hemeroby caused by the

replacement of the original LMCF (Fig. 6) was partially

offset by the increases in secondary forest area and a

reduction in pasture area (Table 3). On the contrary, the

increase in plantation and agroforest areas between 1952

and 1996 has had a negative effect on the landscape

hemeroby. The rapid increase in the size of urban areas did

not have a marked influence on the overall landscape

hemeroby because urban areas still remained a small pro-

portion of the watershed. One factor with a major influence

on the individual and total hemeroby was the presence of

non-native species in all land use categories, particularly

Fraxinus uhdei and Melinis minutiflora.

Discussion

Changes in land use pattern and driving forces

The LUC changes are driven by particular biophysical and

socioeconomic settings (Bonilla-Moheno et al. 2012;

Mather and Needle 2000; Schulz et al. 2011; Souza Soler

and Verburg 2010), which make them non-random pro-

cesses. Therefore, even in the context of massive regional

and global deforestation (e.g., FAO 2011; Pacheco et al.

2011a), there is growing evidence that the forest is

recovering in certain areas of the Neotropics (Aide et al.

Table 4 Estimation of the human influence (hemeroby) on landscape units

LUC categories Tree layer Ground vegetation Hemeroby

(ha)Tree cover

([5 m)

CF

Cover of

native species

SpF

Non-native

tree speices

SpN

Cover of non-

native species

SpNG

Cover of

graminoid

species

CG

Lower montane cloud forest

(Schneider 2001)

Obs

(%)

[60 100 0 0 \5

IV 2.5 2 1 2 1 9

Secondary forest Obs

(%)

55 40 1.5 1.3 \1

IV 2.5 1 1 2 1 6.5

Shrubland Obs

(%)

30 39 4.3 3.6 \3

IV 1.5 1 1 2 1 5.5

Pasture Obs

(%)

5 51 3.3 6 48.1

IV 0.5 1 1 0.5 0.5 2.8

Agro forest (Gutierrez and

Gaviria 2009)

Obs

(%)

20–70 \10 \10 \10 5–10

IV 1.5 0.25 0.5 0.5 1 2.7

Plantation (Gutierrez and

Gaviria 2009)

Obs

(%)

60 0 100 \10 5–10

IV 2.5 0 0 0.5 1 2.5

The higher the hemeroby value the lower the human influencea h = (CF*SpF *SpN) ? (SpN* CG) ? 2; IV: indicator values see Table 2. Obs. = documented or observed value

Table 5 Changes of main landscape features and hemeroby index for

the lower montane cloud forest belt of the Mucujun watershed from

1952–2009

1952 1987 1996 2009

Total area (ha) 3,663.3 3,663.3 3,663.3 3,663.3

Number of patches 142.0 162.0 182.0 252.0

Patch density (n/100 ha) 3.9 4.4 5.0 6.9

Mean patch size (ha) 25.8 22.6 20.1 14.5

S.D. patch size (ha) 79.5 57.6 59.2 54.1

Richness 6.0 8.0 8.0 8.0

Shannon 1.4 1.6 1.7 1.5

Inverse Simpson 3.2 4.2 4.4 3.7

Dominance 0.44 0.44 0.41 0.55

Hemeroby 5 5 6 6

N. Gutierrez B. et al.

123

2012; Redo et al. 2012). Our study in the Mucujun

watershed in the LMCF provides a detailed insight into this

little studied but quite widespread process along neotropi-

cal mountains. These local examples of forest recovery are

related to relatively particular environmental conditions,

like the conditions found in the montane cloud forest

(Lozano 2006 and Rodriguez 2005 unpublished data). The

combination of specific conditions either favors or hinders

land use. For instance, forests on level mesotrophic sites

with mild temperatures are more likely to be rapidly

replaced by crop fields or pastures than are steep oligo-

trophic slopes with high rainfall (e.g., Allan et al. 2002).

Therefore, analyzing regions with comparable environ-

mental conditions would be a good approach to study, in

more detail, forest transition in socio-ecological conditions

which generally favor deforestation.

