Sustainable Land Use Management Needed to Conserve the Dragon’s Blood Tree of Socotra Island, a Vulnerable Endemic Umbrella Species

Abstract

Unsustainable overgrazing is one of the most important threats to the endemic and endangered population of dragon’s blood tree (Dracaena cinnabari) on Socotra Island (Republic of Yemen). However, there is a lack of information about the exact population size and its conservation status. We estimated the population size of D. cinnabari using remote sensing data. The age structure was inferred using a relationship between crown projection area and the number of branch sections. The conservation importance of each sub-population was assessed using a specially developed index. Finally, the future population development (extinction time) was predicted using population matrices. The total population size estimated consists of 80,134 individuals with sub-populations varying from 14 to 32,196 individuals, with an extinction time ranging from 31 to 564 years. Community forestry controlled by a local certification system is suggested as a sustainable land management approach providing traditional and new benefits and enabling the reforestation of endemic tree species on Socotra Island.

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sustainability
Article
Sustainable Land Use Management Needed to
Conserve the Dragon’s Blood Tree of Socotra Island, a
Vulnerable Endemic Umbrella Species
Petr Madˇera 1, *, Daniel Volaˇrík1, Zdenˇek Patoˇcka 2, Hana Kalivodová1, Josef Divín2,
Martin Rejžek 1, Jan Vybíral 1,3, Samuel Lvonˇcík1, David Jeník4, Pavel Hanáˇcek 4,
Abdullateef Saad Amer 5and Petr Vahalík2
1
Department of Forest Botany, Dendrology and Geobiocoenology, Mendel University in Brno, Zemˇedˇelsk
á
3,
613 00 Brno, Czech Republic
2
Department of Forest Management and Applied Geoinformatics, Mendel University in Brno, Zemˇedˇelsk
á
3,
613 00 Brno, Czech Republic
3Lower Morava Biosphere Reserve, Zámeckénámˇestí69, 691 44 Lednice, Czech Republic
4Department of Plant Biology, Mendel University in Brno, Zemˇedˇelská3, 613 00 Brno, Czech Republic
5UNEP/GEF Project, Hadibo, Socotra, Republic of Yemen
*Correspondence: petrmad@mendelu.cz
Received: 13 May 2019; Accepted: 26 June 2019; Published: 28 June 2019


Abstract:
Unsustainable overgrazing is one of the most important threats to the endemic and
endangered population of dragon’s blood tree (Dracaena cinnabari) on Socotra Island (Republic of
Yemen). However, there is a lack of information about the exact population size and its conservation
status. We estimated the population size of D. cinnabari using remote sensing data. The age structure
was inferred using a relationship between crown projection area and the number of branch sections.
The conservation importance of each sub-population was assessed using a specially developed index.
Finally, the future population development (extinction time) was predicted using population matrices.
The total population size estimated consists of 80,134 individuals with sub-populations varying from
14 to 32,196 individuals, with an extinction time ranging from 31 to 564 years. Community forestry
controlled by a local certification system is suggested as a sustainable land management approach
providing traditional and new benefits and enabling the reforestation of endemic tree species on
Socotra Island.
Keywords: Soqotra; population decline; overgrazing; Conservation Importance Index
1. Introduction
Socotra Island is the largest island (3600 km
2
) in the Socotra Archipelago (Figure 1).
This archipelago is continental in origin and was separated from Africa during the middle Miocene at
about 17 Ma [
1
], at which time pre-adaptation of the modern lineage of Dracaena to arid environments
started [
2
]. The archipelago is part of the Republic of Yemen, and is located where the Indian Ocean
meets the Arabian Sea, in the Gulf of Aden. The climate of Socotra Island is in accord with its occurrence
in the arid tropical zone [
3
], with the island therefore hosting xeromorphic, desert and semi-desert plant
communities [
4
–
8
]. Long-term separation from the continent has led to a high degree of endemism,
with 37% of the almost 850 plant species there being endemic [
4
,
5
,
9
,
10
]. Due to the unstable political
situation in the country, the island is relatively isolated from civilization. For thousands of years, the
main source of livelihood for its inhabitants has been raising goats and sheep, and, less frequently,
cattle and camels [4].
Sustainability 2019,11, 3557; doi:10.3390/su11133557 www.mdpi.com/journal/sustainability

Sustainability 2019,11, 3557 2 of 20
The dragon’s blood tree, Dracaena cinnabari (Asparagaceae), endemic to Socotra Island and with a
distinctive appearance, is widely recognized as a flagship species of that location. It is an exceptional
tree belonging to the monocotyledonous plants, and along with several other arborescent species in
Dracaena forms the dragon tree group [
11
]. Crucially, it is a cenozoic relict, with the island’s forests of
dragon’s blood trees belonging to one of the oldest ecosystems in the world [4,5,12,13].
Sustainability 2019, 11, x FOR PEER REVIEW 2 of 20
the unstable political situation in the country, the island is relatively isolated from civilization. For
thousands of years, the main source of livelihood for its inhabitants has been raising goats and sheep,
and, less frequently, cattle and camels [4].
The dragon’s blood tree, Dracaena cinnabari (Asparagaceae), endemic to Socotra Island and with
a distinctive appearance, is widely recognized as a flagship species of that location. It is an exceptional
tree belonging to the monocotyledonous plants, and along with several other arborescent species in
Dracaena forms the dragon tree group [11]. Crucially, it is a cenozoic relict, with the island’s forests
of dragon’s blood trees belonging to one of the oldest ecosystems in the world [4,5,12,13].
Figure 1. Socotra Island and its toponyms.
D. cinnabari is categorized as “vulnerable” on the Red List of the International Union for
Conservation of Nature [14], and its conservation has been recognized as important [15]. In addition
to its restricted distribution, ancientness, vulnerable status and distinctive appearance, its
conservation is important because of the significant ecological roles that it plays. Rejžek et al. [16]
have shown D. cinnabari to be an important nurse plant that facilitates the establishment and growth
of different plant species seedlings under and in the vicinity of canopies. With the decline of its
populations likely to adversely affect overall plant diversity and the abundance of rare endemic
plants, its loss may lead to a homogenization of vegetation. Similarly, García and Vasconcelos [17]
described important mutualistic relationships between D. cinnabari and endemic geckos which
belong to the pollinators of this species. Fruits of D. cinnabari serve as food for birds, which
disseminate the seeds [18]. Furthermore, it can be expected that the occurrence of a wide range of
insect species depend on D. cinnabari [19]. Moreover, dragon’s blood stands function as cloud forests,
catching water from horizontal precipitation, fog, drizzle and mist [20], and thus are very important
to the hydrology of the island. A decreasing dragon’s blood tree population density may cause land
aridification and desertification [21], followed by increasing soil erosion (Figure S1). Given its
ecological importance, D. cinnabari has been characterized as an umbrella species of Socotra Island,
with its conservation essential to conserving the island’s native biota [15,17,20,22–24].
Figure 1. Socotra Island and its toponyms.
D. cinnabari is categorized as “vulnerable” on the Red List of the International Union for
Conservation of Nature [
14
], and its conservation has been recognized as important [
15
]. In addition
to its restricted distribution, ancientness, vulnerable status and distinctive appearance, its conservation
is important because of the significant ecological roles that it plays. Rejžek et al. [
16
] have shown D.
cinnabari to be an important nurse plant that facilitates the establishment and growth of different plant
species seedlings under and in the vicinity of canopies. With the decline of its populations likely to
adversely affect overall plant diversity and the abundance of rare endemic plants, its loss may lead to a
homogenization of vegetation. Similarly, Garc
í
a and Vasconcelos [
17
] described important mutualistic
relationships between D. cinnabari and endemic geckos which belong to the pollinators of this species.
Fruits of D. cinnabari serve as food for birds, which disseminate the seeds [
18
]. Furthermore, it can be
expected that the occurrence of a wide range of insect species depend on D. cinnabari [
19
]. Moreover,
dragon’s blood stands function as cloud forests, catching water from horizontal precipitation, fog,
drizzle and mist [
20
], and thus are very important to the hydrology of the island. A decreasing dragon’s
blood tree population density may cause land aridification and desertification [
21
], followed by
increasing soil erosion (Figure S1). Given its ecological importance, D. cinnabari has been characterized
as an umbrella species of Socotra Island, with its conservation essential to conserving the island’s
native biota [15,17,20,22–24].
Remote sensing provides data for vegetation mapping with lower prices and faster and easier
repeatability in comparison with the field-based surveys. Remote sensing can cover wider and
inaccessible areas [
25
] like Socotra. Nevertheless, it has limitations caused by clouds [
26
] and uses