The increase in forest cover in the Mucujun is in

agreement with regional ongoing pattern of forest transi-

tion in Latin America and the Caribbean Region (Aide

et al. 2012; Grau and Aide 2008; Lugo 2002; Redo et al.

2012; Sanchez-Cuervo et al. 2012), especially in peri-urban

montane areas (Aide et al. 2012; Baptista 2008; Grau et al.

2008; Pares-Ramos et al. 2008). In Venezuela, the change

from deforestation to forest transition was driven mainly by

the consolidation of the oil industry which after 1920

promoted the development of the industrial and service

sectors (Hernandez 2007; Wunder 2003). The oil wealth

altered demographic patterns in Venezuela first by accel-

erating the population growth and second by promoting

migration from rural to urban areas (Hernandez 2007;

Velazquez 2001). The urban population growth in the state

of Merida characterizes very well the situation in the

Venezuelan Andes where about 90 % of the population is

now living in urban areas (over 2,500 inhabitants). Meri-

da’s urban sprawl continues unabated swallowing up of

more land in the watershed.

However, the continued population growth and built-up

area (Fig. 7) has not reduced the forest cover, it has in fact

increased. The main reasons for this are (1) that urbani-

zation took place on the flatter areas of the valley bottom

which were not forested before (Fig. 2), reducing the areas

formerly occupied by agriculture, as also shown in others

countries (Lugo 2002), and (2) jobs and higher wages in the

city of Merida persuaded farmers to abandon marginal

fields and begin commuting to jobs in the city. The later is

Fig. 6 Hemeroby development of the study area over time. The

hemeroby categories within the bars reflect the proportion of the total

area of the Mucujun watershed with different degrees of human

impact (HI). The lower the value, the higher the human influence in

the ecosystem (1 polyhemerob: artificial cover; 9 ahemerob: natural

cover)

Fig. 7 Development of the

number of houses and

inhabitants in the Mucujun

watershed 1950–2001. During

the first decade, the inhabitants

and houses increase

proportionately; after 1980,

there is a high increase in

houses indicating a reduction in

the number of occupants per

house which could also be

responsible for the urban

expansion. (data source: Dıaz

and Castaneda 1963

unpublished data; Dugarte and

Zambrano 1988 unpublished

data. INE 2004)

The recovery of the lower montane cloud forest

123

supported by the decreasing number of incomes reported as

coming from agricultural activities. By 2000, only 6.7 % of

the working population living in the Mucujun watershed

still claimed incomes from agricultural activities (MAC

2000). Additionally, the oil boom reduced the pressure on

traditional agricultural lands to grow crops, especially in

montane areas, by enabling massive imports of raw mate-

rials and food, and also by developing large-scale mecha-

nized agriculture in the lowland plains (Hernandez 2007).

The wealth generated from oil production and urban

economic development resulted in higher meat consumption

and a consequent need for new cattle grazing areas (Wunder

2003). However, the impacts of increased cattle ranching in

the Mucujun were partially offset by conservation efforts.

The growing need for water and concerns over water quality

caused a call for the protection of the Mucujun watershed. It

currently supplies about 70 % of the water consumption

in Merida (Vilanova et al. 2008). In 1985, the Mucujun

watershed was designated a protected area, and in 1989, the

upper part declared as the Sierra de la Culata National Park.

New regulations stemming from the designation seem to be

helping in the conservation of old-growth forest and the

recovery of deforested areas. However, it should be noted

that even before the designation, there was a noticeable

increase in secondary forest area and abandoned pastures.

On the contrary the protected area seems to have a low

influence on urban sprawl, because the population and built-

up area have been steadily increasing since 1980s (Fig. 7).

Between 2001 and 2005, a new agrarian reform in Ven-

ezuela was aimed at redistributing land held in large estates

and recently abandoned productive land (Beltran Zerpa

2008). The reform was reinforced by incentives such as farm

credits and lower taxes for agricultural products. These

additional monetary incentives were made possible due to

the high price of oil. They resulted in waves of squatters

coming to occupy the land. To stop the squatters and the

expropriation of unused land by the state, the former land

owners responded by intensifying their use of the land. This

could result in the reopening of pastures on secondary

forests and shrublands observed between 1996 and 2009.