Sustainability 2019,11, 3557 3 of 20
spatial resolution. The use of spaceborne remote sensing data began in the 1970s [
27
]. The spatial
resolution of these satellite images was a limiting factor for studies at a tree level. Since the end of the
1990s, spaceborne remote sensing has provided data with a very high spatial resolution, under 1 m,
so that studies of individual trees have become more possible [
28
]. Attore et al. [
22
,
29
], Malatesta et
al. [
30
] and Habrov
á
[
31
] used advantages of remote sensing data for vegetation mapping on Socotra
Island. Attore et al. [
22
] and Kr
á
l and Pavliš [
32
] first used remote sensing data for evaluating of D.
cinnabari distribution.
Attore et al. [
22
] estimated that D. cinnabari now occupies only 5% of its potential habitat on the
island. Kr
á
l and Pavliš [
32
] found that although the habitats hosting D. cinnabari comprise a total of
7230 ha, these include only 230 ha of forests and 800 ha of mixed mountains forests, with the rest of the
area (6200 ha) consisting of woodlands with low tree densities and overmatured populations [
15
,
26
].
Dragon’s blood tree population decline has been attributed not only to the ongoing harvesting of the
tree’s resin, known since ancient times as dragon’s blood, but also crucially to overgrazing, which
makes natural population regeneration impossible [
4
,
5
,
12
,
23
,
24
,
33
,
34
]. Moreover, the traditional
silvopastoral system of using inflorescences, fruits and leaves of D. cinnabari as fodder for livestock in
dry times poses an additional threat [
4
]. Dragon’s blood tree population decline has been predicted
to continue without changes to the above-described current pastoral practices and habits of local
inhabitants [
13
,
35
]. Conservation of the dragon’s blood tree will depend upon implementation of land
management practices to protect both natural [24] and artificial [36] seedling regeneration.
The population size and structure of different dragon tree species has been the subject of interest
in several studies. Research at a population level is very important in small, fragmented and isolated
populations of endemic species, as it is generally in dragon tree species. Such population structures
can impose further evolutionary constraints due to low genetic diversity, and it has been predicted that
D. cambodiana, as with other long-lived species with a narrow genetic base, will not be able to adapt
to new selection pressures brought about by changes in environmental and climate conditions [
37
].
Fragmentation, isolation and the remoteness of D. cambodiana populations may lead to a decrease
in gene flow, while an increase in the probability of divergent natural selection [
37
], a significant
phylogeographic structure and genetic differentiation among populations were detected [
38
]. The
population size was described for D. ombet in Gebel Elba NP [
39
,
40
], D. tamaranae on Grand Canaria
Island [
41
], D. draco subsp. draco on Tenerife Island [
42
], D. kaweesakii in Thailand [
43
] and D. cambodiana
on Hainan Island [37].
Generally, the life cycle of trees can be divided into vegetative (juvenile) and generative (adult)
phases. Attorre et al. [
22
] used more details to divide dragon’s blood tree ontogeny into four stages.
Within the juvenile phase, the plant consists of a single leaf rosette without a trunk in the first ontogeny
stage, whereas in the second stage the plant has an added trunk. Within the adult phase, the third
stage in this classification is represented by a plant with more than one leaf rosette, as well as a crown
with a diameter less than 2.5 m, while in the fourth stage the plant has a crown larger than 2.5 m
in diameter. More recently, Madˇera et al. [
36
] described four juvenile stages of dragon’s blood tree
development and estimated that the juvenile phase lasts between 100 and 200 or more years, and that
its end is defined by the onset of first flowering [
12
]. The lifespan of the adult phase of the dragon’s
blood tree has been estimated to be more than 500 years [
34
]. Due to the longevity of the dragon’s
blood tree, which may exceed 700 years, this species isn’t actually threatened by extinction but by
overmaturity [
13
]. However, Habrov
á
et al. [
13
] have predicted population development only for the
next 105 years, they didn
´
t develop the model finishing by extinction. Thus, the question of how much
time we have for effective conservation measures remains unanswered.
The objectives of the present study were to document the abundance of D. cinnabari as accurately
as possible using remote sensing and ground data, and to evaluate the conservation importance of
its overall population and sub-populations including the modelling of extinction time. These steps
are needed to help identify and prioritize sustainable management for this flagship umbrella species,
which in turn would advance conservation of the island’s other endemic species associated with it.