The reduction in the rate of change of all LUC catego-

ries could indicate a tendency for stabilizing the land use

and cover of the LMCF belt in the Mucujun watershed,

which is one of the last phases of the forests transition

(Mather 1992; Redo et al. 2012). This has been observed

on steeper slopes but unless protective measures are taken

the valley bottoms will be converted into urban areas.

Forest recovery

The current increase in the area of secondary forest could

be an opportunity to halt further biodiversity losses and

species extinctions (Aide and Grau 2004; Wright and

Muller-Landau 2006). It will also help to secure and pos-

sibly increase ecosystem goods and services provided by

the native forests (Grau et al. 2003, 2008). Especially, the

expansion of forests on steep slopes and along rivers makes

that the benefits from this case of forest transition go well

beyond what would result from a simple increase in forest

area because these areas are very important for soil and

watershed protection.

The changes observed in landscape structure included

the aggregation of forest patches. This improvement in

connectivity helps to facilitate movements by forest spe-

cies. However, there are still numerous patches with high

border/area relation which could not be suitable for original

LMCF species.

Pastures that dominated the landscape structure since

1952 have been fragmented mainly by expanding second-

ary forest and urbanization. The new landscape mosaic has

had a positive effect on forest recovery when former pas-

tures have become interspersed with woody secondary

vegetation undergoing succession. On the contrary, where

pastures have been maintained or replaced, unrestricted

urban growth continues minimizing the opportunity for

forest recovery. Maintaining small pasture patches sur-

rounded by secondary forest could be a strategy for

increasing connectivity in the valley where non-forest land

uses dominate.

Although the general tendencies of pasture fragmentation

and forest aggregation are clear on the photograph sequence,

the size, form, and distribution of the patches are influenced

by the photo scale used (Tasser et al. 2009). In our case we

had to reduce the scale of the more detailed aerial photo-

graphs to make all orthophotos comparable. This affected

mainly the LUC with restricted distribution such as crop

fields, urban areas, plantations and agroforests but did not

put the general pattern of forest transition in doubt.

The indicators considered in the hemeroby evaluation

showed a positive trend toward ecosystem recovery. After

30–50 years of natural succession, the secondary forests in

the Mucujun watershed have some structural and func-

tional properties comparable to old-growth cloud forests

(Gutierrez et al. 2012; Schneider 2001). However, the

complete recovery of the original floristic composition and

the diversity of mountain forest ecosystems seems to be an

especially long-term process (Howorth and Pendry 2004;

Kappelle et al. 1996).

One common aspect in the recovery of degraded agri-

cultural landscapes is the presence of non-native species

(Aide et al. 2000; Grau et al. 2008; Lugo 2009). In our

study area, pine (Pinus spp.) and tropical ash (Fraxinus

uhdei) plantations accounted for an important increase in

the forested area. The F. uhdei have become naturalized

and can be found even in the older secondary forests where

they are regenerating well (Gutierrez et al. 2012).

N. Gutierrez B. et al.

123

Biodiversity and species composition can be directly

related to ecosystem functioning (Hooper et al. 2005).

Therefore, the presence and abundance of native and non-

native species and the structural features in a cultural

landscape are objective indicators of ecosystem quality in a

cultural landscape. These indicators are integrated in an

index, as hemeroby, and could be used to rapidly assess

and compare the quality of changes in the LUC analysis as

shown here in our study and has been done in other situ-

ations (Brentrup et al. 2002; Grabherr et al. 1998; Stoll

2007, 2008).

Aide and Grau (2004) pointed out that the recovery of

ecosystems and their services could be enhanced by

restricting land use to the more productive areas and

restoring forests in the less productive areas. This was

happening passively in the Mucujun and in other regions

(e.g., Lugo 2002). The recovery of natural forest cover may

enhance environmental services as has been documented

for other peri-urban mountain areas (Grau et al. 2008).

However, further research is needed to study the effects of

non-native species in the recovery process of the LMCF

and their influences on ecosystem functioning and provi-

sion of environmental services. This novel forest (sensu

Lugo 2009), with a new mix of species, is the replacement

ecosystem in current tropical landscapes; therefore, it is

important to study its value for active conservation.

Special attention should be given to the impacts of

intensified land use. In the study area, urban and productive

areas were small compared to forested areas and areas of

other land use types. However, on large intensively managed

agricultural lands, there is a high and frequent use of agro-

chemicals, which in conjunction with urban sprawl (Fig. 7)

has reduced the quantity and quality of the water coming

from the watershed (Bejarano 1997 and Grimaldo 1978

unpublished data). These practices are compromising eco-

system goods and services gains achieved by forest recovery.

Our study shows that in the LMCF, in the short term, after

pastures are abandoned, the land does not regain the original

ecosystem characteristics and evidence of human activities

can remain for several decades. But a reduction in human

activities allows native vegetation to reestablish and con-

nectivity to increase thereby helping the process of ecosystem

restoration. However, as mentioned previously, special

attention should be given to the impacts resulting from land

use intensification, invasions of non-native species and

ongoing urbanization because of their potential for offsetting

and obstructing the recovery of ecosystems in transition.

Conclusions

In contrast to the high deforestation rates in the whole of

Venezuela, the forested area in the Mucujun watershed has

enlarged during the study period. This is the first study that

provides evidence of a forest in transition in Venezuela.

There is, however, evidence that this process is ongoing in

other mountainous areas of the country and on the whole

continent (Aide et al. 2012).

As in other parts of the world (Bonilla-Moheno et al.

2012; Foster 1992; Grau et al. 2007a; Mather 2001; Pares-

Ramos et al. 2008; Thomlinson et al. 1996), regions in

Venezuela were moving away from subsistence farming

and ranching toward an income based industrial economy.

This has led to the abandonment of farms and pastures and

allowed for the renewal of the forest onto lands formerly

occupied. In Venezuela, this process was abetted by the

wealth coming from oil production. Another important

factor allowing forests to expand is the proximity of these

lands to urban areas. This facilitated the farmer’s access to

other income sources. Additionally, the increase in envi-

ronmental awareness appear to have a positive effects, as

has been also observed in other peri-urban areas (e.g.,

Baptista 2008; Grau et al. 2008). However, even with the

forest recovering at higher elevations and on steep slopes,

the ongoing urban sprawl in the valley bottom could offset

any benefits associated with the ecosystem recovery.

Acknowledgments The study was possible with the support from

the Fundacion Gran Mariscal de Ayacucho (FUNDAYACUCHO) and

the German Academic Exchange Service (DAAD), the Verband der

Freunde der Universitat Freiburg i. Br., the International Ph.D. Pro-

gramme ‘‘Forestry in Transition’’ of the Faculty of Forest and Envi-

ronmental Sciences of the University of Freiburg, and the Universidad

de Los Andes. The authors gratefully acknowledge the personal of the

Jardın Botanico de la Facultad de Ciencias of the Universidad de los

Andes, Gerardo Avendano and Darwin Gutierrez for the support

during the visual interpretation and sampling. We are very thankful

for the supportive comments received by two anonymous reviewers.

We thank Bernhard Thiel for improving the English.

References

Aide TM, Grau HR (2004) Ecology: globalization, migration, and

Latin American ecosystems. Science 305:1915–1916. doi:

10.1126/science.1103179

Aide TM, Zimmerman JK, Pascarella JB et al (2000) Forest

regeneration in a chronosequence of tropical abandoned pastures:

implications for restoration ecology. Restor Ecol 8:328–338

Aide TM, Clark ML, Grau HR et al (2012) Deforestation and

Reforestation of Latin America and the Caribbean (2001–2010).