Sustainability 2019,11, 3557 4 of 20
In our study, we wanted to find answers to the following questions: How large is the population of D.
cinnabari on Socotra Island? Which habitats are the most important in terms of occurrence of dragon’s
blood trees? Are there any differences in conservation importance among particular sub-populations?
Is there a threat of extinction of any sub-populations? How is it possible to conserve D. cinnabari on
Socotra Island?
2. Materials and Methods
2.1. Detection of Dragon’s Blood Trees on Socotra Island
Detection of individual trees was carried out using a combination of automatic object-based
classification and manual vectorization of remotely sensed data. For this purpose, we used Pleiades 1A
satellite images provided by Airbus and the National Centre for Space Studies (CNES) available from
Google orthophoto imagery. Analysis was done using ArcGIS (ESRI) and eCognition (Trimble) software.
Object-based classification was conducted in several steps. Distinction between vegetation and
bare land was accomplished using analysis of the normalized difference vegetation index (NDVI).
Clusters of pixels covered with vegetation were segmented into geometric structures. All circular
structures indicative of dragon’s blood tree crowns were identified and distinguished from other
circular structures (circular bush or grassland structures) by the presence of sickle-shaped shadows of
tree crowns.
The whole dataset was verified and corrected using a process of manual vectorization. The area
of the island was segmented into a grid of 1 km
2
squares. Each square with dragon’s blood tree
presence (588 of 3600 squares) was selected, misclassified trees were deleted and unclassified trees were
vectorized. The output polygon vector layer was projected onto a WGS 1984 Web Mercator (auxiliary
sphere) coordinate system and the area of the tree crowns was calculated.
In mountain areas covered by an evergreen mosaic of vegetation, individual trees cannot be
detected using a remote sensing approach; therefore, all mountainous areas over 1100 m a.s.l. (0.54%
of the island’s total area) were excluded from the analysis. Although some dragon’s blood trees occur
in mixed forests at these higher elevations, we roughly estimate, based on personal field observation,
that they constitute only 2–3% of the island’s entire overall population.
Mis-identification with other species was also considered. Euphorbia arbuscula and Euphorbia
socotrana also have the top-view circular crown shape typical of D. cinnabari.E. arbuscular, inhabits the
territory below the lowest elevational limits of D. cinnabari, while E. socotrana inhabits high elevations,
and such areas (above 1100 m a.s.l.) were excluded from the analytical model. In cases where Euphorbia
species occurred in the analyzed territory, they were misidentified as D. cinnabari, but this only
happened sporadically, and not in significant numbers (six examples of E. arbuscula were identified
inside Dracaena woodland in the Homehill protected area).
2.2. Accuracy of Abundance Estimation
To explore the accuracy of our estimation of the abundance and age-classification of trees based on
remote sensing, we compared our results with those produced by the statistical inventory of dragon’s
blood tree forest that had been done in the Firmihin area by Adolt et al. [
33
]. That locality hosts the
best-preserved dragon’s blood tree forest and the most abundant population. In that inventory, for a
total of 107 randomly generated circle plots 25 m in radius, the coordinates and crown projections of
1930 individual trees were recorded. We spatially overlaid the dataset of remotely detected trees on the
inventory dataset to quantify the analytical errors caused by misclassification or by overlapping crown
structures invisible in satellite data.
2.3. Growth Characteristics and Age Estimation
To estimate the ages of all remotely detected dragon’s blood trees, we needed to first infer the
number of branch orders represented in each tree, which could then be used to estimate age. The

Sustainability 2019,11, 3557 5 of 20
number of branch orders was estimated using the relationship between them and the area of crown
projection based on field data collected from ~800 trees in the Firmihin dragon’s blood tree forest
remnant during a statistical forest inventory [
33
] as well as a few hundred other trees evaluated
over the course of 20 years of field work on the island. Specifically, the relationship between crown
area and number of branch orders was calculated using a linear regression model in the R software
environment [
44
]. Crown area was cube-root transformed to linearize the relationship before fitting
the linear regression model.
The age of each dragon’s blood tree was then estimated using the methodology of Adolt et al. [
34
].
The principles of this methodology were formulated by Adolt and Pavliš [
12
], who also made the first
attempt to derive a statistical linear model for crown age prediction. Adolt et al. [
34
] parametrized
this model as a more suitable logistic regression model using mean number of branch orders as the
only explanatory variable; the correspondence between crown ages and numbers of branch orders are
shown in Table S1.
To improve the clarity of age structure description, the trees inside sub-populations were grouped
into ontogenetic developmental stages. Juvenile trees are individuals with only leaf rosette, early adult
trees are individuals with 1 to 5 branch orders, adult trees possess 6 to 17 branch orders, late adult
trees possess 18 to 24 branch orders and senescent possess more than 25 branch orders.
2.4. Sub-Population Delimitation
Dragon’s blood trees occur typically in small dense groups and occupy plateaus of higher
elevations. However, in some areas it is possible to find large sparse groups in a high range of
elevations. That is why neighboring distance and altitude were used as explanatory variables to
separate the whole population into the groups (sub-populations). Using average nearest neighbor
analysis, we obtained an index value for each tree describing its average distance from neighboring
trees. We then performed a geographically weighted regression (GWR) to delimitate individual
sub-populations using the index of the average distance as the dependent variable and elevation as an
independent explanatory variable. The GWR kernel was defined as adaptive to allow variations in
tree density in extent as a function. Optimal distance and number of used neighbors were analytically
defined using the Akaike information criterion. The spatial context (the Gaussian kernel) was a
function of a specified number of neighbors. Where tree distribution was dense, the spatial context was
smaller. Where tree distribution was sparse, the spatial context was larger. The area of each distinct
sub-population was delineated by the generation of a concave hull over the input point vector layer.
2.5. Modelling of Sub-Population Extinction Times
Population matrices [
45
] were used for modelling the future development of particular
sub-populations. The starting point was zero natural regeneration, which is the current situation.
The input data for the model were mortality rate in specific age category (see below), and probability
that the tree will stay in a specific age category or move to the next age category if the population
becomes older by one year. Specific age category was expressed by the number of branch sections,
and probability the tree will stay (P
S
=(D
n−
1)/D
n
) or move (P
M
=1/D
n
) to the next age category
was given by the duration (D
n
) of this category (Table S1). The same approach has already been
used for modelling the population development of dragon’s blood trees by Habrov
á
et al. [
13
] and
Hubálková[35]. We used a more robust dataset that allowed us to build our model more precisely.
The mortality curve expressing the probability that a tree in a specific age category will die used
field data collected in a 200-m-wide transect (Figure 2) across the Firmihin area in 2013. The transect
encompassed 100 m on each side of the road leading across the Firmihin area, and for all recently dead
trees visible from the road we recorded the number of branch orders. Recently dead trees comprised
those that had no signs of decay, thus indicating that they had died within the past year. The field data
from those dead trees was then combined with the number and ages of all living trees in the transect,
inferred from remotely sensed data using the methods described above (see Sections 2.1 and 2.3),