Biotropica n/a–n/a. doi:10.1111/j.1744-7429.2012.00908.x

Aldana AA, Bosque JB (2008) Cambios ocurridos en la cobertura/uso

de la tierra del Parque Nacional Sierra de la Culata. Merida-

Venezuela. perıodo 1988–2003. Geofocus 8:139–168

Allan JD, Brenner AJ, Erazo J et al (2002) Land use in watersheds of

the Venezuelan Andes: a comparative analysis. Conserv Biol

16:527–538. doi:10.1046/j.1523-1739.2002.02301.x

Alo CA, Pontius RG Jr (2008) Identifying systematic land-cover

transitions using remote sensing and GIS: the fate of forests

inside and outside protected areas of Southwestern Ghana.

Environ Plan B 35:280–295

The recovery of the lower montane cloud forest

123

Ataroff M (2001) Venezuela. In: Kappelle M, Brown AD (eds)

Bosques nublados del neotropico. Instituto Nacional de Biodiv-

ersidad (INBio), Costa Rica, pp 397–442

Ataroff M, Rada F (2000) Deforestation impacts on water dynamics

in a Venezuelan Andean cloud forest. Ambio 29:440–444

Ataroff M, Sarmiento L (2003) Diversidad en los Andes de

Venezuela. I mapa de unidades ecologicas del estado Merida.

CD-ROM. Universidad de Los Andes, Merida, Venezuela

Baker WL, Cai Y (1992) The r.le programs for multiscale analysis of

landscape structure using the GRASS geographical information

system. Landsc Ecol 7:291–302. doi:10.1007/BF00131258

Baptista SR (2008) Metropolitanization and forest recovery in

southern Brazil: a multiscale analysis of the Florianopolis city-

region, Santa Catarina State, 1970 to 2005. Ecol Soc 13:5

Beltran Zerpa K (2008) Polıticas de tierras en Venezuela

(1999–2007), en referencia al desarrollo sustentable. Rev Venez

Cien Polit 34:61–74

Biswas S, Wagner H (2012) Landscape contrast: a solution to hidden

assumptions in the metacommunity concept? Landsc Ecol

27:621–631. doi:10.1007/s10980-012-9732-5

Bonilla-Moheno M, Aide TM, Clark ML (2012) The influence of

socioeconomic, environmental, and demographic factors on

municipality-scale land-cover change in Mexico. Reg Environ

Change 12:543–557. doi:10.1007/s10113-011-0268-z

Brentrup F, Kusters J, Lammel J, Kuhlmann H (2002) Life cycle

impact assessment of land use based on the hemeroby concept.

Int J Life Cycle Assess 7:339–348. doi:10.1007/BF02978681

Briceno F (2003) Cambios de cobertura de la tierra en el Valle del Rıo

Momboy, Estado Trujillo. Geoensenanza 8:91–100

Chen L, Fu B, Zhao W (2008) Source-sink landscape theory and its

ecological significance. Front Biol China 3:131–136. doi:

10.1007/s11515-008-0026-x

Cohen J (1960) A coefficient of agreement for nominal scales. Educ

Psychol Meas 20:37–46. doi:10.1177/001316446002000104

Congalton RG (1988) A comparison of sampling schemes used in

generating error matrices for assessing the accuracy of maps

generated from remotely sensed data. Photogramm Eng Rem S

54:593–600

Congalton RG, Green K (2009) Assessing the accuracy of remotely

sensed data: principles and practices, 2nd edn. CRC Press, Boca

Raton

Csorba P, Szabo S (2012) The application of landscape indices in

landscape ecology. Perspectives on nature conservation–paterns,

pressures and prospects direccion de cartografıa nacional (1975)

Mapas topograficos de Venezuela

FAO (2011) The state of world’s forests 2011. FAO, Rome

Foster DR (1992) Land-use history (1730–1990) and vegetation

dynamics in central New England, USA. J Ecol 80:753–771. doi:

10.2307/2260864

Geist HJ, Lambin EF (2001) What drives tropical deforestation? A

meta-analysis of proximate and underlying causes of deforestation

based on subnational case study evidence. LUCC International

Project Office, LUCC Report Series no. 4, Louvain-la-Neuve

Grabherr G, Koch G, Kirchmeir H, Reiter K (1998) Hemerobie

osterreichischer Waldokosysteme. Universitatsverlag Wagner,

Innsbruck

GRASS Development Team (2010) Geographic resources analysis

support system (GRASS) software, version 6.4.0. Open Source

Geospatial Foundation

Grau HR, Aide M (2008) Globalization and land-use transitions in

Latin America. Ecol Soc 13:16

Grau HR, Aide TM, Zimmerman JK et al (2003) The ecological

consequences of socioeconomic and land-use changes in

postagriculture Puerto Rico. Bioscience 53:1159–1168

Grau HR, Gasparri NI, Morales M et al (2007a) Regeneracion

ambiental en el Noroeste argentino: oportunidades para la

conservacion y restauracion de ecosistemas. Cienc Hoy 17:

43–56

Grau HR, Perez Ceballos M, Martinuzzi S et al (2007b) Cambios

socioeconomicos y regeneracion del bosque en la Republica

Dominicana. In: Gonzalez-Espinosa JM, Rey-Benayas JM,

Ramırez-Marcial N (eds) Restauracion de bosques en America

Latina. Fundacion Internacional para la restauracion de Ecosist-

emas (FIRE) & Editorial Mundi-Prensa, Mexico, D.F.,

pp 211–227

Grau HR, Hernandez ME, Gutierrez J et al (2008) A peri-urban

neotropical forest transition and its consequences for environ-

mental services. Ecol Soc 13:35

Gutierrez N, Gaviria JC (2009) Analisis de la vegetacion del Parque

Metropolitano Albarregas, Merida, Venezuela. Rev Forest Lat

Am 24:9–29

Gutierrez N, Gartner S, Gaviria JC et al (2012) Successional

vegetation patterns in abandoned pastures of the lower montane

cloud forest zone in the Venezuelan Andes. Phytocoenologia

42:101–132

Hernandez JL (2007) Elementos claves para la discusion sobre la

problematica agraria venezolana. Universidad de Los Andes,

Facultad de Ciencias Jurıdicas y Polıticas, Maestrıa en Desarr-

ollo Agrario, Centro de Estudios Rurales Andinos, Merida,

Venezuela

Hernandez E, Pozzobon E (2002) Tasas de deforestacion en cuatro

cuencas montanosas del occidentede Venezuela. Rev For Venez

46:35–42

Hooper DU, Chapin FS, Ewel JJ et al (2005) Effects of biodiversity

on ecosystem functioning: a consensus of current knowledge.

Ecol Monogr 75:3–35

Houghton RA (1994) The worldwide extent of land-use change.

Bioscience 44:305–313. doi:10.2307/1312380

Howorth RT, Pendry CA (2004) Post-cultivation secondary succes-

sion in a Venezuelan lower montane rain forest. Biodivers

Conserv 15:673–715

Hueck K (1961) Die Walder Venezuelas. P. Parey, Hamburg

INE (2004) Censo del Estado Merida 2001. Instituto Nacional de

Estadıstica, Caracas

Jalas J (1955) Hemerobe und hemerochore Pflanzenarten. Ein

terminologischer Reformversuch. Acta Soc pro Fauna et Flora

Fennica 72:1–15

Kappelle M, Geuze T, Leal ME, Cleef AM (1996) Successional age

and forest structure in a Costa Rican upper montane Quercus

forest. J Trop Ecol 12:681–698

Kim Y-M, Zerbe S, Kowarik I (2002) Human impact on flora and

habitats in Korean rural settlements UR -. Preslia Praha 74:409–419

Lambin E, Turner B, Geist H, Agbol B (2001) The causes of land-use

and land-cover change: moving beyond the myths. Global

Environ Change 11:261–269

Lugo AE (2002) Can we manage tropical landscapes? An answer

from the Caribbean perspective. Landsc Ecol 17:601–615. doi:

10.1023/A:1021419815480

Lugo AE (2009) The emerging era of novel tropical forests.