Sustainability 2019,11, 3557 6 of 20
which enabled estimation of mortality rates (MR) in relation to the number of branch sections (NBS).
This was done using the R software environment, by fitting a non-linear model (Equation (1)).
MR =a+b∗NBS ∗exp(c ∗NBS), (1)
where a, b and c are parameters estimated using the non-linear model.
Sustainability 2019, 11, x FOR PEER REVIEW 6 of 20
in the transect, inferred from remotely sensed data using the methods described above (see Sections
2.1. and 2.3.), which enabled estimation of mortality rates (MR) in relation to the number of branch
sections (NBS). This was done using the R software environment, by fitting a non-linear model
(Equation (1)).
MR = a + b * NBS * exp(c * NBS), (1)
where a, b and c are parameters estimated using the non-linear model.
Figure 2. Transect along the road across Firmihin with dead and living dragon’s blood trees.
2.6. Sub-Population Conservation Importance Evaluation
We developed a conservation importance index (CI) to evaluate the conservation importance of
each individual sub-population, which was calculated using Equation (2). CI expresses the relative
importance of individual sub-populations of D. cinnabari for conservation of this species. CI contains
information about the sub-population characteristics that are possible to estimate from remote
sensing data. A higher value of CI means greater importance of sub-population for conservation,
while a lower value of CI means a higher priority of some conservation measures.
CI = HD + AM + DT + SP + TN, (2)
where HD = habitat degradation index, AM = mean age index, DT = tree density index, SP = sub-
population size index and TN = tree number index.
The habitat degradation index was calculated, using Equation (3), as the sum of the products of
individual landcover classes’ [32] areas (LCAs) within each sub-population multiplied by the
coefficient of naturalness of the landcover class (CN, Table 1). This sum was divided by the total sub-
population area (S) so that the maximal value was 1.00. Forests had the highest coefficient, woodland
and shrubland had progressively lower values and grasslands had the lowest value. Thus, the
coefficient of naturalness expressed the degree of degradation for each habitat relative to the potential
Figure 2. Transect along the road across Firmihin with dead and living dragon’s blood trees.
2.6. Sub-Population Conservation Importance Evaluation
We developed a conservation importance index (CI) to evaluate the conservation importance of
each individual sub-population, which was calculated using Equation (2). CI expresses the relative
importance of individual sub-populations of D. cinnabari for conservation of this species. CI contains
information about the sub-population characteristics that are possible to estimate from remote sensing
data. A higher value of CI means greater importance of sub-population for conservation, while a lower
value of CI means a higher priority of some conservation measures.
CI=HD+AM+DT+SP+TN, (2)
where H
D
=habitat degradation index, A
M
=mean age index, D
T
=tree density index, S
P
=
sub-population size index and TN=tree number index.
The habitat degradation index was calculated, using Equation (3), as the sum of the products of
individual landcover classes’ [
32
] areas (LCAs) within each sub-population multiplied by the coefficient
of naturalness of the landcover class (C
N
, Table 1). This sum was divided by the total sub-population

Sustainability 2019,11, 3557 7 of 20
area (S) so that the maximal value was 1.00. Forests had the highest coefficient, woodland and
shrubland had progressively lower values and grasslands had the lowest value. Thus, the coefficient
of naturalness expressed the degree of degradation for each habitat relative to the potential vegetation
(i.e., forests with occurrence of D. cinnabari) and the H
D
index described the degree to which the overall
area occupied by the sub-population has been degraded.
HD=
n
X
i=1
(LCAi∗CN)/S, (3)
Table 1. Coefficients of naturalness (CN) for individual landcover classes according to Král and Pavliš [32].
Landcover class. CN
Dracaena forest 1.0
Frankincense forest 1.0
Montane forest 1.0
Dracaena woodland 0.6
Frankincense woodland 0.6
Montane mosaic 0.6
Submontane shrubland 0.4
High shrubland with succulents 0.4
Low Croton-Jatropha shrubland 0.3
Sparse dwarf shrubland 0.2
Submontane grassland 0.2
Montane grassland 0.2
Limestone rocks 1.0
Basement rocks 1.0
Wadi 0.1
Date palm plantations 0.0
We identified the area of individual landcover classes for dragon’s blood tree occurrence by
overlaying a landcover map [32] and the trees’ distribution map.
Using Equation (4), the mean age index (A
M
) was calculated as the ratio of the difference between
the highest mean crown age among the sub-populations (A
max
) and the mean crown age of the
evaluated sub-population (A) to the difference between the highest and lowest (A
min
) mean crown
ages of sub-populations. The maximum value for A
M
was 1.00, and high values indicate the evaluated
sub-population was relatively young compared with the other sub-populations.
AM=(Amax −A)/(Amax −Amin), (4)
The tree density index (D
T
) was calculated, as shown in Equation (5), as the mean tree density (D) of
the individual sub-population (in terms of trees per ha) divided by 50. Fifty trees per hectare with
an average crown area of 20 m
2
(this value was estimated from the dataset of field-measured trees)
meant coverage of 10%, which is minimal forest cover according to the FAO forest definition. In the
theoretical case in which the sub-population mean tree density D reaches 50, the value of the tree
density index would be 1.
DT=D/50, (5)
The sub-population size index (Equation (6)) was calculated as the ratio of the size of the evaluated
sub-population (S) to the size of the largest sub-population (S
max
). S
P
index expresses the importance
of the sub-population area in comparison with the largest sub-population area.
SP=S/Smax, (6)

Sustainability 2019,11, 3557 8 of 20
The tree number index (Equation (7)) was calculated as the proportion of number of trees in the
evaluated sub-population (T) to the number of trees in the most abundant sub-population (T
max
).
The T
N
index expresses the importance of sub-population tree numbers in comparison with the tree
number of the most abundant sub-population.
TN=T/Tmax, (7)
Theoretically, the value of C
I
can to range from 0 to 5, with higher values denoting an increasing
importance of the sub-population from a conservation point of view; therefore, we expressed the
conservation importance index as a relative value from potential (maximal) value CI rel. (Table 4).
3. Results
3.1. Abundance of Dragon’s Blood Tree Population on Socotra Island
A total of 80,134 individual trees were detected using the combination of object-based classification
and manual vectorization of remote sensing data in the area of the whole island up to the elevational
limit of 1100 m a.s.l. Of these, more than 40% (32,195) were located in the Firmihin area, where dragon’s
blood trees occur with the highest density.
The comparison of field inventory and remote sensing data revealed that 18 of the trees identified
by remote sensing in the inventory area did not actually exist, with this number representing only 0.9%
of the total number of 1930 inventoried trees. The ground-truthing also showed there were 152 trees
(7.9% of the 1930 trees actually present) that were not counted in the image analysis based on remote
sensing because they were members of pairs of closely occurring trees or because they were smaller
trees covered by neighboring crowns. These overcounting and undercounting rates produced by our
analysis of remote-sensing images came from the locality with the highest density of trees and with one
of the youngest sub-populations, making it very likely to be the area most susceptible to such errors.
Comparison of field and remotely sensed data from the Firmihin location hosting the only
remaining Dracaena forest habitat also showed different age structures. Specifically, field data revealed
a greater representation of younger trees (by a total of 28% in age classes from 0 to 13 branch sections).
This indicates that our results originating from remote sensed data had a tendency to overestimate
crown projection. Such large discrepancies in age structure were not found for other habitats—Dracaena
woodlands, shrublands and grasslands—where population densities were lower and individual trees
were more visible from satellite imagery due to being farther from each other and with bigger crown
projection (older).
3.2. Spatial Distribution of Dragon’s Blood Tree Population on Socotra Island
The dataset was separated into 20 sub-populations (Table 2, Figure 3) using geographically
weighted regression. The most numerous sub-population, in the Firmihin area, consisted of 32,195
individuals. The smallest remnant sub-population, comprising 15 individuals, was found in an isolated
enclave of area of tree occurrence in the southwest of the island (No. 16). The total area of occupancy
of the focal species was 51,963 ha. The sub-population (No. 10) occupying the largest area occupies
10,408 ha southwest of Dixam and consists of 1252 trees. The smallest area (11 ha) occupied by a
sub-population (No. 11) is located south of Dixam and comprises only 22 trees.

Sustainability 2019,11, 3557 9 of 20
Table 2. Sub-populations and their main characteristics.
Sub-Pop. No Area
[ha] No. of Trees Tree Density
[tree per ha]
Mean Elevation
[m a.s.l.]
1 1635.73 3553 2.17 482
2 1675.31 1392 0.83 476
3 923.91 1872 2.03 358
4 4873.31 223 0.05 465
5 1900.13 2867 1.51 427
6 4383.35 10 890 2.48 530
7 1089.69 32 195 29.55 638
8 7952.07 9343 1.17 611
9 4487.63 7005 1.56 900
10 10 408.80 1252 0.12 745
11 11.19 22 1.97 513
12 3105.48 1089 0.35 744
13 2632.81 719 0.27 771
14 1162.84 311 0.27 630
15 77.21 111 1.44 479
16 462.07 15 0.03 549
17 871.86 81 0.09 403
18 3714.82 6551 1.76 445
19 48.45 76 1.57 495
20 546.52 565 1.03 475
Sustainability 2019, 11, x FOR PEER REVIEW 9 of 20
13 2632.81 719 0.27 771
14 1162.84 311 0.27 630
15 77.21 111 1.44 479
16 462.07 15 0.03 549
17 871.86 81 0.09 403
18 3714.82 6551 1.76 445
19 48.45 76 1.57 495
20 546.52 565 1.03 475
The distribution of dragon’s blood trees is mostly in the highlands and mountains of the central
and eastern parts of the island. Dragon’s blood trees are missing from the seaside plains and lowlands
(below 180 m a.s.l.), and their absence from the western part of the island is very conspicuous.
Figure 3. Map of dragon’s blood tree sub-populations on Socotra Island.
The highest densities of dragon’s blood trees were found in the Dracaena forests, Dracaena
woodlands and limestone rocks; the highest numbers of dragon’s blood trees were recorded in
Dracaena woodlands, submontane grasslands, Dracaena forests and submontane shrublands (Table
3). Dracaena forests and woodlands hosted more than 50% of the population on 10% of the area of the
dragon’s blood tree distribution. Woodlands, grasslands and shrublands with occurrence of dragon’s
blood trees were degraded habitats originating from Dracaena forests.
Table 3. Occurrence of dragon’s blood trees within landcover classes [25] on Socotra Island.
Landcover Class No. of Trees Area
[ha]
Tree Density
[tree per ha]
Dracaena
forests 16 979 240.00 70.75
Dracaena
woodland 26 869 4 883.54 5.50
Limestone rocks 59 19.96 2.96
Submontane grassland 17 043 15 413.97 1.11
Submontane shrubland 11 483 14 829.53 0.77
High shrubland with succulents 4 596 7 899.90 0.58
Montane forest 119 214.87 0.55
Frankincense woodland 1 762 4 049.95 0.44
Frankincense forest 356 927.21 0.38
Montane mosaic 518 1 603.33 0.32
Montane grassland 82 329.00 0.25
Sparse dwarf shrubland 25 103.14 0.24
Low
Croton
-
Jatropha
shrubland 233 1 211.76 0.19
Wadi 9 78.55 0.11
Basement rocks 6 67.28 0.09
Figure 3. Map of dragon’s blood tree sub-populations on Socotra Island.
The distribution of dragon’s blood trees is mostly in the highlands and mountains of the central
and eastern parts of the island. Dragon’s blood trees are missing from the seaside plains and lowlands
(below 180 m a.s.l.), and their absence from the western part of the island is very conspicuous.
The highest densities of dragon’s blood trees were found in the Dracaena forests, Dracaena
woodlands and limestone rocks; the highest numbers of dragon’s blood trees were recorded in Dracaena
woodlands, submontane grasslands, Dracaena forests and submontane shrublands (Table 3). Dracaena
forests and woodlands hosted more than 50% of the population on 10% of the area of the dragon’s
blood tree distribution. Woodlands, grasslands and shrublands with occurrence of dragon’s blood
trees were degraded habitats originating from Dracaena forests.

Sustainability 2019,11, 3557 10 of 20
Table 3. Occurrence of dragon’s blood trees within landcover classes [25] on Socotra Island.
Landcover Class No. of Trees Area
[ha]
Tree Density
[tree per ha]
Dracaena forests 16,979 240.00 70.75
Dracaena woodland 26,869
4883.54
5.50
Limestone rocks 59 19.96 2.96
Submontane grassland 17,043
15,413.97
1.11
Submontane shrubland 11,483
14,829.53
0.77
High shrubland with
succulents 4596
7899.90
0.58
Montane forest 119 214.87 0.55
Frankincense woodland 1762
4049.95
0.44
Frankincense forest 356 927.21 0.38
Montane mosaic 518
1603.33
0.32
Montane grassland 82 329.00 0.25
Sparse dwarf shrubland 25 103.14 0.24
Low Croton-Jatropha shrubland
233
1211.76
0.19
Wadi 9 78.55 0.11
Basement rocks 6 67.28 0.09
Date palm plantations 0 72.36 0.00
3.3. Age Structure of Dracaena cinnabari Population on Socotra Island
To infer the crown age of dragon’s blood trees identified from satellite imagery, we need to know
the relationship between crown projection area and number of branch sections (Figure 4). Using this
relationship, we estimated the crown age of all detected trees in all sub-populations according to
Adolt et al. [
34
] and counted the mean crown age for each sub-population (Table 4). The youngest
sub-population had a mean crown age of 200 years and the oldest a mean of 466 years.
1
Figure 4.
The relationship between crown projection area (CPA) and number of branch sections (NBS).
NBS =(CPAˆ(1/3)-0.868425)/0.106529.