Biotropica 41:589–591. doi:10.1111/j.1744-7429.2009.00550.x

MAC (2000) VI Censo Agrıcola Nacional. Ministerio de Agricultura

y Crıa, Caracas Venezuela

MAT (2008) VII Censo Agrıcola Nacional. http://200.47.151.243/

redatam/. Accessed 15 Oct 2011

Mather AS (1992) The forest transition. Area 24:367–379

Mather AS (2001) The transition from deforestation to reforestation in

Europe. In: Angelsen A, Kaimowitz D (eds) Agricultural

technologies and tropical deforestation. CABI Publication

CIFOR, Wallingford Oxon UK, New York, pp 35–52

Mather AS, Needle CL (2000) The relationships of population and

forest trends. Geogr J 166:2–13. doi:10.1111/j.1475-4959.

2000.tb00002.x

N. Gutierrez B. et al.

123

McRoberts RE, Winter S, Chirici G, LaPoint E (2012) Assessing

forest naturalness. For Sci 58:294–309. doi:10.5849/forsci.

10-075

Millennium Ecosystem Assessment (2005) Ecosystems and human

well-being: biodiversity synthesis. World Resources Institute,

Washington

Mueller-Dombois D, Ellenberg H (1974) Aims and methods of

vegetation ecology. Wiley, New York

Neteler M, Mitasova H (2008) Open source GIS, 3rd edn. Springer,

Berlin

O’Neill R, Krummel J, Gardner R et al (1988) Indices of landscape

pattern. Landsc Ecol 1:153–162

Oliveira-Miranda M, Huber O, Rodriguez JP et al (2010) Riesgo de

eliminacion de los ecosistemas terrestres de Venezuela. In:

Rodrıguez JP, Rojas-Suarez F, Giraldo-Hernandez D (eds) Libro

rojo de los ecosistemas terrestres de Venezuela. Provita,

Shell Venezuela, Lenovo (Venezuela), Caracas, Venezuela,

pp 107–236

Pacheco A, Barrios A (2004) Evaluacion de metodos de interpolacion

en la construccion de un modelo digital de elevacion con fines

hidrologicos: aplicacion en una cuenca andina de montana. Rev

For Venez 48:118–126

Pacheco C, Aguado I, Mollicone D (2011a) Dinamica de la

deforestacion en Venezuela: analisis de los cambios a partir de

mapas historicos. Interciencia 36:578–586

Pacheco C, Aguado I, Mollicone D (2011b) Las causas de la

deforestacion en Venezuela: un estudio retrospectivo. BioLlania

10:281–292

Pares-Ramos IK, Gould WA, Aide TM (2008) Agricultural abandon-

ment, suburban growth, and forest expansion in Puerto Rico

between 1991 and 2000. Ecol Soc 13:1

Parsons JJ (1972) Spread of African pasture grasses to the American

Tropics. J Range Manag 25:12–17. doi:10.2307/3896654

Perz SG (2007) Grand theory and context-specificity in the study of

forest dynamics: forest transition theory and other directions.

Prof Geogr 59:105–114. doi:10.1111/j.1467-9272.2007.00594.x

Pontius RG Jr, Shusas E, McEachern M (2004) Detecting important

categorical land changes while accounting for persistence. Agric

Ecosyst Environ 101:251–268. doi:10.1016/j.agee.2003.09.008

Pozzobon E, Hernandez E, Dugarte I (2004) Evaluacion del proceso

de deforestacion en tres cuencas del piedemonte lacustrino de la

cordillera de Los Andes. Rev For Venez 48:14–22

Puyravaud JP (2003) Standardizing the calculation of the annual rate

of deforestation. For Ecol Manag 177:593–596

Ramos O (1995) Movimiento general de la poblacion del estado

Merida en el perıodo 1936–1990. Rev Econ 10:169–207

Redo DJ, Grau HR, Aide TM, Clark ML (2012) Asymmetric forest

transition driven by the interaction of socioeconomic develop-

ment and environmental heterogeneity in Central America.