Sustainability 2019,11, 3557 11 of 20
Table 4. Age structure and representation of ontogenetic stages within sub-populations.
Sub-Pop.
No
Mean Age
[years] SD SE Min
[years]
Max
[years]
Juve-Nile
[%]
Early
Adult
[%]
Adult
[%]
Late
Adult
[%]
Sene-
Scent
[%]
Extinction
Time
[years]
1 299 87.01 1.46 0 545 0.03 4.28 69.43 22.63 3.63 509
2 307 86.00 2.31 56 529 0.00 3.52 65.88 26.51 4.09 451
3 335 80.44 1.86 56 513 0.00 2.08 54.01 38.14 5.77 442
4 200 89.95 6.02 56 428 0.00 29.15 65.47 5.38 0.00 470
5 332 81.65 1.52 56 521 0.00 1.95 55.14 37.32 5.58 461
6 346 87.46 0.84 56 552 0.00 2.18 47.14 39.70 10.98 506
7 327 82.27 0.46 56 559 0.00 2.45 58.11 34.48 4.96 546
8 352 87.98 0.91 27 545 0.00 1.51 47.00 36.68 14.81 495
9 282 89.35 1.07 27 529 0.00 7.35 71.83 18.93 1.88 564
10 355 89.38 2.53 56 545 0.00 1.36 47.04 34.27 17.33 395
11 355 46.08 9.82 238 438 0.00 0.00 45.45 54.55 0.00 117
12 230 109.66 3.32 27 521 0.00 24.33 60.33 11.75 3.58 545
13 314 97.18 3.62 56 529 0.00 6.82 57.02 28.23 7.93 428
14 364 85.28 4.84 27 529 0.00 1.93 41.16 39.87 17.04 313
15 343 70.33 6.68 108 504 0.00 0.90 54.05 37.84 7.21 226
16 466 61.20 15.80 353 559 0.00 0.00 7.14 21.43 71.43 31
17 388 63.43 7.05 198 504 0.00 0.00 22.22 59.26 18.52 162
18 350 78.35 0.97 27 545 0.00 0.72 48.05 41.87 9.36 461
19 363 71.67 8.22 177 504 0.00 0.00 40.79 51.32 7.89 194
20 365 74.76 3.15 27 537 0.00 0.35 42.48 43.54 13.63 311
The age structures of the whole population on Socotra Island and individual sub-populations
are shown in Figure 5and Table 4. Figure 5clearly depicts the over-maturity of the dragon’s blood
tree population, as there is a visibly low representation of young age classes, especially juvenile trees
with only a top rosette, as defined by Madera et al. [
36
]. Even though Table 4clearly shows differences
among individual sub-populations, juvenile and early adult ontogenetic stages are insufficient among
all sub-populations.
Sustainability 2019, 11, x FOR PEER REVIEW 10 of 20
Date palm plantations 0 72.36 0.00
3.3. Age Structure of Dracaena cinnabari Population on Socotra Island
To infer the crown age of dragon’s blood trees identified from satellite imagery, we need to know
the relationship between crown projection area and number of branch sections (Figure 4). Using this
relationship, we estimated the crown age of all detected trees in all sub-populations according to
Adolt et al. [34] and counted the mean crown age for each sub-population (Table 4). The youngest
sub-population had a mean crown age of 200 years and the oldest a mean of 466 years.
Figure 4. The relationship between crown projection area (CPA) and number of branch sections
(NBS). NBS = (CPA^(1/3)-0.868425) / 0.106529.
The age structures of the whole population on Socotra Island and individual sub-populations
are shown in Figure 5 and Table 4. Figure 5 clearly depicts the over-maturity of the dragon’s blood
tree population, as there is a visibly low representation of young age classes, especially juvenile trees
with only a top rosette, as defined by Madera et al. [36]. Even though Table 4 clearly shows differences
among individual sub-populations, juvenile and early adult ontogenetic stages are insufficient
among all sub-populations.
0
10
20
30
40
50
60
juvenile early adult adult late adult senescent
Figure 5.
Age structure of the whole dragon’s blood tree population on Socotra Island. Vertical axis
expresses frequency [%] of ontogenetic developmental stages.
3.4. Prediction of Dragon’s Blood Tree Extinction
3.4.1. Mortality Curve
In total, within the belt described above (Figure 2), 3805 living and 33 dead trees were detected,
which means the mean mortality rate was very low (less than 1% per year). The mortality curve
(Figure 6) shows that tree mortality is near zero until the tree crown reaches the age of 400 years
(expressed by having 20 branch sections) and substantially increases after the tree crown reaches the
age of 450 years (expressed by having 25 branch sections).

Sustainability 2019,11, 3557 12 of 20
1
Figure 6. Mortality curve of Dracaena cinnabari.
3.4.2. Extinction Time of Dragon’s Blood Tree Sub-Populations
The estimated extinction times of individual sub-populations are shown in Table 4. The extinction
time varied from 31 to 564 years. The most endangered are sub-populations with a high mean crown
age and low numbers of trees. Due to the dragon’s blood tree being a long-lived organism, there are
only a small number of sub-populations with short predicted extinction times, including only one
under 100 years (No. 16) and only three under 200 years (Nos. 11, 17 and 19). These sub-populations
are the most vulnerable to the local extinction. The sub-population matrices are shown in Table S2,
with a visualization of sub-population extinction in Video S1.
3.5. The Conservation Importance of the Dragon’s Blood Tree Sub-Populations on Socotra Island
From a conservation status point of view (Table 5), sub-population 7 (Firmihin) is the most
important (C
I rel
>50%), and although it occupies only 2% of the dragon’s blood tree occurrence area,
it hosts more than 40% of the species’ trees. Sub-populations 4, 6, 8, 9, 10, 12 and 13 are characterized
by a C
I rel
greater than 30% but less than 40%, with their conservation importance thus average,
together covering nearly 73% of the dragon’s blood tree area of occurrence and hosting 38% of the
trees. Sub-populations 1, 2, 3, 5 14 and 18 have relatively lower conservation importance (C
I rel
greater
than 20%, but less than 30%), and altogether comprise 21% of the species’ distribution area and host
20% of its trees. The conservation importance of sub-populations 11, 15, 17, 19 and 20 is relatively very
low (C
I rel
greater than 10% but less than 20%), comprising 3% of the dragon’s blood tree occurrence
area while harboring 1% of the trees. Sub-population 16 had the lowest conservation importance index
value (C
I rel
under 10%) and comprises only a negligible number of trees. However, this sub-population,
as it represents the most western ridge of the species’ occurrence, is nevertheless very important to
demonstrating the former distribution of dragon’s blood trees in the whole central highlands.

Sustainability 2019,11, 3557 13 of 20
Table 5. Conservation importance index values for all sub-populations on Socotra Island.
Sub-Pop. No HDAMDTSPTNCICI rel
7 0.58 0.52 0.59 0.10 1.00 2.79 55.90
4 0.42 1.00 0.00 0.47 0.01 1.90 37.97
8 0.38 0.43 0.02 0.76 0.29 1.88 37.66
10 0.40 0.42 0.00 1.00 0.04 1.86 37.22
9 0.44 0.69 0.03 0.43 0.22 1.81 36.28
12 0.55 0.89 0.01 0.30 0.03 1.77 35.48
6 0.37 0.45 0.05 0.42 0.34 1.63 32.66
13 0.66 0.57 0.01 0.25 0.02 1.51 30.19
18 0.37 0.44 0.04 0.36 0.20 1.40 28.04
1 0.39 0.63 0.04 0.16 0.11 1.32 26.47
14 0.66 0.38 0.01 0.11 0.01 1.16 23.30
5 0.36 0.50 0.03 0.18 0.09 1.16 23.26
2 0.33 0.60 0.02 0.16 0.04 1.15 23.03
3 0.37 0.49 0.04 0.09 0.06 1.05 21.03
20 0.43 0.38 0.02 0.05 0.02 0.90 18.02
15 0.38 0.46 0.03 0.01 0.00 0.88 17.66
19 0.38 0.39 0.03 0.00 0.00 0.80 16.00
11 0.30 0.42 0.04 0.00 0.00 0.76 15.13
17 0.32 0.29 0.00 0.08 0.00 0.70 14.07
16 0.30 0.00 0.00 0.04 0.00 0.35 6.93
3.6. Sustainable Land Management Measures to Conserve Dragon’s Blood Tree Population
We found sub-population 7 (Firmihin) to be the most important from a nature conservation
perspective. Thus, we selected this sub-population as a pilot area where we incorporated components
of sustainable forest management into the currently practiced traditional community silvo-pastoral
system (Supplement S1). There were 153 inhabitants in 4 villages within the Firmihin pilot area (size
778.9 ha), who kept 1168 goats, 217 sheep, 137 cows, 2 donkeys, 6 camels and 14 beehives. The livestock
density was approximately 2 animals per ha.
We prepared a concept for a certified sustainable community forestry system, which we
implemented using a farmer field school [46]. In the pilot area, we established:
1. a forest nursery to produce tree seedlings from local tree species populations;
2. a trial demonstration focused on the presentation of the advantages of rotational pastures;
3. a trial demonstration focused on reforestation using artificial regeneration (Figure S1);
4.
a trial agro-forestry demonstration focused on dragon’s blood tree natural regeneration together
with Aloe perryi plantation.
Local certified community forestry is based on an implementation of forest regeneration into
the traditional rules of silvo-pastoral land use (Supplement S1). The local certification authority (the
newly established Forests office within the Agriculture office) will control compliance with sustainable
community forestry rules.
Within the farmer field school, training sessions are organized for local inhabitants on how to
make products with added value from aloe juice, dragon’s blood, myrrh and frankincense, local herbs
and honey, and also on how to set up value chains with these products to bring higher benefits from
agricultural and forestry lands.
4. Discussion
4.1. Conservation Status
As noted above, the dragon’s blood tree is categorized in the IUCN Red List [
14
] as a vulnerable
species. Our results confirm this evaluation. Extent of occurrence (EOO) was measured as 77,239 ha and