PNAS 109:8839–8844. doi:10.1073/pnas.1201664109

Reif A, Walentowski H (2008) The assessment of naturalness and its

role for nature conservation and forestry in Europe. For Ecol

Landsc Res Natl Conserv 6:63–76

Sanchez-Cuervo AM, Aide TM, Clark ML, Etter A (2012) Land

cover change in Colombia: surprising forest recovery trends

between 2001 and 2010. PLoS ONE 7:e43943. doi:10.1371/

journal.pone.0043943

Sarmiento G, Monasterio M, Azocar A et al (1971) Vegetacion

natural. Estudio integral de la cuenca de los rıos Chama y

Capazon. Instituto de Geografıa, Universidad de los Andes,

Merida, Venezuela

Schneider J (2001) Diversity, structure, and biogeography of a

successional and mature upper montane rain forest of the

Venezuelan Andes. PhD thesis, Johann Wolfgang Goethe-

Universitat

Schulz JJ, Cayuela L, Rey-Benayas JM, Schroder B (2011) Factors

influencing vegetation cover change in Mediterranean Central

Chile (1975–2008). Appl Veg Sci 14:571–582. doi:10.1111/

j.1654-109X.2011.01135.x

Souza Soler L, Verburg PH (2010) Combining remote sensing and

household level data for regional scale analysis of land cover

change in the Brazilian Amazon. Reg Environ Change

10:371–386. doi:10.1007/s10113-009-0107-7

Stoll A (2007) Hemerobia: una medida para evaluar el estado de

conservacion de comunidades vegetales nativas. Chloris Chil-

ensis 10, N�2. http://www.chlorischile.cl/

Stoll A (2008) Hemerobia, un ejemplo de calculo: el estado de

conservacion del bosque de pitao y coigue (Pitavio-Nothofage-

tum) en Quebrada Honda, Region del Maule (VII), Chile.

Chloris Chilensis 11, N�1. http://www.chlorischile.cl/

Sukopp H (1969) Der Einfluss des Menschen auf die Vegetation.

Vegetatio 17:360–371

Tasser E, Sternbach E, Tappeiner U (2008) Biodiversity indicators for

sustainability monitoring at municipality level: an example of

implementation in an alpine region. Ecol Indic 8:204–223. doi:

10.1016/j.ecolind.2007.01.005

Tasser E, Ruffini F, Tappeiner U (2009) An integrative approach for

analysing landscape dynamics in diverse cultivated and natural

mountain areas. Landsc Ecol 24:611–628. doi:10.1007/s10980-

009-9337-9

Thomlinson JR, Serrano MI, Lopez T del M et al (1996) Land-use

dynamics in a post-agricultural Puerto Rican landscape

(1936–1988). Biotropica 28:525–536. doi:10.2307/2389094

Turner MG (1989) Landscape ecology: the effect of pattern on

process. Annu Rev Ecol Syst 20:171–197

Velazquez N (2001) Inmigrantes, Cambios tecnologicos y diversifi-

cacion agrıcola en los Andes venezolanos. Agroalimentaria 13:

87–98

Vilanova E, Torres-Lezama A, Ramırez Angulo HC (2008) Propuesta

para la creacion de un bosque modelo en la subcuenca del rıo

Mucujun, Merida, Venezuela, utilizando el enfoque de manejo

de paisajes. Rev For Venez 52:159–171

Wright SJ, Muller-Landau HC (2006) The future of tropical forest

species. Biotropica 38:287–301

Wunder S (2003) Oil wealth and the fate of the forest: a comparative

study of eight tropical countries. Routledge, London

Zebisch M, Wechsung F, Kenneweg H (2004) Landscape response

functions for biodiversity—assessing the impact of land-use

changes at the county level. Landsc Urban Plan 67:157–172. doi:

10.1016/S0169-2046(03)00036-7

The recovery of the lower montane cloud forest

123