Sustainability 2019,11, 3557 14 of 20
area of occupancy (AOO) as 51,963 ha, which substantially differ from the area of landcover classes with
occurrence of dragon’s blood trees estimated by Král and Pavliš [32]. Král and Pavliš [32] mentioned
occurrences of dragon’s blood trees only in three landcover classes—Dracaena forests, Dracaena
woodlands and mixed mountain forests, altogether totaling an area a little bit over 7000 ha. However,
we also distinguished also different types of shrublands and grasslands with sparse occurrences of
dragon’s blood trees, which is why the recently estimated dragon’s blood tree area of occupancy is
seven times greater than the area reported by Král and Pavliš [32].
The overall population was relatively abundant, with more than 80,000 individuals counted,
and an extinction time estimated to be centuries due to the longevity of this species [
34
], which is
in agreement with the results of Hub
á
lkov
á
[
35
]. The threat lies especially in the absence of natural
regeneration [
4
,
5
,
12
,
23
,
24
,
33
,
34
], which has likely lasted for more than a hundred years [
33
]. Thus, the
population has an unbalanced age structure [
12
,
15
,
33
]; according to our results, more than half of the
trees were adult, with more than one-third belonging to the late-adult ontogenetic stage, and 7% of
trees being senescent. Only 3% of trees belonged to the early adult ontogenetic stage. Juvenile trees
almost do not occur, as confirmed by our field observations.
There is a close correlation (y =0.4439eˆ0,0026x; R
2
=0.7903) comparing the conservation
importance index and extinction time of sub-populations of dragon’s blood trees, which confirms the
correctness of our approach in assessing the importance of sub-populations from a nature conservation
point of view using the available data.
4.2. Driving Forces of Dragon’s Blood Tree Population Decline
Societal changes, improved medical care and better nutrition security caused a permanent increase
in the inhabitant population on Socotra after World War II. Increased inhabitant population is directly
connected to the growth of livestock abundance. While at the beginning of 1950s the number of goats
and sheep was estimated at 19,000 and 26,000, respectively, estimates indicate there were 220,000
livestock in 1999 [47].
Several decades of overgrazing in all habitats with dragon’s blood tree occurrence has led to their
gradual degradation. Over half of the trees are located in more or less degraded habitats on most of
area of occupancy, and Dracaena woodlands, shrublands and grasslands contain three-quarters of the
trees. Kr
á
l and Pavliš [
32
] characterized only 230 ha as Dracaena forest, meaning that less than 0.5% of
area of occupancy hosts one-quarter of the trees. However, current dragon’s blood tree distribution
is only a fragment of its previous occurrence. Attorre et al. [
22
] estimated that dragon’s blood trees
now occupy only 5% of its potential habitat on the island, as defined in terms of moisture index, mean
annual temperature and slope. However, their model used very sparse climatological data and did not
use any data regarding the occurrence of orographic (horizontal) precipitation at all, which is very
important for the growth of dragon’s blood trees [4,18].
Such a huge contraction of dragon’s blood tree distribution cannot be explained only by overgrazing.
All ethnobotanical uses performed without the local inhabitants ensuring the regeneration of dragon’s
blood trees have contributed to the population decline, including: dragon’s blood harvesting (which
has been practiced for thousands of years [
4
,
48
]); cutting of inflorescences, infructescences and leaves as
fodder for livestock [
4
]; making beehives from stems [
49
,
50
]; and winding cordage from leaf veins [
4
].
These practices have continued in continental populations of dragon trees (D. ombet,D. serrulata)
until today. In particular, cutting of branches, leaves and infructescences as a fodder for camels has
accelerated population decline, as sparse studies prove [
39
,
40
,
50
]. Such direct damage, accompanied
by overgrazing, can lead to local population extinction. Local rules on Socotra don’t allow cutting
whole branches of dragon’s blood trees (cuttings of single leaves as a fodder is allowed only in the dry
season—see Supplement S1). This could be the reason why dragon’s blood trees on Socotra are not
damaged to the degree of similar continental species.
The above-described long-term unsustainable management practices of dragon’s blood tree
populations, some of which persist to this day, can be intensified by current environmental

Sustainability 2019,11, 3557 15 of 20
developmental changes. Global climate changes [
22
] or long-term climate oscillations [
51
] can
contribute to direct (disturbance by cyclones) and indirect (increasing drought) negative impacts on
dragon’s blood tree populations.
Progressive decline of dragon’s blood tree forests and woodlands that function as “cloud forests”
may cause land aridification and desertification [
21
], followed by increasing soil erosion and loss of
land productivity. Decreasing pasture productivity and increasing numbers of livestock intensify the
impact of grazing on remnant vegetation, including tree regeneration, except for poisonous species.
Although local herders claim that a large amount of fodder from dragon’s blood trees cause scour
in goats (and so can only be fed in very small quantities [
4
]), a recent author’s observation confirms
the increasing resistance of goats to this feed. Moreover, because juvenile trees are slow-growing, it
can take at least 50 years [
36
] for plants to escape the “browsing zone”, making them particularly
susceptible to consumption by herbivorous mammals [
36
]. Every unprotected seedling is immediately
grazed [
24
], along with leaves from broken mature trees. Therefore, any reforestation efforts must be
accompanied by sufficient, substantial fencing over a long time [36].
4.3. Extinction Model for the Dragon’s Blood Tree
Population viability analysis is a collection of methods for evaluating the threats faced by
populations of species, their risks of extinction or decline and their chances for recovery, based on
species-specific data and models [
52
]. Thus, each model is individually based [
53
]. Our extinction model
for dragon’s blood trees belongs to the stage-based population models, which are used advantageously
just for tree populations [
54
]. For example, such models were developed for threatened tree species
Taxus baccata [
55
], Fraxinus pennsylvanica [
53
] and Swietenia macrophylla [
56
]. The dragon’s blood tree’s
extinction model is unique because individual stages have been expressed by the number of branch
sections that are directly correlated with the age of the tree [12,34].
The survival probability of individual development stages as one of the important inputs for
modelling is shown in mortality curve (Figure 6). This curve comprises all factors that affect the
mortality of a studied species at a normal level without having to specifically detect them. The mortality
rate of individual development stages is a crucial input in population extinction models and has
the largest influence on resulting extinction time. Hub
á
lkov
á
[
35
] used a less-robust dataset for the
mortality rate of the dragon’s blood tree, and the mortality rate of older development stages was
under-evaluated; thus, old trees were cumulated in the published model. In this sense our model could
be closer to reality, because we investigated the mortality rate in transect with 3838 trees. Our model
doesn’t include the influence of catastrophic disturbances, such as the two cyclones in November
2015. Such events (that could repeat more often with global climate changes) bring uncertainty to our
extinction model.
Habrov
á
et al. [
13
] retrospectively evaluated the decline of small populations on Skant mountain,
directly comparing two photographs taken from the same place (one from 1899 and second from 2004).
They found decline in the number of dragon’s blood trees by 44.22% from 199 trees in the original 1899
photograph to only 111 individuals in 2004. Also, the absence of trees from younger age classes was
evident in the 2004 photo. The recorded decline of more than 40% during 105 years is very similar to
that indicated by our model.
4.4. Suggestions for Sustainable Land Management Measures
There is clear evidence that there was a highly organized agricultural land use system on Socotra
in the past, as a high density of wall systems around the island not only served various functions
for agricultural production and management of incense, dragon’s blood and aloes, but were also
used in water and soil management [
57
]. Recently, this system has unfortunately completely been
forgotten, and free grazing is now practiced on the whole island. Not only the dragon’s blood
tree, but also many other endemic trees including Boswellia spp. [
58
,
59
], Commiphora spp. [
60
] and
others [
4
] are strongly endangered. Unfortunately, strict conservation of selected valuable areas, as

Sustainability 2019,11, 3557 16 of 20
the Socotra Archipelago Master Plan supposed in 2002, are now not feasible on Socotra due to the
weak governmental administration and a complicated land tenure system based on tribal organization
of society.
To save the island’s biodiversity, we have to find any land management measure enabling
tree regeneration that will be acceptable by local inhabitants because it brings them new benefits.
One approach that is already proven on Socotra but not very efficient is planting home gardens [
61
],
where, besides vegetable and fruit species, the local people plant native species for medical use, as
fodder or for shade. Thus, during 20 years of Czech developmental aid, the establishment of 320 home
gardens have been supported and more than 10,000 native trees have been planted [
62
]. This solution
is very expensive in terms of costs per tree, and insufficient in terms of the number of planted trees,
but has huge educational significance.
Reforestation activities are very rare and small-scale on Socotra [
36
]. They are also mostly without
success, as the tree species on Socotra are slow-growing, and grazing animals need to be excluded for
many years, typically exceeding the lifetime of any given fence. Attacks from goats often break fences
when they see green vegetation inside during the dry season.
A community forestry approach [
63
], including a return to careful land use planning, rotational
pasturing along with a wall system and exclusion of particularly valuable parts of forests and woodlands
from pasture land, could achieve regeneration of degraded forest. Such an approach is very much
connected to the following three objectives: (1) alleviating poverty of forest users; (2) empowering
them; and (3) improving the condition of the forests [
64
]. All these objectives are needed to be attained
on Socotra, which would again bring benefits from traditional non-timber forest products (such
as incense, dragon’s blood, honey, aloe juice) to local communities, compensating for pasture area
reduction. To achieve success in community forestry, it is necessary to take into account the main
socio-economic success factors [
65
] and establish the appropriate rules of natural resource management
and use, as well as evaluation indicators [
50
]. Some forest certification systems provide opportunities
to implement such management rules and evaluation indicators [
66
] that enable the sale of non-timber
products at fair prices, and allow part of these profits to return to forest regeneration and biodiversity
conservation [67].
Finally, it should be noted that Dracaena serrulata (Arabian peninsula) and D. ombet (northeast Africa,
Arabian peninsula), also in the dragon tree group, are listed on the IUCN Red List as endangered [
14
],
and also face threats from grazing [
39
,
50
,
68
,
69
]. These species may therefore benefit from similar
conservation approaches to those we propose here.
Among other members of the dragon tree group [
11
] only sporadic efforts for practical conservation
measures are known. Wild populations of critically endangered D. tamaranae count only 76 individuals
from which only five generate fruits; 2900 seeds were collected and 350 seedlings were replanted,
while others were sent to different botanical gardens [
70
]. Trials with the replantation of adult trees of
D. serrulata endangered by road construction were carried out in Oman with 54% mortality after one
year [71].
5. Conclusions
The dragon’s blood tree population on Socotra Island has suffered from a lack of natural
regeneration due to direct and indirect human influences for decades and maybe centuries. The result
is a creeping population decline consisting of increasing mean population age and senescent trees
gradually dying without recruitment of young seedlings. Thus, the population is thinning from forests
to woodlands, shrublands and eventually grasslands with individual sparse trees. The final stage of
this decline is eroded treeless land used by local herders as low productivity pasture.
We found the six sub-populations (Nos. 11, 15, 16, 17, 19 and 20) with very low conservation
importance indices, low numbers of trees, low tree densities and relatively high threats of local
extinction, and thus with a high priority on conservation measures. On the other hand, seven
sub-populations (Nos. 4, 6, 8, 9, 10, 12, 13) were defined as very important source sub-populations

Sustainability 2019,11, 3557 17 of 20
with high numbers of trees, high tree densities and low probabilities of local extinction. In these
sub-populations it is very important to induce sustainable land management practices enabling
tree regeneration.
According to our models, there is no danger of imminent extinction of endemic dragon’s blood
trees, but the long-term future of this species is not hopeful without changing the current, unsustainable
livestock management approach. Implementing sustainable land management, especially regarding
the grazing system, is necessary to save the dragon’s blood tree and other endangered endemic tree
species on Socotra Island.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2071-1050/11/13/3557/s1,
Figure S1: Population decline and artificial reforestation; Table S1: Crown age estimation for D. cinnabari using fit
model for Firmihin locality; Table S2: Future sub-population development models, Video S1: Visualization of
sub-populations extinction, Supplement S1: Description of Firmihin pilot area.
Author Contributions:
Conceptualization, P.M. and P.V.; methodology, P.M. and P.V.; literature review, H.K. and
P.M.; data curation, P.M., Z.P., J.D., P.V., H.K., M.R., S.L., D.J., P.H., A.S.A., J.V. and D.V.; writing—original draft
preparation, P.M., P.V., D.V., M.R. and H.K.; map creation, P.V.; project administration, H.K.
Funding:
This research was funded by the Internal Grant Agency of the Faculty of Forestry and Wood Technology,
Mendel University in Brno (grant numbers LDF_VP_2017012 and LDF_VT_2017009), and by UNEP-GEF (grant
number 5347).
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
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